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merged auto-level into old level node

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This commit is contained in:
Alexander G. Morano
2025-09-04 23:15:00 -04:00
parent 07edee1be9
commit a28214a015
5 changed files with 56 additions and 81 deletions
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README.md
+2 -2
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@@ -138,8 +138,8 @@ Nodes that have been migrated:
[Migrated to Jovi_GLSL](https://github.com/Amorano/Jovi_GLSL)
**2025/09/04** @2.1.24:
* `AUTO LEVEL` node
**2025/09/04** @2.1.25:
* Auto-level for `LEVEL` node
* `HISTOGRAM` node
* new support for cozy_comfy (v3+ comfy node spec)
core/adjust.py
+51 -16
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@@ -3,6 +3,7 @@
import sys
from enum import Enum
from typing import Any
from typing_extensions import override
import comfy.model_management
from comfy_api.latest import ComfyExtension, io
@@ -28,7 +29,8 @@ from cozy_comfyui.image.adjust import \
image_contrast, image_brightness, image_equalize, image_gamma, \
image_exposure, image_pixelate, image_pixelscale, \
image_posterize, image_quantize, image_sharpen, image_morphology, \
image_emboss, image_blur, image_edge, image_color
image_emboss, image_blur, image_edge, image_color, \
image_autolevel, image_autolevel_histogram
from cozy_comfyui.image.channel import \
channel_solid
@@ -52,6 +54,11 @@ JOV_CATEGORY = "ADJUST"
# === ENUMERATION ===
# ==============================================================================
class EnumAutoLevel(Enum):
MANUAL = 10
AUTO = 20
HISTOGRAM = 30
class EnumAdjustLight(Enum):
EXPOSURE = 10
GAMMA = 20
@@ -229,7 +236,8 @@ class AdjustLevelNode(CozyImageNode):
NAME = "ADJUST: LEVELS (JOV)"
CATEGORY = JOV_CATEGORY
DESCRIPTION = """
Manual or automatic adjust image levels so that the darkest pixel becomes black
and the brightest pixel becomes white, enhancing overall contrast.
"""
@classmethod
@@ -243,7 +251,14 @@ class AdjustLevelNode(CozyImageNode):
"label": ["LOW", "MID", "HIGH"]}),
Lexicon.RANGE: ("VEC2", {
"default": (0, 1), "mij": 0, "maj": 1, "step": 0.01,
"label": ["IN", "OUT"]})
"label": ["IN", "OUT"]}),
Lexicon.MODE: (EnumAutoLevel._member_names_, {
"default": EnumAutoLevel.MANUAL.name,
"tooltip": "Autolevel linearly or with Histogram bin values, per channel"
}),
"clip": ("FLOAT", {
"default": 0.5, "min": 0, "max": 1.0, "step": 0.01
})
}
})
return Lexicon._parse(d)
@@ -252,18 +267,29 @@ class AdjustLevelNode(CozyImageNode):
pA = parse_param(kw, Lexicon.IMAGE, EnumConvertType.IMAGE, None)
LMH = parse_param(kw, Lexicon.LMH, EnumConvertType.VEC3, (0,0.5,1))
inout = parse_param(kw, Lexicon.RANGE, EnumConvertType.VEC2, (0,1))
params = list(zip_longest_fill(pA, LMH, inout))
mode = parse_param(kw, Lexicon.MODE, EnumAutoLevel, EnumAutoLevel.AUTO.name)
clip = parse_param(kw, "clip", EnumConvertType.FLOAT, 0.5, 0, 1)
params = list(zip_longest_fill(pA, LMH, inout, mode, clip))
images = []
pbar = ProgressBar(len(params))
for idx, (pA, LMH, inout) in enumerate(params):
for idx, (pA, LMH, inout, mode, clip) in enumerate(params):
pA = channel_solid() if pA is None else tensor_to_cv(pA)
'''
h, s, v = hsv
img_new = image_hsv(img_new, h, s, v)
'''
low, mid, high = LMH
start, end = inout
pA = image_levels(pA, low, mid, high, start, end)
match mode:
case EnumAutoLevel.MANUAL:
low, mid, high = LMH
start, end = inout
pA = image_levels(pA, low, mid, high, start, end)
case EnumAutoLevel.AUTO:
pA = image_autolevel(pA)
case EnumAutoLevel.HISTOGRAM:
pA = image_autolevel_histogram(pA, clip)
images.append(cv_to_tensor_full(pA))
pbar.update_absolute(idx)
return image_stack(images)
@@ -463,15 +489,13 @@ Sharpen the pixels of an image.
return image_stack(images)
class AdjustSharpenNodev3(CozyImageNodev3):
NAME = "ADJUST: SHARPEN (JOV)"
CATEGORY = JOV_CATEGORY
DESCRIPTION = """
Sharpen the pixels of an image.
"""
@classmethod
def define_schema(cls, **kwarg) -> io.Schema:
schema = super(**kwarg).define_schema()
# schema.
schema.display_name = "ADJUST: SHARPEN (JOV)"
schema.category = JOV_CATEGORY
schema.description = "Sharpen the pixels of an image."
schema.inputs.extend([
io.MultiType.Input(
id=Lexicon.IMAGE[0],
@@ -504,7 +528,8 @@ Sharpen the pixels of an image.
