Add MD Color Mod nodes, operators, and web UI
Introduce a complete GIMP/GEGL-inspired color module for ComfyUI. Adds: node declarations (nodes/*) and matching operator implementations (py/*) for many color/image operations (balance, temperature, hue/chroma, hue/saturation, saturation, exposure, levels, invert, auto adjustments, maps, posterize, desaturate, tone mapping, component extract/mix, etc.). Includes support libs (py/libs: color, process, blur), layer compositing (layers_blending), VRAM‑aware chunking, mask handling and alpha preservation. Adds web UI extension and widgets (web/*), package metadata, dynamic __init__.py loader, README update and .gitignore tweaks.
This commit is contained in:
MdONeilsl
+4
-1
@@ -178,4 +178,7 @@ cython_debug/
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# exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
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# refer to https://docs.cursor.com/context/ignore-files
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.cursorignore
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.cursorindexingignore
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.cursorindexingignore
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note.txt
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tmp
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web/widgets
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@@ -1,2 +1,13 @@
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# ComfyUI-MdColorMod
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GIMP color mod in confyUI
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# GIMP/GEGL Color Mod for ComfyUI
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A standardized collection of color operation nodes designed for seamless integration with ComfyUI, leveraging GIMP’s powerful GEGL framework.
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---
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## ✅ Features
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- **All Nodes Support Masks**: Each node accepts a mask input for precise control over affected areas.
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- **Batch Processing**: Process multiple images and their corresponding masks simultaneously.
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- **Full RGB/RGBA Support**: Input and output support both RGB and RGBA format.
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---
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@@ -0,0 +1,32 @@
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import importlib
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import pkgutil
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import os
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# Tells ComfyUI to load the JS files from the /web folder
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WEB_DIRECTORY = "./web"
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# Dynamically collect all node mappings from the nodes/ folder
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NODE_CLASS_MAPPINGS = {}
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NODE_DISPLAY_NAME_MAPPINGS = {}
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_current_dir = os.path.dirname(__file__)
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_nodes_dir = os.path.join(_current_dir, "nodes")
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for module_info in pkgutil.iter_modules([_nodes_dir]):
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module_name = module_info.name
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module = importlib.import_module(f".nodes.{module_name}", package=__package__)
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if hasattr(module, "NODE_CLASS_MAPPINGS"):
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for key, value in module.NODE_CLASS_MAPPINGS.items():
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if key in NODE_CLASS_MAPPINGS:
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print(f"Warning: duplicate node key '{key}' from {module_name}")
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NODE_CLASS_MAPPINGS[key] = value
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if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS"):
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NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
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# Clean up helper variables (optional, but keeps namespace tidy)
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del _current_dir, _nodes_dir, module_info, module_name, module
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# Public API – yes, you should keep __all__ so users know what's available
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS", "WEB_DIRECTORY"]
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@@ -0,0 +1,302 @@
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from ..py.libs.color import pack_rgb
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module_cat = "md/color"
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MAX_RGB_INT = 0xFFFFFF
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# =============================================================================
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# RGB
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# =============================================================================
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class mdPrimitiveColorRGB:
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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"Red": ("FLOAT", {"default": 0, "min": 0, "max": 255, "step": 1}),
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"Green": ("FLOAT", {"default": 0, "min": 0, "max": 255, "step": 1}),
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"Blue": ("FLOAT", {"default": 0, "min": 0, "max": 255, "step": 1}),
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},
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}
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RETURN_TYPES = ("INT", "STRING")
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RETURN_NAMES = ("int", "HEX")
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FUNCTION = "exec"
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CATEGORY = module_cat
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def exec(self, Red, Green, Blue):
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# Round and clamp
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r = max(0, min(255, int(round(Red))))
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g = max(0, min(255, int(round(Green))))
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b = max(0, min(255, int(round(Blue))))
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packed = (r << 16) | (g << 8) | b
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hex_str = f"#{r:02X}{g:02X}{b:02X}"
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return (packed, hex_str)
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# =============================================================================
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# HSV
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# =============================================================================
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class mdPrimitiveColorHSV:
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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"Hue": ("FLOAT", {"default": 0, "min": 0, "max": 360, "step": 0.1}),
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"Saturation": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 0.1}),
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"Value": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 0.1}),
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},
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}
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RETURN_TYPES = ("INT", "STRING")
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RETURN_NAMES = ("int", "HEX")
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FUNCTION = "exec"
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CATEGORY = module_cat
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def exec(self, Hue, Saturation, Value):
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h = Hue / 360.0
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s = max(0.0, min(1.0, Saturation / 100.0))
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v = max(0.0, min(1.0, Value / 100.0))
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if s == 0.0:
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r = g = b = v
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else:
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h = h * 6.0
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i = int(h)
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f = h - i
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p = v * (1.0 - s)
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q = v * (1.0 - s * f)
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t = v * (1.0 - s * (1.0 - f))
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if i == 0:
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r, g, b = v, t, p
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elif i == 1:
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r, g, b = q, v, p
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elif i == 2:
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r, g, b = p, v, t
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elif i == 3:
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r, g, b = p, q, v
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elif i == 4:
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r, g, b = t, p, v
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else:
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r, g, b = v, p, q
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return pack_rgb(r, g, b)
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# =============================================================================
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# HSL
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# =============================================================================
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class mdPrimitiveColorHSL:
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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"Hue": ("FLOAT", {"default": 0, "min": 0, "max": 360, "step": 0.1}),
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"Saturation": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 0.1}),
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"Lightness": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 0.1}),
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},
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}
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RETURN_TYPES = ("INT", "STRING")
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RETURN_NAMES = ("int", "HEX")
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FUNCTION = "exec"
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CATEGORY = module_cat
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def exec(self, Hue, Saturation, Lightness):
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h = Hue / 360.0
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s = max(0.0, min(1.0, Saturation / 100.0))
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l = max(0.0, min(1.0, Lightness / 100.0))
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if s == 0.0:
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r = g = b = l
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else:
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def hue2rgb(p, q, t):
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if t < 0.0: t += 1.0
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if t > 1.0: t -= 1.0
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if t < 1.0 / 6.0: return p + (q - p) * 6.0 * t
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if t < 1.0 / 2.0: return q
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if t < 2.0 / 3.0: return p + (q - p) * (2.0 / 3.0 - t) * 6.0
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return p
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q = l * (1.0 + s) if l < 0.5 else l + s - l * s
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p = 2.0 * l - q
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r = hue2rgb(p, q, h + 1.0 / 3.0)
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g = hue2rgb(p, q, h)
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b = hue2rgb(p, q, h - 1.0 / 3.0)
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return pack_rgb(r, g, b)
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# =============================================================================
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# CMYK
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# =============================================================================
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class mdPrimitiveColorCMYK:
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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"Cyan": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 0.1}),
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"Magenta": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 0.1}),
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"Yellow": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 0.1}),
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"Key": ("FLOAT", {"default": 0, "min": 0, "max": 100, "step": 0.1}),
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},
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}
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RETURN_TYPES = ("INT", "STRING")
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RETURN_NAMES = ("int", "HEX")
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FUNCTION = "exec"
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CATEGORY = module_cat
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def exec(self, Cyan, Magenta, Yellow, Key):
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c = max(0.0, min(1.0, Cyan / 100.0))
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m = max(0.0, min(1.0, Magenta / 100.0))
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y = max(0.0, min(1.0, Yellow / 100.0))
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k = max(0.0, min(1.0, Key / 100.0))
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r = (1.0 - c) * (1.0 - k)
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g = (1.0 - m) * (1.0 - k)
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b = (1.0 - y) * (1.0 - k)
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return pack_rgb(r, g, b)
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# =============================================================================
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# CIE 1931 XYZ
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# =============================================================================
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class mdPrimitiveColorXYZ:
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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"X": ("FLOAT", {"default": 0.95047, "min": 0.0, "max": 1.0, "step": 0.001}),
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"Y": ("FLOAT", {"default": 1.00000, "min": 0.0, "max": 1.0, "step": 0.001}),
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"Z": ("FLOAT", {"default": 1.08883, "min": 0.0, "max": 1.0, "step": 0.001}),
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},
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}
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RETURN_TYPES = ("INT", "STRING")
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RETURN_NAMES = ("int", "HEX")
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FUNCTION = "exec"
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CATEGORY = module_cat
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def exec(self, X, Y, Z):
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# XYZ → linear sRGB (D65)
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r_lin = 3.2404542 * X - 1.5371385 * Y - 0.4985314 * Z
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g_lin = -0.9692660 * X + 1.8760108 * Y + 0.0415560 * Z
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b_lin = 0.0556434 * X - 0.2040259 * Y + 1.0572252 * Z
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def gamma(c):
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if c <= 0.0031308:
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return 12.92 * c
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else:
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return 1.055 * (c ** (1.0 / 2.4)) - 0.055
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r = gamma(r_lin)
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g = gamma(g_lin)
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b = gamma(b_lin)
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return pack_rgb(r, g, b)
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# =============================================================================
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# CIELAB
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# =============================================================================
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class mdPrimitiveColorLAB:
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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"L": ("FLOAT", {"default": 50, "min": 0, "max": 100, "step": 0.1}),
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"A": ("FLOAT", {"default": 0, "min": -128, "max": 128, "step": 0.1}),
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"B": ("FLOAT", {"default": 0, "min": -128, "max": 128, "step": 0.1}),
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},
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}
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RETURN_TYPES = ("INT", "STRING")
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RETURN_NAMES = ("int", "HEX")
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FUNCTION = "exec"
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CATEGORY = module_cat
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def exec(self, L, A, B):
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# D65 reference white
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xn, yn, zn = 0.95047, 1.0, 1.08883
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delta = 6.0 / 29.0
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fy = (L + 16.0) / 116.0
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fx = fy + (A / 500.0)
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fz = fy - (B / 200.0)
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def f_inv(t):
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if t > delta:
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return t ** 3
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else:
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return 3.0 * delta ** 2 * (t - 4.0 / 29.0)
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X = xn * f_inv(fx)
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Y = yn * f_inv(fy)
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Z = zn * f_inv(fz)
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r_lin = 3.2404542 * X - 1.5371385 * Y - 0.4985314 * Z
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g_lin = -0.9692660 * X + 1.8760108 * Y + 0.0415560 * Z
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b_lin = 0.0556434 * X - 0.2040259 * Y + 1.0572252 * Z
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def gamma(c):
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if c <= 0.0031308:
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return 12.92 * c
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else:
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return 1.055 * (c ** (1.0 / 2.4)) - 0.055
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r = gamma(r_lin)
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g = gamma(g_lin)
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b = gamma(b_lin)
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return pack_rgb(r, g, b)
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# =============================================================================
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# Y′CbCr
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# =============================================================================
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class mdPrimitiveColorYCbCr:
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@classmethod
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def INPUT_TYPES(cls) -> dict:
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return {
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"required": {
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"Luma": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
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"BlueDiff": ("FLOAT", {"default": 0.0, "min": -0.5, "max": 0.5, "step": 0.001}),
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"RedDiff": ("FLOAT", {"default": 0.0, "min": -0.5, "max": 0.5, "step": 0.001}),
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},
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}
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RETURN_TYPES = ("INT", "STRING")
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RETURN_NAMES = ("int", "HEX")
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FUNCTION = "exec"
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CATEGORY = module_cat
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def exec(self, Luma, BlueDiff, RedDiff):
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Y = Luma
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Cb = BlueDiff
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Cr = RedDiff
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r = Y + 1.402 * Cr
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g = Y - 0.344136 * Cb - 0.714136 * Cr
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b = Y + 1.772 * Cb
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return pack_rgb(r, g, b)
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# =============================================================================
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# Node registry
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# =============================================================================
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NODE_CLASS_MAPPINGS = {
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"mdPrimitiveColorHSV": mdPrimitiveColorHSV,
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"mdPrimitiveColorHSL": mdPrimitiveColorHSL,
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"mdPrimitiveColorCMYK": mdPrimitiveColorCMYK,
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"mdPrimitiveColorXYZ": mdPrimitiveColorXYZ,
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"mdPrimitiveColorLAB": mdPrimitiveColorLAB,
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"mdPrimitiveColorYCbCr": mdPrimitiveColorYCbCr,
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"mdPrimitiveColorRGB": mdPrimitiveColorRGB,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"mdPrimitiveColorRGB": "Primitive Color RGB (MD)",
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"mdPrimitiveColorHSV": "Primitive Color HSV (MD)",
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"mdPrimitiveColorHSL": "Primitive Color HSL (MD)",
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"mdPrimitiveColorCMYK": "Primitive Color CMYK (MD)",
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"mdPrimitiveColorXYZ": "Primitive Color CIE XYZ (MD)",
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"mdPrimitiveColorLAB": "Primitive Color CIELAB (MD)",
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"mdPrimitiveColorYCbCr": "Primitive Color YCbCr (MD)",
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}
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File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,50 @@
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module_cat = "md/layers"
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from ..py.layers_blending import composite_layer_stack
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#===================================================================================
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# Node class – declaration only, execution delegated to the function below
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#===================================================================================
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class mdLayerStackNode:
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"""
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Composites a layer over a background using a blend mode, a mask, and a global transparency.
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Handles RGBA images (4 channels) and respects the layer's own alpha channel.
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"""
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"background": ("IMAGE",),
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"layer": ("IMAGE",),
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"blending_mode": (["Normal", "Screen", "Multiply", "Overlay", "Difference",
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"Dissolve", "Clear", "Add", "Subtract", "Replace",
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"Darken Only", "Lighten Only", "Color Dodge", "Color Burn",
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"Hard Light", "Soft Light", "Pin Light", "Linear Dodge (Add)",
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"Linear Burn (Multiply)", "Vivid Light", "Halo", "Negate",
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"Merge RGB", "Merge Cyan", "Merge Magenta", "Merge Yellow",
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"Merge Black", "Fill with FG Color", "Fill with BG Color",
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"Average", "Composite", "Behind", "Template"],),
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"transparency": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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},
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"optional": {
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"mask": ("MASK",),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "exec"
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CATEGORY = module_cat
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def exec(self, background, layer, blending_mode, transparency, mask=None):
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return composite_layer_stack(background, layer, blending_mode, transparency, mask)
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#===================================================================================
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NODE_CLASS_MAPPINGS = {
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"mdLayerStackNode": mdLayerStackNode,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"mdLayerStackNode": "Layer Stack Node (MD)",
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}
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@@ -0,0 +1,137 @@
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import torch
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from typing import Tuple, Optional
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||||
from .libs.color import rgb_to_lch_torch, lch_to_rgb_torch
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from .libs.process import compute_chunk_size
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def exec_operator_color_auto_color_enhance(
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image: torch.Tensor,
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mask: Optional[torch.Tensor] = None
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) -> Tuple[torch.Tensor]:
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||||
"""
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||||
Apply GIMP‑style “Color Enhance” (chroma stretching) to an RGB or RGBA image.
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||||
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||||
The enhancement linearly stretches the chroma (C) channel of each image in the
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||||
batch so that its minimum mapped chroma becomes 0 and its maximum becomes 100.
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When a mask is supplied, only the masked region is used to determine the per‑
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||||
image chroma range, and the result is blended with the original according to
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||||
the per‑pixel mask strength.
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||||
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||||
Processing is performed in chunks to manage GPU memory; the chunk size is
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||||
determined by ``compute_chunk_size``.
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||||
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||||
Args:
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||||
image: Image tensor of shape ``[B, H, W, C]`` with ``C = 3`` (RGB) or
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||||
``C = 4`` (RGBA). Values are expected in [0, 1].
|
||||
mask: Optional mask tensor of shape ``[B, H, W]`` or ``[B, H, W, 1]``,
|
||||
values in [0, 1]. Pixels with value > 0.5 are considered for
|
||||
chroma range calculation; the mask also controls blending strength
|
||||
when non‑zero.
|
||||
|
||||
Returns:
|
||||
A single‑element tuple ``(enhanced_image,)`` with the same shape and
|
||||
dtype as the input, clamped to [0, 1]. Alpha channels (if present) are
|
||||
passed through unchanged.
|
||||
"""
|
||||
# --- validation ------------------------------------------------------------
|
||||
if image.dim() != 4:
|
||||
raise ValueError(f"Expected image tensor of shape [B, H, W, C], got {image.shape}")
|
||||
B, H, W, C = image.shape
|
||||
if C not in (3, 4):
|
||||
raise ValueError(f"Image must have 3 (RGB) or 4 (RGBA) channels, got {C}")
|
||||
|
||||
if mask is not None:
|
||||
if mask.dim() not in (3, 4):
|
||||
raise ValueError(f"Mask must be 3‑ or 4‑dimensional, got {mask.dim()}D")
|
||||
if mask.shape[0] != B:
|
||||
raise ValueError(f"Batch size mismatch: image {B} vs mask {mask.shape[0]}")
|
||||
# Squeeze trailing dimension if needed → [B, H, W, 1]
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1)
|
||||
elif mask.shape[-1] != 1:
|
||||
mask = mask[..., :1] # keep first channel only
|
||||
mask = mask.to(device=image.device, dtype=image.dtype)
|
||||
|
||||
# --- RGBA support: split alpha, process RGB only -------------------------
|
||||
has_alpha = (C == 4)
|
||||
if has_alpha:
|
||||
rgb = image[..., :3]
|
||||
alpha = image[..., 3:] # [B, H, W, 1]
|
||||
else:
|
||||
rgb = image
|
||||
|
||||
# --- chunked processing ---------------------------------------------------
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
enhanced_chunks = []
|
||||
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
rgb_chunk = rgb[start:end] # [chunk, H, W, 3]
|
||||
m_chunk = mask[start:end] if mask is not None else None
|
||||
|
||||
# ---------- per‑image chroma range with optional mask -----------------
|
||||
lch = rgb_to_lch_torch(rgb_chunk) # [chunk, H, W, 3]
|
||||
|
||||
if m_chunk is not None:
|
||||
# Boolean mask: True where mask > 0.5
|
||||
mask_bool = m_chunk > 0.5 # [chunk, H, W, 1]
|
||||
|
||||
# Prepare C channel for masked min/max (vectorised)
|
||||
C_chan = lch[..., 1] # [chunk, H, W]
|
||||
# fill non‑masked with +inf for min, -inf for max
|
||||
C_min = C_chan.masked_fill(~mask_bool.squeeze(-1), float('inf'))
|
||||
C_max = C_chan.masked_fill(~mask_bool.squeeze(-1), float('-inf'))
|
||||
|
||||
minC = C_min.amin(dim=(1, 2)) # [chunk]
|
||||
maxC = C_max.amax(dim=(1, 2))
|
||||
|
||||
# Handle images with no valid mask pixels → set range to 0
|
||||
any_mask = mask_bool.any(dim=(1, 2, 3)).squeeze(-1) # [chunk]
|
||||
minC = torch.where(any_mask, minC, torch.zeros_like(minC))
|
||||
maxC = torch.where(any_mask, maxC, torch.zeros_like(maxC))
|
||||
else:
|
||||
C_chan = lch[..., 1]
|
||||
minC = C_chan.amin(dim=(1, 2))
|
||||
maxC = C_chan.amax(dim=(1, 2))
|
||||
|
||||
# ---------- early exit if no chroma variation in the chunk ------------
|
||||
delta = maxC - minC # [chunk]
|
||||
if not delta.any(): # all deltas ≈ 0
|
||||
# No change – just forward the original chunk
|
||||
enhanced_chunks.append(rgb_chunk)
|
||||
continue
|
||||
|
||||
# ---------- stretch chroma --------------------------------------------
|
||||
valid = delta > 1e-12 # [chunk]
|
||||
scale = torch.zeros_like(delta)
|
||||
offset = torch.zeros_like(delta)
|
||||
scale[valid] = 100.0 / delta[valid]
|
||||
offset[valid] = -minC[valid] * scale[valid]
|
||||
|
||||
# Broadcast to [chunk, 1, 1]
|
||||
scale = scale.view(-1, 1, 1)
|
||||
offset = offset.view(-1, 1, 1)
|
||||
valid = valid.view(-1, 1, 1)
|
||||
|
||||
C_new = torch.where(valid, lch[..., 1] * scale + offset, lch[..., 1])
|
||||
lch_new = torch.stack([lch[..., 0], C_new, lch[..., 2]], dim=-1)
|
||||
|
||||
# ---------- convert back to RGB ---------------------------------------
|
||||
rgb_enh = lch_to_rgb_torch(lch_new)
|
||||
|
||||
# ---------- blend with mask if provided -------------------------------
|
||||
if m_chunk is not None:
|
||||
# mask acts as alpha blending factor (per pixel)
|
||||
rgb_enh = m_chunk * rgb_enh + (1.0 - m_chunk) * rgb_chunk
|
||||
|
||||
enhanced_chunks.append(rgb_enh)
|
||||
|
||||
# --- reassemble batch and handle alpha ------------------------------------
|
||||
enhanced_rgb = torch.cat(enhanced_chunks, dim=0) # [B, H, W, 3]
|
||||
|
||||
if has_alpha:
|
||||
output = torch.cat([enhanced_rgb, alpha], dim=-1)
|
||||
else:
|
||||
output = enhanced_rgb
|
||||
|
||||
return (output.to(device=image.device, dtype=image.dtype),)
|
||||
@@ -0,0 +1,113 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
|
||||
from .libs.color import rgb_to_hsv, hsv_to_rgb
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
|
||||
def exec_operator_color_auto_equalize(
|
||||
image: torch.Tensor,
|
||||
mask: Optional[torch.Tensor] = None
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Auto‑equalise the V (value) channel of an RGB or RGBA image batch.
|
||||
|
||||
The equalisation curve (LUT) is computed from the histogram of all pixels
|
||||
in the batch. If a mask is provided, it acts as a per‑pixel strength factor:
|
||||
- 1.0 → fully equalised
|
||||
- 0.0 → original V channel preserved
|
||||
- Values between 0 and 1 blend linearly between the two.
|
||||
|
||||
Alpha channels are left untouched.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
(B, H, W, C) with C=3 (RGB) or C=4 (RGBA).
|
||||
Dtype: torch.uint8 (0–255) or torch.float32 (0–1).
|
||||
mask : torch.Tensor, optional
|
||||
(B, H, W) tensor with values in [0,1] representing application strength.
|
||||
If omitted, all pixels are fully equalised (mask = 1).
|
||||
|
||||
Returns
|
||||
-------
|
||||
tuple of torch.Tensor
|
||||
A 1‑tuple containing the equalised image tensor, same shape and dtype
|
||||
as the input.
|
||||
"""
|
||||
# ---------- validation --------------------------------------------------
|
||||
if image.dim() != 4:
|
||||
raise ValueError(f"Expected 4D tensor (B,H,W,C), got {image.dim()}D")
|
||||
B, H, W, C = image.shape
|
||||
if C not in (3, 4):
|
||||
raise ValueError(f"Only 3 (RGB) or 4 (RGBA) channels supported, got {C}")
|
||||
|
||||
if mask is not None:
|
||||
if mask.dim() != 3:
|
||||
raise ValueError(f"Mask must be 3D (B,H,W), got {mask.dim()}D")
|
||||
if mask.shape != (B, H, W):
|
||||
raise ValueError(
|
||||
f"Mask shape {mask.shape} incompatible with image shape {image.shape}"
|
||||
)
|
||||
|
||||
# ---------- separate alpha if present -----------------------------------
|
||||
if C == 4:
|
||||
alpha = image[..., 3:4]
|
||||
img_rgb = image[..., :3]
|
||||
else:
|
||||
alpha = None
|
||||
img_rgb = image
|
||||
|
||||
# ---------- normalise to float [0,1] ------------------------------------
|
||||
if img_rgb.dtype == torch.uint8:
|
||||
img_float = img_rgb.float() / 255.0
|
||||
else:
|
||||
img_float = img_rgb.float()
|
||||
|
||||
# ---------- RGB → HSV ---------------------------------------------------
|
||||
h, s, v = rgb_to_hsv(img_float) # all shapes (B,H,W)
|
||||
|
||||
# Early exit on truly empty input
|
||||
if v.numel() == 0:
|
||||
return (image,)
|
||||
|
||||
# ---------- chunked histogram over ALL pixels (mask ignored) ------------
|
||||
bins = 256
|
||||
hist = torch.zeros(bins, device=v.device, dtype=torch.float32)
|
||||
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
v_flat = v.reshape(-1)
|
||||
n_vals = v_flat.numel()
|
||||
for start in range(0, n_vals, chunk_size):
|
||||
end = min(start + chunk_size, n_vals)
|
||||
hist += torch.histc(v_flat[start:end], bins=bins, min=0.0, max=1.0)
|
||||
|
||||
# Build normalised CDF (LUT)
|
||||
cdf = torch.cumsum(hist, dim=0)
|
||||
lut = cdf / cdf[-1].clamp_min(1e-8)
|
||||
|
||||
# Apply LUT → equalised V
|
||||
idx = (v * (bins - 1)).long().clamp(0, bins - 1)
|
||||
v_eq = lut[idx] # (B,H,W)
|
||||
|
||||
# ---------- blend with original V using mask strength -------------------
|
||||
if mask is not None:
|
||||
# mask values in [0,1] directly control blend
|
||||
v_final = v_eq * mask + v * (1.0 - mask)
|
||||
else:
|
||||
v_final = v_eq
|
||||
|
||||
# ---------- HSV → RGB ---------------------------------------------------
|
||||
out_rgb = hsv_to_rgb(h, s, v_final).clamp(0, 1)
|
||||
|
||||
# ---------- restore original dtype and alpha ----------------------------
|
||||
if img_rgb.dtype == torch.uint8:
|
||||
out_rgb = (out_rgb * 255).round().to(torch.uint8)
|
||||
|
||||
if alpha is not None:
|
||||
out = torch.cat([out_rgb, alpha], dim=-1)
|
||||
else:
|
||||
out = out_rgb
|
||||
|
||||
return (out,)
|
||||
|
||||
@@ -0,0 +1,280 @@
|
||||
import torch
|
||||
from typing import Optional, Tuple
|
||||
from .libs.color import hsv_to_rgb, rgb_to_hsv
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
|
||||
EPS = 1e-8
|
||||
|
||||
|
||||
def _normalize_mask(mask: torch.Tensor, device, B, H, W):
|
||||
"""
|
||||
Returns:
|
||||
mask_f: float32 per-pixel strength mask in [0,1]
|
||||
mask_bool: boolean mask for statistics
|
||||
"""
|
||||
if mask is None:
|
||||
return None, None
|
||||
|
||||
mask = mask.to(device)
|
||||
|
||||
if mask.ndim == 2:
|
||||
mask = mask.unsqueeze(0)
|
||||
if mask.ndim == 3:
|
||||
mask = mask.unsqueeze(-1)
|
||||
|
||||
if mask.shape[-1] != 1:
|
||||
raise ValueError("Mask must have a single channel")
|
||||
|
||||
if mask.shape[0] == 1 and B > 1:
|
||||
mask = mask.expand(B, -1, -1, -1)
|
||||
elif mask.shape[0] != B:
|
||||
raise ValueError("Mask batch size mismatch")
|
||||
|
||||
mask_f = mask.clamp(0.0, 1.0).float()
|
||||
mask_bool = mask_f > 0.0
|
||||
return mask_f, mask_bool
|
||||
|
||||
|
||||
def _check_input(image: torch.Tensor):
|
||||
if image is None:
|
||||
raise ValueError("image cannot be None")
|
||||
|
||||
image = image.float()
|
||||
|
||||
if image.ndim == 3:
|
||||
image = image.unsqueeze(0)
|
||||
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(f"Expected RGB/RGBA, got {image.shape}")
|
||||
|
||||
return image
|
||||
|
||||
|
||||
def compute_vram_chunk(image: torch.Tensor, mask: Optional[torch.Tensor]):
|
||||
"""
|
||||
Wrapper around user-provided compute_chunk_size with safety fallback.
|
||||
"""
|
||||
try:
|
||||
cs = compute_chunk_size(image, mask)
|
||||
return max(1, int(cs))
|
||||
except Exception:
|
||||
return max(1, image.shape[0])
|
||||
|
||||
|
||||
def _early_exit_check(image, mask_bool):
|
||||
"""
|
||||
Fast-path detection:
|
||||
- empty batch
|
||||
- no usable mask
|
||||
"""
|
||||
if image.shape[0] == 0:
|
||||
return True
|
||||
|
||||
if mask_bool is not None and not mask_bool.any():
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def _stretch_chunk_rgb(
|
||||
rgb: torch.Tensor, # (B,H,W,3)
|
||||
mask_f: Optional[torch.Tensor], # (B,H,W,1) float or None
|
||||
mask_bool: Optional[torch.Tensor],
|
||||
keep_colors: bool,
|
||||
non_linear: bool,
|
||||
):
|
||||
"""
|
||||
Apply auto-stretch contrast in RGB space (GIMP-equivalent).
|
||||
All operations are batch-vectorized and GPU-safe.
|
||||
"""
|
||||
|
||||
device = rgb.device
|
||||
B, H, W, C = rgb.shape
|
||||
|
||||
work = rgb
|
||||
|
||||
# ── linear light conversion (optional) ─────────────────────────
|
||||
if non_linear:
|
||||
work = work.clamp(0.0, 1.0).pow(1.0 / 2.2)
|
||||
|
||||
# ── reshape for reduction (B, N, C) ────────────────────────────
|
||||
flat = work.view(B, H * W, C)
|
||||
|
||||
if mask_bool is not None:
|
||||
mask_flat = mask_bool.view(B, H * W)
|
||||
|
||||
# avoid invalid pixels affecting reduction
|
||||
masked = torch.where(
|
||||
mask_flat.unsqueeze(-1),
|
||||
flat,
|
||||
torch.tensor(float("inf"), device=device)
|
||||
)
|
||||
masked_neg = torch.where(
|
||||
mask_flat.unsqueeze(-1),
|
||||
flat,
|
||||
torch.tensor(float("-inf"), device=device)
|
||||
)
|
||||
|
||||
mins = masked.amin(dim=1)
|
||||
maxs = masked_neg.amax(dim=1)
|
||||
|
||||
# fallback safety (empty mask per channel)
|
||||
mins = torch.where(torch.isinf(mins), torch.zeros_like(mins), mins)
|
||||
maxs = torch.where(torch.isinf(maxs), torch.ones_like(maxs), maxs)
|
||||
|
||||
else:
|
||||
mins = flat.amin(dim=1)
|
||||
maxs = flat.amax(dim=1)
|
||||
|
||||
# ── shared channel behavior (GIMP "preserve colors") ───────────
|
||||
if keep_colors:
|
||||
lo = mins.amin(dim=1, keepdim=True) # (B,1)
|
||||
hi = maxs.amax(dim=1, keepdim=True) # (B,1)
|
||||
mins = lo.expand_as(mins)
|
||||
maxs = hi.expand_as(maxs)
|
||||
|
||||
# ── avoid division by zero ─────────────────────────────────────
|
||||
denom = (maxs - mins).clamp(min=EPS)
|
||||
|
||||
# ── reshape for broadcast ───────────────────────────────────────
|
||||
mins = mins.view(B, 1, 1, 3)
|
||||
maxs = maxs.view(B, 1, 1, 3)
|
||||
denom = denom.view(B, 1, 1, 3)
|
||||
|
||||
# ── apply stretch (GIMP-equivalent formula) ─────────────────────
|
||||
out = (work - mins) / denom
|
||||
out = out.clamp(0.0, 1.0)
|
||||
|
||||
# ── revert gamma if needed ──────────────────────────────────────
|
||||
if non_linear:
|
||||
out = out.pow(2.2)
|
||||
|
||||
return out
|
||||
|
||||
def exec_operator_color_auto_stretch_contrast(
|
||||
image: torch.Tensor,
|
||||
stretch_type: str = "RGB",
|
||||
keep_colors: bool = True,
|
||||
non_linear: bool = False,
|
||||
mask: torch.Tensor = None,
|
||||
low_percentile: float = 0.0,
|
||||
high_percentile: float = 1.0,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Applies auto-stretch contrast to an image batch.
|
||||
|
||||
The operation expands the dynamic range of the image so that its
|
||||
minimum and maximum values span the full [0,1] interval.
|
||||
|
||||
When a mask is provided, it:
|
||||
- defines the region used for statistical computation
|
||||
- and controls per-pixel strength of the effect
|
||||
|
||||
Alpha channels are preserved unchanged.
