rename color_correct_brightness&contrast.py to color_correct_brightness_and_contrast.py; color_correct_shadow&highlight.py to color_correct_shadow_and_highlight.py; rembg_ultra.py to rmbg_ultra.py; rembg_ultra_v2.py to rmbg_ultra.py

2024-06-27 09:37:44 +08:00
parent fc239893aa
commit 49004bb3d1
4 changed files with 59 additions and 59 deletions
@@ -1,60 +1,60 @@
-from .imagefunc import *
-
-NODE_NAME = 'Brightness & Contrast'
-
-class ColorCorrectBrightnessAndContrast:
-
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(self):
-
-        return {
-            "required": {
-                "image": ("IMAGE", ),  #
-                "brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
-                "contrast": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
-                "saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
-            },
-            "optional": {
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("image",)
-    FUNCTION = 'color_correct_brightness_and_contrast'
-    CATEGORY = '😺dzNodes/LayerColor'
-
-    def color_correct_brightness_and_contrast(self, image, brightness, contrast, saturation):
-
-        ret_images = []
-
-        for i in image:
-            i = torch.unsqueeze(i,0)
-            __image = tensor2pil(i)
-            ret_image = __image.convert('RGB')
-            if brightness != 1:
-                brightness_image = ImageEnhance.Brightness(ret_image)
-                ret_image = brightness_image.enhance(factor=brightness)
-            if contrast != 1:
-                contrast_image = ImageEnhance.Contrast(ret_image)
-                ret_image = contrast_image.enhance(factor=contrast)
-            if saturation != 1:
-                color_image = ImageEnhance.Color(ret_image)
-                ret_image = color_image.enhance(factor=saturation)
-
-            if __image.mode == 'RGBA':
-                ret_image = RGB2RGBA(ret_image, __image.split()[-1])
-            ret_images.append(pil2tensor(ret_image))
-
-        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
-        return (torch.cat(ret_images, dim=0),)
-
-NODE_CLASS_MAPPINGS = {
-    "LayerColor: Brightness & Contrast": ColorCorrectBrightnessAndContrast
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "LayerColor: Brightness & Contrast": "LayerColor: Brightness & Contrast"
+from .imagefunc import *
+
+NODE_NAME = 'Brightness & Contrast'
+
+class ColorCorrectBrightnessAndContrast:
+
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(self):
+
+        return {
+            "required": {
+                "image": ("IMAGE", ),  #
+                "brightness": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+                "contrast": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+                "saturation": ("FLOAT", {"default": 1, "min": 0.0, "max": 3, "step": 0.01}),
+            },
+            "optional": {
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = 'color_correct_brightness_and_contrast'
+    CATEGORY = '😺dzNodes/LayerColor'
+
+    def color_correct_brightness_and_contrast(self, image, brightness, contrast, saturation):
+
+        ret_images = []
+
+        for i in image:
+            i = torch.unsqueeze(i,0)
+            __image = tensor2pil(i)
+            ret_image = __image.convert('RGB')
+            if brightness != 1:
+                brightness_image = ImageEnhance.Brightness(ret_image)
+                ret_image = brightness_image.enhance(factor=brightness)
+            if contrast != 1:
+                contrast_image = ImageEnhance.Contrast(ret_image)
+                ret_image = contrast_image.enhance(factor=contrast)
+            if saturation != 1:
+                color_image = ImageEnhance.Color(ret_image)
+                ret_image = color_image.enhance(factor=saturation)
+
+            if __image.mode == 'RGBA':
+                ret_image = RGB2RGBA(ret_image, __image.split()[-1])
+            ret_images.append(pil2tensor(ret_image))
+
+        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
+        return (torch.cat(ret_images, dim=0),)
+
+NODE_CLASS_MAPPINGS = {
+    "LayerColor: Brightness & Contrast": ColorCorrectBrightnessAndContrast
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerColor: Brightness & Contrast": "LayerColor: Brightness & Contrast"
 }