MachinePainting
3a574494eb
None of the nodes showed description text in ComfyUI because ComfyUI reads obj_class.DESCRIPTION when it builds node_info - it does not read the class docstring, which is what these nodes had. Every registered node now carries a one-line DESCRIPTION, kept to 29-67 characters so it fits the limited space in ComfyUI Manager and the node search results. Image Transform Pro's four-sentence description is shortened to match. Descriptions are derived from each node's existing docstring, or from the README for the five classes that had no docstring (ImageBlendPro, ColorMatchBlend and the three Boolean switch nodes). README also updated for the recent changes: the New Nodes table now lists the real "Rotate & Scale" category, the XY placement direction is documented on both nodes (positive X right, positive Y up), and the v2.2.0 changelog records the placement convention, the node outputs and the MP_ImageTransform internal name.
233 lines
8.6 KiB
Python
233 lines
8.6 KiB
Python
import torch
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import numpy as np
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import cv2
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from PIL import Image
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import folder_paths
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import os
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class HistogramView:
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"""
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Displays RGB and luminance histogram with in-node preview.
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"""
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DESCRIPTION = "RGB and luminance histogram displayed in-node."
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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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"image": ("IMAGE",),
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},
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"optional": {
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"show_rgb": ("BOOLEAN", {"default": True}),
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"show_luminance": ("BOOLEAN", {"default": True}),
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"line_thickness": ("INT", {"default": 1, "min": 1, "max": 5, "step": 1}),
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"background": (["black", "gray", "white"], {"default": "black"}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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}
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}
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RETURN_TYPES = ()
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OUTPUT_NODE = True
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FUNCTION = "create_histogram"
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CATEGORY = "MachinePaintingNodes/Analysis"
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def create_histogram(self, image, show_rgb=True, show_luminance=True,
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line_thickness=1, background="black", unique_id=None):
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img = (image[0].cpu().numpy() * 255).astype(np.uint8)
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# Fixed size
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width = 400
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height = int(width * 0.6)
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# Background color
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bg_colors = {"black": (0, 0, 0), "white": (255, 255, 255), "gray": (40, 40, 40)}
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bg_color = bg_colors.get(background, (0, 0, 0))
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# Create histogram canvas
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hist_img = np.full((height, width, 3), bg_color, dtype=np.uint8)
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# Calculate histograms
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colors = []
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hists = []
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if show_rgb:
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hist_r = cv2.calcHist([img], [0], None, [256], [0, 256]).flatten()
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hists.append(hist_r)
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colors.append((255, 80, 80))
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hist_g = cv2.calcHist([img], [1], None, [256], [0, 256]).flatten()
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hists.append(hist_g)
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colors.append((80, 255, 80))
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hist_b = cv2.calcHist([img], [2], None, [256], [0, 256]).flatten()
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hists.append(hist_b)
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colors.append((80, 80, 255))
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if show_luminance:
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gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
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hist_l = cv2.calcHist([gray], [0], None, [256], [0, 256]).flatten()
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hists.append(hist_l)
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colors.append((200, 200, 200))
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if hists:
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max_val = max(h.max() for h in hists)
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if max_val == 0:
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max_val = 1
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bin_width = width / 256
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for hist, color in zip(hists, colors):
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hist_normalized = (hist / max_val * (height - 20)).astype(np.int32)
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points = []
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for i in range(256):
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x = int(i * bin_width)
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y = height - 10 - hist_normalized[i]
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points.append((x, y))
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for i in range(len(points) - 1):
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cv2.line(hist_img, points[i], points[i+1], color, line_thickness, cv2.LINE_AA)
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# Grid lines
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grid_color = (60, 60, 60) if background == "black" else (180, 180, 180)
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for val in [64, 128, 192]:
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x = int(val * bin_width)
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cv2.line(hist_img, (x, 0), (x, height), grid_color, 1)
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# Labels
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font = cv2.FONT_HERSHEY_SIMPLEX
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label_color = (100, 100, 100)
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cv2.putText(hist_img, "Shadows", (5, height - 3), font, 0.35, label_color, 1, cv2.LINE_AA)
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cv2.putText(hist_img, "Mids", (width//2 - 15, height - 3), font, 0.35, label_color, 1, cv2.LINE_AA)
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cv2.putText(hist_img, "Highlights", (width - 55, height - 3), font, 0.35, label_color, 1, cv2.LINE_AA)
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# Save preview
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preview_results = self.save_preview(hist_img, unique_id)
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return {"ui": {"images": preview_results}}
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def save_preview(self, img_np, unique_id):
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temp_dir = folder_paths.get_temp_directory()
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filename = f"histogram_{unique_id}.png"
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filepath = os.path.join(temp_dir, filename)
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img_pil = Image.fromarray(img_np)
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img_pil.save(filepath)
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return [{"filename": filename, "subfolder": "", "type": "temp"}]
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class ColorWheelView:
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"""
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Vectorscope-style color wheel showing color distribution.