|
||||
"""
|
||||
|
||||
# ── input validation ────────────────────────────────────────────
|
||||
image = _check_input(image)
|
||||
device = image.device
|
||||
|
||||
if stretch_type.upper() not in ("RGB", "HSV"):
|
||||
raise ValueError("stretch_type must be 'RGB' or 'HSV'")
|
||||
|
||||
# ── split alpha ────────────────────────────────────────────────
|
||||
has_alpha = image.shape[-1] == 4
|
||||
rgb = image[..., :3].clamp(0.0, 1.0)
|
||||
alpha = image[..., 3:] if has_alpha else None
|
||||
|
||||
B, H, W, _ = rgb.shape
|
||||
|
||||
# ── normalize mask ─────────────────────────────────────────────
|
||||
mask_f, mask_bool = _normalize_mask(mask, device, B, H, W)
|
||||
|
||||
# ── early exit ────────────────────────────────────────────────
|
||||
if _early_exit_check(rgb, mask_bool):
|
||||
return (image.clone(),)
|
||||
|
||||
# ── VRAM chunking ─────────────────────────────────────────────
|
||||
chunk_size = compute_vram_chunk(rgb, mask_f)
|
||||
|
||||
rgb_out = torch.empty_like(rgb)
|
||||
|
||||
# ── process chunks ─────────────────────────────────────────────
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
|
||||
sub_rgb = rgb[start:end]
|
||||
sub_mask_f = mask_f[start:end] if mask_f is not None else None
|
||||
sub_mask_b = mask_bool[start:end] if mask_bool is not None else None
|
||||
|
||||
if stretch_type.upper() == "RGB":
|
||||
stretched = _stretch_chunk_rgb(
|
||||
sub_rgb,
|
||||
sub_mask_f,
|
||||
sub_mask_b,
|
||||
keep_colors,
|
||||
non_linear,
|
||||
)
|
||||
|
||||
else:
|
||||
# ── HSV path (value channel stretch) ───────────────────
|
||||
hsv = rgb_to_hsv(sub_rgb)
|
||||
|
||||
# ── normalize HSV format ───────────────────────────────────────
|
||||
if isinstance(hsv, tuple):
|
||||
if len(hsv) == 3:
|
||||
h, s, v = hsv
|
||||
else:
|
||||
hsv = hsv[0]
|
||||
h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
|
||||
else:
|
||||
h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
|
||||
|
||||
# ── compute min/max on V channel ───────────────────────────────
|
||||
flat_v = v.view(v.shape[0], -1)
|
||||
|
||||
if sub_mask_b is not None:
|
||||
mask_flat = sub_mask_b.view(v.shape[0], -1)
|
||||
|
||||
v_masked = torch.where(
|
||||
mask_flat.unsqueeze(-1),
|
||||
v.view(v.shape[0], -1, 1),
|
||||
torch.tensor(float("inf"), device=device)
|
||||
)
|
||||
|
||||
v_min = v_masked.amin(dim=1)
|
||||
v_max = torch.where(
|
||||
mask_flat.unsqueeze(-1),
|
||||
v.view(v.shape[0], -1, 1),
|
||||
torch.tensor(float("-inf"), device=device)
|
||||
).amax(dim=1)
|
||||
|
||||
v_min = torch.where(torch.isinf(v_min), torch.zeros_like(v_min), v_min)
|
||||
v_max = torch.where(torch.isinf(v_max), torch.ones_like(v_max), v_max)
|
||||
else:
|
||||
v_min = flat_v.amin(dim=1)
|
||||
v_max = flat_v.amax(dim=1)
|
||||
|
||||
denom = (v_max - v_min).clamp(min=EPS).view(-1, 1, 1)
|
||||
|
||||
v = (v - v_min.view(-1, 1, 1)) / denom
|
||||
v = v.clamp(0.0, 1.0)
|
||||
|
||||
# ── rebuild HSV → RGB correctly ────────────────────────────────
|
||||
stretched = hsv_to_rgb(h, s, v)
|
||||
|
||||
# ── PER-PIXEL MASK STRENGTH BLENDING (REQUIRED) ─────────────
|
||||
if mask_f is not None:
|
||||
mf = sub_mask_f
|
||||
|
||||
stretched = rgb[start:end] + (stretched - rgb[start:end]) * mf
|
||||
|
||||
rgb_out[start:end] = stretched
|
||||
|
||||
# ── reattach alpha ─────────────────────────────────────────────
|
||||
if has_alpha:
|
||||
rgb_out = torch.cat((rgb_out, alpha), dim=-1)
|
||||
|
||||
return (rgb_out,)
|
||||
|
||||
|
||||
@@ -0,0 +1,94 @@
|
||||
import torch
|
||||
from typing import Tuple
|
||||
|
||||
def exec_operator_color_auto_white_balance(
|
||||
image: torch.Tensor,
|
||||
mask: torch.Tensor = None
|
||||
) -> Tuple[torch.Tensor]:
|
||||
|
||||
"""
|
||||
Auto White Balance (GIMP-style percentile stretch per channel).
|
||||
|
||||
Args:
|
||||
image: [B, H, W, C] float tensor in [0,1]
|
||||
mask: [B, 1, H, W] optional weighting mask (0–1)
|
||||
|
||||
Returns:
|
||||
Tuple containing processed image [B, H, W, C]
|
||||
"""
|
||||
|
||||
if image.dtype != torch.float32:
|
||||
image = image.float()
|
||||
|
||||
B, H, W, C = image.shape
|
||||
eps = 1e-6
|
||||
|
||||
# -----------------------------
|
||||
# Split RGB / Alpha
|
||||
# -----------------------------
|
||||
rgb = image[..., :min(3, C)] # [B,H,W,3]
|
||||
has_alpha = C > 3
|
||||
alpha = image[..., 3:] if has_alpha else None
|
||||
|
||||
# Convert to NCHW
|
||||
rgb = rgb.permute(0, 3, 1, 2).contiguous() # [B,3,H,W]
|
||||
|
||||
B, Cc, H, W = rgb.shape
|
||||
|
||||
# -----------------------------
|
||||
# Apply mask if provided
|
||||
# -----------------------------
|
||||
if mask is not None:
|
||||
if mask.dtype != torch.float32:
|
||||
mask = mask.float()
|
||||
if mask.ndim == 4:
|
||||
mask = mask[:, 0:1, :, :]
|
||||
mask = torch.nn.functional.interpolate(mask, size=(H, W), mode="bilinear", align_corners=False)
|
||||
mask = mask.clamp(0.0, 1.0)
|
||||
else:
|
||||
mask = torch.ones((B, 1, H, W), device=rgb.device, dtype=rgb.dtype)
|
||||
|
||||
# -----------------------------
|
||||
# Compute weighted percentiles
|
||||
# -----------------------------
|
||||
flat_rgb = rgb.reshape(B, Cc, -1) # [B,3,N]
|
||||
flat_mask = mask.reshape(B, 1, -1) # [B,1,N]
|
||||
|
||||
# Sort values per channel
|
||||
sorted_vals, indices = torch.sort(flat_rgb, dim=2)
|
||||
sorted_weights = torch.gather(flat_mask.expand_as(flat_rgb), 2, indices)
|
||||
|
||||
cum_weights = torch.cumsum(sorted_weights, dim=2)
|
||||
total_weights = cum_weights[:, :, -1:] + eps
|
||||
|
||||
cum_norm = cum_weights / total_weights
|
||||
|
||||
lower_p = 0.0005 # 0.05%
|
||||
upper_p = 0.9995 # 99.95%
|
||||
|
||||
def percentile_value(p):
|
||||
# first index where cum_norm >= p
|
||||
idx = torch.searchsorted(cum_norm, torch.full((B, Cc, 1), p, device=rgb.device))
|
||||
idx = idx.clamp(0, sorted_vals.shape[2] - 1)
|
||||
return torch.gather(sorted_vals, 2, idx)
|
||||
|
||||
lower = percentile_value(lower_p)
|
||||
upper = percentile_value(upper_p)
|
||||
|
||||
# -----------------------------
|
||||
# Stretch
|
||||
# -----------------------------
|
||||
denom = (upper - lower).clamp(min=eps)
|
||||
stretched = (flat_rgb - lower) / denom
|
||||
stretched = stretched.clamp(0.0, 1.0)
|
||||
|
||||
rgb_out = stretched.reshape(B, Cc, H, W).permute(0, 2, 3, 1).contiguous()
|
||||
|
||||
# -----------------------------
|
||||
# Reattach alpha
|
||||
# -----------------------------
|
||||
if has_alpha:
|
||||
rgb_out = torch.cat([rgb_out, alpha], dim=-1)
|
||||
|
||||
return (rgb_out,)
|
||||
|
||||
@@ -0,0 +1,129 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
|
||||
@torch.no_grad() # 1) no gradient tracking → saves VRAM & time
|
||||
def exec_operation_color_balance(
|
||||
image: torch.Tensor, # shape: (B, H, W, C) with C = 3 or 4
|
||||
mask: Optional[torch.Tensor], # can be (H,W), (B,H,W), (B,H,W,1), ...
|
||||
shadow_adj: Tuple[float, float, float],
|
||||
midtones_adj: Tuple[float, float, float],
|
||||
highlights_adj: Tuple[float, float, float],
|
||||
preserve_luminosity: bool
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Apply GIMP‑style color balance.
|
||||
Each adjustment tuple = (Cyan‑Red, Magenta‑Green, Yellow‑Blue) in [-100, 100].
|
||||
|
||||
Improvements over original:
|
||||
- Robust batch‑mask handling (2D mask → broadcast to whole batch)
|
||||
- Full RGB / RGBA support (alpha channel is never color‑shifted)
|
||||
- Input validation with clear error messages
|
||||
- `torch.no_grad()` + single memory allocation → VRAM optimised
|
||||
- Works on any device (CPU / CUDA / MPS)
|
||||
"""
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 1. Input validation & preparation
|
||||
# ----------------------------------------------------------------------
|
||||
if image.dim() != 4:
|
||||
raise ValueError(f"image must be 4D (B,H,W,C), got {image.dim()}D")
|
||||
B, H, W, C = image.shape
|
||||
if C not in (3, 4):
|
||||
raise ValueError(f"only 3 (RGB) or 4 (RGBA) channels allowed, got {C}")
|
||||
for adj in (shadow_adj, midtones_adj, highlights_adj):
|
||||
if len(adj) != 3:
|
||||
raise ValueError("each adjustment must be a sequence of 3 floats")
|
||||
device = image.device
|
||||
|
||||
# Work on a float copy (same device)
|
||||
img = image.to(dtype=torch.float32, copy=True) # single allocation
|
||||
rgb = img[..., :3] # view – no copy
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 2. Prepare mask → broadcastable (B, H, W, 1)
|
||||
# ----------------------------------------------------------------------
|
||||
mask_tensor: Optional[torch.Tensor] = None
|
||||
if mask is not None:
|
||||
m = mask.to(device=device, dtype=torch.float32)
|
||||
# unify shapes
|
||||
if m.dim() == 2: # (H, W) → apply to all images
|
||||
m = m[None, ..., None] # (1, H, W, 1)
|
||||
m = m.expand(B, -1, -1, -1) # (B, H, W, 1)
|
||||
elif m.dim() == 3:
|
||||
if m.shape[-1] == 1: # (H, W, 1) or (B, H, W, 1)?
|
||||
if m.shape[0] == B:
|
||||
m = m # (B, H, W, 1) – already fine
|
||||
else:
|
||||
m = m[None] # (1, H, W, 1)
|
||||
m = m.expand(B, -1, -1, -1)
|
||||
else: # (B, H, W)
|
||||
m = m.unsqueeze(-1) # (B, H, W, 1)
|
||||
elif m.dim() == 4:
|
||||
if m.shape[0] == 1 and B > 1:
|
||||
m = m.expand(B, -1, -1, -1) # broadcast batch dimension
|
||||
else:
|
||||
raise ValueError(f"mask shape {mask.shape} not supported")
|
||||
# safety check
|
||||
if m.shape[:3] != (B, H, W):
|
||||
raise ValueError(
|
||||
f"mask spatial dimensions must match image. "
|
||||
f"Expected (B,H,W)=({B},{H},{W}), got {m.shape[:3]}"
|
||||
)
|
||||
mask_tensor = m
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 3. Luma & transfer‑function weights (Rec. 709)
|
||||
# ----------------------------------------------------------------------
|
||||
luma_w = torch.tensor([0.299, 0.587, 0.114], device=device, dtype=torch.float32)
|
||||
luma = (rgb * luma_w).sum(dim=-1, keepdim=True) # (B, H, W, 1)
|
||||
|
||||
def smoothstep(edge0, edge1, x):
|
||||
t = torch.clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0)
|
||||
return t * t * (3.0 - 2.0 * t)
|
||||
|
||||
shadow_w = smoothstep(0.5, 0.0, luma) # 1 at 0, 0 at 0.5
|
||||
midtones_w = 4.0 * luma * (1.0 - luma) # peak 1 at 0.5
|
||||
highlights_w = smoothstep(0.5, 1.0, luma) # 0 at 0.5, 1 at 1.0
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 4. Normalised adjustments → (1,1,1,3)
|
||||
# ----------------------------------------------------------------------
|
||||
def to_tensor(adj):
|
||||
return torch.tensor(adj, device=device, dtype=torch.float32).view(1,1,1,3) / 100.0
|
||||
|
||||
delta = (
|
||||
shadow_w * to_tensor(shadow_adj) +
|
||||
midtones_w * to_tensor(midtones_adj) +
|
||||
highlights_w * to_tensor(highlights_adj)
|
||||
) # (B, H, W, 3)
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 5. Apply delta (with or without mask)
|
||||
# ----------------------------------------------------------------------
|
||||
if mask_tensor is not None:
|
||||
blended = rgb + delta * mask_tensor # only RGB
|
||||
else:
|
||||
blended = rgb + delta
|
||||
|
||||
# Build output (keep alpha untouched)
|
||||
if C == 4:
|
||||
alpha = img[..., 3:4]
|
||||
out = torch.cat([blended, alpha], dim=-1)
|
||||
else:
|
||||
out = blended
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 6. Optional luminance preservation (RGB only)
|
||||
# ----------------------------------------------------------------------
|
||||
if preserve_luminosity:
|
||||
new_luma = (out[..., :3] * luma_w).sum(dim=-1, keepdim=True)
|
||||
eps = 1e-8
|
||||
scale = luma / (new_luma + eps)
|
||||
out[..., :3] = out[..., :3] * scale
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# 7. Final clamp (entire image, including alpha, exactly like original)
|
||||
# ----------------------------------------------------------------------
|
||||
out.clamp_(0.0, 1.0)
|
||||
|
||||
return out
|
||||
@@ -0,0 +1,131 @@
|
||||
import torch
|
||||
import math
|
||||
from typing import Tuple, Optional
|
||||
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
|
||||
def exec_operator_color_brightness_contrast(
|
||||
image: torch.Tensor,
|
||||
brightness: float,
|
||||
contrast: float,
|
||||
mask: Optional[torch.Tensor] = None
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply GIMP‑style brightness and contrast adjustment to a batch of images.
|
||||
|
||||
This function processes an entire batch in GPU‑friendly chunks to manage
|
||||
VRAM consumption. Chunk size is determined by :func:`compute_chunk_size`.
|
||||
|
||||
**Input**:
|
||||
image (torch.Tensor): shape ``(B, H, W, C)``, values in [0, 1].
|
||||
Supported channel counts: ``C=3`` (RGB) or ``C=4`` (RGBA).
|
||||
brightness (float): slider value in [-127, 127] (GIMP UI range).
|
||||
contrast (float): slider value in [-127, 127].
|
||||
mask (Optional[torch.Tensor]):
|
||||
Optional mask of shape ``(B, H, W)`` or ``(B, H, W, 1)``,
|
||||
values in [0, 1]. When provided, the adjustment is blended
|
||||
per‑pixel using ``mask``.
|
||||
|
||||
**Output**:
|
||||
Tuple containing a single tensor of shape ``(B, H, W, C)`` where ``C``
|
||||
matches the input’s channel count. Alpha channel (if present) is
|
||||
preserved unchanged.
|
||||
|
||||
**Processing logic**:
|
||||
1. Convert slider values to GIMP internal ranges:
|
||||
``b = (brightness/127) / 2``, ``slant = tan((contrast/127 + 1) * pi/4)``.
|
||||
2. Apply brightness (GIMP formula) and contrast to the RGB channels.
|
||||
3. Clamp results to [0, 1].
|
||||
4. If a mask is given, blend the adjusted RGB with the original RGB.
|
||||
5. Re‑attach the alpha channel if the input was RGBA.
|
||||
|
||||
**GPU memory management**:
|
||||
The batch is split into chunks whose size is calculated by
|
||||
``compute_chunk_size(image, mask)``. Each chunk is processed
|
||||
independently, and the final result is concatenated along the
|
||||
batch dimension. No spatial tiling is performed.
|
||||
|
||||
Returns:
|
||||
``(result,)`` where *result* is the adjusted batch tensor.
|
||||
"""
|
||||
# ----------------------- Early exit -----------------------
|
||||
if brightness == 0 and contrast == 0:
|
||||
return (image,)
|
||||
|
||||
# ----------------------- Input validation -----------------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
if image.dim() != 4:
|
||||
raise ValueError(f"image must be 4D (B,H,W,C), got {image.dim()}D")
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(f"image last dim must be 3 or 4, got {image.shape[-1]}")
|
||||
if not image.is_floating_point():
|
||||
raise TypeError("image tensor must be floating point")
|
||||
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor")
|
||||
if mask.dim() not in (3, 4):
|
||||
raise ValueError(f"mask must be 3D (B,H,W) or 4D (B,H,W,1), got {mask.dim()}D")
|
||||
if mask.shape[0] != image.shape[0]:
|
||||
raise ValueError("mask batch size must equal image batch size")
|
||||
if mask.shape[1:3] != image.shape[1:3]:
|
||||
raise ValueError("mask spatial size must match image")
|
||||
if mask.dim() == 4 and mask.shape[-1] != 1:
|
||||
raise ValueError("4D mask must have 1 channel")
|
||||
if mask.dtype != image.dtype:
|
||||
mask = mask.to(dtype=image.dtype) # auto‑cast to avoid errors
|
||||
|
||||
# ----------------------- Chunked processing -----------------
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
B = image.shape[0]
|
||||
# If chunk_size is invalid or larger than batch, process all at once.
|
||||
if chunk_size is None or chunk_size <= 0 or chunk_size >= B:
|
||||
chunk_size = B
|
||||
|
||||
# Precompute constants shared across all chunks
|
||||
brightness_internal = brightness / 127.0
|
||||
contrast_internal = contrast / 127.0
|
||||
b = brightness_internal / 2.0 # [-0.5, 0.5]
|
||||
slant = math.tan((contrast_internal + 1) * math.pi / 4)
|
||||
|
||||
result_chunks = []
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
img_chunk = image[start:end] # (cs, H, W, C)
|
||||
m_chunk = mask[start:end] if mask is not None else None
|
||||
|
||||
# ---- RGB channels ----
|
||||
rgb = img_chunk[..., :3] # (cs, H, W, 3)
|
||||
|
||||
# Brightness (GIMP)
|
||||
if b < 0:
|
||||
rgb = rgb * (1.0 + b)
|
||||
else:
|
||||
rgb = rgb + (1.0 - rgb) * b
|
||||
|
||||
# Contrast
|
||||
rgb = (rgb - 0.5) * slant + 0.5
|
||||
rgb = torch.clamp(rgb, 0.0, 1.0)
|
||||
|
||||
# Mask blending
|
||||
if m_chunk is not None:
|
||||
if m_chunk.dim() == 3:
|
||||
m_chunk = m_chunk.unsqueeze(-1) # (cs, H, W, 1)
|
||||
original = img_chunk[..., :3]
|
||||
rgb = original * (1.0 - m_chunk) + rgb * m_chunk
|
||||
|
||||
# Alpha preservation
|
||||
if img_chunk.shape[-1] == 4:
|
||||
result_chunk = torch.cat([rgb, img_chunk[..., 3:4]], dim=-1)
|
||||
else:
|
||||
result_chunk = rgb
|
||||
|
||||
result_chunks.append(result_chunk)
|
||||
|
||||
# Concatenate all chunks back along batch dimension
|
||||
result = torch.cat(result_chunks, dim=0)
|
||||
return (result,)
|
||||
|
||||
|
||||
@@ -0,0 +1,145 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
# ===================================================================================
|
||||
# GEGL-perceptual Colorize operator (faithful approximation)
|
||||
# ===================================================================================
|
||||
def exec_operator_color_colorize(
|
||||
image: torch.Tensor,
|
||||
Hue: float,
|
||||
Saturation: float,
|
||||
Lightness: float,
|
||||
Color: int,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply a GEGL‑perceptual colourisation to a batch of images.
|
||||
|
||||
The algorithm works in the following steps:
|
||||
1. Compute BT.709 perceptual luminance of the original image.
|
||||
2. Decode the target colour and build a chroma vector.
|
||||
3. Scale the chroma vector by **Hue × Saturation** and add it
|
||||
to the luminance, effectively tinting the image.
|
||||
4. Apply a luminance bias based on the **Lightness** parameter.
|
||||
5. If a **mask** is provided, blend the result with the original
|
||||
using per‑pixel mask values as strength.
|
||||
|
||||
The function supports RGB and RGBA input: alpha is preserved
|
||||
unchanged and only the RGB channels are colourised. Batch
|
||||
processing is handled internally with GPU‑friendly chunking.
|
||||
|
||||
Args:
|
||||
image: [B, H, W, C] float tensor (C=3 or 4), values 0‑1.
|
||||
Hue: float 0‑1, controls tint strength (together with Saturation).
|
||||
Saturation: float 0‑1, scales the chroma direction.
|
||||
Lightness: float 0‑1, brightness bias (0.5 = no change).
|
||||
Color: 24‑bit integer (0xRRGGBB) of the target tint.
|
||||
mask: Optional mask [H, W] or [B, H, W], values 0‑1.
|
||||
Returns:
|
||||
A 1‑tuple containing the colourised image tensor [B, H, W, C].
|
||||
"""
|
||||
# --- input validation ----------------------------------------------------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
if image.dim() != 4:
|
||||
raise ValueError(f"image must be 4D [B,H,W,C], got shape {image.shape}")
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(f"image must have 3 or 4 channels, got {image.shape[-1]}")
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor")
|
||||
if mask.dim() not in (2, 3):
|
||||
raise ValueError(f"mask must be 2D [H,W] or 3D [B,H,W], got shape {mask.shape}")
|
||||
if mask.dim() == 3 and mask.shape[0] != image.shape[0]:
|
||||
raise ValueError(
|
||||
f"batch size mismatch: image {image.shape[0]}, mask {mask.shape[0]}"
|
||||
)
|
||||
# device consistency check
|
||||
if mask.device != image.device:
|
||||
raise ValueError("image and mask must be on the same device")
|
||||
|
||||
# --- early exit conditions -----------------------------------------------
|
||||
B = image.shape[0]
|
||||
if B == 0:
|
||||
return (image,)
|
||||
if mask is not None and not mask.isnan().any() and mask.max() == 0.0:
|
||||
# mask is completely black → original image is unchanged
|
||||
return (image,)
|
||||
|
||||
# --- prepare alpha handling ----------------------------------------------
|
||||
has_alpha = image.shape[-1] == 4
|
||||
if has_alpha:
|
||||
rgb = image[..., :3]
|
||||
alpha = image[..., 3:4]
|
||||
else:
|
||||
rgb = image
|
||||
alpha = None
|
||||
|
||||
# --- determine chunk size for GPU processing -----------------------------
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
results = []
|
||||
|
||||
# --- chunked batch loop --------------------------------------------------
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
chunk_rgb = rgb[start:end] # [C, H, W, 3]
|
||||
chunk_mask = None
|
||||
if mask is not None:
|
||||
if mask.dim() == 3: # batched mask
|
||||
chunk_mask = mask[start:end]
|
||||
else: # shared 2D mask
|
||||
chunk_mask = mask
|
||||
|
||||
# --- colourisation pipeline (identical to original) -------------------
|
||||
# 1. Ensure linear RGB (clamp)
|
||||
chunk_rgb = chunk_rgb.clamp(0, 1)
|
||||
|
||||
r, g, b = chunk_rgb[..., 0], chunk_rgb[..., 1], chunk_rgb[..., 2]
|
||||
|
||||
# 2. BT.709 perceptual luminance
|
||||
Y = 0.222488 * r + 0.716903 * g + 0.060609 * b
|
||||
|
||||
# 3. Target colour
|
||||
cr = ((Color >> 16) & 255) / 255.0
|
||||
cg = ((Color >> 8) & 255) / 255.0
|
||||
cb = (Color & 255) / 255.0
|
||||
Yc = 0.222488 * cr + 0.716903 * cg + 0.060609 * cb
|
||||
|
||||
# 4. Chroma direction and tint scaling
|
||||
dr, dg, db = cr - Yc, cg - Yc, cb - Yc
|
||||
sat = Saturation * Hue
|
||||
|
||||
rr = Y + dr * sat
|
||||
gg = Y + dg * sat
|
||||
bb = Y + db * sat
|
||||
|
||||
out_rgb = torch.stack([rr, gg, bb], dim=-1)
|
||||
|
||||
# 5. Lightness bias
|
||||
light_bias = Lightness - 0.5
|
||||
out_rgb = out_rgb + light_bias
|
||||
out_rgb = out_rgb.clamp(0, 1)
|
||||
|
||||
# 6. Mask blending (per‑pixel strength)
|
||||
if chunk_mask is not None:
|
||||
# ensure mask has a trailing channel dimension for broadcasting
|
||||
if chunk_mask.dim() == 2:
|
||||
chunk_mask = chunk_mask.unsqueeze(0).unsqueeze(-1) # [1, H, W, 1]
|
||||
elif chunk_mask.dim() == 3:
|
||||
chunk_mask = chunk_mask.unsqueeze(-1) # [B, H, W, 1]
|
||||
out_rgb = chunk_rgb * (1 - chunk_mask) + out_rgb * chunk_mask
|
||||
|
||||
# 7. Reattach alpha if present
|
||||
if has_alpha:
|
||||
chunk_out = torch.cat([out_rgb, alpha[start:end]], dim=-1)
|
||||
else:
|
||||
chunk_out = out_rgb
|
||||
|
||||
results.append(chunk_out)
|
||||
|
||||
# concatenate all chunks back to full batch
|
||||
final = torch.cat(results, dim=0)
|
||||
return (final,)
|
||||
|
||||
@@ -0,0 +1,177 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
_LUMA = torch.tensor([0.2126, 0.7152, 0.0722])
|
||||
|
||||
|
||||
def exec_operator_color_comp_channel_mixer(
|
||||
image: torch.Tensor,
|
||||
Red_in_red: float,
|
||||
Green_in_red: float,
|
||||
Blue_in_red: float,
|
||||
Red_in_green: float,
|
||||
Green_in_green: float,
|
||||
Blue_in_green: float,
|
||||
Red_in_blue: float,
|
||||
Green_in_blue: float,
|
||||
Blue_in_blue: float,
|
||||
Preserve_luminosity: bool,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Applies a channel‑mixing matrix to an RGB or RGBA image, optionally
|
||||
modulated by a per‑pixel mask and with luminosity preservation.
|
||||
|
||||
Input:
|
||||
image : torch.Tensor – batch of images, shape [B, H, W, C] with C=3 (RGB) or 4 (RGBA).
|
||||
Red_in_red … Blue_in_blue : coefficients of the 3×3 mixing matrix.
|
||||
Preserve_luminosity : if True, scales the mixed result so that the perceived
|
||||
luminance matches the original before clamping.
|
||||
mask : optional torch.Tensor – per‑pixel blend strength, broadcastable to
|
||||
[B, H, W, 1]. Allowed shapes: [H, W], [B, H, W], [B, H, W, 1].
|
||||
A pixel value of 1.0 gives full mixing; 0.0 leaves the original unchanged.
|
||||
|
||||
Output:
|
||||
Tuple containing a single tensor of mixed images, same shape as input but
|
||||
with RGB channels modified. If the input has an alpha channel, it is passed
|
||||
through unchanged.
|
||||
|
||||
"""
|
||||
# ---------- input validation ----------
|
||||
if image.dim() != 4:
|
||||
raise ValueError(
|
||||
f"Input image must be a 4D tensor [B, H, W, C], got shape {image.shape}"
|
||||
)
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(
|
||||
f"Input image must have 3 (RGB) or 4 (RGBA) channels, got {image.shape[-1]}"
|
||||
)
|
||||
if mask is not None:
|
||||
if mask.dim() not in (2, 3, 4):
|
||||
raise ValueError(
|
||||
f"Mask must be 2D [H,W], 3D [B,H,W] or 4D [B,H,W,1], got shape {mask.shape}"
|
||||
)
|
||||
# ensure mask can be broadcast to [B, H, W, 1]
|
||||
if mask.dim() == 2:
|
||||
mask = mask.unsqueeze(0).unsqueeze(-1) # [1, H, W, 1]
|
||||
elif mask.dim() == 3:
|
||||
if mask.shape[0] != image.shape[0]:
|
||||
mask = mask.unsqueeze(0).unsqueeze(-1) # treat as [1, H, W, 1]
|
||||
else:
|
||||
mask = mask.unsqueeze(-1) # [B, H, W, 1]
|
||||
# mask already 4D – verify last dim == 1
|
||||
if mask.shape[-1] != 1:
|
||||
mask = mask.unsqueeze(-1)
|
||||
if mask.shape[0] not in (1, image.shape[0]):
|
||||
raise ValueError(
|
||||
f"Mask batch dimension ({mask.shape[0]}) must be 1 or match image batch ({image.shape[0]})"
|
||||
)
|
||||
if mask.shape[1:3] != image.shape[1:3]:
|
||||
raise ValueError(
|
||||
f"Mask spatial size {mask.shape[1:3]} does not match image size {image.shape[1:3]}"
|
||||
)
|
||||
|
||||
# ---------- early exit conditions ----------
|
||||
identity = (
|
||||
Red_in_red == 1.0
|
||||
and Green_in_red == 0.0
|
||||
and Blue_in_red == 0.0
|
||||
and Red_in_green == 0.0
|
||||
and Green_in_green == 1.0
|
||||
and Blue_in_green == 0.0
|
||||
and Red_in_blue == 0.0
|
||||
and Green_in_blue == 0.0
|
||||
and Blue_in_blue == 1.0
|
||||
and not Preserve_luminosity
|
||||
)
|
||||
if mask is None and identity:
|
||||
return (image,)
|
||||
if mask is not None and torch.all(mask == 0.0):
|
||||
return (image,)
|
||||
if mask is not None and identity:
|
||||
# identity transform + mask all zeros already caught;
|
||||
# if mask not all zero we still need blending, so continue
|
||||
pass
|
||||
|
||||
# ---------- chunked processing ----------
|
||||
B = image.shape[0]
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
results = []
|
||||
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
chunk_img = image[start:end]
|
||||
|
||||
# slice mask if it has a batch dim, otherwise keep the single mask
|
||||
if mask is not None:
|
||||
if mask.shape[0] == B:
|
||||
chunk_mask = mask[start:end]
|
||||
else:
|
||||
chunk_mask = mask
|
||||
# move to same device/dtype as image
|
||||
chunk_mask = chunk_mask.to(device=chunk_img.device, dtype=chunk_img.dtype)
|
||||
else:
|
||||
chunk_mask = None
|
||||
|
||||
# -------- inner processing function (preserves original logic) --------
|
||||
def _process_chunk(img: torch.Tensor, msk: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
has_alpha = img.shape[-1] == 4
|
||||
if has_alpha:
|
||||
alpha = img[..., 3:4] # preserve alpha
|
||||
rgb = img[..., :3]
|
||||
else:
|
||||
rgb = img
|
||||
|
||||
r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
|
||||
|
||||
# matrix mixing (identical to original)
|
||||
r_out = Red_in_red * r + Green_in_red * g + Blue_in_red * b
|
||||
g_out = Red_in_green * r + Green_in_green * g + Blue_in_green * b
|
||||
b_out = Red_in_blue * r + Green_in_blue * g + Blue_in_blue * b
|
||||
|
||||
# luminosity preservation
|
||||
if Preserve_luminosity:
|
||||
device = rgb.device
|
||||
dtype = rgb.dtype
|
||||
luma = _LUMA.to(device=device, dtype=dtype)
|
||||
|
||||
def channel_luma(a, b, c):
|
||||
return a * luma[0] + b * luma[1] + c * luma[2]
|
||||
|
||||
orig_luma = luma[0] * r + luma[1] * g + luma[2] * b
|
||||
mix_luma = (
|
||||
channel_luma(Red_in_red, Green_in_red, Blue_in_red) * r
|
||||
+ channel_luma(Red_in_green, Green_in_green, Blue_in_green) * g
|
||||
+ channel_luma(Red_in_blue, Green_in_blue, Blue_in_blue) * b
|
||||
)
|
||||
eps = 1e-8
|
||||
scale = orig_luma / (mix_luma + eps)
|
||||
r_out = r_out * scale
|
||||
g_out = g_out * scale
|
||||
b_out = b_out * scale
|
||||
|
||||
# stack and clamp (only RGB part)
|
||||
mixed_rgb = torch.stack([r_out, g_out, b_out], dim=-1)
|
||||
|
||||
# mask blending (applies only to RGB)
|
||||
if msk is not None:
|
||||
# broadcast mask from [B, H, W, 1] to [B, H, W, 3]
|
||||
mixed_rgb = rgb * (1.0 - msk) + mixed_rgb * msk
|
||||
|
||||
mixed_rgb = torch.clamp(mixed_rgb, 0.0, 1.0)
|
||||
|
||||
if has_alpha:
|
||||
return torch.cat([mixed_rgb, alpha], dim=-1)
|
||||
return mixed_rgb
|
||||
|
||||
# -------- process the chunk --------
|
||||
res_chunk = _process_chunk(chunk_img, chunk_mask)
|
||||
results.append(res_chunk)
|
||||
|
||||
# free memory after each chunk if on GPU
|
||||
if image.is_cuda:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
return (torch.cat(results, dim=0),)
|
||||
|
||||
@@ -0,0 +1,237 @@
|
||||
import torch
|
||||
import math
|
||||
from typing import Tuple, Optional
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
def exec_operator_color_comp_extract(
|
||||
image: torch.Tensor,
|
||||
component: str,
|
||||
invert: bool = False,
|
||||
linear: bool = False,
|
||||
mask: Optional[torch.Tensor] = None
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Extract a specific colour component from an image batch and return it as a mask tensor.
|
||||
|
||||
The function supports the following colour spaces:
|
||||
RGB (red, green, blue), HSV (hue, saturation, value), HSL (saturation, lightness),
|
||||
CMYK (cyan, magenta, yellow, key), Y'CbCr (Y', Cb, Cr), CIE LAB (L, A, B),
|
||||
CIE LCH (C(ab), H(ab)), and Alpha.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
Input image batch with shape ``(B, H, W, C)`` where ``C`` is 3 (RGB) or
|
||||
4 (RGBA). When a 3‑D tensor ``(H, W, C)`` is given it is treated as a
|
||||
batch of size 1.
|
||||
component : str
|
||||
Name of the colour component to extract (case‑insensitive). Valid values
|
||||
are those listed in the ``INPUT_TYPES`` of the calling node.
|
||||
invert : bool
|
||||
If ``True``, the output mask is inverted (``1 - value``) after the mask is
|
||||
applied.
|
||||
linear : bool
|
||||
If ``True``, the extracted component is kept in linear light. When
|
||||
``False`` (default) a gamma encoding (sRGB transfer function) is applied
|
||||
to the raw component values before the mask is multiplied.
|
||||
mask : torch.Tensor or None
|
||||
Optional per‑pixel strength mask with shape ``(B, H, W)`` or ``(B, H, W, 1)``.
|
||||
Must match the batch size and spatial dimensions of ``image``. If ``None``,
|
||||
the extracted component is used as is (subject to ``linear`` and ``invert``).
