add reference code

2025-06-23 20:42:49 +08:00
parent 1c44578d9a
commit ca2c13e0ef
131 changed files with 18966 additions and 0 deletions
@@ -0,0 +1,80 @@
+import torch
+import copy
+import cv2
+import numpy as np
+from PIL import Image
+from .imagefunc import log
+
+
+
+class SharpAndSoft:
+
+    def __init__(self):
+        self.NODE_NAME = 'Sharp & Soft'
+
+    @classmethod
+    def INPUT_TYPES(self):
+
+        enhance_list = ['very sharp', 'sharp', 'soft', 'very soft', 'None']
+
+        return {
+            "required": {
+                "images": ("IMAGE",),
+                "enhance": (enhance_list, ),
+
+            },
+            "optional": {
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+    FUNCTION = 'sharp_and_soft'
+    CATEGORY = '😺dzNodes/LayerFilter'
+
+    def sharp_and_soft(self, images, enhance, ):
+
+        if enhance == 'very sharp':
+            filter_radius = 1
+            denoise = 0.6
+            detail_mult = 2.8
+        if enhance == 'sharp':
+            filter_radius = 3
+            denoise = 0.12
+            detail_mult = 1.8
+        if enhance == 'soft':
+            filter_radius = 8
+            denoise = 0.08
+            detail_mult = 0.5
+        if enhance == 'very soft':
+            filter_radius = 15
+            denoise = 0.06
+            detail_mult = 0.01
+        else:
+            return (images,)
+
+        d = int(filter_radius * 2) + 1
+        s = 0.02
+        n = denoise / 10
+        dup = copy.deepcopy(images.cpu().numpy())
+
+        from cv2.ximgproc import guidedFilter
+        for index, image in enumerate(dup):
+            imgB = image
+            if denoise > 0.0:
+                imgB = cv2.bilateralFilter(image, d, n, d)
+            imgG = np.clip(guidedFilter(image, image, d, s), 0.001, 1)
+            details = (imgB / imgG - 1) * detail_mult + 1
+            dup[index] = np.clip(details * imgG - imgB + image, 0, 1)
+
+        log(f"{self.NODE_NAME} Processed {dup.shape[0]} image(s).", message_type='finish')
+        return (torch.from_numpy(dup),)
+
+
+NODE_CLASS_MAPPINGS = {
+    "LayerFilter: Sharp & Soft": SharpAndSoft
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "LayerFilter: Sharp & Soft": "LayerFilter: Sharp & Soft"
+}