Update FV_ColorCorrection.py

2023-11-01 03:20:12 +01:00
parent 81e82540ff
commit 86d1a62579
1 changed files with 0 additions and 80 deletions
@@ -1,81 +1 @@
-import cv2
-import numpy as np
-from skimage.exposure import match_histograms
-from PIL import Image
-from enum import Enum
-import torch

-# PIL to Tensor
-def pil2tensor(image):
-    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
-
-# Tensor to PIL
-def tensor2pil(image):
-    return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
-    
-
-# Define 'BlendType' and 'blendLayers' as needed
-def blendLayers(image1, image2):
-    # Extract the luminance channel from both images
-    image1_luminance = image1.convert("L")
-    image2_luminance = image2.convert("L")
-
-    # Combine the luminance channel from image1 with the color channels of image2
-    r, g, b = image2.split()
-    blended_image = Image.merge("RGB", [image1_luminance, g, b])
-
-    return blended_image
-
-
-class ColorCorrection:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "original_image": ("IMAGE",),
-                "correction": ("IMAGE",),  # Add this line
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "color_correction"
-    CATEGORY = "Fictiverse"
-
-
-        
-        
-    def color_correction(self, original_image, correction):
-
-        pil_original_image = np.array(tensor2pil(original_image))
-        pil_correction = np.array(tensor2pil(correction))
-
-        original_lab = cv2.cvtColor(pil_original_image, cv2.COLOR_RGB2LAB)
-        corrected_lab = cv2.cvtColor(pil_correction, cv2.COLOR_RGB2LAB)
-        corrected_image = cv2.cvtColor(match_histograms(original_lab, corrected_lab, channel_axis=2), cv2.COLOR_LAB2RGB).astype("uint8")
-
-        # Use 'correction' as the template image
-        template_image = corrected_image  # Use the 'correction' as the template image
-
-        # Perform template matching with 'correction' as the template
-        result = cv2.matchTemplate(corrected_image, template_image, cv2.TM_CCOEFF_NORMED)
-        
-        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
-        top_left = max_loc
-        h, w = template_image.shape[:2]
-        bottom_right = (top_left[0] + w, top_left[1] + h)
-        
-        # Draw a rectangle around the matched area (you can modify this part)
-        cv2.rectangle(corrected_image, top_left, bottom_right, (0, 0, 255), 2)
-
-        # Convert the result back to a PIL image
-        result_image = Image.fromarray(corrected_image)
-
-        img = pil2tensor(result_image)
-        return (img,)
- 
-NODE_CLASS_MAPPINGS = {
-        "Color correction": ColorCorrection
-}