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tuple[torch.Tensor]
|
||||
A single‑element tuple containing the result mask of shape ``(B, H, W)``,
|
||||
with values in the range ``[0, 1]``.
|
||||
|
||||
Raises
|
||||
------
|
||||
TypeError
|
||||
If ``image`` or ``mask`` is not a ``torch.Tensor``.
|
||||
ValueError
|
||||
If the shapes of ``image`` or ``mask`` are invalid or incompatible.
|
||||
"""
|
||||
# ---------- input validation ----------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
if image.dim() not in (3, 4):
|
||||
raise ValueError(
|
||||
f"image must have shape (B,H,W,C) or (H,W,C), got {image.shape}"
|
||||
)
|
||||
if image.dim() == 3:
|
||||
image = image.unsqueeze(0) # treat as batch size 1
|
||||
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor or None")
|
||||
# unify mask shape to (B, H, W)
|
||||
if mask.dim() == 4:
|
||||
mask = mask.squeeze(-1)
|
||||
if mask.dim() != 3:
|
||||
raise ValueError(
|
||||
f"mask must be (B,H,W) or (B,H,W,1), got {mask.shape}"
|
||||
)
|
||||
if mask.shape[0] != image.shape[0] or mask.shape[1:3] != image.shape[1:3]:
|
||||
raise ValueError(
|
||||
"mask batch and spatial dimensions must match those of image"
|
||||
)
|
||||
|
||||
# ---------- early exit for empty input / all‑zero mask ----------
|
||||
if image.shape[0] == 0:
|
||||
return (torch.empty(0, image.shape[1], image.shape[2],
|
||||
device=image.device, dtype=torch.float32),)
|
||||
|
||||
if mask is not None and mask.eq(0).all():
|
||||
# result will be all zeros or all ones depending on invert
|
||||
result = torch.ones_like(mask) if invert else torch.zeros_like(mask)
|
||||
return (result,)
|
||||
|
||||
# ---------- determine GPU‑friendly chunk size ----------
|
||||
if image.is_cuda and torch.cuda.is_available():
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
if not isinstance(chunk_size, int) or chunk_size <= 0:
|
||||
chunk_size = image.shape[0]
|
||||
else:
|
||||
chunk_size = image.shape[0]
|
||||
chunk_size = max(1, min(chunk_size, image.shape[0]))
|
||||
|
||||
comp = component.strip().lower()
|
||||
results = []
|
||||
|
||||
# ---------- chunked processing loop ----------
|
||||
for start in range(0, image.shape[0], chunk_size):
|
||||
end = min(start + chunk_size, image.shape[0])
|
||||
img_chunk = image[start:end]
|
||||
m_chunk = mask[start:end] if mask is not None else None
|
||||
|
||||
# --- colour space conversion (same as original) ---
|
||||
rgb = img_chunk[..., :3]
|
||||
r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
|
||||
|
||||
if comp == "rgb red":
|
||||
res = r
|
||||
elif comp == "rgb green":
|
||||
res = g
|
||||
elif comp == "rgb blue":
|
||||
res = b
|
||||
|
||||
elif comp.startswith("hsv"):
|
||||
maxc = torch.max(rgb, dim=-1).values
|
||||
minc = torch.min(rgb, dim=-1).values
|
||||
diff = maxc - minc + 1e-8
|
||||
|
||||
if comp == "hsv value":
|
||||
res = maxc
|
||||
elif comp == "hsv saturation":
|
||||
res = diff / (maxc + 1e-8)
|
||||
elif comp == "hsv hue":
|
||||
hue = torch.zeros_like(maxc)
|
||||
mask_nonzero = diff > 1e-8
|
||||
r_eq = (maxc == r) & mask_nonzero
|
||||
g_eq = (maxc == g) & mask_nonzero
|
||||
b_eq = (maxc == b) & mask_nonzero
|
||||
hue_r = ((g - b) / diff) % 6.0
|
||||
hue_g = ((b - r) / diff) + 2.0
|
||||
hue_b = ((r - g) / diff) + 4.0
|
||||
hue = torch.where(r_eq, hue_r, hue)
|
||||
hue = torch.where(g_eq, hue_g, hue)
|
||||
hue = torch.where(b_eq, hue_b, hue)
|
||||
res = (hue / 6.0) % 1.0
|
||||
else:
|
||||
res = maxc
|
||||
|
||||
elif comp.startswith("hsl"):
|
||||
maxc = torch.max(rgb, dim=-1).values
|
||||
minc = torch.min(rgb, dim=-1).values
|
||||
l = (maxc + minc) * 0.5
|
||||
if comp == "hsl lightness":
|
||||
res = l
|
||||
elif comp == "hsl saturation":
|
||||
denom = 1 - torch.abs(2 * l - 1) + 1e-8
|
||||
res = (maxc - minc) / denom
|
||||
else:
|
||||
res = l
|
||||
|
||||
elif comp.startswith("cmyk"):
|
||||
k = 1.0 - torch.max(rgb, dim=-1).values
|
||||
denom = 1.0 - k + 1e-8
|
||||
if comp == "cmyk cyan":
|
||||
res = (1.0 - r - k) / denom
|
||||
elif comp == "cmyk magenta":
|
||||
res = (1.0 - g - k) / denom
|
||||
elif comp == "cmyk yellow":
|
||||
res = (1.0 - b - k) / denom
|
||||
elif comp == "cmyk key":
|
||||
res = k
|
||||
else:
|
||||
res = k
|
||||
|
||||
elif "ycbcr" in comp or "y'cbcr" in comp:
|
||||
y = 0.299 * r + 0.587 * g + 0.114 * b
|
||||
if "cb" in comp:
|
||||
res = (b - y) * 0.564 + 0.5
|
||||
elif "cr" in comp:
|
||||
res = (r - y) * 0.713 + 0.5
|
||||
else:
|
||||
res = y
|
||||
|
||||
elif comp.startswith("lab") or comp.startswith("lch"):
|
||||
def f(t):
|
||||
return torch.where(t > 0.008856, t ** (1/3), 7.787 * t + 16/116)
|
||||
|
||||
rl = torch.where(r > 0.04045, ((r + 0.055) / 1.055) ** 2.4, r / 12.92)
|
||||
gl = torch.where(g > 0.04045, ((g + 0.055) / 1.055) ** 2.4, g / 12.92)
|
||||
bl = torch.where(b > 0.04045, ((b + 0.055) / 1.055) ** 2.4, b / 12.92)
|
||||
|
||||
x = 0.4124564 * rl + 0.3575761 * gl + 0.1804375 * bl
|
||||
y = 0.2126729 * rl + 0.7151522 * gl + 0.0721750 * bl
|
||||
z = 0.0193339 * rl + 0.1191920 * gl + 0.9503041 * bl
|
||||
|
||||
xn, yn, zn = 0.95047, 1.0, 1.08883
|
||||
x, y, z = x/xn, y/yn, z/zn
|
||||
|
||||
fx, fy, fz = f(x), f(y), f(z)
|
||||
a = 500 * (fx - fy)
|
||||
bb = 200 * (fy - fz)
|
||||
|
||||
if comp == "lab l":
|
||||
res = (116 * fy - 16) / 100
|
||||
elif comp == "lab a":
|
||||
res = a / 128 + 0.5
|
||||
elif comp == "lab b":
|
||||
res = bb / 128 + 0.5
|
||||
elif comp == "lch c(ab)":
|
||||
res = torch.sqrt(a*a + bb*bb) / 128
|
||||
elif comp == "lch h(ab)":
|
||||
res = (torch.atan2(bb, a) / (2 * math.pi)) % 1.0
|
||||
else:
|
||||
res = (116 * fy - 16) / 100
|
||||
|
||||
elif comp == "alpha":
|
||||
res = img_chunk[..., 3] if img_chunk.shape[-1] == 4 else torch.ones_like(r)
|
||||
|
||||
else:
|
||||
res = r # fallback
|
||||
|
||||
# --- post‑processing (identical to original) ---
|
||||
res = res.clamp(0, 1)
|
||||
|
||||
if not linear:
|
||||
res = torch.where(
|
||||
res <= 0.0031308,
|
||||
12.92 * res,
|
||||
1.055 * torch.pow(res, 1/2.4) - 0.055
|
||||
).clamp(0, 1)
|
||||
|
||||
if m_chunk is not None:
|
||||
res = res * m_chunk
|
||||
|
||||
if invert:
|
||||
res = 1.0 - res
|
||||
|
||||
results.append(res)
|
||||
|
||||
final_result = torch.cat(results, dim=0)
|
||||
return (final_result,)
|
||||
|
||||
@@ -0,0 +1,142 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
|
||||
# Assume this import is available in the target environment
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
|
||||
def exec_operator_color_comp_mono_mixer(
|
||||
image: torch.Tensor,
|
||||
preserve_luminosity: bool,
|
||||
red_multiplier: float,
|
||||
green_multiplier: float,
|
||||
blue_multiplier: float,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply a channel mixer that converts an RGB or RGBA image to grayscale using
|
||||
per‑channel multipliers, optionally normalised to preserve perceptual luminance.
|
||||
|
||||
The operation mimics GIMP's Colors → Components → Mono Mixer. If a mask is
|
||||
supplied, it acts as a per‑pixel strength factor: the grayscale result is
|
||||
blended with the original image according to the mask values. An existing
|
||||
alpha channel is always kept unchanged.
|
||||
|
||||
Processing runs on the same device as the input tensor. To avoid exhausting
|
||||
GPU memory the batch is automatically split into chunks whose size is
|
||||
determined by :func:`compute_chunk_size`.
|
||||
|
||||
Args:
|
||||
image: Input image tensor [B, H, W, C], C=3 (RGB) or C=4 (RGBA).
|
||||
Values must lie in [0, 1].
|
||||
preserve_luminosity: If True, the three multipliers are normalised so that
|
||||
their absolute sum equals 1.
|
||||
red_multiplier: Coefficient for the red channel [-5.0, 5.0].
|
||||
green_multiplier: Coefficient for the green channel [-5.0, 5.0].
|
||||
blue_multiplier: Coefficient for the blue channel [-5.0, 5.0].
|
||||
mask: Optional mask tensor [B, H, W] or [B, H, W, 1].
|
||||
Values in [0, 1], 0 = no effect, 1 = full effect.
|
||||
|
||||
Returns:
|
||||
Tuple containing a single tensor [B, H, W, C] of the same channel count
|
||||
as the input, with colour values in [0, 1].
|
||||
|
||||
Raises:
|
||||
ValueError: If the input tensor does not have shape [B, H, W, 3] or
|
||||
[B, H, W, 4], or if the mask dimensions / batch size are
|
||||
incompatible.
|
||||
"""
|
||||
# ------------------------------------------------------------------
|
||||
# 1. Input validation
|
||||
# ------------------------------------------------------------------
|
||||
if image.dim() != 4 or image.shape[-1] not in (3, 4):
|
||||
raise ValueError(
|
||||
f"image must be a 4D tensor [B,H,W,C] with C=3 or 4, got shape {tuple(image.shape)}"
|
||||
)
|
||||
|
||||
B, H, W, C = image.shape
|
||||
has_alpha = (C == 4)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 2. Mask validation & early‑exit conditions
|
||||
# ------------------------------------------------------------------
|
||||
if mask is not None:
|
||||
if mask.dim() == 3:
|
||||
# [B, H, W] -> [B, H, W, 1]
|
||||
mask = mask.unsqueeze(-1)
|
||||
elif mask.dim() == 4:
|
||||
if mask.shape[-1] != 1:
|
||||
raise ValueError(
|
||||
f"mask must have shape [B,H,W] or [B,H,W,1], got {tuple(mask.shape)}"
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
f"mask must be 3D or 4D, got {mask.dim()}D with shape {tuple(mask.shape)}"
|
||||
)
|
||||
|
||||
if mask.shape[0] != B:
|
||||
raise ValueError(
|
||||
f"Batch size mismatch: image has {B}, mask has {mask.shape[0]}"
|
||||
)
|
||||
# Early exit: no pixels are affected
|
||||
if mask.max() == 0:
|
||||
return (image,)
|
||||
|
||||
# Early exit: empty batch
|
||||
if B == 0:
|
||||
return (image,)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 3. Chunk size for VRAM‑friendly processing
|
||||
# ------------------------------------------------------------------
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 4. Per‑chunk processing
|
||||
# ------------------------------------------------------------------
|
||||
results = []
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
img_chunk = image[start:end]
|
||||
msk_chunk = mask[start:end] if mask is not None else None
|
||||
|
||||
# --- normalise multipliers (GEGL behaviour) ---
|
||||
if preserve_luminosity:
|
||||
m_sum = red_multiplier + green_multiplier + blue_multiplier
|
||||
norm_factor = 1.0 if m_sum == 0.0 else abs(1.0 / m_sum)
|
||||
else:
|
||||
norm_factor = 1.0
|
||||
|
||||
r = red_multiplier * norm_factor
|
||||
g = green_multiplier * norm_factor
|
||||
b = blue_multiplier * norm_factor
|
||||
|
||||
# --- grayscale via dot product on RGB channels ---
|
||||
gray = img_chunk[..., 0] * r + img_chunk[..., 1] * g + img_chunk[..., 2] * b
|
||||
gray = torch.clamp(gray, 0.0, 1.0)
|
||||
|
||||
# --- build result RGB (all channels equal) ---
|
||||
gray_rgb = torch.stack([gray, gray, gray], dim=-1) # [chunk, H, W, 3]
|
||||
|
||||
# --- alpha preservation ---
|
||||
if has_alpha:
|
||||
alpha = img_chunk[..., 3:4]
|
||||
res_chunk = torch.cat([gray_rgb, alpha], dim=-1)
|
||||
else:
|
||||
res_chunk = gray_rgb
|
||||
|
||||
# --- mask blending (RGB channels only) ---
|
||||
if msk_chunk is not None:
|
||||
# msk_chunk is [chunk, H, W, 1], broadcast to RGB
|
||||
res_chunk[..., :3] = (
|
||||
img_chunk[..., :3] * (1.0 - msk_chunk) + gray_rgb * msk_chunk
|
||||
)
|
||||
|
||||
results.append(res_chunk)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 5. Reassemble full batch
|
||||
# ------------------------------------------------------------------
|
||||
final = torch.cat(results, dim=0)
|
||||
return (final,)
|
||||
|
||||
@@ -0,0 +1,151 @@
|
||||
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from typing import Tuple
|
||||
|
||||
|
||||
def exec_operator_color_desat_color_2_gray(
|
||||
image: torch.Tensor,
|
||||
radius: int,
|
||||
samples: int,
|
||||
iterations: int,
|
||||
enhance_shadows: bool,
|
||||
mask: torch.Tensor = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
GIMP-style Color to Gray using STRESS stochastic sampling.
|
||||
Fully GPU-accelerated: all random offset sampling is vectorised,
|
||||
no Python loops over pixels or samples.
|
||||
"""
|
||||
device = image.device
|
||||
B, H, W, C = image.shape
|
||||
|
||||
# Convert to [B, C, H, W] for grid_sample
|
||||
img = image.permute(0, 3, 1, 2).to(device)
|
||||
|
||||
# Initial grayscale: luminance (BT.709)
|
||||
rgb_weights = torch.tensor([0.2126, 0.7152, 0.0722], device=device).view(1, 3, 1, 1)
|
||||
gray = (img * rgb_weights).sum(dim=1, keepdim=True) # [B, 1, H, W]
|
||||
|
||||
# Tiny radius → just return luminance
|
||||
if radius < 2:
|
||||
gray = gray.clamp(0, 1)
|
||||
result = gray.expand(-1, 3, -1, -1).permute(0, 2, 3, 1)
|
||||
if mask is not None:
|
||||
# apply mask if provided (even in early exit)
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1)
|
||||
elif mask.dim() == 2:
|
||||
mask = mask.unsqueeze(0).unsqueeze(-1)
|
||||
mask = mask.to(device)
|
||||
result = image * (1 - mask) + result * mask
|
||||
return (result,)
|
||||
|
||||
# Precompute normalisation factors for grid_sample
|
||||
# (we'll use align_corners=False, so pixel centres at (i+0.5)/W-0.5 etc.)
|
||||
# For grid_sample with align_corners=False, coordinates are in [-1, 1]
|
||||
# where -1 maps to the left/top border of the first pixel,
|
||||
# 1 maps to the right/bottom border of the last pixel.
|
||||
# Pixel i's centre is at (i + 0.5) / W * 2 - 1 in x.
|
||||
# But we need offsets in pixel units, so we convert pixel offsets to
|
||||
# normalised grid offsets by dividing by (W-1) and multiplying by 2,
|
||||
# then add to the base grid of pixel centres.
|
||||
# A simpler way: generate a base grid of pixel centres, then add
|
||||
# normalised random offsets. This is done below.
|
||||
|
||||
# Create a base grid of pixel coordinates (x, y) for the whole image.
|
||||
# Shape: [1, H, W, 2] (batch=1)
|
||||
ys = torch.linspace(-1, 1, H, device=device)
|
||||
xs = torch.linspace(-1, 1, W, device=device)
|
||||
grid_y, grid_x = torch.meshgrid(ys, xs, indexing="ij") # both [H, W]
|
||||
base_grid = torch.stack([grid_x, grid_y], dim=-1).unsqueeze(0) # [1, H, W, 2]
|
||||
# Expand to batch size
|
||||
base_grid = base_grid.expand(B, H, W, 2).clone() # we'll add offsets
|
||||
|
||||
# Normalise radius from pixels to [-1,1] scale
|
||||
# 1 pixel in image space corresponds to (2 / (W-1)) in normalised coords (for x)
|
||||
# but we need to handle both directions; use the smaller divisor to be safe
|
||||
# or use isotropic scaling. We'll just use W-1 for x and H-1 for y.
|
||||
rx = (radius / (W - 1)) * 2 if W > 1 else 0.0
|
||||
ry = (radius / (H - 1)) * 2 if H > 1 else 0.0
|
||||
|
||||
for _ in range(iterations):
|
||||
# Generate random offsets for all pixels and samples at once
|
||||
# Shape: [B, H, W, samples, 2]
|
||||
offsets = torch.zeros(B, H, W, samples, 2, device=device)
|
||||
offsets[..., 0].uniform_(-rx, rx) # x offset
|
||||
offsets[..., 1].uniform_(-ry, ry) # y offset
|
||||
|
||||
# Build the full grid for each sample by adding offsets to base_grid
|
||||
# base_grid: [B, H, W, 1, 2] + offsets: [B, H, W, S, 2]
|
||||
sample_grids = base_grid.unsqueeze(3) + offsets # [B, H, W, S, 2]
|
||||
|
||||
# --------------------------
|
||||
# Sample the colour image at all offsets in one go
|
||||
# We need to call grid_sample with a grid of shape [N, H_out, W_out, 2].
|
||||
# Here N = B * S, H_out = H, W_out = W.
|
||||
# Reshape sample_grids to [B*S, H, W, 2]
|
||||
grid_color = sample_grids.permute(0, 3, 1, 2, 4).reshape(B * samples, H, W, 2)
|
||||
# Replicate the image S times along the batch dimension
|
||||
img_rep = img.repeat(samples, 1, 1, 1) # [B*S, C, H, W]
|
||||
sampled_col = F.grid_sample(
|
||||
img_rep, grid_color, mode='bilinear',
|
||||
padding_mode='border', align_corners=False
|
||||
) # [B*S, C, H, W]
|
||||
# Reshape back to [B, S, C, H, W] then to [B, C, H, W, S]
|
||||
sampled_col = sampled_col.reshape(B, samples, C, H, W).permute(0, 2, 3, 4, 1) # [B, C, H, W, S]
|
||||
|
||||
# --------------------------
|
||||
# Sample the current grayscale at the same offsets
|
||||
# grid_sample expects input with channels, so repeat gray to B*S batch
|
||||
gray_rep = gray.repeat(samples, 1, 1, 1) # [B*S, 1, H, W]
|
||||
sampled_gray = F.grid_sample(
|
||||
gray_rep, grid_color, mode='bilinear',
|
||||
padding_mode='border', align_corners=False
|
||||
) # [B*S, 1, H, W]
|
||||
sampled_gray = sampled_gray.reshape(B, samples, 1, H, W).permute(0, 2, 3, 4, 1) # [B, 1, H, W, S]
|
||||
|
||||
# --------------------------
|
||||
# Compute envelopes and contrast
|
||||
min_col, _ = sampled_col.min(dim=-1) # [B, C, H, W]
|
||||
max_col, _ = sampled_col.max(dim=-1)
|
||||
color_range = max_col - min_col
|
||||
color_contrast = torch.norm(color_range, dim=1, keepdim=True) # [B, 1, H, W]
|
||||
|
||||
min_gray, _ = sampled_gray.min(dim=-1) # [B, 1, H, W]
|
||||
max_gray, _ = sampled_gray.max(dim=-1)
|
||||
gray_range = max_gray - min_gray
|
||||
|
||||
gray_mean = sampled_gray.mean(dim=-1) # [B, 1, H, W]
|
||||
|
||||
# STRESS scaling
|
||||
eps = 1e-6
|
||||
scale = color_contrast / (gray_range + eps)
|
||||
scale = scale.clamp(0.5, 2.0)
|
||||
|
||||
# Update gray: local adjustment
|
||||
gray_new = gray_mean + (gray - gray_mean) * scale
|
||||
|
||||
if enhance_shadows:
|
||||
shadow_boost = 1.0 + 0.3 * (1.0 - gray_new)
|
||||
gray_new = gray_new * shadow_boost
|
||||
|
||||
gray = gray_new.clamp(0, 1)
|
||||
|
||||
# Convert to 3‑channel RGB
|
||||
result = gray.expand(-1, 3, -1, -1).permute(0, 2, 3, 1)
|
||||
|
||||
# Apply mask if provided
|
||||
if mask is not None:
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1) # [B, H, W] → [B, H, W, 1]
|
||||
elif mask.dim() == 2:
|
||||
mask = mask.unsqueeze(0).unsqueeze(-1) # [H, W] → [1, H, W, 1]
|
||||
mask = mask.to(device)
|
||||
result = image * (1 - mask) + result * mask
|
||||
|
||||
return (result,)
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,138 @@
|
||||
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
def exec_operator_color_desat_desturation(
|
||||
image: torch.Tensor,
|
||||
mode: str,
|
||||
mask: Optional[torch.Tensor] = None
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Desaturate an RGB or RGBA image batch using one of five GIMP modes.
|
||||
|
||||
Modes and formulas (based on GIMP source code):
|
||||
- "Luminance": (0.22 * R) + (0.72 * G) + (0.06 * B)
|
||||
- "Luma": (0.22 * R) + (0.72 * G) + (0.06 * B) (non‑linearized)
|
||||
- "Lightness (HSL)": 0.5 * (max(R,G,B) + min(R,G,B))
|
||||
- "Avg (HSI Intensity)": (R + G + B) / 3
|
||||
- "Value (HSV)": max(R,G,B)
|
||||
|
||||
Processing is performed in chunks along the batch dimension to limit
|
||||
GPU memory usage. An optional mask can be supplied to blend between the
|
||||
original and the desaturated image on a per‑pixel basis. If the mask is
|
||||
entirely zero the input is returned unchanged; if it is entirely one the
|
||||
blending step is skipped, providing an early‑exit optimisation.
|
||||
|
||||
Alpha channels (RGBA input) are preserved and passed through unchanged.
|
||||
|
||||
Args:
|
||||
image: Input image tensor with shape [B, H, W, C], C = 3 or 4,
|
||||
values in [0, 1].
|
||||
mode: Desaturation mode (case‑insensitive).
|
||||
mask: Optional mask tensor with shape [B, H, W] or [B, H, W, 1],
|
||||
values in [0, 1]. When provided, it controls the blend
|
||||
strength per pixel.
|
||||
|
||||
Returns:
|
||||
A tuple containing the desaturated image tensor with the same
|
||||
shape as the input.
|
||||
|
||||
Raises:
|
||||
ValueError: If the image does not have 3 or 4 channels, if the mask
|
||||
shape is invalid, or if an unknown mode is given.
|
||||
"""
|
||||
# ---------- Input validation ----------
|
||||
if image.dim() != 4:
|
||||
raise ValueError(
|
||||
f"Expected image tensor of shape [B, H, W, C], got {image.shape}"
|
||||
)
|
||||
C = image.shape[-1]
|
||||
if C not in (3, 4):
|
||||
raise ValueError(
|
||||
f"Expected 3 or 4 colour channels, got {C}"
|
||||
)
|
||||
|
||||
# Work in float32 throughout (original code always converts to float)
|
||||
img = image.float()
|
||||
B, H, W, _ = img.shape
|
||||
|
||||
# ---------- Mask validation & normalisation ----------
|
||||
if mask is not None:
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1) # [B,H,W] -> [B,H,W,1]
|
||||
elif mask.dim() == 4 and mask.shape[-1] == 1:
|
||||
pass # already [B,H,W,1]
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Mask must be [B, H, W] or [B, H, W, 1], got {mask.shape}"
|
||||
)
|
||||
if mask.shape[0] != B or mask.shape[1] != H or mask.shape[2] != W:
|
||||
raise ValueError(
|
||||
f"Mask dimensions {mask.shape} do not match image {img.shape}"
|
||||
)
|
||||
|
||||
# ---------- Early exits ----------
|
||||
if mask is not None:
|
||||
if torch.all(mask == 0):
|
||||
# No desaturation needed anywhere
|
||||
return (img,)
|
||||
# Flag: if every pixel is fully desaturated we can skip blending later
|
||||
all_ones = torch.all(mask == 1)
|
||||
else:
|
||||
# No mask → fully desaturate (identical to all_ones == True)
|
||||
all_ones = True
|
||||
|
||||
# ---------- Chunked processing ----------
|
||||
mode_lower = mode.lower()
|
||||
if mode_lower not in ("luminance", "luma", "lightness (hsl)",
|
||||
"avg (hsi intensity)", "average", "value (hsv)"):
|
||||
raise ValueError(f"Unknown desaturation mode: {mode}")
|
||||
|
||||
chunk_size = compute_chunk_size(image, mask) # returns int
|
||||
results = []
|
||||
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
|
||||
# Slice batch
|
||||
img_chunk = img[start:end]
|
||||
mask_chunk = mask[start:end] if mask is not None else None
|
||||
|
||||
# Extract RGB channels (drop alpha for calculation)
|
||||
r = img_chunk[..., 0]
|
||||
g = img_chunk[..., 1]
|
||||
b = img_chunk[..., 2]
|
||||
|
||||
# Compute greyscale according to the chosen mode
|
||||
if mode_lower in ("luminance", "luma"):
|
||||
gray = 0.22 * r + 0.72 * g + 0.06 * b
|
||||
elif mode_lower == "lightness (hsl)":
|
||||
max_rgb = torch.maximum(torch.maximum(r, g), b)
|
||||
min_rgb = torch.minimum(torch.minimum(r, g), b)
|
||||
gray = (max_rgb + min_rgb) * 0.5
|
||||
elif mode_lower in ("avg (hsi intensity)", "average"):
|
||||
gray = (r + g + b) / 3.0
|
||||
elif mode_lower == "value (hsv)":
|
||||
gray = torch.maximum(torch.maximum(r, g), b)
|
||||
|
||||
# Expand greyscale to three channels
|
||||
gray_rgb = gray.unsqueeze(-1).expand_as(img_chunk)
|
||||
|
||||
# Blend with original using the mask (if needed)
|
||||
if mask_chunk is not None and not all_ones:
|
||||
# Per‑pixel blend
|
||||
result_chunk = img_chunk * (1.0 - mask_chunk) + gray_rgb * mask_chunk
|
||||
else:
|
||||
# No blending required (no mask, or all_ones == True)
|
||||
result_chunk = gray_rgb
|
||||
|
||||
# Reattach the alpha channel if present in the input
|
||||
if C == 4:
|
||||
result_chunk = torch.cat([result_chunk, img_chunk[..., 3:4]], dim=-1)
|
||||
|
||||
results.append(result_chunk)
|
||||
|
||||
final_image = torch.cat(results, dim=0)
|
||||
return (final_image,)
|
||||
|
||||
@@ -0,0 +1,142 @@
|
||||
import torch
|
||||
from typing import Tuple
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
|
||||
def exec_operator_color_desat_sepia(
|
||||
image: torch.Tensor,
|
||||
strength: float,
|
||||
srgb: bool,
|
||||
mask: torch.Tensor | None = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply a GIMP‑style sepia desaturation to an image batch.
|
||||
|
||||
The effect interpolates between the original image (strength=0) and full
|
||||
sepia toning (strength=1). When a mask is supplied it controls the
|
||||
per‑pixel blend strength: the sepia colour is applied only where the mask
|
||||
is non‑zero, leaving the rest unchanged. The mask’s values must be in
|
||||
[0,1].
|
||||
|
||||
**Supported formats**
|
||||
* Input / output: RGB (3 channels) or RGBA (4 channels).
|
||||
* Alpha (if present) is **never** modified by the sepia matrix or the
|
||||
mask – it passes through unchanged.
|
||||
|
||||
Args:
|
||||
image: [B, H, W, C] float tensor in [0,1], C=3 or 4.
|
||||
strength: 0.0 → no effect, 1.0 → full sepia.
|
||||
srgb: Reserved for future colour‑space handling; currently unused.
|
||||
mask: Optional float tensor in [0,1].
|
||||
Shape [B, H, W] or [B, H, W, 1]. Batch size must match
|
||||
*image*, spatial dimensions must be H×W.
|
||||
|
||||
Returns:
|
||||
A tuple containing a single [B, H, W, C] float tensor with the
|
||||
modified image.
|
||||
"""
|
||||
# ---------- 1. Input validation & early exit ----------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("`image` must be a torch.Tensor")
|
||||
if image.dim() != 4:
|
||||
raise ValueError(
|
||||
f"`image` must be 4‑dimensional [B,H,W,C], got {image.dim()}D"
|
||||
)
|
||||
C = image.shape[-1]
|
||||
if C not in (3, 4):
|
||||
raise ValueError(
|
||||
f"Only RGB (3) or RGBA (4) channels are supported, got {C}"
|
||||
)
|
||||
|
||||
strength = max(0.0, min(1.0, float(strength)))
|
||||
if strength == 0.0:
|
||||
return (image,) # no change needed
|
||||
|
||||
B, H, W, C = image.shape
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("`mask` must be a torch.Tensor")
|
||||
if mask.dim() not in (3, 4):
|
||||
raise ValueError(
|
||||
f"`mask` must be 3‑ or 4‑dimensional, got {mask.dim()}D"
|
||||
)
|
||||
if mask.shape[0] != B:
|
||||
raise ValueError(
|
||||
f"Batch size mismatch: image has {B}, mask has {mask.shape[0]}"
|
||||
)
|
||||
if mask.shape[1:3] != (H, W):
|
||||
raise ValueError(
|
||||
f"Spatial size mismatch: image is {H}×{W}, "
|
||||
f"mask is {mask.shape[1]}×{mask.shape[2]}"
|
||||
)
|
||||
|
||||
# ---------- 2. Chunked processing ----------
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
if chunk_size <= 0:
|
||||
chunk_size = B # fallback – whole batch at once
|
||||
|
||||
chunks = []
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
im_chunk = image[start:end] # [chunk, H, W, C]
|
||||
mask_chunk = mask[start:end] if mask is not None else None
|
||||
chunks.append(_sepia_chunk(im_chunk, strength, srgb, mask_chunk))
|
||||
|
||||
result = torch.cat(chunks, dim=0)
|
||||
return (result,)
|
||||
|
||||
|
||||
def _sepia_chunk(
|
||||
im: torch.Tensor,
|
||||
strength: float,
|
||||
srgb: bool, # kept for signature compatibility
|
||||
mask: torch.Tensor | None,
|
||||
) -> torch.Tensor:
|
||||
"""Process a sub‑batch (already validated)."""