])
return schema
def run(self, **kw) -> RGBAMaskType:
@classmethod
def execute(self, *arg, **kw) -> io.NodeOutput:
pA = parse_param(kw, Lexicon.IMAGE, EnumConvertType.IMAGE, None)
amount = parse_param(kw, Lexicon.AMOUNT, EnumConvertType.FLOAT, 0)
threshold = parse_param(kw, Lexicon.THRESHOLD, EnumConvertType.FLOAT, 0)
@@ -516,4 +541,14 @@ Sharpen the pixels of an image.
pA = image_sharpen(pA, amount / 2., threshold=threshold / 25.5)
images.append(cv_to_tensor_full(pA))
pbar.update_absolute(idx)
return image_stack(images)
return io.NodeOutput(image_stack(images))
class AdjustExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
AdjustSharpenNodev3
]
async def comfy_entrypoint() -> AdjustExtension:
return AdjustExtension()
core/compose.py
+1 -60
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@@ -1,7 +1,5 @@
""" Jovimetrix - Composition """
from enum import Enum
import numpy as np
from comfy.utils import ProgressBar
@@ -23,8 +21,7 @@ from cozy_comfyui.image import \
from cozy_comfyui.image.adjust import \
EnumThreshold, EnumThresholdAdapt, \
image_histogram2, image_invert, image_filter, image_threshold, \
image_autolevel, image_autolevel_histogram
image_histogram2, image_invert, image_filter, image_threshold
from cozy_comfyui.image.channel import \
EnumPixelSwizzle, \
@@ -47,66 +44,10 @@ from cozy_comfyui.image.misc import \
JOV_CATEGORY = "COMPOSE"
# ==============================================================================
# === ENUMERATION ===
# ==============================================================================
class EnumAutoLevel(Enum):
AUTO = 10
HISTOGRAM = 20
# ==============================================================================
# === CLASS ===
# ==============================================================================
class AutoLevelNode(CozyImageNode):
NAME = "AUTO LEVEL (JOV)"
CATEGORY = JOV_CATEGORY
DESCRIPTION = """
Automatically adjusts image levels so that the darkest pixel becomes black
and the brightest pixel becomes white, enhancing overall contrast.
"""
@classmethod
def INPUT_TYPES(cls) -> InputType:
d = super().INPUT_TYPES()
d = deep_merge(d, {
"required": {
Lexicon.IMAGE: (COZY_TYPE_IMAGE, {
"tooltip": "Pixel Data (RGBA, RGB, or Grayscale)"
}),
Lexicon.MODE: (EnumAutoLevel._member_names_, {
"default": EnumAutoLevel.AUTO.name,
"tooltip": "Autolevel linearly or with Histogram bin values, per channel"
}),
"clip": ("FLOAT", {
"default": 0.5, "min": 0, "max": 1.0, "step": 0.01
})
}
})
return Lexicon._parse(d)
def run(self, **kw) -> RGBAMaskType:
pA = parse_param(kw, Lexicon.IMAGE, EnumConvertType.IMAGE, None)
mode = parse_param(kw, Lexicon.MODE, EnumAutoLevel, EnumAutoLevel.AUTO.name)
clip = parse_param(kw, "clip", EnumConvertType.FLOAT, 0.5, 0, 1)
params = list(zip_longest_fill(pA, mode, clip))
images = []
pbar = ProgressBar(len(params))
for idx, (pA, mode, clip) in enumerate(params):
img = tensor_to_cv(pA)
match mode:
case EnumAutoLevel.AUTO:
leveled = image_autolevel(img)
case EnumAutoLevel.HISTOGRAM:
leveled = image_autolevel_histogram(img, clip)
images.append(cv_to_tensor_full(leveled))
pbar.update_absolute(idx)
return image_stack(images)
class BlendNode(CozyImageNode):
NAME = "BLEND (JOV) ⚗️"
CATEGORY = JOV_CATEGORY
node_list.json
+1 -2
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@@ -3,14 +3,13 @@
"ADJUST: COLOR (JOV)": "Enhance and modify images with various blur effects",
"ADJUST: EDGE (JOV)": "Enhanced edge detection",
"ADJUST: EMBOSS (JOV)": "Emboss boss mode",
"ADJUST: LEVELS (JOV)": "",
"ADJUST: LEVELS (JOV)": "Manual or automatic adjust image levels so that the darkest pixel becomes black\nand the brightest pixel becomes white, enhancing overall contrast",
"ADJUST: LIGHT (JOV)": "Tonal adjustments",
"ADJUST: MORPHOLOGY (JOV)": "Operations based on the image shape",
"ADJUST: PIXEL (JOV)": "Pixel-level transformations",
"ADJUST: SHARPEN (JOV)": "Sharpen the pixels of an image",
"AKASHIC (JOV) \ud83d\udcd3": "Visualize data",
"ARRAY (JOV) \ud83d\udcda": "Processes a batch of data based on the selected mode",
"AUTO LEVEL (JOV)": "Automatically adjusts image levels so that the darkest pixel becomes black\nand the brightest pixel becomes white, enhancing overall contrast",
"BATCH TO LIST (JOV)": "Convert a batch of values into a pure python list of values",
"BIT SPLIT (JOV) \u2b44": "Split an input into separate bits",
"BLEND (JOV) \u2697\ufe0f": "Combine two input images using various blending modes, such as normal, screen, multiply, overlay, etc",
pyproject.toml
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@@ -1,7 +1,7 @@
[project]
name = "jovimetrix"
description = "Animation via tick. Parameter manipulation with wave generator. Unary and Binary math support. Value convert int/float/bool, VectorN and Image, Mask types. Shape mask generator. Stack images, do channel ops, split, merge and randomize arrays and batches. Load images & video from anywhere. Dynamic bus routing. Save output anywhere! Flatten, crop, transform; check colorblindness or linear interpolate values."
version = "2.1.24"
version = "2.1.25"
license = { file = "LICENSE" }
readme = "README.md"
authors = [{ name = "Alexander G. Morano", email = "amorano@gmail.com" }]