Upload ComfyUI-LightColorCulling_test_v4.py

ComfyUI-LightColorCulling_test_v4.py

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+import numpy as np
+from PIL import Image
+import torch
+import cv2
+
+def numpy2tensor(image: Image.Image):
+    return torch.from_numpy(image).unsqueeze(0)
+
+class LightColorCulling:
+    @classmethod
+    def INPUT_TYPES(self):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "white_threshold": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 0.9, "step": 0.01}),
+                "transition_range": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01}),
+            },
+        }
+    RETURN_TYPES = ("IMAGE", )
+    RETURN_NAMES = ("image", )
+    FUNCTION = 'Preprocessing'
+    CATEGORY = 'StickerEdit/LightColorCulling'
+
+    def Preprocessing(self, image, white_threshold=0.7, transition_range=0.2):
+        image_np = (image.cpu().numpy().squeeze()*255).astype(np.uint8)
+        # 检查图像是否有透明通道
+        has_alpha = image_np.shape[2] == 4
+
+        # 转换为灰度图
+        gray = cv2.cvtColor(image_np, cv2.COLOR_BGR2GRAY)
+
+        # 归一化灰度图像到0-1范围
+        normalized_gray = gray / 255.0
+
+        # 创建结果图像
+        result_image = image_np.copy()
+
+            # 创建透明度通道
+        if has_alpha:
+            b_channel, g_channel, r_channel, alpha_channel = cv2.split(result_image)
+        else:
+            alpha_channel = np.ones_like(gray, dtype=float) * 255
+            b_channel, g_channel, r_channel = cv2.split(result_image)
+        
+        # 计算渐变透明度
+        mask = normalized_gray >= white_threshold
+        transition_mask = (normalized_gray >= (white_threshold - transition_range)) & (normalized_gray < white_threshold)
+        alpha_channel[mask] = 0
+        alpha_channel[transition_mask] = (1 - (normalized_gray[transition_mask] - (white_threshold - transition_range)) / transition_range) * 255
+
+        # 更新结果图像的alpha通道
+        # import pdb;pdb.set_trace()
+        result_image = cv2.merge((b_channel, g_channel, r_channel, alpha_channel.astype(np.uint8)))
+
+        res_tensor = numpy2tensor(result_image/255.0)
+        return (res_tensor,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LightColorCulling": LightColorCulling,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LightColorCulling": "LightColorCulling",
+}