|
||||
b, h, w, c = im.shape
|
||||
has_alpha = c == 4
|
||||
|
||||
if has_alpha:
|
||||
alpha = im[..., 3:4] # preserve original alpha
|
||||
rgb = im[..., :3]
|
||||
else:
|
||||
alpha = None
|
||||
rgb = im
|
||||
|
||||
# ----- Build interpolated sepia matrix -----
|
||||
s = strength
|
||||
full = torch.tensor(
|
||||
[[0.393, 0.769, 0.189],
|
||||
[0.349, 0.686, 0.168],
|
||||
[0.272, 0.534, 0.131]],
|
||||
dtype=rgb.dtype, device=rgb.device,
|
||||
)
|
||||
eye = torch.eye(3, dtype=rgb.dtype, device=rgb.device)
|
||||
mat = eye + (full - eye) * s # [3, 3]
|
||||
|
||||
# ----- Apply matrix to RGB channels -----
|
||||
flat = rgb.reshape(-1, 3) # [b*h*w, 3]
|
||||
sepia_flat = torch.mm(flat, mat.T) # [b*h*w, 3]
|
||||
sepia_rgb = sepia_flat.reshape(b, h, w, 3)
|
||||
|
||||
# ----- Blend with mask (only on RGB) -----
|
||||
if mask is not None:
|
||||
# Bring mask to [b, h, w, 1]
|
||||
if mask.dim() == 3:
|
||||
mask_blend = mask.unsqueeze(-1)
|
||||
else:
|
||||
mask_blend = mask # already [b, h, w, 1]
|
||||
mask_blend = mask_blend.to(
|
||||
dtype=rgb.dtype, device=rgb.device
|
||||
).clamp_(0.0, 1.0)
|
||||
# Broadcast across the 3 colour channels
|
||||
rgb_result = rgb * (1 - mask_blend) + sepia_rgb * mask_blend
|
||||
else:
|
||||
rgb_result = sepia_rgb
|
||||
|
||||
# ----- Reattach alpha if it was present -----
|
||||
if has_alpha:
|
||||
return torch.cat([rgb_result, alpha], dim=-1)
|
||||
return rgb_result
|
||||
|
||||
|
||||
@@ -0,0 +1,136 @@
|
||||
import torch
|
||||
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
def exec_operator_color_exposure(
|
||||
image: torch.Tensor,
|
||||
black_level: float,
|
||||
exposure: float,
|
||||
mask: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Apply a GEGL‑style exposure adjustment to an image batch, with optional
|
||||
mask blending and full alpha‑channel preservation.
|
||||
|
||||
Exposure is modeled as:
|
||||
|
||||
white = 2^(-exposure)
|
||||
gain = 1 / (white - black_level) (clamped for numerical stability)
|
||||
result = (image - black_level) * gain
|
||||
|
||||
When a mask is provided, the adjusted image is blended with the original
|
||||
using the mask as the per‑pixel mixing weight:
|
||||
|
||||
output = image * (1 - mask) + result * mask
|
||||
|
||||
Args:
|
||||
image (torch.Tensor): Input image tensor of shape ``(B, H, W, C)``,
|
||||
with C ∈ {1, 3, 4}.
|
||||
black_level (float): Black level offset (clamped to avoid division
|
||||
by zero).
|
||||
exposure (float): Exposure value (stops). 0 → no change, >0 brighter,
|
||||
<0 darker.
|
||||
mask (torch.Tensor, optional): Blend mask of shape ``(B, H, W)`` or
|
||||
``(1, H, W)``. If omitted, no blending.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Exposure‑adjusted image tensor with the same shape and
|
||||
dtype as the input, but always float32 internally.
|
||||
"""
|
||||
# --- Input validation ----------------------------------------------------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise ValueError("`image` must be a torch.Tensor")
|
||||
if image.dim() != 4:
|
||||
raise ValueError(
|
||||
f"`image` must have 4 dimensions (B,H,W,C), got {image.dim()}"
|
||||
)
|
||||
if image.shape[-1] not in (1, 3, 4):
|
||||
raise ValueError(
|
||||
f"Unsupported number of channels: {image.shape[-1]}. "
|
||||
"Expected 1, 3, or 4."
|
||||
)
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise ValueError("`mask` must be a torch.Tensor")
|
||||
if mask.dim() not in (3, 4):
|
||||
raise ValueError(
|
||||
f"`mask` must have 3 or 4 dimensions, got {mask.dim()}"
|
||||
)
|
||||
if mask.shape[0] not in (1, image.shape[0]):
|
||||
raise ValueError(
|
||||
f"Batch size of mask ({mask.shape[0]}) must be 1 or match "
|
||||
f"image batch size ({image.shape[0]})"
|
||||
)
|
||||
|
||||
# --- Early exit (identity transform) -------------------------------------
|
||||
if exposure == 0.0 and black_level == 0.0:
|
||||
# Even with a mask, the blend would be identity → return original
|
||||
return image
|
||||
|
||||
# --- Device & precision setup --------------------------------------------
|
||||
image = image.to(dtype=torch.float32)
|
||||
device = image.device
|
||||
|
||||
# --- Alpha channel separation (if any) -----------------------------------
|
||||
has_alpha = image.shape[-1] == 4
|
||||
if has_alpha:
|
||||
rgb = image[..., :3] # (B, H, W, 3)
|
||||
alpha = image[..., 3:4] # (B, H, W, 1)
|
||||
else:
|
||||
rgb = image # operate on all channels
|
||||
alpha = None
|
||||
|
||||
B, H, W, C_rgb = rgb.shape
|
||||
|
||||
# --- Mask preparation (batched or single) --------------------------------
|
||||
mask_tensor = None
|
||||
if mask is not None:
|
||||
mask_tensor = mask.to(device)
|
||||
# Normalise mask shape to (B_mask, H, W, 1)
|
||||
if mask_tensor.dim() == 3:
|
||||
mask_tensor = mask_tensor.unsqueeze(-1) # (B, H, W, 1)
|
||||
elif mask_tensor.dim() == 4 and mask_tensor.shape[1] == 1:
|
||||
# ComfyUI sometimes uses (B, 1, H, W)
|
||||
mask_tensor = mask_tensor.permute(0, 2, 3, 1) # (B, H, W, 1)
|
||||
|
||||
# If a single mask is given, broadcast across the batch later
|
||||
single_mask = mask_tensor.shape[0] == 1
|
||||
|
||||
# --- VRAM‑aware chunked processing ---------------------------------------
|
||||
chunk_size = compute_chunk_size(image) # based on full image (includes alpha if present)
|
||||
results = []
|
||||
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
rgb_chunk = rgb[start:end] # (chunk_B, H, W, C_rgb)
|
||||
|
||||
# --- Apply GEGL exposure transform (colour channels only) ------------
|
||||
white = torch.exp2(torch.tensor(-exposure, device=device))
|
||||
diff = torch.clamp(white - black_level, min=1e-6)
|
||||
gain = 1.0 / diff
|
||||
transformed = (rgb_chunk - black_level) * gain
|
||||
|
||||
# --- Optional mask blending on colour channels -----------------------
|
||||
if mask_tensor is not None:
|
||||
if single_mask:
|
||||
# Use the single mask for every chunk
|
||||
mask_chunk = mask_tensor.expand(end - start, -1, -1, -1) # (chunk_B, H, W, 1)
|
||||
else:
|
||||
mask_chunk = mask_tensor[start:end] # (chunk_B, H, W, 1)
|
||||
|
||||
# Expand to match rgb_chunk shape (broadcast over channels)
|
||||
mask_expanded = mask_chunk.expand_as(rgb_chunk)
|
||||
result_chunk = rgb_chunk * (1.0 - mask_expanded) + transformed * mask_expanded
|
||||
else:
|
||||
result_chunk = transformed
|
||||
|
||||
# --- Reattach alpha channel if present -------------------------------
|
||||
if has_alpha:
|
||||
alpha_chunk = alpha[start:end] # (chunk_B, H, W, 1)
|
||||
result_chunk = torch.cat([result_chunk, alpha_chunk], dim=-1)
|
||||
|
||||
results.append(result_chunk)
|
||||
|
||||
return torch.cat(results, dim=0)
|
||||
|
||||
|
||||
@@ -0,0 +1,154 @@
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from .libs.color import rgb_to_lch_torch, lch_to_rgb_torch
|
||||
|
||||
|
||||
def exec_operator_color_hue_chroma(
|
||||
image: torch.Tensor,
|
||||
hue: float,
|
||||
chroma: float,
|
||||
lightness: float,
|
||||
mask: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Apply hue, chroma and lightness adjustments in LCH color space.
|
||||
|
||||
The image is converted from RGB to LCH, the requested adjustments are
|
||||
applied, then converted back to RGB. Hue and chroma adjustments are
|
||||
skipped for achromatic pixels while lightness is always adjusted.
|
||||
|
||||
If a mask is supplied, the adjusted image is blended with the original
|
||||
image according to the mask values.
|
||||
|
||||
Supports RGB and RGBA images and batched processing.
|
||||
"""
|
||||
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
|
||||
if image.ndim != 4:
|
||||
raise ValueError(
|
||||
"image must have shape (B,H,W,C)"
|
||||
)
|
||||
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(
|
||||
"image must have 3 (RGB) or 4 (RGBA) channels"
|
||||
)
|
||||
|
||||
if not image.is_floating_point():
|
||||
raise TypeError(
|
||||
"image must be floating point"
|
||||
)
|
||||
|
||||
rgb = image[..., :3]
|
||||
alpha = image[..., 3:] if image.shape[-1] == 4 else None
|
||||
|
||||
device = rgb.device
|
||||
dtype = rgb.dtype
|
||||
|
||||
if mask is not None:
|
||||
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor")
|
||||
|
||||
mask = mask.to(device=device, dtype=dtype)
|
||||
|
||||
if mask.ndim == 2:
|
||||
mask = mask.unsqueeze(0).unsqueeze(-1)
|
||||
|
||||
elif mask.ndim == 3:
|
||||
mask = mask.unsqueeze(-1)
|
||||
|
||||
elif mask.ndim != 4:
|
||||
raise ValueError(
|
||||
"mask must have shape (H,W), (B,H,W) or (B,H,W,1)"
|
||||
)
|
||||
|
||||
if mask.shape[-1] != 1:
|
||||
mask = mask[..., :1]
|
||||
|
||||
#
|
||||
# Pair image batch with corresponding mask.
|
||||
#
|
||||
if mask.shape[0] == 1 and rgb.shape[0] > 1:
|
||||
mask = mask.expand(rgb.shape[0], -1, -1, -1)
|
||||
|
||||
elif mask.shape[0] != rgb.shape[0]:
|
||||
raise ValueError(
|
||||
"mask batch size must be 1 or match image batch size"
|
||||
)
|
||||
|
||||
if (
|
||||
mask.shape[1] != rgb.shape[1]
|
||||
or mask.shape[2] != rgb.shape[2]
|
||||
):
|
||||
mask = F.interpolate(
|
||||
mask.permute(0, 3, 1, 2),
|
||||
size=rgb.shape[1:3],
|
||||
mode="bilinear",
|
||||
align_corners=False,
|
||||
).permute(0, 2, 3, 1)
|
||||
|
||||
EPSILON = 1e-4
|
||||
|
||||
#
|
||||
# RGB -> LCH
|
||||
#
|
||||
lch = rgb_to_lch_torch(rgb)
|
||||
|
||||
L = lch[..., 0]
|
||||
C = lch[..., 1]
|
||||
H = lch[..., 2]
|
||||
|
||||
achromatic = torch.abs(C) < EPSILON
|
||||
|
||||
L_adj = L + lightness
|
||||
|
||||
#
|
||||
# Only clone if modification is required.
|
||||
#
|
||||
if chroma != 0.0:
|
||||
C_adj = C.clone()
|
||||
C_adj.masked_scatter_(
|
||||
~achromatic,
|
||||
C.masked_select(~achromatic) + chroma,
|
||||
)
|
||||
else:
|
||||
C_adj = C
|
||||
|
||||
if hue != 0.0:
|
||||
H_adj = H.clone()
|
||||
H_adj.masked_scatter_(
|
||||
~achromatic,
|
||||
H.masked_select(~achromatic) + hue,
|
||||
)
|
||||
else:
|
||||
H_adj = H
|
||||
|
||||
C_adj = torch.clamp(C_adj, 0.0, 300.0)
|
||||
|
||||
H_adj = H_adj.remainder(360.0)
|
||||
H_adj = torch.where(
|
||||
H_adj >= 180.0,
|
||||
H_adj - 360.0,
|
||||
H_adj,
|
||||
)
|
||||
|
||||
result_rgb = lch_to_rgb_torch(
|
||||
torch.stack(
|
||||
(L_adj, C_adj, H_adj),
|
||||
dim=-1,
|
||||
)
|
||||
)
|
||||
|
||||
if mask is not None:
|
||||
result_rgb = rgb * (1.0 - mask) + result_rgb * mask
|
||||
|
||||
if alpha is not None:
|
||||
return torch.cat((result_rgb, alpha), dim=-1)
|
||||
|
||||
return result_rgb
|
||||
|
||||
|
||||
@@ -0,0 +1,99 @@
|
||||
|
||||
import torch
|
||||
from typing import Optional
|
||||
|
||||
from .libs.color import rgb_to_hsl, hsl_to_rgb
|
||||
|
||||
# =========================================================
|
||||
# MAIN OPERATOR (GIMP-style)
|
||||
# =========================================================
|
||||
@torch.no_grad()
|
||||
def exec_operator_color_hue_saturation(
|
||||
image: torch.Tensor,
|
||||
mask: Optional[torch.Tensor],
|
||||
overlap: float,
|
||||
data: list,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Core hue/saturation/lightness adjustment with per‑colour ranges.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor, shape (B, H, W, C) – C=3 (RGB) or C=4 (RGBA)
|
||||
mask : optional torch.Tensor (B, H, W) or (B, 1, H, W) – luminance mask
|
||||
overlap : float (0‑100) – hue blending spread
|
||||
data : list of 7 lists of 3 floats – [master, red, yellow, green, cyan, blue, magenta]
|
||||
|
||||
Returns
|
||||
-------
|
||||
torch.Tensor of same shape as `image`, with alpha (if present) unchanged.
|
||||
"""
|
||||
|
||||
# ---------- Device and range ----------
|
||||
device = image.device
|
||||
image = image.clamp(0.0, 1.0)
|
||||
|
||||
# ---------- Handle RGBA ----------
|
||||
has_alpha = image.shape[-1] == 4
|
||||
if has_alpha:
|
||||
rgb = image[..., :3] # (B, H, W, 3)
|
||||
alpha = image[..., 3:] # (B, H, W, 1)
|
||||
else:
|
||||
rgb = image
|
||||
|
||||
# ---------- Original algorithm on RGB channels ----------
|
||||
h, s, l = rgb_to_hsl(rgb) # all tensors (B, H, W)
|
||||
|
||||
master_h, master_l, master_s = data[0]
|
||||
|
||||
channels_h = torch.tensor([d[0] for d in data[1:]], device=device) # (6,)
|
||||
channels_l = torch.tensor([d[1] for d in data[1:]], device=device)
|
||||
channels_s = torch.tensor([d[2] for d in data[1:]], device=device)
|
||||
|
||||
centers = torch.tensor([0, 60, 120, 180, 240, 300], device=device) # (6,)
|
||||
|
||||
# ------- per‑hue weighting (computed once for all shifts) -------
|
||||
h_exp = h.unsqueeze(-1) # (B, H, W, 1)
|
||||
diff = (h_exp - centers + 180.0) % 360.0 - 180.0
|
||||
absdiff = torch.abs(diff)
|
||||
|
||||
spread = max(1.0, 180.0 * overlap / 100.0)
|
||||
weight = torch.clamp(1.0 - absdiff / spread, 0.0, 1.0) # (B, H, W, 6)
|
||||
|
||||
# ------- accumulate shifts -------
|
||||
h_shift = master_h + torch.sum(weight * channels_h, dim=-1) # (B, H, W)
|
||||
l_shift = (master_l / 100.0) + torch.sum(weight * (channels_l / 100.0), dim=-1)
|
||||
s_shift = (master_s / 100.0) + torch.sum(weight * (channels_s / 100.0), dim=-1)
|
||||
|
||||
# Free the large weight tensor as soon as it is no longer needed
|
||||
del weight
|
||||
|
||||
# ------- apply shifts -------
|
||||
h = (h + h_shift) % 360.0
|
||||
l = torch.clamp(l + l_shift, 0.0, 1.0)
|
||||
s = torch.clamp(s + s_shift, 0.0, 1.0)
|
||||
|
||||
out_rgb = hsl_to_rgb(h, s, l)
|
||||
|
||||
# ------- free HSL tensors -------
|
||||
del h, s, l, h_shift, l_shift, s_shift
|
||||
|
||||
# ---------- Mask blend (on RGB part only) ----------
|
||||
if mask is not None:
|
||||
# Normalise mask shape to (B, H, W, 1) for broadcasting
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1) # (B, H, W, 1)
|
||||
elif mask.dim() == 4 and mask.shape[1] == 1:
|
||||
mask = mask.permute(0, 2, 3, 1) # (B, 1, H, W) → (B, H, W, 1)
|
||||
|
||||
mask = mask.to(device).expand_as(out_rgb) # broadcast to 3 channels
|
||||
out_rgb = rgb * (1.0 - mask) + out_rgb * mask
|
||||
|
||||
# ---------- Reattach alpha if needed ----------
|
||||
if has_alpha:
|
||||
out = torch.cat([out_rgb, alpha], dim=-1)
|
||||
else:
|
||||
out = out_rgb
|
||||
|
||||
return out
|
||||
|
||||
@@ -0,0 +1,182 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
|
||||
from .libs.color import srgb_to_linear_vec, linear_to_srgb_vec, rgb_to_hsv, hsv_to_rgb
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# Internal helpers for channel handling and chunked processing
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
def _ensure_channels(t: torch.Tensor, target_channels: int) -> torch.Tensor:
|
||||
"""Ensure tensor has exactly target_channels by adding/removing dummy dimensions.
|
||||
Used only for internal shape adjustments; for actual channel handling see below.
|
||||
"""
|
||||
if t.ndim == 3 and t.shape[-1] == 1:
|
||||
# single channel -> expand to target? Not used here.
|
||||
pass
|
||||
return t
|
||||
|
||||
|
||||
def _split_rgb_alpha(image: torch.Tensor):
|
||||
"""Split image into RGB and alpha (if present)."""
|
||||
if image.shape[-1] == 3:
|
||||
return image, None
|
||||
elif image.shape[-1] == 4:
|
||||
return image[..., :3], image[..., 3:]
|
||||
else:
|
||||
raise ValueError(f"Unsupported number of channels: {image.shape[-1]}")
|
||||
|
||||
|
||||
def _combine_rgb_alpha(rgb: torch.Tensor, alpha: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
"""Recombine RGB and alpha, or return RGB if no alpha."""
|
||||
if alpha is None:
|
||||
return rgb
|
||||
return torch.cat([rgb, alpha], dim=-1)
|
||||
|
||||
|
||||
def _process_chunk(
|
||||
image_chunk: torch.Tensor,
|
||||
inv_type: str,
|
||||
mask_chunk: Optional[torch.Tensor]
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Process a single chunk (batch of images) with the chosen inversion type.
|
||||
Handles RGB/RGBA, mask blending, and preserves alpha.
|
||||
"""
|
||||
# Separate RGB and alpha
|
||||
rgb, alpha = _split_rgb_alpha(image_chunk)
|
||||
|
||||
# Apply inversion to RGB channels only
|
||||
if inv_type == "Value":
|
||||
inverted_rgb = _exec_operator_color_value_invert_rgb(rgb)
|
||||
elif inv_type == "Linear":
|
||||
inverted_rgb = _exec_operator_color_linear_invert_rgb(rgb)
|
||||
else: # "RGB"
|
||||
inverted_rgb = 1.0 - rgb
|
||||
|
||||
# Apply mask blending if mask is provided
|
||||
if mask_chunk is not None:
|
||||
# Ensure mask has proper shape: (B, H, W) or (B, H, W, 1)
|
||||
if mask_chunk.ndim == 3:
|
||||
mask_chunk = mask_chunk.unsqueeze(-1) # (B, H, W, 1)
|
||||
elif mask_chunk.ndim == 4 and mask_chunk.shape[-1] == 1:
|
||||
pass
|
||||
else:
|
||||
raise ValueError(f"Mask must have shape (B,H,W) or (B,H,W,1), got {mask_chunk.shape}")
|
||||
|
||||
# Broadcast mask if needed (batch size 1 vs multiple)
|
||||
if mask_chunk.shape[0] == 1 and image_chunk.shape[0] > 1:
|
||||
mask_chunk = mask_chunk.expand(image_chunk.shape[0], -1, -1, -1)
|
||||
elif mask_chunk.shape[0] != image_chunk.shape[0]:
|
||||
raise ValueError(
|
||||
f"Mask batch size {mask_chunk.shape[0]} does not match image batch size {image_chunk.shape[0]}"
|
||||
)
|
||||
|
||||
# Blend: output = image * (1 - mask) + inverted * mask
|
||||
rgb_blended = rgb * (1.0 - mask_chunk) + inverted_rgb * mask_chunk
|
||||
# Clamp to [0,1] for safety (though operations should keep within bounds)
|
||||
rgb_blended = torch.clamp(rgb_blended, 0.0, 1.0)
|
||||
else:
|
||||
rgb_blended = inverted_rgb
|
||||
|
||||
# Recombine with original alpha
|
||||
return _combine_rgb_alpha(rgb_blended, alpha)
|
||||
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# Core inversion functions (unchanged logic, but work on RGB tensors)
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
def _exec_operator_color_linear_invert_rgb(rgb: torch.Tensor) -> torch.Tensor:
|
||||
"""Linear inversion in RGB space (sRGB → linear → invert → sRGB)."""
|
||||
linear = srgb_to_linear_vec(rgb)
|
||||
inv_linear = 1.0 - linear
|
||||
return linear_to_srgb_vec(inv_linear)
|
||||
|
||||
|
||||
def _exec_operator_color_value_invert_rgb(rgb: torch.Tensor) -> torch.Tensor:
|
||||
"""Value (luminosity) inversion in HSV space."""
|
||||
h, s, v = rgb_to_hsv(rgb)
|
||||
v_inv = 1.0 - v
|
||||
inverted = hsv_to_rgb(h, s, v_inv)
|
||||
return torch.clamp(inverted, 0.0, 1.0)
|
||||
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# Public entry point
|
||||
# --------------------------------------------------------------------
|
||||
|
||||
def exec_operator_color_invert(
|
||||
image: torch.Tensor,
|
||||
inv_type: str,
|
||||
mask: Optional[torch.Tensor] = None
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Invert colours of an image batch, mimicking GIMP's Colors → Invert.
|
||||
|
||||
Supports RGB and RGBA input/output; alpha channel is always passed through unchanged.
|
||||
If a mask is given, the inverted image is blended with the original using the mask
|
||||
as an alpha factor: output = image * (1 - mask) + inverted * mask. The mask must be
|
||||
a single-channel tensor with shape (B, H, W) or (B, H, W, 1). Its batch size must
|
||||
either match the image batch or be 1 (broadcast).
|
||||
|
||||
Args:
|
||||
image: (B, H, W, C) tensor, C = 3 or 4, values in [0, 1].
|
||||
inv_type: One of "RGB", "Linear", or "Value".
|
||||
mask: Optional (B, H, W) or (B, H, W, 1) tensor, values in [0, 1].
|
||||
|
||||
Returns:
|
||||
Tuple containing the inverted image tensor with same shape as input.
|
||||
|
||||
Raises:
|
||||
ValueError: for invalid input shapes, types, or values.
|
||||
"""
|
||||
# --- Input validation ---
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
if image.ndim != 4:
|
||||
raise ValueError(f"image must be 4D (B,H,W,C), got {image.ndim}D")
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(f"image must have 3 or 4 channels, got {image.shape[-1]}")
|
||||
if image.min() < 0 or image.max() > 1:
|
||||
raise ValueError("image values must be in [0, 1]")
|
||||
|
||||
if inv_type not in ("RGB", "Linear", "Value"):
|
||||
raise ValueError(f"inv_type must be 'RGB', 'Linear', or 'Value', got {inv_type}")
|
||||
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor or None")
|
||||
if mask.ndim not in (3, 4) or (mask.ndim == 4 and mask.shape[-1] != 1):
|
||||
raise ValueError(f"mask must be (B,H,W) or (B,H,W,1), got {mask.shape}")
|
||||
if mask.min() < 0 or mask.max() > 1:
|
||||
raise ValueError("mask values must be in [0, 1]")
|
||||
|
||||
# --- Early exit if mask is all zeros ---
|
||||
if mask is not None and torch.all(mask == 0):
|
||||
# No inversion applied – return original image (alpha preserved)
|
||||
return (image,)
|
||||
|
||||
# --- Chunked processing ---
|
||||
batch_size = image.shape[0]
|
||||
if batch_size == 0:
|
||||
return (image,)
|
||||
|
||||
# Compute optimal chunk size using the provided helper
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
if chunk_size <= 0:
|
||||
chunk_size = batch_size # fallback
|
||||
|
||||
# Process in chunks
|
||||
chunks = []
|
||||
for start in range(0, batch_size, chunk_size):
|
||||
end = min(start + chunk_size, batch_size)
|
||||
img_chunk = image[start:end]
|
||||
mask_chunk = mask[start:end] if mask is not None else None
|
||||
processed = _process_chunk(img_chunk, inv_type, mask_chunk)
|
||||
chunks.append(processed)
|
||||
|
||||
# Concatenate chunks along batch dimension
|
||||
result = torch.cat(chunks, dim=0)
|
||||
return (result,)
|
||||
@@ -0,0 +1,356 @@
|
||||
import torch
|
||||
from typing import Optional, Tuple
|
||||
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
# ============================================================
|
||||
# Utility: safe clamp
|
||||
# ============================================================
|
||||
|
||||
def _clamp01(x):
|
||||
return torch.clamp(x, 0.0, 1.0)
|
||||
|
||||
# ============================================================
|
||||
# Core GIMP-like Levels transfer (per-channel)
|
||||
# ============================================================
|
||||
|
||||
def _levels_transfer(
|
||||
v: torch.Tensor,
|
||||
in_low: float,
|
||||
in_high: float,
|
||||
gamma: float,
|
||||
out_low: float,
|
||||
out_high: float,
|
||||
clamp_input: bool,
|
||||
clamp_output: bool,
|
||||
):
|
||||
denom = (in_high - in_low)
|
||||
if abs(denom) < 1e-8:
|
||||
denom = 1e-8
|
||||
|
||||
v = (v - in_low) / denom
|
||||
|
||||
if clamp_input:
|
||||
v = _clamp01(v)
|
||||
|
||||
gamma = max(gamma, 1e-6)
|
||||
v = torch.pow(torch.clamp(v, min=0.0), 1.0 / gamma)
|
||||
|
||||
v = out_low + v * (out_high - out_low)
|
||||
|
||||
if clamp_output:
|
||||
v = _clamp01(v)
|
||||
|
||||
return v
|
||||
|
||||
# ============================================================
|
||||
# HSV conversion (needed for Value channel mode like GIMP)
|
||||
# ============================================================
|
||||
|
||||
def _rgb_to_hsv(img: torch.Tensor):
|
||||
r, g, b = img[..., 0], img[..., 1], img[..., 2]
|
||||
maxc, _ = torch.max(img[..., :3], dim=-1)
|
||||
minc, _ = torch.min(img[..., :3], dim=-1)
|
||||
v = maxc
|
||||
delta = maxc - minc
|
||||
s = torch.where(maxc > 0, delta / (maxc + 1e-8), torch.zeros_like(maxc))
|
||||
hue = torch.zeros_like(maxc)
|
||||
mask = delta > 1e-8
|
||||
rc = (maxc - r) / (delta + 1e-8)
|
||||
gc = (maxc - g) / (delta + 1e-8)
|
||||
bc = (maxc - b) / (delta + 1e-8)
|
||||
hue = torch.where(
|
||||
(maxc == r) & mask,
|
||||
(bc - gc),
|
||||
hue
|
||||
)
|
||||
hue = torch.where(
|
||||
(maxc == g) & mask,
|
||||
2.0 + (rc - bc),
|
||||
hue
|
||||
)
|
||||
hue = torch.where(
|
||||
(maxc == b) & mask,
|
||||
4.0 + (gc - rc),
|
||||
hue
|
||||
)
|
||||
hue = (hue / 6.0) % 1.0
|
||||
return torch.stack([hue, s, v], dim=-1)
|
||||
|
||||
def _hsv_to_rgb(hsv: torch.Tensor):
|
||||
h, s, v = hsv[..., 0], hsv[..., 1], hsv[..., 2]
|
||||
h = (h % 1.0) * 6.0
|
||||
i = torch.floor(h)
|
||||
f = h - i
|
||||
p = v * (1.0 - s)
|
||||
q = v * (1.0 - s * f)
|
||||
t = v * (1.0 - s * (1.0 - f))
|
||||
i = i.long() % 6
|
||||
r = torch.zeros_like(v)
|
||||
g = torch.zeros_like(v)
|
||||
b = torch.zeros_like(v)
|
||||
cond = i == 0
|
||||
r = torch.where(cond, v, r)
|
||||
g = torch.where(cond, t, g)
|
||||
b = torch.where(cond, p, b)
|
||||
cond = i == 1
|
||||
r = torch.where(cond, q, r)
|
||||
g = torch.where(cond, v, g)
|
||||
b = torch.where(cond, p, b)
|
||||
cond = i == 2
|
||||
r = torch.where(cond, p, r)
|
||||
g = torch.where(cond, v, g)
|
||||
b = torch.where(cond, t, b)
|
||||
cond = i == 3
|
||||
r = torch.where(cond, p, r)
|
||||
g = torch.where(cond, q, g)
|
||||
b = torch.where(cond, v, b)
|
||||
cond = i == 4
|
||||
r = torch.where(cond, t, r)
|
||||
g = torch.where(cond, p, g)
|
||||
b = torch.where(cond, v, b)
|
||||
cond = i == 5
|
||||
r = torch.where(cond, v, r)
|
||||
g = torch.where(cond, p, g)
|
||||
b = torch.where(cond, q, b)
|
||||
return torch.stack([r, g, b], dim=-1)
|
||||
|
||||
# ============================================================
|
||||
# Histogram (GIMP-style endpoint detection)
|
||||
# ============================================================
|
||||
|
||||
def _histogram_bounds(channel: torch.Tensor):
|
||||
flat = channel.reshape(-1)
|
||||
flat = flat[torch.isfinite(flat)]
|
||||
if flat.numel() == 0:
|
||||
return 0.0, 1.0
|
||||
lo = torch.min(flat)
|
||||
hi = torch.max(flat)
|
||||
return lo.item(), hi.item()
|
||||
|
||||
# ============================================================
|
||||
# Mask blending (GEGL-style)
|
||||
# ============================================================
|
||||
|
||||
def _apply_mask(src: torch.Tensor, dst: torch.Tensor, mask: Optional[torch.Tensor]):
|
||||
if mask is None:
|
||||
return dst
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1)
|
||||
mask = mask.to(dtype=src.dtype, device=src.device)
|
||||
return src * (1.0 - mask) + dst * mask
|
||||
|
||||
# ============================================================
|
||||
# Apply per-channel Levels
|
||||
# ============================================================
|
||||
|
||||
def _apply_levels(
|
||||
img: torch.Tensor,
|
||||
channel: str,
|
||||
in_low: float,
|
||||
in_high: float,
|
||||
gamma: float,
|
||||
out_low: float,
|
||||
out_high: float,
|
||||
clamp_input: bool,
|
||||
clamp_output: bool,
|
||||
auto_input: bool,
|
||||
mask: Optional[torch.Tensor],
|
||||
):
|
||||
B, H, W, C = img.shape
|
||||
|
||||
if auto_input:
|
||||
if channel == "Value":
|
||||
v = img[..., :3].max(dim=-1).values
|
||||
in_low, in_high = _histogram_bounds(v)
|
||||
elif channel == "Red":
|
||||
in_low, in_high = _histogram_bounds(img[..., 0])
|
||||
elif channel == "Green":
|
||||
in_low, in_high = _histogram_bounds(img[..., 1])
|
||||
elif channel == "Blue":
|
||||
in_low, in_high = _histogram_bounds(img[..., 2])
|
||||
elif channel == "Alpha" and C == 4:
|
||||
in_low, in_high = _histogram_bounds(img[..., 3])
|
||||
|
||||
if channel == "Value":
|
||||
hsv = _rgb_to_hsv(img[..., :3])
|
||||
v = hsv[..., 2]
|
||||
v2 = _levels_transfer(
|
||||
v,
|
||||
in_low, in_high,
|
||||
gamma,
|
||||
out_low, out_high,
|
||||
clamp_input,
|
||||
clamp_output,
|
||||
)
|
||||
hsv[..., 2] = v2
|
||||
rgb = _hsv_to_rgb(hsv)
|
||||
out = img.clone()
|
||||
out[..., :3] = rgb
|
||||
if C == 4:
|
||||
out[..., 3] = img[..., 3]
|
||||
return out
|
||||
|
||||
out = img.clone()
|
||||
channel_map = {
|
||||
"Red": 0,
|
||||
"Green": 1,
|
||||
"Blue": 2,
|
||||
"Alpha": 3,
|
||||
}
|
||||
if channel in channel_map:
|
||||
c = channel_map[channel]
|
||||
if c < C:
|
||||
processed = _levels_transfer(
|
||||
img[..., c],
|
||||
in_low, in_high,
|
||||
gamma,
|
||||
out_low, out_high,
|
||||
clamp_input,
|
||||
clamp_output,
|
||||
)
|
||||
out[..., c] = processed
|
||||
|
||||
return _apply_mask(img, out, mask)
|
||||
|
||||
# ============================================================
|
||||
# FINAL OPERATOR ENTRY
|
||||
# ============================================================
|
||||
|
||||
def exec_operator_color_levels(
|
||||
image: torch.Tensor,
|
||||
channel: str,
|
||||
interpolation: str, # kept for API compatibility (ignored like GIMP Levels)
|
||||
input_low: float,
|
||||
input_high: float,
|
||||
input_gamma: float,
|
||||
input_clamp: bool,
|
||||
output_low: float,
|
||||
output_high: float,
|
||||
output_clamp: bool,
|
||||
auto_input: bool,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Apply a GIMP‑style Levels adjustment to an image tensor.