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"""
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DESCRIPTION = "Vectorscope colour wheel showing hue and saturation spread."
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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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"image": ("IMAGE",),
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},
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"optional": {
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"intensity": ("FLOAT", {"default": 1.0, "min": 0.2, "max": 3.0, "step": 0.1, "display": "slider"}),
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"show_skin_line": ("BOOLEAN", {"default": True}),
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"show_color_targets": ("BOOLEAN", {"default": True}),
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},
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"hidden": {
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"unique_id": "UNIQUE_ID",
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}
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}
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RETURN_TYPES = ()
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OUTPUT_NODE = True
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FUNCTION = "create_vectorscope"
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CATEGORY = "MachinePaintingNodes/Analysis"
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def create_vectorscope(self, image, intensity=1.0, show_skin_line=True,
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show_color_targets=True, unique_id=None):
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img = (image[0].cpu().numpy() * 255).astype(np.uint8)
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# Fixed size
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size = 400
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# Convert to YUV
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yuv = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
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# Create scope
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scope = np.zeros((size, size, 3), dtype=np.float32)
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center = size // 2
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radius = size // 2 - 15
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# Draw circle outlines
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cv2.circle(scope, (center, center), radius, (0.2, 0.2, 0.2), 1)
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cv2.circle(scope, (center, center), int(radius * 0.75), (0.15, 0.15, 0.15), 1)
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cv2.circle(scope, (center, center), int(radius * 0.5), (0.12, 0.12, 0.12), 1)
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cv2.circle(scope, (center, center), int(radius * 0.25), (0.1, 0.1, 0.1), 1)
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# Draw crosshairs
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cv2.line(scope, (center, 10), (center, size - 10), (0.15, 0.15, 0.15), 1)
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cv2.line(scope, (10, center), (size - 10, center), (0.15, 0.15, 0.15), 1)
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# Color targets
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if show_color_targets:
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targets = [
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(0.5, -0.4, (0.4, 0.1, 0.1), "R"),
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(0.3, 0.5, (0.1, 0.4, 0.1), "G"),
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(-0.4, -0.5, (0.1, 0.1, 0.4), "B"),
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(-0.5, 0.4, (0.1, 0.3, 0.3), "C"),
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(-0.3, -0.5, (0.3, 0.1, 0.3), "M"),
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(0.4, 0.5, (0.3, 0.3, 0.1), "Y"),
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]
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for u, v, color, label in targets:
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tx = int(center + u * radius * 0.85)
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ty = int(center - v * radius * 0.85)
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cv2.rectangle(scope, (tx-4, ty-4), (tx+4, ty+4), color, 1)
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# Skin tone line
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if show_skin_line:
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angle = np.radians(123)
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x1 = int(center + radius * 0.1 * np.cos(angle))
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y1 = int(center - radius * 0.1 * np.sin(angle))
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x2 = int(center + radius * np.cos(angle))
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y2 = int(center - radius * np.sin(angle))
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cv2.line(scope, (x1, y1), (x2, y2), (0.5, 0.35, 0.25), 2)
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# Sample pixels (subsample for large images)
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img_h, img_w = img.shape[:2]
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sample_step = max(1, int(np.sqrt(img_h * img_w / 50000)))
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u_vals = yuv[::sample_step, ::sample_step, 1].flatten().astype(np.float32) - 128
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v_vals = yuv[::sample_step, ::sample_step, 2].flatten().astype(np.float32) - 128
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scale = radius / 128
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x_coords = (center + u_vals * scale).astype(np.int32)
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y_coords = (center - v_vals * scale).astype(np.int32)
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# Accumulate with color
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for x, y in zip(x_coords, y_coords):
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if 0 <= x < size and 0 <= y < size:
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hue = np.arctan2(center - y, x - center)
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r = 0.5 + 0.5 * np.cos(hue)
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g = 0.5 + 0.5 * np.cos(hue - 2.094)
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b = 0.5 + 0.5 * np.cos(hue + 2.094)
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scope[y, x] += np.array([r, g, b]) * intensity * 0.05
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scope = np.clip(scope, 0, 1)
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scope_uint8 = (scope * 255).astype(np.uint8)
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# Save preview
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preview_results = self.save_preview(scope_uint8, unique_id)
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return {"ui": {"images": preview_results}}
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def save_preview(self, img_np, unique_id):
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temp_dir = folder_paths.get_temp_directory()
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filename = f"colorwheel_{unique_id}.png"
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filepath = os.path.join(temp_dir, filename)
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img_pil = Image.fromarray(img_np)
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img_pil.save(filepath)
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return [{"filename": filename, "subfolder": "", "type": "temp"}]
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