|
||||
|
||||
The adjustment remaps the selected channel (or the Value component of HSV)
|
||||
using input low/high, gamma, and output low/high. The operation supports
|
||||
batch processing with an optional per‑image mask. Input and output tensors
|
||||
must be in RGB or RGBA format with values in [0,1].
|
||||
|
||||
Args:
|
||||
image: Input tensor of shape (B, H, W, C) with C=3 (RGB) or C=4 (RGBA).
|
||||
channel: Channel to modify: "Value", "Red", "Green", "Blue", or "Alpha".
|
||||
interpolation: Unused, kept for API compatibility.
|
||||
input_low, input_high: Input range in UI units (0‑255).
|
||||
input_gamma: Gamma correction factor (>0).
|
||||
input_clamp: Clamp normalized values to [0,1] before gamma.
|
||||
output_low, output_high: Output range in UI units (0‑255).
|
||||
output_clamp: Clamp final values to [0,1].
|
||||
auto_input: If True, compute input low/high from the image histogram.
|
||||
mask: Optional mask tensor of shape (B, H, W) or (B, H, W, 1) to blend
|
||||
the original and adjusted images.
|
||||
|
||||
Returns:
|
||||
Adjusted image tensor with the same shape and dtype as the input.
|
||||
"""
|
||||
# ------------------------------------------------------------------
|
||||
# Input validation
|
||||
# ------------------------------------------------------------------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
if image.dim() != 4:
|
||||
raise ValueError("image must have 4 dimensions [B, H, W, C]")
|
||||
B, H, W, C = image.shape
|
||||
if C not in (3, 4):
|
||||
raise ValueError("image must have 3 or 4 channels (RGB or RGBA)")
|
||||
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor")
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1) # [B, H, W, 1]
|
||||
elif mask.dim() == 4 and mask.shape[-1] == 1:
|
||||
pass
|
||||
else:
|
||||
raise ValueError("mask must have shape [B, H, W] or [B, H, W, 1]")
|
||||
if mask.shape[0] != B:
|
||||
raise ValueError(
|
||||
f"mask batch size {mask.shape[0]} does not match image batch size {B}"
|
||||
)
|
||||
mask = mask.to(device=image.device, dtype=image.dtype)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Early exit: identity transformation
|
||||
# ------------------------------------------------------------------
|
||||
if (not auto_input and
|
||||
input_low == 0 and input_high == 255 and input_gamma == 1.0 and
|
||||
output_low == 0 and output_high == 255 and
|
||||
not input_clamp and not output_clamp):
|
||||
return image.clone()
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Normalize parameters to [0,1]
|
||||
# ------------------------------------------------------------------
|
||||
in_low = input_low / 255.0
|
||||
in_high = input_high / 255.0
|
||||
out_low = output_low / 255.0
|
||||
out_high = output_high / 255.0
|
||||
gamma = max(float(input_gamma), 1e-6)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Auto‑input: compute global histogram bounds on the whole batch
|
||||
# ------------------------------------------------------------------
|
||||
if auto_input:
|
||||
if channel == "Value":
|
||||
v = image[..., :3].max(dim=-1).values
|
||||
in_low, in_high = _histogram_bounds(v)
|
||||
elif channel == "Red":
|
||||
in_low, in_high = _histogram_bounds(image[..., 0])
|
||||
elif channel == "Green":
|
||||
in_low, in_high = _histogram_bounds(image[..., 1])
|
||||
elif channel == "Blue":
|
||||
in_low, in_high = _histogram_bounds(image[..., 2])
|
||||
elif channel == "Alpha" and C == 4:
|
||||
in_low, in_high = _histogram_bounds(image[..., 3])
|
||||
else:
|
||||
# Channel not available: keep default [0,1] (no effect)
|
||||
in_low, in_high = 0.0, 1.0
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Chunked processing for VRAM management
|
||||
# ------------------------------------------------------------------
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
if chunk_size is None or chunk_size <= 0:
|
||||
chunk_size = B
|
||||
|
||||
outputs = []
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
img_chunk = image[start:end]
|
||||
mask_chunk = mask[start:end] if mask is not None else None
|
||||
|
||||
out_chunk = _apply_levels(
|
||||
img_chunk,
|
||||
channel=channel,
|
||||
in_low=in_low,
|
||||
in_high=in_high,
|
||||
gamma=gamma,
|
||||
out_low=out_low,
|
||||
out_high=out_high,
|
||||
clamp_input=input_clamp,
|
||||
clamp_output=output_clamp,
|
||||
auto_input=False, # already computed globally
|
||||
mask=mask_chunk,
|
||||
)
|
||||
outputs.append(out_chunk)
|
||||
|
||||
if outputs:
|
||||
result = torch.cat(outputs, dim=0)
|
||||
else:
|
||||
result = image # should not happen for non‑empty batch
|
||||
|
||||
return result.to(image.dtype).clamp(0.0, 1.0)
|
||||
|
||||
|
||||
@@ -0,0 +1,246 @@
|
||||
import torch
|
||||
import math
|
||||
from typing import Tuple, Optional
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
# ===================================================================================
|
||||
def rgb_to_hsl_torch(image: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Convert an RGB image to HSL.
|
||||
Input: (B, H, W, 3) in [0, 1]
|
||||
Output: (B, H, W, 3) with H, S, L in [0, 1]
|
||||
"""
|
||||
r, g, b = image[..., 0], image[..., 1], image[..., 2]
|
||||
max_val, _ = torch.max(image, dim=-1)
|
||||
min_val, _ = torch.min(image, dim=-1)
|
||||
l = (max_val + min_val) / 2.0
|
||||
|
||||
delta = max_val - min_val
|
||||
# Saturation: 0 if delta == 0
|
||||
s = torch.where(delta == 0, torch.zeros_like(delta),
|
||||
delta / (1.0 - torch.abs(2.0 * l - 1.0)))
|
||||
|
||||
h = torch.zeros_like(l)
|
||||
mask = delta != 0
|
||||
r_eq_max = (max_val == r) & mask
|
||||
g_eq_max = (max_val == g) & mask
|
||||
# Otherwise b_eq_max (implicit)
|
||||
h = torch.where(r_eq_max, ((g - b) / delta) % 6.0, h)
|
||||
h = torch.where(g_eq_max, ((b - r) / delta) + 2.0, h)
|
||||
h = torch.where(~r_eq_max & ~g_eq_max & mask, ((r - g) / delta) + 4.0, h)
|
||||
h = h / 6.0
|
||||
h = torch.where(h < 0, h + 1.0, h)
|
||||
h = torch.where(h >= 1.0, h - 1.0, h)
|
||||
|
||||
return torch.stack([h, s, l], dim=-1)
|
||||
|
||||
|
||||
def hsl_to_rgb_torch(image: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Convert an HSL image to RGB.
|
||||
Input: (B, H, W, 3) with H, S, L in [0, 1]
|
||||
Output: (B, H, W, 3) in [0, 1]
|
||||
"""
|
||||
h, s, l = image[..., 0], image[..., 1], image[..., 2]
|
||||
|
||||
c = (1.0 - torch.abs(2.0 * l - 1.0)) * s
|
||||
x = c * (1.0 - torch.abs((h * 6.0) % 2.0 - 1.0))
|
||||
m = l - c / 2.0
|
||||
|
||||
sector = (h * 6.0).to(torch.int64) % 6
|
||||
zeros = torch.zeros_like(h)
|
||||
|
||||
r = torch.where(sector == 0, c,
|
||||
torch.where(sector == 1, x,
|
||||
torch.where(sector == 2, zeros,
|
||||
torch.where(sector == 3, zeros,
|
||||
torch.where(sector == 4, x, c)))))
|
||||
g = torch.where(sector == 0, x,
|
||||
torch.where(sector == 1, c,
|
||||
torch.where(sector == 2, c,
|
||||
torch.where(sector == 3, x,
|
||||
torch.where(sector == 4, zeros, zeros)))))
|
||||
b = torch.where(sector == 0, zeros,
|
||||
torch.where(sector == 1, zeros,
|
||||
torch.where(sector == 2, x,
|
||||
torch.where(sector == 3, c,
|
||||
torch.where(sector == 4, c, x)))))
|
||||
|
||||
return torch.stack([r + m, g + m, b + m], dim=-1)
|
||||
|
||||
|
||||
def exec_operator_color_map_alien(
|
||||
image: torch.Tensor,
|
||||
model: str,
|
||||
freq1: float, freq2: float, freq3: float,
|
||||
shift1: float, shift2: float, shift3: float,
|
||||
keep1: bool, keep2: bool, keep3: bool,
|
||||
mask: Optional[torch.Tensor] = None
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply the Alien Map colour transformation to a batch of images.
|
||||
|
||||
The transformation mimics the GIMP “Alien Map” filter. For each colour
|
||||
channel that is not “kept”, the operation
|
||||
|
||||
out = 0.5 * (1 + sin( (2*in - 1) * freq + phase ))
|
||||
|
||||
is applied, where *freq* = `frequency` × π and *phase* = π × `shift` / 180°.
|
||||
The colour space can be either RGB or HSL; when HSL is chosen the image is
|
||||
temporarily converted, transformed, and then converted back to RGB.
|
||||
|
||||
**Batch processing** – if `mask` is a batch, the i‑th image is processed
|
||||
with the i‑th mask. A single mask is broadcast across the whole batch.
|
||||
|
||||
**RGB / RGBA** – 3‑channel (RGB) and 4‑channel (RGBA) images are accepted.
|
||||
For RGBA the alpha channel is **preserved unchanged**. Mask blending
|
||||
affects only the RGB channels.
|
||||
|
||||
**Early exit** – when all three `keep` flags are `True` the transformation
|
||||
is an identity; the function returns the original image immediately.
|
||||
|
||||
**GPU acceleration & VRAM management** – all work is done on the same
|
||||
device as the input tensor. To avoid out‑of‑memory errors the batch is
|
||||
processed in chunks whose size is determined by
|
||||
:func:`compute_chunk_size`. Spatial (tiled) splitting is **not** used.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
Input image(s), shape ``(B, H, W, 3)`` or ``(B, H, W, 4)``, values in [0, 1].
|
||||
model : str
|
||||
``"RGB"`` or ``"HSL"``.
|
||||
freq1, freq2, freq3 : float
|
||||
Frequency multipliers (0 – 20).
|
||||
shift1, shift2, shift3 : float
|
||||
Phase shifts in degrees (0 – 360).
|
||||
keep1, keep2, keep3 : bool
|
||||
If ``True`` the corresponding channel is left unchanged.
|
||||
mask : torch.Tensor, optional
|
||||
Per‑pixel strength of the effect, shape ``(B, H, W)`` or ``(B, H, W, 1)``,
|
||||
values in [0, 1]. The output is blended linearly:
|
||||
``mask * transformed + (1 - mask) * original``.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tuple[torch.Tensor]
|
||||
A single‑element tuple containing the transformed image tensor
|
||||
``(B, H, W, 3)`` or ``(B, H, W, 4)``, same shape and device as the input.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If the input image has an unsupported shape, or if the mask dimensions
|
||||
are incompatible with the image.
|
||||
"""
|
||||
# ----------------------- input validation ----------------------------------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise ValueError("image must be a torch.Tensor")
|
||||
if image.dim() != 4 or image.shape[-1] not in (3, 4):
|
||||
raise ValueError(
|
||||
f"image must be 4D (B, H, W, C) with C=3 or 4, got {image.shape}"
|
||||
)
|
||||
|
||||
batch_size, height, width, channels = image.shape
|
||||
has_alpha = (channels == 4)
|
||||
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise ValueError("mask must be a torch.Tensor or None")
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1) # (B, H, W, 1)
|
||||
elif mask.dim() != 4 or mask.shape[-1] != 1:
|
||||
raise ValueError(
|
||||
f"mask must be 3D (B,H,W) or 4D (B,H,W,1), got {mask.shape}"
|
||||
)
|
||||
if mask.shape[1:3] != (height, width):
|
||||
raise ValueError(
|
||||
f"mask spatial size {mask.shape[1:3]} must match image {(height, width)}"
|
||||
)
|
||||
mask_batch = mask.shape[0]
|
||||
if mask_batch != 1 and mask_batch != batch_size:
|
||||
raise ValueError(
|
||||
f"mask batch size {mask_batch} must be 1 or equal to image batch size {batch_size}"
|
||||
)
|
||||
|
||||
# ----------------------- early exit ----------------------------------------
|
||||
if all([keep1, keep2, keep3]):
|
||||
# The transformation is an identity; no need to compute anything.
|
||||
return (image,)
|
||||
|
||||
# ----------------------- prepare data --------------------------------------
|
||||
img = image.float()
|
||||
img = torch.clamp(img, 0.0, 1.0)
|
||||
|
||||
# Separate RGB and alpha (if present)
|
||||
if has_alpha:
|
||||
rgb = img[..., :3].clone()
|
||||
alpha = img[..., 3:4].clone()
|
||||
original_rgb = rgb.clone()
|
||||
else:
|
||||
rgb = img
|
||||
alpha = None
|
||||
original_rgb = img.clone() # used for mask blending later
|
||||
|
||||
device = rgb.device
|
||||
|
||||
# Colour‑space conversion (RGB → HSL) if required
|
||||
if model == "HSL":
|
||||
rgb = rgb_to_hsl_torch(rgb)
|
||||
|
||||
# Transformation constants (on correct device)
|
||||
freq = torch.tensor(
|
||||
[freq1 * math.pi, freq2 * math.pi, freq3 * math.pi],
|
||||
dtype=torch.float32, device=device
|
||||
)
|
||||
phase = torch.tensor(
|
||||
[math.pi * shift1 / 180.0,
|
||||
math.pi * shift2 / 180.0,
|
||||
math.pi * shift3 / 180.0],
|
||||
dtype=torch.float32, device=device
|
||||
)
|
||||
keep = torch.tensor([keep1, keep2, keep3], dtype=torch.bool, device=device)
|
||||
|
||||
# ----------------------- chunked processing --------------------------------
|
||||
chunk_size = compute_chunk_size(image, mask) # returns int
|
||||
if chunk_size <= 0 or chunk_size > batch_size:
|
||||
chunk_size = batch_size
|
||||
|
||||
out_rgb = torch.empty_like(rgb)
|
||||
|
||||
for start in range(0, batch_size, chunk_size):
|
||||
end = min(start + chunk_size, batch_size)
|
||||
|
||||
rgb_chunk = rgb[start:end]
|
||||
out_chunk = rgb_chunk.clone()
|
||||
|
||||
# Apply the Alien Map formula per channel
|
||||
for i in range(3):
|
||||
if not keep[i]:
|
||||
val = 2.0 * rgb_chunk[..., i] - 1.0
|
||||
out_chunk[..., i] = 0.5 * (1.0 + torch.sin(val * freq[i] + phase[i]))
|
||||
|
||||
out_chunk = torch.clamp(out_chunk, 0.0, 1.0)
|
||||
|
||||
# Back to RGB if we were in HSL
|
||||
if model == "HSL":
|
||||
out_chunk = hsl_to_rgb_torch(out_chunk)
|
||||
|
||||
# Blend with mask (RGB channels only)
|
||||
if mask is not None:
|
||||
mask_chunk = mask[start:end] # will broadcast correctly if mask batch=1
|
||||
# original_rgb_chunk has the original clamped values
|
||||
original_chunk = original_rgb[start:end]
|
||||
out_chunk = mask_chunk * out_chunk + (1.0 - mask_chunk) * original_chunk
|
||||
|
||||
out_rgb[start:end] = out_chunk
|
||||
|
||||
# ----------------------- recombine alpha (if present) -----------------------
|
||||
if has_alpha:
|
||||
out = torch.cat([out_rgb, alpha], dim=-1)
|
||||
else:
|
||||
out = out_rgb
|
||||
|
||||
return (out,)
|
||||
|
||||
|
||||
@@ -0,0 +1,166 @@
|
||||
import torch
|
||||
from typing import Optional, Tuple
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
# ===================================================================================
|
||||
|
||||
def exec_operator_color_map_color_exchange(
|
||||
image: torch.Tensor,
|
||||
from_color: int,
|
||||
to_color: int,
|
||||
red_threshold: float,
|
||||
green_threshold: float,
|
||||
blue_threshold: float,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Color Exchange (RGB/RGBA) with soft mask, batch chunking and alpha preservation.
|
||||
|
||||
Pixels whose RGB components *all* lie within the given thresholds around a
|
||||
source colour are recoloured to a target colour, preserving each channel's
|
||||
original offset from the source::
|
||||
|
||||
out_channel = target_channel + (pixel_channel - source_channel)
|
||||
|
||||
The result is clamped to [0,1]. When a soft mask is supplied, the
|
||||
recolouring is blended per‑pixel using the mask value as the interpolation
|
||||
strength.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
Batched image tensor of shape ``(B, H, W, 3)`` or ``(B, H, W, 4)``,
|
||||
values in [0, 1], dtype float16/32/64.
|
||||
from_color : int
|
||||
Packed source colour (0xRRGGBB).
|
||||
to_color : int
|
||||
Packed target colour (0xRRGGBB).
|
||||
red_threshold, green_threshold, blue_threshold : float
|
||||
Per‑channel tolerance in [0, 1].
|
||||
mask : torch.Tensor, optional
|
||||
Soft mask of shape ``(B, H, W)`` or ``(B, H, W, 1)``, or a single
|
||||
mask broadcastable to the batch size. Values are clamped to [0, 1]
|
||||
and used as per‑pixel blend strength.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tuple[torch.Tensor]
|
||||
A single‑element tuple containing the recoloured image with the same
|
||||
shape and number of channels as the input.
|
||||
"""
|
||||
# ---- Validate input --------------------------------------------------------
|
||||
if image.ndim != 4:
|
||||
raise ValueError(f"image must be 4D (B,H,W,C), got shape {image.shape}")
|
||||
C = image.shape[-1]
|
||||
if C not in (3, 4):
|
||||
raise ValueError(f"image must have 3 or 4 channels, got {C}")
|
||||
if not torch.is_floating_point(image):
|
||||
raise TypeError("image must be a floating point tensor")
|
||||
|
||||
if not (0 <= from_color <= 0xFFFFFF):
|
||||
raise ValueError(f"from_color must be in [0, 0xFFFFFF], got {from_color:#x}")
|
||||
if not (0 <= to_color <= 0xFFFFFF):
|
||||
raise ValueError(f"to_color must be in [0, 0xFFFFFF], got {to_color:#x}")
|
||||
for th, name in zip(
|
||||
(red_threshold, green_threshold, blue_threshold),
|
||||
("red_threshold", "green_threshold", "blue_threshold"),
|
||||
):
|
||||
if not (0.0 <= th <= 1.0):
|
||||
raise ValueError(f"{name} must be in [0, 1], got {th}")
|
||||
|
||||
device = image.device
|
||||
dtype = image.dtype
|
||||
B = image.shape[0]
|
||||
|
||||
# ---- Mask validation & early exit ------------------------------------------
|
||||
if mask is not None:
|
||||
if mask.ndim == 3:
|
||||
mask = mask.unsqueeze(-1) # (B,H,W) -> (B,H,W,1)
|
||||
elif mask.ndim == 4:
|
||||
if mask.shape[-1] != 1:
|
||||
raise ValueError("mask with 4 dims must have last dim == 1")
|
||||
else:
|
||||
raise ValueError(f"mask must be 3D or 4D, got shape {mask.shape}")
|
||||
|
||||
# Move mask to the correct device/dtype, clamp
|
||||
mask = mask.to(device=device, dtype=dtype).clamp(0.0, 1.0)
|
||||
|
||||
# Check batch compatibility
|
||||
if mask.shape[0] != 1 and mask.shape[0] != B:
|
||||
raise ValueError(
|
||||
f"mask batch size {mask.shape[0]} must be 1 or match image batch size {B}"
|
||||
)
|
||||
|
||||
# Early exit if mask is entirely zero
|
||||
if mask.count_nonzero() == 0:
|
||||
return (image,)
|
||||
|
||||
# ---- Pre‑compute colour thresholds (RGB only) --------------------------------
|
||||
from_rgb = image.new_tensor([
|
||||
((from_color >> 16) & 0xFF) / 255.0,
|
||||
((from_color >> 8) & 0xFF) / 255.0,
|
||||
(from_color & 0xFF) / 255.0,
|
||||
])
|
||||
to_rgb = image.new_tensor([
|
||||
((to_color >> 16) & 0xFF) / 255.0,
|
||||
((to_color >> 8) & 0xFF) / 255.0,
|
||||
(to_color & 0xFF) / 255.0,
|
||||
])
|
||||
thresholds = image.new_tensor([red_threshold, green_threshold, blue_threshold])
|
||||
|
||||
# Broadcast dimensions: (1,1,1,3)
|
||||
from_rgb = from_rgb.view(1, 1, 1, 3)
|
||||
to_rgb = to_rgb.view(1, 1, 1, 3)
|
||||
thresholds = thresholds.view(1, 1, 1, 3)
|
||||
|
||||
# ---- RGBA support: separate RGB and alpha ----------------------------------
|
||||
if C == 4:
|
||||
image_rgb = image[..., :3]
|
||||
image_alpha = image[..., 3:4] # keep as (B,H,W,1)
|
||||
else:
|
||||
image_rgb = image
|
||||
|
||||
# ---- Determine chunk size and process in sub‑batches -----------------------
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
result_chunks = []
|
||||
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
img_chunk = image_rgb[start:end] # (chunk, H, W, 3)
|
||||
|
||||
# Extract matching mask chunk (handle broadcast)
|
||||
if mask is not None:
|
||||
if mask.shape[0] == 1:
|
||||
mask_chunk = mask # broadcast across chunk
|
||||
else:
|
||||
mask_chunk = mask[start:end]
|
||||
else:
|
||||
mask_chunk = None
|
||||
|
||||
# ----- Original colour exchange logic (applied to RGB) ------------------
|
||||
diff = (img_chunk - from_rgb).abs()
|
||||
match = (diff <= thresholds).all(dim=-1, keepdim=True) # (chunk,H,W,1)
|
||||
|
||||
exchanged = to_rgb + (img_chunk - from_rgb)
|
||||
exchanged = torch.clamp(exchanged, 0.0, 1.0)
|
||||
result_rgb = torch.where(match, exchanged, img_chunk)
|
||||
|
||||
# ----- Soft mask blending (applies only to RGB) -------------------------
|
||||
if mask_chunk is not None:
|
||||
# mask_chunk is (1,H,W,1) or (chunk,H,W,1) – already prepared
|
||||
result_rgb = img_chunk * (1.0 - mask_chunk) + result_rgb * mask_chunk
|
||||
|
||||
# ----- Reassemble RGBA if needed ----------------------------------------
|
||||
if C == 4:
|
||||
alpha_chunk = image_alpha[start:end]
|
||||
result_chunk = torch.cat([result_rgb, alpha_chunk], dim=-1)
|
||||
else:
|
||||
result_chunk = result_rgb
|
||||
|
||||
result_chunks.append(result_chunk)
|
||||
|
||||
# Concatenate all chunks back to a single batch tensor
|
||||
result = torch.cat(result_chunks, dim=0)
|
||||
|
||||
return (result,)
|
||||
|
||||
@@ -0,0 +1,252 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
|
||||
from .libs.color import hsv_to_rgb, rgb_to_hsv
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Angular helpers (unchanged)
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
def _cw_distance(start: float, angle: torch.Tensor) -> torch.Tensor:
|
||||
"""Clockwise angular distance [0,360)."""
|
||||
return (angle - start) % 360.0
|
||||
|
||||
|
||||
def _ccw_distance(start: float, angle: torch.Tensor) -> torch.Tensor:
|
||||
"""Counter-clockwise angular distance [0,360)."""
|
||||
return (start - angle) % 360.0
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Core color rotate logic (identical per-pixel behaviour, no mask/alpha)
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
def _color_rotate_core(
|
||||
rgb_chunk: torch.Tensor, # (B, H, W, 3) in [0,1]
|
||||
mask_chunk: Optional[torch.Tensor], # (B, H, W, 1) in [0,1] or None
|
||||
Source_from: float,
|
||||
Source_to: float,
|
||||
Source_clockwise: bool,
|
||||
Destination_from: float,
|
||||
Destination_to: float,
|
||||
Destination_clockwise: bool,
|
||||
Gray_threshold: float,
|
||||
Mode: str,
|
||||
Hue: float,
|
||||
Saturation: float,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Applies GIMP-style color rotate to a chunk of RGB images,
|
||||
optionally blending with a soft mask.
|
||||
Returns an RGB tensor of the same shape as `rgb_chunk`.
|
||||
"""
|
||||
# RGB → HSV
|
||||
hue, sat, val = rgb_to_hsv(rgb_chunk)
|
||||
hue = hue * 360.0
|
||||
|
||||
# Gray handling
|
||||
is_gray = sat <= Gray_threshold
|
||||
replacement_hue = torch.full_like(hue, Hue % 360.0)
|
||||
replacement_sat = torch.full_like(sat, float(max(0.0, min(1.0, Saturation))))
|
||||
|
||||
if Mode == "Change to this":
|
||||
rotate_hue = hue
|
||||
rotate_sat = sat
|
||||
hue = torch.where(is_gray, replacement_hue, hue)
|
||||
sat = torch.where(is_gray, replacement_sat, sat)
|
||||
rotate_mask = ~is_gray
|
||||
else: # "Treat as this"
|
||||
rotate_hue = torch.where(is_gray, replacement_hue, hue)
|
||||
rotate_sat = torch.where(is_gray, replacement_sat, sat)
|
||||
hue = rotate_hue
|
||||
sat = rotate_sat
|
||||
rotate_mask = torch.ones_like(is_gray)
|
||||
|
||||
# Source interval
|
||||
if Source_clockwise:
|
||||
span = (Source_to - Source_from) % 360.0
|
||||
dist = _cw_distance(Source_from, rotate_hue)
|
||||
else:
|
||||
span = (Source_from - Source_to) % 360.0
|
||||
dist = _ccw_distance(Source_from, rotate_hue)
|
||||
|
||||
if span < 1e-6:
|
||||
span = 360.0
|
||||
|
||||
inside = dist <= span
|
||||
rotate_mask &= inside
|
||||
|
||||
# Normalised position
|
||||
t = (dist / span).clamp(0.0, 1.0)
|
||||
|
||||
# Destination interval
|
||||
if Destination_clockwise:
|
||||
dst_span = (Destination_to - Destination_from) % 360.0
|
||||
new_hue = (Destination_from + t * dst_span) % 360.0
|
||||
else:
|
||||
dst_span = (Destination_from - Destination_to) % 360.0
|
||||
new_hue = (Destination_from - t * dst_span) % 360.0
|
||||
|
||||
hue = torch.where(rotate_mask, new_hue, hue)
|
||||
|
||||
# HSV → RGB
|
||||
new_rgb = hsv_to_rgb(hue / 360.0, sat.clamp(0.0, 1.0), val)
|
||||
|
||||
# Soft mask blending
|
||||
if mask_chunk is not None:
|
||||
rgb_out = rgb_chunk * (1.0 - mask_chunk) + new_rgb * mask_chunk
|
||||
else:
|
||||
rgb_out = new_rgb
|
||||
|
||||
return rgb_out
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Main entry point – batch processing, alpha preservation, chunking, early exit
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
def exec_operator_color_map_color_rotate(
|
||||
image: torch.Tensor,
|
||||
Source_from: float,
|
||||
Source_to: float,
|
||||
Source_clockwise: bool,
|
||||
Destination_from: float,
|
||||
Destination_to: float,
|
||||
Destination_clockwise: bool,
|
||||
Gray_threshold: float,
|
||||
Mode: str,
|
||||
Hue: float,
|
||||
Saturation: float,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply a GIMP‑style color‑rotate effect to a batch of RGB or RGBA images.
|
||||
|
||||
The operation remaps a source hue interval to a destination hue interval.
|
||||
An optional per‑pixel soft mask controls the blending strength.
|
||||
Alpha channels (if present) are preserved unchanged.
|
||||
Processing is performed in chunks along the batch dimension to manage
|
||||
GPU memory usage. If the optional mask is entirely zero the function
|
||||
returns the input immediately without any computation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
Input batch of shape (B, H, W, 3) or (B, H, W, 4), values in [0, 1].
|
||||
Source_from, Source_to : float
|
||||
Bounds of the source hue interval in degrees [0, 360].
|
||||
Source_clockwise : bool
|
||||
Direction of the source interval (clockwise if True).
|
||||
Destination_from, Destination_to : float
|
||||
Bounds of the destination hue interval in degrees [0, 360].
|
||||
Destination_clockwise : bool
|
||||
Direction of the destination interval.
|
||||
Gray_threshold : float
|
||||
Saturation below which a pixel is considered gray [0, 1].
|
||||
Mode : str
|
||||
"Change to this" – replace gray pixels with the given Hue/Saturation;
|
||||
"Treat as this" – treat gray pixels as if they had the given Hue/Saturation.
|
||||
Hue : float
|
||||
Replacement/treatment hue for gray pixels [0, 360].
|
||||
Saturation : float
|
||||
Replacement/treatment saturation for gray pixels [0, 1].
|
||||
mask : torch.Tensor, optional
|
||||
Soft mask of shape (B, H, W) or (B, H, W, 1), values in [0, 1].
|
||||
When provided, the effect is blended per‑pixel: output = image*(1‑mask) + effect*mask.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tuple[torch.Tensor]
|
||||
A single‑element tuple containing the processed image batch of the same
|
||||
shape and channel count as the input.
|
||||
"""
|
||||
# ----- Validation --------------------------------------------------------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
if image.dim() != 4:
|
||||
raise ValueError(
|
||||
f"image must be 4‑dimensional (B,H,W,C), got shape {image.shape}"
|
||||
)
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(
|
||||
f"image must have 3 or 4 channels (RGB or RGBA), got {image.shape[-1]}"
|
||||
)
|
||||
|
||||
# ----- Alpha preservation ------------------------------------------------
|
||||
has_alpha = image.shape[-1] == 4
|
||||
if has_alpha:
|
||||
alpha = image[..., 3:4] # keep last dim for concatenation
|
||||
rgb_image = image[..., :3]
|
||||
else:
|
||||
alpha = None
|
||||
rgb_image = image
|
||||
|
||||
# ----- Mask validation ---------------------------------------------------
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor")
|
||||
# Unify shape to (B, H, W, 1)
|
||||
if mask.ndim == 3:
|
||||
mask = mask.unsqueeze(-1)
|
||||
elif mask.ndim != 4 or mask.shape[-1] != 1:
|
||||
raise ValueError(
|
||||
f"mask must have shape (B,H,W) or (B,H,W,1), got {mask.shape}"
|
||||
)
|
||||
if mask.shape[0] != image.shape[0]:
|
||||
raise ValueError(
|
||||
f"mask batch size ({mask.shape[0]}) does not match image ({image.shape[0]})"
|
||||
)
|
||||
if mask.shape[1:3] != image.shape[1:3]:
|
||||
raise ValueError(
|
||||
f"mask spatial size {mask.shape[1:3]} does not match image {image.shape[1:3]}"
|
||||
)
|
||||
# Move to image device/dtype
|
||||
mask = mask.to(device=rgb_image.device, dtype=rgb_image.dtype)
|
||||
|
||||
# ----- Early exit (mask is fully transparent) ----------------------------
|
||||
if mask is not None and mask.max() == 0:
|
||||
return (image,)
|
||||
|
||||
B = rgb_image.shape[0]
|
||||
if B == 0:
|
||||
return (image,)
|
||||
|
||||
# ----- Chunked processing ------------------------------------------------
|
||||
chunk_size = compute_chunk_size(rgb_image, mask)
|
||||
if chunk_size <= 0 or chunk_size > B:
|
||||
chunk_size = B
|
||||
|
||||
out_chunks = []
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
rgb_chunk = rgb_image[start:end]
|
||||
mask_chunk = mask[start:end] if mask is not None else None
|
||||
|
||||
chunk_out = _color_rotate_core(
|
||||
rgb_chunk=rgb_chunk,
|
||||
mask_chunk=mask_chunk,
|
||||
Source_from=Source_from,
|
||||
Source_to=Source_to,
|
||||
Source_clockwise=Source_clockwise,
|
||||
Destination_from=Destination_from,
|
||||
Destination_to=Destination_to,
|
||||
Destination_clockwise=Destination_clockwise,
|
||||
Gray_threshold=Gray_threshold,
|
||||
Mode=Mode,
|
||||
Hue=Hue,
|
||||
Saturation=Saturation,
|
||||
)
|
||||
out_chunks.append(chunk_out)
|
||||
|
||||
rgb_result = torch.cat(out_chunks, dim=0)
|
||||
|
||||
# ----- Recombine alpha ---------------------------------------------------
|
||||
if has_alpha:
|
||||
result = torch.cat([rgb_result, alpha], dim=-1)
|
||||
else:
|
||||
result = rgb_result
|
||||
|
||||
return (result,)
|
||||
|
||||
@@ -0,0 +1,168 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
|
||||
from .libs.color import srgb_to_linear_vec, linear_to_srgb_vec
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
# ============================================================
|
||||
# GEGL-style posterization core
|
||||
# ============================================================
|
||||
|
||||
def _posterize_channel_gegl(x: torch.Tensor, levels: int) -> torch.Tensor:
|
||||
"""
|
||||
GEGL-style uniform quantizer.
|
||||
Equivalent to:
|
||||
floor(x * (levels - 1) + 0.5) / (levels - 1)
|
||||
"""
|
||||
levels = max(2, int(levels))
|
||||
scale = levels - 1
|
||||
return torch.floor(x * scale + 0.5) / scale
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Main operator – improved with batch chunking, alpha preservation,
|
||||
# error handling, and early exit.
|
||||
# ============================================================
|
||||
|
||||
def exec_operator_color_posterize(
|
||||
image: torch.Tensor,
|
||||
levels: int,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
linear: bool = False, # GEGL toggle: operate in linear light
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply GEGL‑style posterisation to a batch of images.
|
||||
|
||||
The input *image* is a float tensor of shape ``(B, H, W, C)`` with
|
||||
``C == 3`` (RGB) or ``C == 4`` (RGBA). *levels* controls the number
|
||||
of discrete levels per colour channel (minimum 2). An optional *mask*
|
||||
tensor of shape ``(H, W)`` (single mask) or ``(B, H, W)`` (per‑image
|
||||
mask) defines the per‑pixel strength of the effect: ``0.0`` leaves the
|
||||
pixel unchanged, ``1.0`` applies full posterisation.
|
||||
|
||||
When the input contains an alpha channel (``C == 4``), the alpha
|
||||
channel is **preserved unchanged**; only the RGB channels are
|
||||
posterised and blended with the mask.
|
||||
|
||||
The function validates input shapes, performs an early exit for empty
|
||||
batches, and processes the batch in GPU‑friendly chunks as determined
|
||||
by ``compute_chunk_size``. No spatial tiling is performed.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
Batch of images (B,H,W,C), float.
|
||||
levels : int
|
||||
Number of posterisation levels (2‑256).
|
||||
mask : torch.Tensor or None
|
||||
Optional strength mask (H,W) or (B,H,W).
|
||||
linear : bool
|
||||
If ``True``, the effect is applied in linear light
|
||||
(sRGB ↔ linear conversion is used).
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tuple[torch.Tensor]
|
||||
A 1‑tuple containing the posterised image batch.
|
||||
"""
|
||||
|
||||
# ---------- input validation ----------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError(f"image must be a torch.Tensor, got {type(image)}")
|
||||
if image.dim() != 4:
|
||||
raise ValueError(f"image must be 4D (B,H,W,C), got shape {image.shape}")
|
||||
C = image.shape[-1]
|
||||
if C not in (1, 3, 4):
|
||||
raise ValueError(f"Unsupported number of channels: {C}. Only 1, 3, or 4 are supported")
|
||||
if image.dtype not in (torch.float32, torch.float64, torch.float16):
|
||||
raise TypeError(f"image dtype must be float, got {image.dtype}")
|
||||
|
||||
levels = max(2, int(levels)) # ensure >= 2
|
||||
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError(f"mask must be a torch.Tensor or None, got {type(mask)}")
|
||||
if mask.dim() == 2:
|
||||
pass # (H,W) will be broadcast
|
||||
elif mask.dim() == 3:
|
||||
if mask.shape[0] != image.shape[0]:
|
||||
raise ValueError(
|
||||
f"Batch dimension mismatch: image batch {image.shape[0]}, "
|
||||
f"mask batch {mask.shape[0]}"
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"mask must be 2D (H,W) or 3D (B,H,W), got shape {mask.shape}")
|
||||
if mask.device != image.device:
|
||||
raise ValueError("image and mask must reside on the same device")
|
||||
|
||||
# ---------- early exit for empty batches ----------
|
||||
if image.shape[0] == 0:
|
||||
return (image,)
|
||||
|
||||
# ---------- alpha channel handling ----------
|
||||
has_alpha = (C == 4)
|
||||
if has_alpha:
|
||||
rgb = image[..., :3]
|
||||
alpha = image[..., 3:4] # keep as (B,H,W,1)
|
||||
else:
|
||||
rgb = image
|
||||
alpha = None
|
||||
|
||||
B = rgb.shape[0]
|
||||
|
||||
# ---------- determine chunk size (VRAM‑aware) ----------
|
||||
# The helper considers both image and mask sizes.
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
chunk_size = max(1, min(chunk_size, B)) # clamp to valid range
|
||||
|
||||
out_chunks = []
|
||||
|
||||
# ---------- chunked processing ----------
|
||||
for start in range(0, B, chunk_size):
|
||||
end = min(start + chunk_size, B)
|
||||
|
||||
chunk_rgb = rgb[start:end].clone() # clone to avoid in‑place issues
|
||||
# Prepare mask chunk
|
||||
chunk_mask = None
|
||||
if mask is not None:
|
||||
if mask.ndim == 3: # per‑image masks
|
||||
chunk_mask = mask[start:end]
|
||||
else: # single (H,W) mask, reuse for all images
|
||||
chunk_mask = mask
|
||||
|
||||
# ---- posterisation (per‑channel) ----
|
||||
x = torch.clamp(chunk_rgb, 0.0, 1.0)
|
||||
|
||||
if linear:
|
||||
x = srgb_to_linear_vec(x)
|
||||
|
||||
x_p = _posterize_channel_gegl(x, levels)
|
||||
|
||||
if linear:
|
||||
x_p = linear_to_srgb_vec(x_p)
|
||||
|
||||
x_p = torch.clamp(x_p, 0.0, 1.0)
|
||||
|
||||
# ---- mask blending (only on RGB) ----
|
||||
if chunk_mask is not None:
|
||||
# Ensure mask has shape (B',H,W,1) for broadcasting
|
||||
if chunk_mask.ndim == 2:
|
||||
chunk_mask = chunk_mask.unsqueeze(0).unsqueeze(-1) # (1,H,W,1)
|
||||
else:
|
||||
chunk_mask = chunk_mask.unsqueeze(-1) # (B',H,W,1)
|
||||
chunk_mask = chunk_mask.to(dtype=x.dtype, device=x.device)
|
||||
x_p = chunk_rgb * (1.0 - chunk_mask) + x_p * chunk_mask
|
||||
|
||||
# ---- reassemble alpha if present ----
|
||||
if has_alpha:
|
||||
chunk_out = torch.cat([x_p, alpha[start:end]], dim=-1)
|
||||
else:
|
||||
chunk_out = x_p
|
||||
|
||||
out_chunks.append(chunk_out)
|
||||
|
||||
# ---------- concatenate chunks ----------
|
||||
final_image = torch.cat(out_chunks, dim=0)
|
||||
|
||||
return (final_image,)
|
||||
|
||||
@@ -0,0 +1,185 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
import logging
|
||||
|
||||
from .libs.color import rgb_to_hsv, hsv_to_rgb, rgb_to_lch, lch_to_rgb, rgb_to_yuv, yuv_to_rgb
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# Core saturation algorithm – processes a single batch on one device
|
||||
# ----------------------------------------------------------------------
|
||||
def _saturation_core(
|
||||
image: torch.Tensor, # (B, H, W, C) C = 3 or 4
|
||||
scale: float,
|
||||
color_space: str,
|
||||
mask: Optional[torch.Tensor] # (B, H, W, C) or None
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Apply saturation scaling to an image batch (RGB or RGBA).
|
||||
Handles exactly one device; no chunking, no fallback.
|
||||
"""
|
||||
|
||||
# Early exit when no change is requested
|
||||
if scale == 1.0:
|
||||
return image
|
||||
|
||||
# Clamp input to valid colour range
|
||||
img = image.clamp(0.0, 1.0)
|
||||
|
||||
# Separate alpha if present
|
||||
if img.shape[-1] == 4:
|
||||
alpha = img[..., 3:4]
|
||||
rgb = img[..., :3]
|
||||
else:
|
||||
alpha = None
|
||||
rgb = img
|
||||
|
||||
# ----- colour space conversion & scaling -----
|
||||
if color_space == "Native":
|
||||
h, s, v = rgb_to_hsv(rgb)
|
||||
s = torch.clamp(s * scale, 0.0, 1.0)
|
||||
result_rgb = hsv_to_rgb(h, s, v)
|
||||
elif color_space == "CIE Lab/Lch":
|
||||
l, c, h = rgb_to_lch(rgb)
|
||||
c = c * scale
|
||||
result_rgb = lch_to_rgb(l, c, h)
|
||||
else: # CIE Yuv
|
||||
y, u, v = rgb_to_yuv(rgb)
|
||||
u = u * scale
|
||||
v = v * scale
|
||||
result_rgb = yuv_to_rgb(y, u, v)
|
||||
|
||||
result_rgb = torch.clamp(result_rgb, 0.0, 1.0)
|
||||
|
||||
# Reassemble RGBA if needed
|
||||
if alpha is not None:
|
||||
result = torch.cat([result_rgb, alpha], dim=-1)
|
||||
else:
|
||||
result = result_rgb
|
||||
|
||||
# Mask blending (applied to all channels, including alpha)
|
||||
if mask is not None:
|
||||
result = img * (1.0 - mask) + result * mask
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ----------------------------------------------------------------------
|
||||
# Public entry point – batch, device, VRAM management, error handling
|
||||
# ----------------------------------------------------------------------
|
||||
def exec_operator_color_saturation(
|
||||
image: torch.Tensor,
|
||||
scale: float,
|
||||
color_space: str,
|
||||
mask: Optional[torch.Tensor] = None
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Adjust the saturation of an image or batch of images.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
Input image(s) in RGB or RGBA format.
|
||||
Shape: (H, W, 3) or (H, W, 4) for a single image,
|
||||
(B, H, W, 3) or (B, H, W, 4) for a batch.
|
||||
scale : float
|
||||
Saturation multiplier. 1.0 = no change, <1 reduces saturation,
|
||||
>1 increases saturation.
|
||||
color_space : str
|
||||
Colour space in which saturation is scaled.
|
||||
One of ``"Native"``, ``"CIE Lab/Lch"``, ``"CIE Yuv"``.
|
||||
mask : torch.Tensor, optional
|
||||
Optional mask defining where the adjustment is applied.
|
||||
Shape must match the image batch size and spatial dimensions.
|
||||
(B, H, W) or (B, 1, H, W) or (B, H, W, 1).
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tuple[torch.Tensor]
|
||||
A tuple containing the saturation‑adjusted image(s) in the same
|
||||
format and shape as the input.
|
||||
"""
|
||||
# --- validate input shapes and types ---
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
if image.dim() not in (3, 4):
|
||||
raise ValueError(f"image must be 3D (H,W,C) or 4D (B,H,W,C), got {image.dim()}D")
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(f"image channels must be 3 or 4, got {image.shape[-1]}")
|
||||
|
||||
if not isinstance(scale, (int, float)):
|
||||
raise TypeError("scale must be a float")
|
||||
if color_space not in ("Native", "CIE Lab/Lch", "CIE Yuv"):
|
||||
raise ValueError(f"unknown color_space '{color_space}'")
|
||||
|
||||
# Normalise to batch format (B, H, W, C)
|
||||
single_input = image.dim() == 3
|
||||
if single_input:
|
||||
image = image.unsqueeze(0) # (1, H, W, C)
|
||||
if mask is not None:
|
||||
mask = mask.unsqueeze(0) # (1, …) whatever dim
|
||||
|
||||
batch_size, h, w, channels = image.shape
|
||||
device = image.device
|
||||
|
||||
# --- mask handling (ensure (B, H, W, C) compatible) ---
|
||||
if mask is not None:
|
||||
mask = mask.to(device)
|
||||
# bring to (B, H, W, 1)
|
||||
if mask.dim() == 2: # (H, W)
|
||||
mask = mask.unsqueeze(0).unsqueeze(-1)
|
||||
elif mask.dim() == 3: # (B, H, W)
|
||||
mask = mask.unsqueeze(-1)
|
||||
elif mask.dim() == 4:
|
||||
if mask.shape[1] == 1: # (B, 1, H, W)
|
||||
mask = mask.permute(0, 2, 3, 1) # (B, H, W, 1)
|
||||
elif mask.shape[-1] != 1:
|
||||
raise ValueError("mask must have a single channel")
|
||||
else:
|
||||
raise ValueError("mask must be 2D, 3D or 4D")
|
||||
|
||||
if mask.shape[0] != batch_size:
|
||||
raise ValueError(f"batch size mismatch: image {batch_size}, mask {mask.shape[0]}")
|
||||
# Expand mask to full channel dimension
|
||||
mask = mask.expand(-1, -1, -1, channels)
|
||||
|
||||
# --- VRAM optimised chunked processing (GPU only) ---
|
||||
VRAM_CHUNK_SIZE = compute_chunk_size(image) # process 4 images at once on GPU to limit peak memory
|
||||
|
||||
def _process_chunked(img_batch, msk_batch):
|
||||
"""Process a whole batch, potentially in chunks."""
|
||||
if device.type == 'cuda' and batch_size > VRAM_CHUNK_SIZE:
|
||||
results = []
|
||||
for i in range(0, batch_size, VRAM_CHUNK_SIZE):
|
||||
end = min(i + VRAM_CHUNK_SIZE, batch_size)
|
||||
sub_img = img_batch[i:end]
|
||||
sub_msk = msk_batch[i:end] if msk_batch is not None else None
|
||||
results.append(_saturation_core(sub_img, scale, color_space, sub_msk))
|
||||
# free GPU memory peaks
|
||||
torch.cuda.empty_cache()
|
||||
return torch.cat(results, dim=0)
|
||||
else:
|
||||
return _saturation_core(img_batch, scale, color_space, msk_batch)
|
||||
|
||||
# --- execute with GPU OOM fallback ---
|
||||
try:
|
||||
result = _process_chunked(image, mask)
|
||||
except RuntimeError as e:
|
||||
if 'out of memory' in str(e) and device.type == 'cuda':
|
||||
logger.warning("CUDA out of memory – falling back to CPU")
|
||||
torch.cuda.empty_cache()
|
||||
cpu_image = image.cpu()
|
||||
cpu_mask = mask.cpu() if mask is not None else None
|
||||
result_cpu = _saturation_core(cpu_image, scale, color_space, cpu_mask)
|
||||
result = result_cpu.to(device)
|
||||
else:
|
||||
raise
|
||||
|
||||
# Remove batch dimension if input was a single image
|
||||
if single_input:
|
||||
result = result.squeeze(0)
|
||||
|
||||
return (result,)
|
||||
|
||||
@@ -0,0 +1,220 @@
|
||||
import torch
|
||||
from typing import Tuple
|
||||
|
||||
from .libs.blur import gaussian_blur_2d
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
|
||||
def _process_core(
|
||||
image: torch.Tensor,
|
||||
Shadows: float,
|
||||
Shadows_color_adj: float,
|
||||
Highlights: float,
|
||||
Highlights_color_adj: float,
|
||||
White_point_adj: float,
|
||||
Radius: float,
|
||||
Compress: float,
|
||||
mask: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Apply the GEGL‑style shadows/highlights correction to a single batch
|
||||
chunk. This function is identical to the original processing logic.
|
||||
"""
|
||||
img = image[..., :3].clamp(0.0, 1.0)
|
||||
device = img.device
|
||||
|
||||
# GEGL luminance (Rec.709)
|
||||
lum = 0.2126 * img[..., 0] + 0.7152 * img[..., 1] + 0.0722 * img[..., 2]
|
||||
|
||||
# White point correction
|
||||
white = 1.0 - (White_point_adj / 100.0)
|
||||
lum = torch.clamp(lum / max(white, 1e-6), 0.0, 1.0)
|
||||
|
||||
# Local context (GEGL radius model)
|
||||
local = gaussian_blur_2d(lum, Radius)
|
||||
|
||||
# Normalise parameters
|
||||
shadows = max(-1.0, min(1.0, float(Shadows) / 100.0))
|
||||
highlights = max(-1.0, min(1.0, float(Highlights) / 100.0))
|
||||
compress = max(0.0, min(0.99, float(Compress) / 100.0))
|
||||
|
||||
# GEGL‑like nonlinear weighting
|
||||
shadow_w = (1.0 - local) ** 1.5
|
||||
highlight_w = local ** 1.5
|
||||
|
||||
lum_out = lum + shadows * shadow_w - highlights * highlight_w
|
||||
|
||||
# Compression (midtone preservation)
|
||||
mid = lum * (1.0 - lum) * 4.0
|
||||
lum_out = lum_out * (1.0 - compress * mid)
|
||||
lum_out = torch.clamp(lum_out, 0.0, 1.0)
|
||||
|
||||
# Saturation model (correct luminance)
|
||||
gray = 0.2126 * img[..., 0:1] + 0.7152 * img[..., 1:2] + 0.0722 * img[..., 2:3]
|
||||
|
||||
sat_shadow = 1.0 - (1.0 - Shadows_color_adj / 100.0) * (1.0 - local.unsqueeze(-1))
|
||||
sat_high = 1.0 - (1.0 - Highlights_color_adj / 100.0) * local.unsqueeze(-1)
|
||||
sat = sat_shadow * sat_high
|
||||
|
||||
rgb = gray + (img - gray) * sat
|
||||
|
||||
# Luminance correction (safe division)
|
||||
rgb = rgb * (lum_out.unsqueeze(-1) / (lum.unsqueeze(-1) + 1e-6))
|
||||
rgb = torch.clamp(rgb, 0.0, 1.0)
|
||||
|
||||
# Mask blending
|
||||
if mask is not None:
|
||||
mask = mask.to(device)
|
||||
if mask.dim() == 3:
|
||||
mask = mask.unsqueeze(-1)
|
||||
elif mask.dim() == 4 and mask.shape[1] == 1:
|
||||
mask = mask.permute(0, 2, 3, 1)
|
||||
mask = mask.expand_as(rgb)
|
||||
rgb = img * (1.0 - mask) + rgb * mask
|
||||
|
||||
# Preserve alpha channel
|
||||
if image.shape[-1] == 4:
|
||||
rgb = torch.cat([rgb, image[..., 3:4]], dim=-1)
|
||||
|
||||
return rgb
|
||||
|
||||
|
||||
def exec_operator_color_shadows_highlights(
|
||||
image: torch.Tensor,
|
||||
Shadows: float = 0.0,
|
||||
Shadows_color_adj: float = 100.0,
|
||||
Highlights: float = 0.0,
|
||||
Highlights_color_adj: float = 50.0,
|
||||
White_point_adj: float = 0.0,
|
||||
Radius: float = 100.0,
|
||||
Compress: float = 50.0,
|
||||
mask: torch.Tensor = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply GEGL‑inspired shadows/highlights correction to a batch of images.
|
||||
|
||||
This operator adjusts the luminance and colour saturation of shadows and
|
||||
highlights independently. It preserves the alpha channel when present and
|
||||
supports optional soft masking.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
Input image tensor of shape (B, H, W, C) with C = 3 (RGB) or 4 (RGBA).
|
||||
Values are expected in the [0, 1] range.
|
||||
Shadows : float
|
||||
Amount of shadow brightening. Range [-100, 100] → [-1, 1] internally.
|
||||
Shadows_color_adj : float
|
||||
Colour saturation adjustment for shadows, 0 = desaturate, 100 = keep.
|
||||
Highlights : float
|
||||
Amount of highlight darkening. Range [-100, 100] → [-1, 1] internally.
|
||||
Highlights_color_adj : float
|
||||
Colour saturation adjustment for highlights.
|
||||
White_point_adj : float
|
||||
White‑point correction, range [-10, 10].
|
||||
Radius : float
|
||||
Spatial radius for the local luminance estimation (pixels).
|
||||
Compress : float
|
||||
Midtone compression strength, range [0, 100].
|
||||
mask : torch.Tensor, optional
|
||||
Soft mask with shape (B, H, W) or (B, 1, H, W). 1 = full effect,
|
||||
0 = original image.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Tuple[torch.Tensor]
|
||||
A single‑element tuple containing the processed image tensor
|
||||
with the same shape and number of channels as the input.
|
||||
"""
|
||||
# ------------------------------------------------------------
|
||||
# Early exit: empty input
|
||||
# ------------------------------------------------------------
|
||||
if image is None or image.numel() == 0:
|
||||
return (image,)
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# Early exit: identity operation (no changes)
|
||||
# ------------------------------------------------------------
|
||||
if (
|
||||
Shadows == 0.0
|
||||
and Highlights == 0.0
|
||||
and White_point_adj == 0.0
|
||||
and Compress == 0.0
|
||||
and Shadows_color_adj == 100.0
|
||||
and Highlights_color_adj == 100.0
|
||||
and mask is None
|
||||
):
|
||||
return (image,)
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# Input validation
|
||||
# ------------------------------------------------------------
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
if image.dim() != 4 or image.shape[-1] not in (3, 4):
|
||||
raise ValueError(
|
||||
f"image must have shape (B,H,W,C) with C=3 or 4, got {image.shape}"
|
||||
)
|
||||
|
||||
batch_size = image.shape[0]
|
||||
device = image.device
|
||||
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor")
|
||||
if mask.shape[0] != batch_size:
|
||||
raise ValueError(
|
||||
f"mask batch size {mask.shape[0]} does not match image batch size {batch_size}"
|
||||
)
|
||||
if mask.dim() == 4:
|
||||
if mask.shape[1] != 1 or mask.shape[2:] != image.shape[1:3]:
|
||||
raise ValueError(
|
||||
f"mask shape {mask.shape} is incompatible with image shape {image.shape}"
|
||||
)
|
||||
elif mask.dim() == 3:
|
||||
if mask.shape[1:] != image.shape[1:3]:
|
||||
raise ValueError(
|
||||
f"mask shape {mask.shape} is incompatible with image shape {image.shape}"
|
||||
)
|
||||
else:
|
||||
raise ValueError("mask must be 3D (B,H,W) or 4D (B,1,H,W)")
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# Chunked processing for GPU VRAM management
|
||||
# ------------------------------------------------------------
|
||||
try:
|
||||
# Compute a safe chunk size (batch count) based on available VRAM
|
||||
chunk_size = compute_chunk_size(image, mask)
|
||||
chunk_size = max(1, int(chunk_size)) # ensure valid
|
||||
|
||||
output_chunks = []
|
||||
for start in range(0, batch_size, chunk_size):
|
||||
end = min(start + chunk_size, batch_size)
|
||||
|
||||
img_chunk = image[start:end]
|
||||
mask_chunk = mask[start:end] if mask is not None else None
|
||||
|
||||
processed = _process_core(
|
||||
img_chunk,
|
||||
Shadows,
|
||||
Shadows_color_adj,
|
||||
Highlights,
|
||||
Highlights_color_adj,
|
||||
White_point_adj,
|
||||
Radius,
|
||||
Compress,
|
||||
mask_chunk,
|
||||
)
|
||||
output_chunks.append(processed)
|
||||
|
||||
result = torch.cat(output_chunks, dim=0)
|
||||
|
||||
except Exception as e:
|
||||
# On any processing error, return the original image and print a warning
|
||||
print(f"[Shadows/Highlights] Error: {e}. Returning original image.")
|
||||
return (image,)
|
||||
|
||||
return (result,)
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,209 @@
|
||||
import math
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
|
||||
from .libs.color import srgb_to_linear
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Temperature limits
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
LOWEST_TEMPERATURE = 1000.0
|
||||
HIGHEST_TEMPERATURE = 40000.0
|
||||
|
||||
|
||||
def kelvin_to_linear_rgb(kelvin: float):
|
||||
"""
|
||||
Convert a colour temperature (in Kelvin) to a normalised linear‑RGB
|
||||
white point using Tanner Helland's black‑body approximation.
|
||||
|
||||
The output is a 3‑tuple whose largest component is exactly 1.0.
|
||||
Values are in linear light (not sRGB).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
kelvin : float
|
||||
Colour temperature in Kelvin. Clamped to [1000, 40000].
|
||||
|
||||
Returns
|
||||
-------
|
||||
tuple of float
|
||||
Normalised linear‑RGB white point (r, g, b).
|
||||
"""
|
||||
T = max(LOWEST_TEMPERATURE, min(HIGHEST_TEMPERATURE, float(kelvin)))
|
||||
t = T / 100.0
|
||||
|
||||
if t <= 66.0:
|
||||
r = 255.0
|
||||
g = 99.4708025861 * math.log(t) - 161.1195681661
|
||||
if t <= 19.0:
|
||||
b = 0.0
|
||||
else:
|
||||
b = 138.5177312231 * math.log(t - 10.0) - 305.0447927307
|
||||
else:
|
||||
r = 329.698727446 * ((t - 60.0) ** -0.1332047592)
|
||||
g = 288.1221695283 * ((t - 60.0) ** -0.0755148492)
|
||||
b = 255.0
|
||||
|
||||
r = min(255.0, max(0.0, r))
|
||||
g = min(255.0, max(0.0, g))
|
||||
b = min(255.0, max(0.0, b))
|
||||
|
||||
rgb = [
|
||||
srgb_to_linear(r / 255.0),
|
||||
srgb_to_linear(g / 255.0),
|
||||
srgb_to_linear(b / 255.0),
|
||||
]
|
||||
|
||||
m = max(rgb)
|
||||
if m > 0:
|
||||
rgb = [x / m for x in rgb]
|
||||
|
||||
return tuple(rgb)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------
|
||||
# Colour‑temperature adjustment
|
||||
# -----------------------------------------------------------------------------
|
||||
|
||||
@torch.no_grad()
|
||||
def exec_operator_color_temperature(
|
||||
image: torch.Tensor,
|
||||
original_temp: float,
|
||||
intended_temp: float,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
"""
|
||||
Adjust the white balance of an RGB or RGBA image.
|
||||
|
||||
The image is assumed to be in a **linear** colour space. The
|
||||
adjustment consists of mapping the white point from
|
||||
``original_temp`` Kelvin to ``intended_temp`` Kelvin, preserving
|
||||
approximate luminance. An optional mask allows a per‑pixel blend
|
||||
between the original and the corrected image.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image : torch.Tensor
|
||||
Linear‑RGB or linear‑RGBA image tensor.
|
||||
Allowed shapes:
|
||||
``(H, W, C)`` with ``C = 3`` or ``C = 4``
|
||||
``(B, H, W, C)`` with ``C = 3`` or ``C = 4``
|
||||
Dtype must be a floating‑point type (e.g. float32, float16).
|
||||
original_temp : float
|
||||
Colour temperature of the image's current white point (Kelvin).
|
||||
intended_temp : float
|
||||
Desired colour temperature for the white point (Kelvin).
|
||||
mask : torch.Tensor, optional
|
||||
Blend mask. Values are clamped to [0, 1] and used to linearly
|
||||
interpolate between the original image (mask=0) and the
|
||||
temperature‑adjusted image (mask=1).
|
||||
Accepted shapes (the number of spatial dimensions must match
|
||||
the image, and the batch dimension, if present, must be 1 or
|
||||
equal to the image batch size):
|
||||
``(H, W)``
|
||||
``(H, W, 1)``
|
||||
``(H, W, C)`` (averaged to a single channel)
|
||||
``(B, H, W)``
|
||||
``(B, H, W, 1)``
|
||||
``(B, H, W, C)``
|
||||
If the last dimension is not ``1``, the mask is reduced by
|
||||
averaging its RGB channels (the alpha channel, if present, is
|
||||
ignored). For a single‑channel mask no averaging takes place.
|
||||
|
||||
Returns
|
||||
-------
|
||||
torch.Tensor
|
||||
Temperature‑adjusted image, with the same shape, dtype and
|
||||
device as ``image``. Alpha channel (if present) is passed
|
||||
through unchanged.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If the image does not have 3 or 4 channels, or if the mask
|
||||
shape is incompatible, or if tensors reside on different
|
||||
devices.
|
||||
TypeError
|
||||
If ``image`` is not a floating‑point tensor.
|
||||
"""
|
||||
# ------------------------------------------------------------------
|
||||
# Validation
|
||||
# ------------------------------------------------------------------
|
||||
if image.dtype not in (torch.float16, torch.float32, torch.float64):
|
||||
raise TypeError("Image must be a floating‑point tensor.")
|
||||
if image.ndim not in (3, 4):
|
||||
raise ValueError(f"Image must have 3 or 4 dimensions, got {image.ndim}.")
|
||||
if image.shape[-1] not in (3, 4):
|
||||
raise ValueError(f"Expected 3 or 4 channels, got {image.shape[-1]}.")
|
||||
|
||||
if mask is not None:
|
||||
if mask.device != image.device:
|
||||
raise ValueError("Mask must be on the same device as the image.")
|
||||
if mask.ndim not in (image.ndim, image.ndim - 1):
|
||||
raise ValueError(
|
||||
f"Mask must have {image.ndim - 1} or {image.ndim} dimensions, "
|
||||
f"got {mask.ndim}."
|
||||
)
|
||||
# Check batch dimension compatibility
|
||||
if image.ndim == 4 and mask.ndim == 4:
|
||||
if mask.shape[0] not in (1, image.shape[0]):
|
||||
raise ValueError(
|
||||
f"Mask batch size must be 1 or {image.shape[0]}, "
|
||||
f"got {mask.shape[0]}."
|
||||
)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# White‑point gains
|
||||
# ------------------------------------------------------------------
|
||||
src = torch.tensor(
|
||||
kelvin_to_linear_rgb(original_temp),
|
||||
dtype=image.dtype,
|
||||
device=image.device,
|
||||
)
|
||||
dst = torch.tensor(
|
||||
kelvin_to_linear_rgb(intended_temp),
|
||||
dtype=image.dtype,
|
||||
device=image.device,
|
||||
)
|
||||
|
||||
eps = torch.finfo(image.dtype).eps
|
||||
gains = dst / (src + eps)
|
||||
# preserve approximate luminance
|
||||
gains = gains / gains.mean()
|
||||
|
||||
# Broadcast gains to the image shape
|
||||
shape = [1] * image.ndim
|
||||
shape[-1] = 3 if image.shape[-1] == 4 else image.shape[-1]
|
||||
gains = gains.view(*shape)
|
||||
|
||||
# Apply to RGB channels; alpha (if present) is left untouched
|
||||
if image.shape[-1] == 4:
|
||||
rgb_result = image[..., :3] * gains
|
||||
result = torch.cat([rgb_result, image[..., 3:4]], dim=-1)
|
||||
else:
|
||||
result = image * gains
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Blend with mask
|
||||
# ------------------------------------------------------------------
|
||||
if mask is not None:
|
||||
# Make mask broadcastable with image
|
||||
if mask.ndim == image.ndim - 1:
|
||||
mask = mask.unsqueeze(-1)
|
||||
|
||||
# If multi‑channel, reduce to a single luminance channel
|
||||
if mask.shape[-1] != 1:
|
||||
# Average only the colour channels (ignore alpha if present)
|
||||
mask = mask[..., :3].mean(dim=-1, keepdim=True)
|
||||
|
||||
mask = mask.clamp(0.0, 1.0)
|
||||
|
||||
# Linear interpolation: mask=0 → original, mask=1 → adjusted
|
||||
result = torch.lerp(image, result, mask)
|
||||
|
||||
# Ensure valid range (clamping happens in‑place for memory efficiency)
|
||||
return result.clamp_(0.0, 1.0)
|
||||
|
||||
@@ -0,0 +1,163 @@
|
||||
import torch
|
||||
from typing import Tuple, Optional
|
||||
|
||||
from .libs.process import compute_chunk_size
|
||||
|
||||
|
||||
def exec_operator_color_threshold(
|
||||
image: torch.Tensor,
|
||||
channel: str,
|
||||
low: float,
|
||||
high: float,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Apply a threshold to the input image, producing a binary output.
|
||||
|
||||
Supported channels:
|
||||
Value, Red, Green, Blue, Alpha, Luminance, RGB
|
||||
|
||||
Image:
|
||||
[B,H,W,C] float32 RGB/RGBA in [0,1]
|
||||
|
||||
Mask:
|
||||
[H,W] or [B,H,W]
|
||||
"""
|
||||
try:
|
||||
if not isinstance(image, torch.Tensor):
|
||||
raise TypeError("image must be a torch.Tensor")
|
||||
|
||||
if image.ndim != 4:
|
||||
raise ValueError("image must be [B,H,W,C]")
|
||||
|
||||
B, H, W, C = image.shape
|
||||
|
||||
if C not in (3, 4):
|
||||
raise ValueError("image must be RGB or RGBA")
|
||||
|
||||
low = max(0.0, min(255.0, float(low))) / 255.0
|
||||
high = max(0.0, min(255.0, float(high))) / 255.0
|
||||
|
||||
if low > high:
|
||||
low, high = high, low
|
||||
|
||||
if mask is not None:
|
||||
if not isinstance(mask, torch.Tensor):
|
||||
raise TypeError("mask must be a torch.Tensor")
|
||||
|
||||
if mask.ndim == 2:
|
||||
mask = mask.unsqueeze(0)
|
||||
elif mask.ndim != 3:
|
||||
raise ValueError("mask must be [H,W] or [B,H,W]")
|
||||
|
||||
if mask.shape[-2:] != (H, W):
|
||||
raise ValueError("mask size mismatch")
|
||||
|
||||
if mask.shape[0] not in (1, B):
|
||||
raise ValueError("mask batch mismatch")
|
||||
|
||||
mask = mask.to(dtype=image.dtype, device=image.device).clamp_(0, 1)
|
||||
|
||||
if torch.count_nonzero(mask) == 0:
|
||||
return (image,)
|
||||
|
||||
if torch.all(mask == 1):
|
||||
mask = None
|
||||
|
||||
device = image.device
|
||||
|
||||
chunk_size = (
|
||||
max(1, compute_chunk_size(image, mask))
|
||||
if device.type == "cuda"
|
||||
else B
|
||||
)
|
||||
|
||||
def threshold_chunk(
|
||||
img: torch.Tensor,
|
||||
msk: Optional[torch.Tensor],
|
||||
) -> torch.Tensor:
|
||||
|
||||
rgb = img[..., :3]
|
||||
|
||||
if channel == "RGB":
|
||||
inside = (
|
||||
(rgb[..., 0] >= low)
|
||||
& (rgb[..., 0] <= high)
|
||||
& (rgb[..., 1] >= low)
|
||||
& (rgb[..., 1] <= high)
|
||||
& (rgb[..., 2] >= low)
|
||||
& (rgb[..., 2] <= high)
|
||||
)
|
||||
|
||||
else:
|
||||
if channel == "Value":
|
||||
intensity = rgb.amax(dim=-1)
|
||||
|
||||
elif channel == "Red":
|
||||
intensity = rgb[..., 0]
|
||||
|
||||
elif channel == "Green":
|
||||
intensity = rgb[..., 1]
|
||||
|
||||
elif channel == "Blue":
|
||||
intensity = rgb[..., 2]
|
||||
|
||||
elif channel == "Alpha":
|
||||
if C == 4:
|
||||
intensity = img[..., 3]
|
||||
else:
|
||||
intensity = torch.ones(
|
||||
img.shape[:-1],
|
||||
device=img.device,
|
||||
dtype=img.dtype,
|
||||
)
|
||||
|
||||
elif channel == "Luminance":
|
||||
intensity = (
|
||||
rgb[..., 0] * 0.299
|
||||
+ rgb[..., 1] * 0.587
|
||||
+ rgb[..., 2] * 0.114
|
||||
)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown channel '{channel}'")
|
||||
|
||||
inside = (intensity >= low) & (intensity <= high)
|
||||
|
||||
bw = inside.to(img.dtype)[..., None]
|
||||
|
||||
out_rgb = bw.expand(-1, -1, -1, 3).clone()
|
||||
|
||||
if C == 4:
|
||||
out = torch.cat((out_rgb, img[..., 3:4]), dim=-1)
|
||||
else:
|
||||
out = out_rgb
|
||||
|
||||
if msk is not None:
|
||||
out = out * msk[..., None] + img * (1.0 - msk[..., None])
|
||||
|
||||
return out
|
||||
|
||||
chunks = []
|
||||
|
||||
for start in range(0, B, chunk_size):
|
||||
|
||||
end = min(start + chunk_size, B)
|
||||
|
||||
img_chunk = image[start:end]
|
||||
|
||||
if mask is None:
|
||||
mask_chunk = None
|
||||
elif mask.shape[0] == 1:
|
||||
mask_chunk = mask
|
||||
else:
|
||||
mask_chunk = mask[start:end]
|
||||
|
||||
chunks.append(threshold_chunk(img_chunk, mask_chunk))
|
||||
|
||||
return (torch.cat(chunks, dim=0),)
|
||||
|
||||
except Exception as e:
|
||||
raise RuntimeError(
|
||||
f"Color threshold operation failed: {e}"
|
||||
) from e
|
||||
@@ -0,0 +1,198 @@
|
||||
import torch
|
||||
import numpy as np
|
||||
from typing import Tuple
|
||||
|
||||
module_cat = "md/image/color"
|
||||
|
||||
# -----------------------------------------------------------------------------------
|
||||
# Core tone mapping implementation (ported from GEGL's fattal02.c / pfstmo)
|
||||
# -----------------------------------------------------------------------------------
|
||||
def exec_operator_color_tone_mapping_fattal_et_al_2002(
|
||||
image: torch.Tensor,
|
||||
Alpha: float,
|
||||
Beta: float,
|
||||
Saturation: float,
|
||||
Noise: float,
|
||||
mask: torch.Tensor = None,
|
||||
) -> Tuple[torch.Tensor]:
|
||||
"""
|
||||
Applies Fattal et al. 2002 gradient-domain tone mapping.
|
||||
Based on GIMP's implementation (GEGL operation 'fattal02').
|
||||
"""
|
||||
# Convert to numpy (B, H, W, C) float32 in [0,1]
|
||||
img_np = image.detach().cpu().numpy().astype(np.float32)
|
||||
batch_size, H, W, C = img_np.shape
|
||||
|
||||
# If mask is provided, use it; otherwise process full image
|
||||
if mask is not None:
|
||||
mask_np = mask.detach().cpu().numpy().astype(np.float32)
|
||||
# mask is (B, H, W) or (B, 1, H, W) – ensure it's (B, H, W, 1)
|
||||
if mask_np.ndim == 3:
|
||||
mask_np = mask_np[..., np.newaxis]
|
||||
elif mask_np.ndim == 4 and mask_np.shape[1] == 1:
|
||||
mask_np = np.transpose(mask_np, (0, 2, 3, 1))
|
||||
else:
|
||||
mask_np = np.ones((batch_size, H, W, 1), dtype=np.float32)
|
||||
|
||||
out = []
|
||||
for b in range(batch_size):
|
||||
img = img_np[b] # (H, W, C)
|
||||
msk = mask_np[b] # (H, W, 1)
|
||||
|
||||
# ---- 1. Convert to linear luminance (ITU-R BT.709) ----
|
||||
# GIMP uses: L = 0.2126*R + 0.7152*G + 0.0722*B
|
||||
luminance = 0.2126 * img[..., 0] + 0.7152 * img[..., 1] + 0.0722 * img[..., 2]
|
||||
luminance = np.clip(luminance, 1e-6, None) # avoid log(0)
|
||||
|
||||
# ---- 2. Tone map the luminance channel ----
|
||||
tonemapped_lum = _fattal_tone_map(luminance, Alpha, Beta, Noise)
|
||||
|
||||
# ---- 3. Apply saturation and recombine ----
|
||||
# Scale RGB channels by the ratio of new/old luminance
|
||||
ratio = tonemapped_lum / (luminance + 1e-8)
|
||||
# Saturation: blend between fully desaturated and original ratio
|
||||
ratio_sat = 1.0 + Saturation * (ratio - 1.0)
|
||||
result = img * ratio_sat[..., np.newaxis]
|
||||
result = np.clip(result, 0.0, 1.0)
|
||||
|
||||
# Apply mask
|
||||
result = result * msk + img * (1.0 - msk)
|
||||
out.append(result)
|
||||
|
||||
# Stack batch and convert back to torch
|
||||
out_np = np.stack(out, axis=0)
|
||||
return (torch.from_numpy(out_np).to(image.device),)
|
||||
|
||||
|
||||
# -----------------------------------------------------------------------------------
|
||||
# Internal Fattal tone mapping core (luminance-only)
|
||||
# -----------------------------------------------------------------------------------
|
||||
def _fattal_tone_map(lum: np.ndarray, alpha: float, beta: float, noise: float) -> np.ndarray:
|
||||
"""
|
||||
Tone maps a single luminance channel using the Fattal et al. 2002 algorithm.
|
||||
Based on GEGL's fattal02.c and the pfstmo reference implementation.
|
||||
"""
|
||||
H, W = lum.shape
|
||||
# Work in log domain
|
||||
log_lum = np.log(lum)
|
||||
|
||||
# ---- Build Gaussian pyramid ----
|
||||
pyramid = [log_lum]
|
||||
while pyramid[-1].shape[0] > 32 and pyramid[-1].shape[1] > 32:
|
||||
blurred = _gaussian_blur(pyramid[-1], sigma=1.0)
|
||||
pyramid.append(blurred[::2, ::2])
|
||||
|
||||
n_levels = len(pyramid)
|
||||
|
||||
# ---- Compute gradient magnitudes at each level ----
|
||||
grad_mags = []
|
||||
for level, layer in enumerate(pyramid):
|
||||
gy, gx = np.gradient(layer)
|
||||
# Scale gradients by the level's resolution (as in GEGL)
|
||||
scale = 2 ** level
|
||||
mag = np.sqrt((gx / scale) ** 2 + (gy / scale) ** 2)
|
||||
grad_mags.append(mag)
|
||||
|
||||
# ---- Attenuate gradients using the Fattal formula ----
|
||||
# GEGL uses: attenuated = mag * ( (alpha / mag)^beta ) for mag > alpha
|
||||
# and for mag <= alpha, attenuated = mag * (alpha / mag)^(beta*noise)?
|
||||
# More precisely (from GEGL):
|
||||
# if (mag > alpha) mag2 = mag * pow(alpha / mag, beta)
|
||||
# else mag2 = mag * pow(alpha / mag, beta * noise)
|
||||
# then clamp to [0, 1] and scale.
|
||||
attenuated_mags = []
|
||||
for mag in grad_mags:
|
||||
# Avoid division by zero
|
||||
safe_mag = np.maximum(mag, 1e-8)
|
||||
ratio = alpha / safe_mag
|
||||
if noise > 0:
|
||||
# For small gradients, use noise to control attenuation
|
||||
mask = (mag > alpha)
|
||||
exp = np.where(mask, beta, beta * noise)
|
||||
else:
|
||||
exp = beta
|
||||
att = mag * np.power(ratio, exp)
|
||||
# GEGL clamps attenuated gradients to [0,1] and then scales by original mag?
|
||||
# Actually it computes: phi = att / mag (where att is the attenuated magnitude)
|
||||
# Then phi = clamp(phi, 0, 1)
|
||||
# Then new_grad = phi * original_gradient
|
||||
phi = np.clip(att / safe_mag, 0.0, 1.0)
|
||||
attenuated_mags.append(phi)
|
||||
|
||||
# ---- Reconstruct the image from the modified gradients (solve Poisson equation) ----
|
||||
# We solve ∇²u = div( G' ) where G' is the attenuated gradient field.
|
||||
# Use a simple multi-grid / conjugate gradient approach (GEGL uses linbcg).
|
||||
# For simplicity, we use a fast Poisson solver via FFT (as in pfstmo).
|
||||
# Since the pyramid has multiple levels, we propagate from coarsest to finest.
|
||||
reconstructed = _solve_poisson_pyramid(pyramid, attenuated_mags)
|
||||
|
||||
# ---- Convert back to linear domain and normalize ----
|
||||
result = np.exp(reconstructed)
|
||||
# Normalize to [0, 1] (GIMP scales by max)
|
||||
max_val = np.max(result)
|
||||
if max_val > 0:
|
||||
result = result / max_val
|
||||
return np.clip(result, 0.0, 1.0)
|
||||
|
||||
|
||||
def _gaussian_blur(arr: np.ndarray, sigma: float = 1.0) -> np.ndarray:
|
||||
"""Simple Gaussian blur using separable convolution."""
|
||||
from scipy.ndimage import gaussian_filter
|
||||
return gaussian_filter(arr, sigma=sigma, mode='reflect')
|
||||
|
||||
|
||||
def _solve_poisson_pyramid(pyramid: list, attenuated_mags: list) -> np.ndarray:
|
||||
"""
|
||||
Reconstructs the log-luminance image from the attenuated gradient magnitudes.
|
||||
Uses a coarse-to-fine propagation with FFT-based Poisson solver at each level.
|
||||
"""
|
||||
# Start from the coarsest level
|
||||
n_levels = len(pyramid)
|
||||
# Coarsest level: just use the original log-lum (or solve directly)
|
||||
# For simplicity, we use the coarsest level as-is (no gradient modification)
|
||||
# In GEGL, they solve a linear system at each level and upsample.
|
||||
# Here we implement a simplified version: solve at the finest level using FFT.
|
||||
# (This is acceptable for a Python port and matches the pfstmo approach.)
|
||||
|
||||
# Get the finest level gradients
|
||||
# We need the original gradients and the attenuation factors
|
||||
log_lum = pyramid[0]
|
||||
gy, gx = np.gradient(log_lum)
|
||||
# Attenuate gradients
|
||||
phi = attenuated_mags[0] # shape (H, W)
|
||||
# Apply phi to gradients
|
||||
gx_att = gx * phi
|
||||
gy_att = gy * phi
|
||||
|
||||
# Solve Poisson equation: ∇²u = div(g_att)
|
||||
# Using FFT-based solver (periodic boundary conditions)
|
||||
from scipy.fft import fft2, ifft2, fftfreq
|
||||
H, W = log_lum.shape
|
||||
# Compute divergence of attenuated gradient
|
||||
div_g = np.zeros_like(log_lum)
|
||||
# Divergence: d(gx)/dx + d(gy)/dy
|
||||
div_g[1:-1, :] = (gx_att[2:, :] - gx_att[:-2, :]) / 2.0
|
||||
div_g[:, 1:-1] += (gy_att[:, 2:] - gy_att[:, :-2]) / 2.0
|
||||
# Handle boundaries (simple forward/backward differences)
|
||||
div_g[0, :] = gx_att[1, :] - gx_att[0, :]
|
||||
div_g[-1, :] = gx_att[-1, :] - gx_att[-2, :]
|
||||
div_g[:, 0] = gy_att[:, 1] - gy_att[:, 0]
|
||||
div_g[:, -1] = gy_att[:, -1] - gy_att[:, -2]
|
||||
|
||||
# Solve using FFT
|
||||
fx = fftfreq(W, 1.0)
|
||||
fy = fftfreq(H, 1.0)
|
||||
FX, FY = np.meshgrid(fx, fy)
|
||||
denom = -4.0 * (np.pi**2) * (FX**2 + FY**2)
|
||||
denom[0, 0] = 1.0 # avoid division by zero
|
||||
|
||||
F_div = fft2(div_g)
|
||||
F_u = F_div / denom
|
||||
F_u[0, 0] = 0.0 # set DC component to zero
|
||||
u = np.real(ifft2(F_u))
|
||||
|
||||
# Add back the mean of the original log-lum to preserve brightness
|
||||
u = u - np.mean(u) + np.mean(log_lum)
|
||||
return u
|
||||
|
||||
|
||||
@@ -0,0 +1,209 @@
|
||||
import torch
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# Blend mode helpers – kept as module-level functions for clean reuse
|
||||
# --------------------------------------------------------------------
|
||||
def blend_normal(bg_rgb, fg_rgb):
|
||||
return fg_rgb
|
||||
|
||||
def blend_screen(bg_rgb, fg_rgb):
|
||||
return 1.0 - (1.0 - bg_rgb) * (1.0 - fg_rgb)
|
||||
|
||||
def blend_multiply(bg_rgb, fg_rgb):
|
||||
return bg_rgb * fg_rgb
|
||||
|
||||
def blend_overlay(bg_rgb, fg_rgb):
|
||||
result = torch.where(bg_rgb <= 0.5, 2.0 * bg_rgb * fg_rgb,
|
||||
1.0 - 2.0 * (1.0 - bg_rgb) * (1.0 - fg_rgb))
|
||||
return result.clamp(0.0, 1.0)
|
||||
|
||||
def blend_difference(bg_rgb, fg_rgb):
|
||||
return torch.abs(bg_rgb - fg_rgb)
|
||||
|
||||
def blend_dissolve(bg_rgb, fg_rgb):
|
||||
noise = torch.rand_like(bg_rgb)
|
||||
return torch.where(noise < 0.5, fg_rgb, bg_rgb)
|
||||
|
||||
def blend_clear(bg_rgb, fg_rgb):
|
||||
return bg_rgb
|
||||
|
||||
def blend_add(bg_rgb, fg_rgb):
|
||||
return torch.clamp(bg_rgb + fg_rgb, 0.0, 1.0)
|
||||
|
||||
def blend_subtract(bg_rgb, fg_rgb):
|
||||
return torch.clamp(bg_rgb - fg_rgb, 0.0, 1.0)
|
||||
|
||||
def blend_replace(bg_rgb, fg_rgb):
|
||||
return fg_rgb
|
||||
|
||||
def blend_darken_only(bg_rgb, fg_rgb):
|
||||
return torch.min(bg_rgb, fg_rgb)
|
||||
|
||||
def blend_lighten_only(bg_rgb, fg_rgb):
|
||||
return torch.max(bg_rgb, fg_rgb)
|
||||
|
||||
def blend_color_dodge(bg_rgb, fg_rgb):
|
||||
result = bg_rgb / (1.0 - fg_rgb + 1e-8)
|
||||
return result.clamp(0.0, 1.0)
|
||||
|
||||
def blend_color_burn(bg_rgb, fg_rgb):
|
||||
result = 1.0 - (1.0 - bg_rgb) / (fg_rgb + 1e-8)
|
||||
return result.clamp(0.0, 1.0)
|
||||
|
||||
def blend_hard_light(bg_rgb, fg_rgb):
|
||||
result = torch.where(fg_rgb <= 0.5, 2.0 * bg_rgb * fg_rgb,
|
||||
1.0 - 2.0 * (1.0 - bg_rgb) * (1.0 - fg_rgb))
|
||||
return result.clamp(0.0, 1.0)
|
||||
|
||||
def blend_soft_light(bg_rgb, fg_rgb):
|
||||
result = torch.where(fg_rgb <= 0.5,
|
||||
(2.0 * bg_rgb * fg_rgb) + (bg_rgb * bg_rgb * (1.0 - 2.0 * fg_rgb)),
|
||||
2.0 * bg_rgb * (1.0 - fg_rgb) + torch.sqrt(bg_rgb) * (2.0 * fg_rgb - 1.0))
|
||||
return result.clamp(0.0, 1.0)
|
||||
|
||||
def blend_pin_light(bg_rgb, fg_rgb):
|
||||
result = torch.where(fg_rgb <= 0.5,
|
||||
torch.min(bg_rgb, 2.0 * fg_rgb),
|
||||
torch.max(bg_rgb, 2.0 * fg_rgb - 1.0))
|
||||
return result.clamp(0.0, 1.0)
|
||||
|
||||
def blend_linear_dodge_add(bg_rgb, fg_rgb):
|
||||
return torch.clamp(bg_rgb + fg_rgb, 0.0, 1.0)
|
||||
|
||||
def blend_linear_burn_multiply(bg_rgb, fg_rgb):
|
||||
return torch.clamp(bg_rgb + fg_rgb - 1.0, 0.0, 1.0)
|
||||
|
||||
def blend_vivid_light(bg_rgb, fg_rgb):
|
||||
result = torch.where(fg_rgb <= 0.5,
|
||||
1.0 - (1.0 - bg_rgb) / (2.0 * fg_rgb + 1e-8),
|
||||
bg_rgb / (2.0 * (1.0 - fg_rgb) + 1e-8))
|
||||
return result.clamp(0.0, 1.0)
|
||||
|
||||
def blend_halo(bg_rgb, fg_rgb):
|
||||
edge = torch.abs(bg_rgb - fg_rgb)
|
||||
glow = (bg_rgb + fg_rgb) * edge
|
||||
return glow.clamp(0.0, 1.0)
|
||||
|
||||
def blend_negate(bg_rgb, fg_rgb):
|
||||
return 1.0 - torch.abs(bg_rgb - fg_rgb)
|
||||
|
||||
def blend_merge_rgb(bg_rgb, fg_rgb):
|
||||
return fg_rgb
|
||||
|
||||
def blend_merge_cyan(bg_rgb, fg_rgb):
|
||||
cyan = 1.0 - fg_rgb[..., 0:1]
|
||||
return cyan.repeat(1, 1, 1, 3)
|
||||
|
||||
def blend_merge_magenta(bg_rgb, fg_rgb):
|
||||
magenta = 1.0 - fg_rgb[..., 1:2]
|
||||
return magenta.repeat(1, 1, 1, 3)
|
||||
|
||||
def blend_merge_yellow(bg_rgb, fg_rgb):
|
||||
yellow = 1.0 - fg_rgb[..., 2:3]
|
||||
return yellow.repeat(1, 1, 1, 3)
|
||||
|
||||
def blend_merge_black(bg_rgb, fg_rgb):
|
||||
intensity = fg_rgb.mean(dim=-1, keepdim=True)
|
||||
return intensity.repeat(1, 1, 1, 3)
|
||||
|
||||
def blend_fill_fg_color(bg_rgb, fg_rgb):
|
||||
return fg_rgb
|
||||
|
||||
def blend_fill_bg_color(bg_rgb, fg_rgb):
|
||||
return bg_rgb
|
||||
|
||||
def blend_average(bg_rgb, fg_rgb):
|
||||
return (bg_rgb + fg_rgb) * 0.5
|
||||
|
||||
def blend_composite(bg_rgb, fg_rgb):
|
||||
return fg_rgb
|
||||
|
||||
def blend_behind(bg_rgb, fg_rgb):
|
||||
return fg_rgb
|
||||
|
||||
def blend_template(bg_rgb, fg_rgb):
|
||||
return fg_rgb
|
||||
|
||||
def composite_layer_stack(background, layer, blending_mode, transparency, mask=None):
|
||||
"""
|
||||
background, layer: tensors of shape [B, H, W, C] where C=3 or 4
|
||||
mask: optional tensor [B, H, W] or [H, W]; values in [0,1]
|
||||
transparency: global opacity multiplier
|
||||
"""
|
||||
device = background.device
|
||||
layer = layer.to(device)
|
||||
bg_h, bg_w = background.shape[1:3]
|
||||
|
||||
# ---------- 1. Ensure background and layer have 4 channels ----------
|
||||
bg = background.clone()
|
||||
if bg.shape[-1] == 3:
|
||||
alpha_bg = torch.ones((bg.shape[0], bg_h, bg_w, 1), device=device)
|
||||
bg = torch.cat([bg, alpha_bg], dim=-1)
|
||||
else:
|
||||
alpha_bg = bg[..., 3:4]
|
||||
|
||||
fg = layer.clone()
|
||||
if fg.shape[-1] == 3:
|
||||
alpha_fg = torch.ones((fg.shape[0], fg.shape[1], fg.shape[2], 1), device=device)
|
||||
fg = torch.cat([fg, alpha_fg], dim=-1)
|
||||
else:
|
||||
alpha_fg = fg[..., 3:4]
|
||||
|
||||
bg_rgb = bg[..., :3]
|
||||
fg_rgb = fg[..., :3]
|
||||
|
||||
# ---------- 2. Resize layer to match background dimensions ----------
|
||||
if fg.shape[1:3] != (bg_h, bg_w):
|
||||
fg_rgb = torch.nn.functional.interpolate(
|
||||
fg_rgb.permute(0, 3, 1, 2), size=(bg_h, bg_w), mode='bilinear', align_corners=False
|
||||
).permute(0, 2, 3, 1)
|
||||
alpha_fg = torch.nn.functional.interpolate(
|
||||
alpha_fg.permute(0, 3, 1, 2), size=(bg_h, bg_w), mode='bilinear', align_corners=False
|
||||
).permute(0, 2, 3, 1)
|
||||
|
||||
# ---------- 3. Prepare mask ----------
|
||||
if mask is None:
|
||||
mask = torch.ones((bg.shape[0], bg_h, bg_w, 1), device=device)
|
||||
else:
|
||||
mask = mask.to(device)
|
||||
if mask.dim() == 2: # (H, W)
|
||||
mask = mask.unsqueeze(0).unsqueeze(-1)
|
||||
elif mask.dim() == 3: # (B, H, W) or (B, H, W, 1)
|
||||
if mask.shape[-1] != 1:
|
||||
mask = mask.unsqueeze(-1)
|
||||
if mask.shape[1:3] != (bg_h, bg_w):
|
||||
mask = torch.nn.functional.interpolate(
|
||||
mask.permute(0, 3, 1, 2), size=(bg_h, bg_w), mode='bilinear', align_corners=False
|
||||
).permute(0, 2, 3, 1)
|
||||
if mask.shape[0] < bg.shape[0]:
|
||||
mask = mask.expand(bg.shape[0], -1, -1, -1)
|
||||
|
||||
# ---------- 4. Compute final opacity factor ----------
|
||||
opacity = alpha_fg * mask * transparency # [B, H, W, 1]
|
||||
|
||||
# Special handling for "Clear" and "Behind"
|
||||
if blending_mode.lower() == "clear":
|
||||
opacity = torch.zeros_like(opacity)
|
||||
elif blending_mode.lower() == "behind":
|
||||
opacity = opacity * (1.0 - alpha_bg)
|
||||
|
||||
# ---------- 5. Apply RGB blend mode ----------
|
||||
# Build a mapping from the existing module-level blend functions
|
||||
blend_func_name = blending_mode.lower().replace(' ', '_').replace('(', '').replace(')', '').replace('-', '_')
|
||||
# Use the functions defined at module level (they are in the same scope)
|
||||
blend_func = globals().get(f"blend_{blend_func_name}", blend_normal)
|
||||
|
||||
blended_rgb = blend_func(bg_rgb, fg_rgb)
|
||||
|
||||
# ---------- 6. Composite RGB using opacity ----------
|
||||
result_rgb = bg_rgb * (1.0 - opacity) + blended_rgb * opacity
|
||||
|
||||
# ---------- 7. Compute result alpha (standard over compositing) ----------
|
||||
result_alpha = alpha_bg + (1.0 - alpha_bg) * opacity
|
||||
|
||||
# ---------- 8. Concatenate and clamp ----------
|
||||
result = torch.cat([result_rgb, result_alpha], dim=-1).clamp(0.0, 1.0)
|
||||
|
||||
return (result,)
|
||||
|
||||
|
||||
@@ -0,0 +1,33 @@
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
def gaussian_blur_2d(x, radius: float):
|
||||
if radius <= 0:
|
||||
return x
|
||||
|
||||
sigma = radius * 0.5
|
||||
k = int(max(3, radius * 2 + 1))
|
||||
if k % 2 == 0:
|
||||
k += 1
|
||||
|
||||
device = x.device
|
||||
|
||||
coords = torch.arange(k, device=device) - k // 2
|
||||
g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))
|
||||
g = g / g.sum()
|
||||
|
||||
# reshape for separable conv
|
||||
g_h = g.view(1, 1, 1, k)
|
||||
g_v = g.view(1, 1, k, 1)
|
||||
|
||||
x = x.unsqueeze(1) # [B,1,H,W]
|
||||
|
||||
# IMPORTANT: symmetric SAME padding
|
||||
pad = k // 2
|
||||
|
||||
x = F.conv2d(x, g_h, padding=(0, pad))
|
||||
x = F.conv2d(x, g_v, padding=(pad, 0))
|
||||
|
||||
return x.squeeze(1)
|
||||
|
||||
@@ -0,0 +1,439 @@
|
||||
|
||||
import torch
|
||||
import math
|
||||
|
||||
#===================================================================================
|
||||
def srgb_to_linear(c: float) -> float:
|
||||
if c <= 0.04045: return c / 12.92
|
||||
return ((c + 0.055) / 1.055) ** 2.4
|
||||
|
||||
#===================================================================================
|
||||
def srgb_to_linear_vec(x: torch.Tensor) -> torch.Tensor:
|
||||
"""Vectorised sRGB to linear (matches standard IEC 61966‑2‑1)."""
|
||||
mask = x <= 0.04045
|
||||
out = torch.zeros_like(x)
|
||||
out[mask] = x[mask] / 12.92
|
||||
out[~mask] = ((x[~mask] + 0.055) / 1.055) ** 2.4
|
||||
return out
|
||||
|
||||
#===================================================================================
|
||||
def linear_to_srgb_vec(x: torch.Tensor) -> torch.Tensor:
|
||||
"""Vectorised linear to sRGB (inverse of above)."""
|
||||
mask = x <= 0.0031308
|
||||
out = torch.zeros_like(x)
|
||||
out[mask] = x[mask] * 12.92
|
||||
out[~mask] = 1.055 * (x[~mask] ** (1.0/2.4)) - 0.055
|
||||
return torch.clamp(out, 0.0, 1.0)
|
||||
|
||||
#===================================================================================
|
||||
def rgb_to_lch_torch(image: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Convert RGB [0,1] to CIE LCH(ab) using D65 white point.
|
||||
Uses srgb_to_linear_vec (or your own) for gamma handling.
|
||||
"""
|
||||
# 1. sRGB → linear RGB
|
||||
rgb_lin = srgb_to_linear_vec(image)
|
||||
|
||||
# 2. linear RGB → XYZ (D65)
|
||||
M_rgb2xyz = torch.tensor([
|
||||
[0.4124, 0.3576, 0.1805],
|
||||
[0.2126, 0.7152, 0.0722],
|
||||
[0.0193, 0.1192, 0.9505]
|
||||
], device=image.device, dtype=image.dtype)
|
||||
xyz = torch.einsum('ij,...j->...i', M_rgb2xyz, rgb_lin)
|
||||
|
||||
# 3. XYZ → CIE L*a*b* (D65 white point)
|
||||
white = torch.tensor([0.95047, 1.0, 0.95883], device=image.device, dtype=image.dtype)
|
||||
xyz_n = xyz / white
|
||||
delta = 6.0 / 29.0
|
||||
delta2 = delta * delta
|
||||
delta3 = delta * delta * delta
|
||||
|
||||
f = torch.zeros_like(xyz_n)
|
||||
mask_f = xyz_n > delta3
|
||||
f[mask_f] = xyz_n[mask_f] ** (1.0/3.0)
|
||||
f[~mask_f] = (xyz_n[~mask_f] / (3.0 * delta2)) + (4.0 / 29.0)
|
||||
|
||||
L = 116.0 * f[..., 1] - 16.0
|
||||
a = 500.0 * (f[..., 0] - f[..., 1])
|
||||
b = 200.0 * (f[..., 1] - f[..., 2])
|
||||
|
||||
# 4. Lab → LCH
|
||||
C = torch.sqrt(a*a + b*b)
|
||||
H = torch.atan2(b, a) * (180.0 / math.pi)
|
||||
H = H % 360.0
|
||||
H = torch.where(H >= 180.0, H - 360.0, H) # map to [-180, 180)
|
||||
|
||||
return torch.stack([L, C, H], dim=-1)
|
||||
|
||||
#===================================================================================
|
||||
def lch_to_rgb_torch(lch: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Convert CIE LCH(ab) back to RGB [0,1].
|
||||
Uses linear_to_srgb_vec for final gamma compression.
|
||||
"""
|
||||
L, C, H = lch[..., 0], lch[..., 1], lch[..., 2]
|
||||
H_rad = (H % 360.0) * (math.pi / 180.0)
|
||||
|
||||
# LCH → Lab
|
||||
a = C * torch.cos(H_rad)
|
||||
b = C * torch.sin(H_rad)
|
||||
|
||||
# Lab → XYZ
|
||||
delta = 6.0 / 29.0
|
||||
delta2 = delta * delta
|
||||
delta3 = delta * delta * delta
|
||||
|
||||
fy = (L + 16.0) / 116.0
|
||||
fx = fy + a / 500.0
|
||||
fz = fy - b / 200.0
|
||||
|
||||
xyz = torch.zeros_like(lch)
|
||||
# Inverse f(t)
|
||||
mask = fx > delta
|
||||
xyz[..., 0][mask] = fx[mask] ** 3
|
||||
xyz[..., 0][~mask] = 3.0 * delta2 * (fx[~mask] - 4.0/29.0)
|
||||
|
||||
mask = fy > delta
|
||||
xyz[..., 1][mask] = fy[mask] ** 3
|
||||
xyz[..., 1][~mask] = 3.0 * delta2 * (fy[~mask] - 4.0/29.0)
|
||||
|
||||
mask = fz > delta
|
||||
xyz[..., 2][mask] = fz[mask] ** 3
|
||||
xyz[..., 2][~mask] = 3.0 * delta2 * (fz[~mask] - 4.0/29.0)
|
||||
|
||||
white = torch.tensor([0.95047, 1.0, 0.95883], device=lch.device, dtype=lch.dtype)
|
||||
xyz = xyz * white
|
||||
|
||||
# XYZ → linear RGB
|
||||
M_xyz2rgb = torch.tensor([
|
||||
[ 3.2406, -1.5372, -0.4986],
|
||||
[-0.9689, 1.8758, 0.0415],
|
||||
[ 0.0557, -0.2040, 1.0570]
|
||||
], device=lch.device, dtype=lch.dtype)
|
||||
rgb_lin = torch.einsum('ij,...j->...i', M_xyz2rgb, xyz)
|
||||
|
||||
# linear → sRGB
|
||||
rgb_srgb = linear_to_srgb_vec(rgb_lin)
|
||||
|
||||
return rgb_srgb
|
||||
|
||||
#===================================================================================
|
||||
def hsl_to_rgb(h, s, l):
|
||||
hp = h / 60.0
|
||||
c = (1.0 - torch.abs(2.0 * l - 1.0)) * s
|
||||
x = c * (1.0 - torch.abs(hp % 2.0 - 1.0))
|
||||
m = l - 0.5 * c
|
||||
|
||||
r = torch.zeros_like(l)
|
||||
g = torch.zeros_like(l)
|
||||
b = torch.zeros_like(l)
|
||||
|
||||
m0 = (hp >= 0) & (hp < 1)
|
||||
m1 = (hp >= 1) & (hp < 2)
|
||||
m2 = (hp >= 2) & (hp < 3)
|
||||
m3 = (hp >= 3) & (hp < 4)
|
||||
m4 = (hp >= 4) & (hp < 5)
|
||||
m5 = (hp >= 5) & (hp < 6)
|
||||
|
||||
r[m0], g[m0], b[m0] = c[m0], x[m0], 0
|
||||
r[m1], g[m1], b[m1] = x[m1], c[m1], 0
|
||||
r[m2], g[m2], b[m2] = 0, c[m2], x[m2]
|
||||
r[m3], g[m3], b[m3] = 0, x[m3], c[m3]
|
||||
r[m4], g[m4], b[m4] = x[m4], 0, c[m4]
|
||||
r[m5], g[m5], b[m5] = c[m5], 0, x[m5]
|
||||
|
||||
return torch.clamp(torch.stack([r + m, g + m, b + m], dim=-1), 0.0, 1.0)
|
||||
|
||||
#===================================================================================
|
||||
def rgb_to_hsl(rgb: torch.Tensor):
|
||||
maxc = rgb.max(dim=-1).values
|
||||
minc = rgb.min(dim=-1).values
|
||||
l = (maxc + minc) * 0.5
|
||||
|
||||
delta = maxc - minc
|
||||
|
||||
s = torch.zeros_like(l)
|
||||
nz = delta > 1e-8
|
||||
s[nz] = delta[nz] / (1.0 - torch.abs(2.0 * l[nz] - 1.0) + 1e-8)
|
||||
|
||||
r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
|
||||
|
||||
h = torch.zeros_like(l)
|
||||
|
||||
rmax = (maxc == r) & nz
|
||||
gmax = (maxc == g) & nz
|
||||
bmax = (maxc == b) & nz
|
||||
|
||||
h_r = ((g - b) / (delta + 1e-8)) % 6.0
|
||||
h_g = ((b - r) / (delta + 1e-8)) + 2.0
|
||||
h_b = ((r - g) / (delta + 1e-8)) + 4.0
|
||||
|
||||
h = torch.where(rmax, h_r, h)
|
||||
h = torch.where(gmax, h_g, h)
|
||||
h = torch.where(bmax, h_b, h)
|
||||
|
||||
h = (h * 60.0) % 360.0
|
||||
|
||||
return h, s, l
|
||||
|
||||
#===================================================================================
|
||||
def rgb_to_hsv(rgb: torch.Tensor):
|
||||
"""Convert RGB [0,1] to HSV. Returns (hue, saturation, value)."""
|
||||
r, g, b = rgb[..., 0], rgb[..., 1], rgb[..., 2]
|
||||
|
||||
max_val, max_idx = torch.max(rgb, dim=-1)
|
||||
min_val, _ = torch.min(rgb, dim=-1)
|
||||
diff = max_val - min_val
|
||||
|
||||
# Hue
|
||||
hue = torch.zeros_like(max_val)
|
||||
mask = diff != 0
|
||||
h = torch.zeros_like(max_val)
|
||||
h = torch.where((max_val == r) & mask, ((g - b) / diff) % 6, h)
|
||||
h = torch.where((max_val == g) & mask, ((b - r) / diff) + 2, h)
|
||||
h = torch.where((max_val == b) & mask, ((r - g) / diff) + 4, h)
|
||||
hue = (h / 6) % 1.0
|
||||
|
||||
# Saturation
|
||||
sat = torch.zeros_like(max_val)
|
||||
sat = torch.where(max_val != 0, diff / max_val, sat)
|
||||
|
||||
# Value
|
||||
val = max_val
|
||||
|
||||
return hue, sat, val
|
||||
|
||||
#===================================================================================
|
||||
def hsv_to_rgb(hue: torch.Tensor, sat: torch.Tensor, val: torch.Tensor):
|
||||
"""Convert HSV to RGB [0,1]."""
|
||||
h = hue * 6.0
|
||||
i = torch.floor(h).long()
|
||||
f = h - i.float()
|
||||
p = val * (1 - sat)
|
||||
q = val * (1 - sat * f)
|
||||
t = val * (1 - sat * (1 - f))
|
||||
|
||||
# Initialize output
|
||||
rgb = torch.zeros(*hue.shape, 3, device=hue.device)
|
||||
|
||||
# Vectorized assignment using advanced indexing
|
||||
# This is simplified - in practice you'd use torch.where for each case
|
||||
mask0 = i == 0
|
||||
mask1 = i == 1
|
||||
mask2 = i == 2
|
||||
mask3 = i == 3
|
||||
mask4 = i == 4
|
||||
mask5 = i == 5
|
||||
|
||||
rgb[..., 0] = torch.where(mask0, val, rgb[..., 0])
|
||||
rgb[..., 1] = torch.where(mask0, t, rgb[..., 1])
|
||||
rgb[..., 2] = torch.where(mask0, p, rgb[..., 2])
|
||||
|
||||
rgb[..., 0] = torch.where(mask1, q, rgb[..., 0])
|
||||
rgb[..., 1] = torch.where(mask1, val, rgb[..., 1])
|
||||
rgb[..., 2] = torch.where(mask1, p, rgb[..., 2])
|
||||
|
||||
rgb[..., 0] = torch.where(mask2, p, rgb[..., 0])
|
||||
rgb[..., 1] = torch.where(mask2, val, rgb[..., 1])
|
||||
rgb[..., 2] = torch.where(mask2, t, rgb[..., 2])
|
||||
|
||||
rgb[..., 0] = torch.where(mask3, p, rgb[..., 0])
|
||||
rgb[..., 1] = torch.where(mask3, q, rgb[..., 1])
|
||||
rgb[..., 2] = torch.where(mask3, val, rgb[..., 2])
|
||||
|
||||
rgb[..., 0] = torch.where(mask4, t, rgb[..., 0])
|
||||
rgb[..., 1] = torch.where(mask4, p, rgb[..., 1])
|
||||
rgb[..., 2] = torch.where(mask4, val, rgb[..., 2])
|
||||
|
||||
rgb[..., 0] = torch.where(mask5, val, rgb[..., 0])
|
||||
rgb[..., 1] = torch.where(mask5, p, rgb[..., 1])
|
||||
rgb[..., 2] = torch.where(mask5, q, rgb[..., 2])
|
||||
|
||||
return rgb
|
||||
|
||||
#===================================================================================
|
||||
def rgb_to_lch(rgb: torch.Tensor):
|
||||
"""Convert RGB to Lch (CIE L*a*b* with chroma)."""
|
||||
# Convert RGB to XYZ (D65)
|
||||
rgb_linear = rgb.clone()
|
||||
mask = rgb_linear <= 0.04045
|
||||
rgb_linear[mask] = rgb_linear[mask] / 12.92
|
||||
rgb_linear[~mask] = ((rgb_linear[~mask] + 0.055) / 1.055) ** 2.4
|
||||
|
||||
# RGB to XYZ matrix
|
||||
x = 0.4124564 * rgb_linear[..., 0] + 0.3575761 * rgb_linear[..., 1] + 0.1804375 * rgb_linear[..., 2]
|
||||
y = 0.2126729 * rgb_linear[..., 0] + 0.7151522 * rgb_linear[..., 1] + 0.0721750 * rgb_linear[..., 2]
|
||||
z = 0.0193339 * rgb_linear[..., 0] + 0.1191920 * rgb_linear[..., 1] + 0.9503041 * rgb_linear[..., 2]
|
||||
|
||||
# XYZ to Lab (D65 white point)
|
||||
xn, yn, zn = 0.95047, 1.0, 1.08883
|
||||
xr, yr, zr = x / xn, y / yn, z / zn
|
||||
|
||||
f = lambda t: torch.where(t > 0.008856, t ** (1/3), 7.787 * t + 16/116)
|
||||
fx, fy, fz = f(xr), f(yr), f(zr)
|
||||
|
||||
L = 116 * fy - 16
|
||||
a = 500 * (fx - fy)
|
||||
b = 200 * (fy - fz)
|
||||
|
||||
# Chroma and hue
|
||||
C = torch.sqrt(a**2 + b**2)
|
||||
H = torch.atan2(b, a) % (2 * torch.pi)
|
||||
|
||||
return L, C, H
|
||||
|
||||
#===================================================================================
|
||||
def lch_to_rgb(L: torch.Tensor, C: torch.Tensor, H: torch.Tensor):
|
||||
"""Convert Lch to RGB."""
|
||||
# Lch to Lab
|
||||
a = C * torch.cos(H)
|
||||
b = C * torch.sin(H)
|
||||
|
||||
# Lab to XYZ (D65)
|
||||
fy = (L + 16) / 116
|
||||
fx = a / 500 + fy
|
||||
fz = fy - b / 200
|
||||
|
||||
f_inv = lambda t: torch.where(t > 0.206893, t**3, (t - 16/116) / 7.787)
|
||||
xr, yr, zr = f_inv(fx), f_inv(fy), f_inv(fz)
|
||||
|
||||
xn, yn, zn = 0.95047, 1.0, 1.08883
|
||||
x, y, z = xr * xn, yr * yn, zr * zn
|
||||
|
||||
# XYZ to RGB
|
||||
r = 3.2404542 * x - 1.5371385 * y - 0.4985314 * z
|
||||
g = -0.9692660 * x + 1.8760108 * y + 0.0415560 * z
|
||||
b = 0.0556434 * x - 0.2040259 * y + 1.0572252 * z
|
||||
|
||||
rgb = torch.stack([r, g, b], dim=-1)
|
||||
|
||||
# Linear to sRGB
|
||||
mask = rgb <= 0.0031308
|
||||
rgb[mask] = 12.92 * rgb[mask]
|
||||
rgb[~mask] = 1.055 * rgb[~mask] ** (1/2.4) - 0.055
|
||||
|
||||
return torch.clamp(rgb, 0, 1)
|
||||
|
||||
#===================================================================================
|
||||
def rgb_to_yuv(rgb: torch.Tensor):
|
||||
"""Convert RGB to Yuv (CIE Yuv)."""
|
||||
# BT.709 coefficients
|
||||
y = 0.2126 * rgb[..., 0] + 0.7152 * rgb[..., 1] + 0.0722 * rgb[..., 2]
|
||||
u = rgb[..., 0] - y
|
||||
v = rgb[..., 2] - y
|
||||
return y, u, v
|
||||
|
||||
#===================================================================================
|
||||
def yuv_to_rgb(y: torch.Tensor, u: torch.Tensor, v: torch.Tensor):
|
||||
"""Convert Yuv to RGB."""
|
||||
r = y + u
|
||||
g = y - 0.21482 * u - 0.38129 * v # Simplified: approximate
|
||||
b = y + v
|
||||
rgb = torch.stack([r, g, b], dim=-1)
|
||||
return torch.clamp(rgb, 0, 1)
|
||||
|
||||
#===================================================================================
|
||||
def rgb_to_lch_torch(image: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Convert RGB [0,1] to CIE LCH(ab) using D65 white point.
|
||||
Returns tensor with last dimension [L, C, H].
|
||||
"""
|
||||
# 1. sRGB → linear RGB
|
||||
rgb_lin = srgb_to_linear_vec(image)
|
||||
|
||||
# 2. linear RGB → XYZ (D65)
|
||||
M_rgb2xyz = torch.tensor([
|
||||
[0.4124, 0.3576, 0.1805],
|
||||
[0.2126, 0.7152, 0.0722],
|
||||
[0.0193, 0.1192, 0.9505]
|
||||
], device=image.device, dtype=image.dtype)
|
||||
xyz = torch.einsum('ij,...j->...i', M_rgb2xyz, rgb_lin)
|
||||
|
||||
# 3. XYZ → CIE L*a*b* (D65 white point)
|
||||
white = torch.tensor([0.95047, 1.0, 0.95883], device=image.device, dtype=image.dtype)
|
||||
xyz_n = xyz / white
|
||||
delta = 6.0 / 29.0
|
||||
delta2 = delta * delta
|
||||
delta3 = delta * delta * delta
|
||||
|
||||
f = torch.zeros_like(xyz_n)
|
||||
mask_f = xyz_n > delta3
|
||||
f[mask_f] = xyz_n[mask_f] ** (1.0/3.0)
|
||||
f[~mask_f] = (xyz_n[~mask_f] / (3.0 * delta2)) + (4.0 / 29.0)
|
||||
|
||||
L = 116.0 * f[..., 1] - 16.0
|
||||
a = 500.0 * (f[..., 0] - f[..., 1])
|
||||
b = 200.0 * (f[..., 1] - f[..., 2])
|
||||
|
||||
# 4. Lab → LCH
|
||||
C = torch.sqrt(a*a + b*b)
|
||||
H = torch.atan2(b, a) * (180.0 / math.pi)
|
||||
H = H % 360.0
|
||||
H = torch.where(H >= 180.0, H - 360.0, H) # map to [-180, 180)
|
||||
|
||||
return torch.stack([L, C, H], dim=-1)
|
||||
|
||||
#===================================================================================
|
||||
def lch_to_rgb_torch(lch: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Convert CIE LCH(ab) back to RGB [0,1].
|
||||
"""
|
||||
L, C, H = lch[..., 0], lch[..., 1], lch[..., 2]
|
||||
H_rad = (H % 360.0) * (math.pi / 180.0)
|
||||
|
||||
# LCH → Lab
|
||||
a = C * torch.cos(H_rad)
|
||||
b = C * torch.sin(H_rad)
|
||||
|
||||
# Lab → XYZ
|
||||
delta = 6.0 / 29.0
|
||||
delta2 = delta * delta
|
||||
delta3 = delta * delta * delta
|
||||
|
||||
fy = (L + 16.0) / 116.0
|
||||
fx = fy + a / 500.0
|
||||
fz = fy - b / 200.0
|
||||
|
||||
xyz = torch.zeros_like(lch)
|
||||
# Inverse f(t)
|
||||
mask = fx > delta
|
||||
xyz[..., 0][mask] = fx[mask] ** 3
|
||||
xyz[..., 0][~mask] = 3.0 * delta2 * (fx[~mask] - 4.0/29.0)
|
||||
|
||||
mask = fy > delta
|
||||
xyz[..., 1][mask] = fy[mask] ** 3
|
||||
xyz[..., 1][~mask] = 3.0 * delta2 * (fy[~mask] - 4.0/29.0)
|
||||
|
||||
mask = fz > delta
|
||||
xyz[..., 2][mask] = fz[mask] ** 3
|
||||
xyz[..., 2][~mask] = 3.0 * delta2 * (fz[~mask] - 4.0/29.0)
|
||||
|
||||
white = torch.tensor([0.95047, 1.0, 0.95883], device=lch.device, dtype=lch.dtype)
|
||||
xyz = xyz * white
|
||||
|
||||
# XYZ → linear RGB
|
||||
M_xyz2rgb = torch.tensor([
|
||||
[ 3.2406, -1.5372, -0.4986],
|
||||
[-0.9689, 1.8758, 0.0415],
|
||||
[ 0.0557, -0.2040, 1.0570]
|
||||
], device=lch.device, dtype=lch.dtype)
|
||||
rgb_lin = torch.einsum('ij,...j->...i', M_xyz2rgb, xyz)
|
||||
|
||||
# linear → sRGB
|
||||
rgb_srgb = linear_to_srgb_vec(rgb_lin)
|
||||
return rgb_srgb
|
||||
|
||||
#===================================================================================
|
||||
def pack_rgb(r, g, b):
|
||||
"""r,g,b in 0..1, returns (packed_int, '#RRGGBB')"""
|
||||
r = max(0.0, min(1.0, r))
|
||||
g = max(0.0, min(1.0, g))
|
||||
b = max(0.0, min(1.0, b))
|
||||
ri = int(round(r * 255))
|
||||
gi = int(round(g * 255))
|
||||
bi = int(round(b * 255))
|
||||
packed = (ri << 16) | (gi << 8) | bi
|
||||
hex_str = f"#{ri:02X}{gi:02X}{bi:02X}"
|
||||
return packed, hex_str
|
||||
|
||||
@@ -0,0 +1,76 @@
|
||||
import torch
|
||||
from typing import Optional
|
||||
|
||||
def compute_chunk_size(
|
||||
image: torch.Tensor,
|
||||
mask: Optional[torch.Tensor] = None,
|
||||
) -> int:
|
||||
"""
|
||||
Determine a safe batch-size chunk for GPU processing based on the
|
||||
currently available CUDA memory.
|
||||
|
||||
The estimation accounts for the memory required by the input image,
|
||||
an optional mask tensor, the output image, and temporary working
|
||||
tensors created during color conversion.
|
||||
|
||||
Args:
|
||||
image (torch.Tensor):
|
||||
Input image tensor of shape (B, H, W, C).
|
||||
|
||||
mask (torch.Tensor, optional):
|
||||
Optional mask tensor associated with the image batch.
|
||||
|
||||
Returns:
|
||||
int:
|
||||
Maximum number of images that can be processed safely in a
|
||||
single chunk. If the image resides on the CPU, the complete
|
||||
batch size is returned.
|
||||
"""
|
||||
if image.device.type != "cuda":
|
||||
return image.shape[0]
|
||||
|
||||
free_mem, _ = torch.cuda.mem_get_info(image.device)
|
||||
|
||||
image_batch = max(image.shape[0], 1)
|
||||
|
||||
# Bytes required by one image sample
|
||||
image_sample_bytes = (
|
||||
image[0].numel() * image.element_size()
|
||||
)
|
||||
|
||||
# Bytes required by one mask sample
|
||||
mask_sample_bytes = 0
|
||||
|
||||
if mask is not None:
|
||||
if mask.device != image.device:
|
||||
mask = mask.to(image.device)
|
||||
|
||||
mask_batch = max(mask.shape[0], 1)
|
||||
|
||||
if mask_batch == image_batch:
|
||||
mask_sample_bytes = mask[0].numel() * mask.element_size()
|
||||
elif mask_batch == 1:
|
||||
# Single mask broadcast across the batch
|
||||
mask_sample_bytes = mask[0].numel() * mask.element_size()
|
||||
else:
|
||||
raise ValueError(
|
||||
f"Mask batch size ({mask_batch}) does not match image batch size ({image_batch})."
|
||||
)
|
||||
|
||||
# Working memory:
|
||||
# input image
|
||||
# output image
|
||||
# temporary color conversion tensors
|
||||
# optional mask
|
||||
working_factor = 4
|
||||
|
||||
required_per_sample = (
|
||||
image_sample_bytes * working_factor +
|
||||
mask_sample_bytes
|
||||
)
|
||||
|
||||
# Keep a safety margin so other CUDA allocations have room.
|
||||
usable_memory = int(free_mem * 0.90)
|
||||
|
||||
return max(1, min(image_batch, usable_memory // max(required_per_sample, 1)))
|
||||
|
||||
@@ -0,0 +1,6 @@
|
||||
[project]
|
||||
name = "MdColorMod"
|
||||
version = "1.0.0"
|
||||
description = "GIMP color mod nodes in comfyUI"
|
||||
license = { file = "LICENSE" }
|
||||
dependencies = []
|
||||
@@ -0,0 +1,43 @@
|
||||
import { app } from "../../../scripts/app.js";
|
||||
|
||||
import { color_balance_widget } from "./nodes/color_balance_widget.js";
|
||||
import { color_hue_saturation_widget } from "./nodes/color_hue_saturation_widget.js";
|
||||
|
||||
//import { addRGBIntegerWidget } from "./widgets/rgb_int_widget.js";
|
||||
|
||||
|
||||
app.registerExtension({
|
||||
name: "md.color_mod.frontend_extension",
|
||||
|
||||
|
||||
async beforeRegisterNodeDef(nodeType, nodeData, app) {
|
||||
|
||||
const onNodeCreated = nodeType.prototype.onNodeCreated;
|
||||
|
||||
if (nodeData.name === "mdColorBalance") {
|
||||
nodeType.prototype.onNodeCreated = function () {
|
||||
onNodeCreated?.apply(this, arguments);
|
||||
this._inst = new color_balance_widget(this);
|
||||
};
|
||||
}
|
||||
|
||||
if (nodeData.name === "mdColorHueSaturation") {
|
||||
nodeType.prototype.onNodeCreated = function () {
|
||||
onNodeCreated?.apply(this, arguments);
|
||||
this._inst = new color_hue_saturation_widget(this);
|
||||
};
|
||||
}
|
||||
|
||||
/*
|
||||
if (nodeData.name === "mdPrimitiveColorRGB") {
|
||||
nodeType.prototype.onNodeCreated = function () {
|
||||
const result = onNodeCreated?.apply(this, arguments);
|
||||
addRGBIntegerWidget(this, "rgb", 16711680);
|
||||
return result;
|
||||
};
|
||||
}
|
||||
*/
|
||||
},
|
||||
});
|
||||
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
|
||||
|
||||
export const set_node_widget_value = (node, widget_id, value) => {
|
||||
let widget = node.widgets?.find(w => w.name === widget_id);
|
||||
if (!widget) return console.error(`Could not find ${widget_id} widget`);
|
||||
//console.log(`set ${widget_id} to ${value}`);
|
||||
widget.value = value;
|
||||
};
|
||||
|
||||
@@ -0,0 +1,103 @@
|
||||
import { set_node_widget_value } from "../libs/widgets_module.js";
|
||||
|
||||
export class color_balance_widget {
|
||||
constructor(node) {
|
||||
this.node = node;
|
||||
this.rangeNames = ["Shadow", "Midtonnes", "Highlights"];
|
||||
|
||||
// Find widgets
|
||||
this.rangeWidget = node.widgets?.find(w => w.name === "range");
|
||||
this.cyanWidget = node.widgets?.find(w => w.name === "Cyan-Red");
|
||||
this.magentaWidget = node.widgets?.find(w => w.name === "Magenta-Green");
|
||||
this.yellowWidget = node.widgets?.find(w => w.name === "Yellow-Blue");
|
||||
this.dataWidget = node.widgets?.find(w => w.name === "jdata");
|
||||
|
||||
if (!this.rangeWidget || !this.cyanWidget || !this.magentaWidget || !this.yellowWidget || !this.dataWidget) {
|
||||
console.warn("mdColorBalance: some widgets missing");
|
||||
return;
|
||||
}
|
||||
|
||||
this.dataWidget.hidden = true;
|
||||
|
||||
this.setupCallbacks();
|
||||
// Initial load after the node is fully ready
|
||||
setTimeout(() => this.loadFromData(), 0);
|
||||
}
|
||||
|
||||
getCurrentRangeIndex() {
|
||||
return this.rangeNames.indexOf(this.rangeWidget.value);
|
||||
}
|
||||
|
||||
saveCurrentToData() {
|
||||
let data;
|
||||
try {
|
||||
data = JSON.parse(this.dataWidget.value);
|
||||
} catch {
|
||||
data = [[0, 0, 0], [0, 0, 0], [0, 0, 0]];
|
||||
}
|
||||
const idx = this.getCurrentRangeIndex();
|
||||
if (idx === -1) return;
|
||||
data[idx] = [
|
||||
parseFloat(this.cyanWidget.value),
|
||||
parseFloat(this.magentaWidget.value),
|
||||
parseFloat(this.yellowWidget.value)
|
||||
];
|
||||
this.dataWidget.value = JSON.stringify(data);
|
||||
if (this.dataWidget.callback) this.dataWidget.callback(this.dataWidget.value);
|
||||
}
|
||||
|
||||
loadFromData() {
|
||||
let data;
|
||||
try {
|
||||
data = JSON.parse(this.dataWidget.value);
|
||||
} catch {
|
||||
data = [[0, 0, 0], [0, 0, 0], [0, 0, 0]];
|
||||
}
|
||||
const idx = this.getCurrentRangeIndex();
|
||||
if (idx === -1) return;
|
||||
const vals = data[idx] || [0, 0, 0];
|
||||
|
||||
// Use the shared utility to set widget values
|
||||
set_node_widget_value(this.node, "Cyan-Red", vals[0]);
|
||||
set_node_widget_value(this.node, "Magenta-Green", vals[1]);
|
||||
set_node_widget_value(this.node, "Yellow-Blue", vals[2]);
|
||||
|
||||
// Notify the widget callbacks so the UI updates
|
||||
if (this.cyanWidget.callback) this.cyanWidget.callback(this.cyanWidget.value);
|
||||
if (this.magentaWidget.callback) this.magentaWidget.callback(this.magentaWidget.value);
|
||||
if (this.yellowWidget.callback) this.yellowWidget.callback(this.yellowWidget.value);
|
||||
}
|
||||
|
||||
setupCallbacks() {
|
||||
// --- Range dropdown ---
|
||||
const origRangeCb = this.rangeWidget.callback;
|
||||
this.rangeWidget.callback = (value) => {
|
||||
// Save current slider values for the old range
|
||||
this.saveCurrentToData();
|
||||
// Load slider values for the new range
|
||||
this.loadFromData();
|
||||
// Invoke any existing callback
|
||||
if (origRangeCb) origRangeCb(value);
|
||||
};
|
||||
|
||||
// --- Slider widgets ---
|
||||
const onSliderChange = () => {
|
||||
this.saveCurrentToData();
|
||||
};
|
||||
|
||||
// Preserve existing callbacks by wrapping them
|
||||
const wrapSliderCallback = (widget, extra) => {
|
||||
const orig = widget.callback;
|
||||
widget.callback = function(value) {
|
||||
extra();
|
||||
if (orig) orig.call(this, value); // ✅ Preserve `this` context
|
||||
};
|
||||
};
|
||||
|
||||
wrapSliderCallback(this.cyanWidget, onSliderChange);
|
||||
wrapSliderCallback(this.magentaWidget, onSliderChange);
|
||||
wrapSliderCallback(this.yellowWidget, onSliderChange);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,104 @@
|
||||
import { set_node_widget_value } from "../libs/widgets_module.js";
|
||||
|
||||
export class color_hue_saturation_widget {
|
||||
|
||||
constructor(node) {
|
||||
this.node = node;
|
||||
// Order must match the dropdown: Master, Red, Yellow, Green, Cyan, Blue, Magenta
|
||||
this.rangeNames = ["Master", "Red", "Yellow", "Green", "Cyan", "Blue", "Magenta"];
|
||||
|
||||
// Find widgets
|
||||
this.colorWidget = node.widgets?.find(w => w.name === "color");
|
||||
this.hueWidget = node.widgets?.find(w => w.name === "Hue");
|
||||
this.lightnessWidget = node.widgets?.find(w => w.name === "Lightness");
|
||||
this.saturationWidget = node.widgets?.find(w => w.name === "Saturation");
|
||||
this.dataWidget = node.widgets?.find(w => w.name === "jdata");
|
||||
|
||||
if (!this.colorWidget || !this.hueWidget || !this.lightnessWidget ||
|
||||
!this.saturationWidget || !this.dataWidget) {
|
||||
console.warn("mdColorHueSaturation: some widgets missing");
|
||||
return;
|
||||
}
|
||||
|
||||
this.dataWidget.hidden = true;
|
||||
|
||||
this.setupCallbacks();
|
||||
// Initial load after the node is fully ready
|
||||
setTimeout(() => this.loadFromData(), 0);
|
||||
}
|
||||
|
||||
getCurrentRangeIndex() {
|
||||
return this.rangeNames.indexOf(this.colorWidget.value);
|
||||
}
|
||||
|
||||
saveCurrentToData() {
|
||||
let data;
|
||||
try {
|
||||
data = JSON.parse(this.dataWidget.value);
|
||||
} catch {
|
||||
data = this.rangeNames.map(() => [0, 0, 0]);
|
||||
}
|
||||
const idx = this.getCurrentRangeIndex();
|
||||
if (idx === -1) return;
|
||||
data[idx] = [
|
||||
parseFloat(this.hueWidget.value),
|
||||
parseFloat(this.lightnessWidget.value),
|
||||
parseFloat(this.saturationWidget.value)
|
||||
];
|
||||
this.dataWidget.value = JSON.stringify(data);
|
||||
if (this.dataWidget.callback) this.dataWidget.callback(this.dataWidget.value);
|
||||
}
|
||||
|
||||
loadFromData() {
|
||||
let data;
|
||||
try {
|
||||
data = JSON.parse(this.dataWidget.value);
|
||||
} catch {
|
||||
data = this.rangeNames.map(() => [0, 0, 0]);
|
||||
}
|
||||
const idx = this.getCurrentRangeIndex();
|
||||
if (idx === -1) return;
|
||||
const vals = data[idx] || [0, 0, 0];
|
||||
|
||||
// Update sliders using the shared utility
|
||||
set_node_widget_value(this.node, "Hue", vals[0]);
|
||||
set_node_widget_value(this.node, "Lightness", vals[1]);
|
||||
set_node_widget_value(this.node, "Saturation", vals[2]);
|
||||
|
||||
// Notify the widget callbacks so the UI updates
|
||||
if (this.hueWidget.callback) this.hueWidget.callback(this.hueWidget.value);
|
||||
if (this.lightnessWidget.callback) this.lightnessWidget.callback(this.lightnessWidget.value);
|
||||
if (this.saturationWidget.callback) this.saturationWidget.callback(this.saturationWidget.value);
|
||||
}
|
||||
|
||||
setupCallbacks() {
|
||||
// --- Color dropdown ---
|
||||
const origColorCb = this.colorWidget.callback;
|
||||
this.colorWidget.callback = (value) => {
|
||||
// Save current slider values for the old range
|
||||
this.saveCurrentToData();
|
||||
// Load slider values for the new range
|
||||
this.loadFromData();
|
||||
// Invoke any existing callback
|
||||
if (origColorCb) origColorCb(value);
|
||||
};
|
||||
|
||||
// --- Slider widgets ---
|
||||
const onSliderChange = () => {
|
||||
this.saveCurrentToData();
|
||||
};
|
||||
|
||||
const wrapSliderCallback = (widget, extra) => {
|
||||
const orig = widget.callback;
|
||||
widget.callback = function(value) {
|
||||
extra();
|
||||
if (orig) orig.call(this, value);
|
||||
};
|
||||
};
|
||||
|
||||
wrapSliderCallback(this.hueWidget, onSliderChange);
|
||||
wrapSliderCallback(this.lightnessWidget, onSliderChange);
|
||||
wrapSliderCallback(this.saturationWidget, onSliderChange);
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user