Update diffusion_webui/utils/preprocces_utils.py

diffusion_webui/utils/preprocces_utils.py

@@ -14,6 +14,10 @@ from controlnet_aux import (
     ZoeDetector,
 )
 
+import numpmy as np
+import cv2
+
+
 PREPROCCES_DICT = {
     "Hed": HEDdetector.from_pretrained("lllyasviel/Annotators"),
     "Midas": MidasDetector.from_pretrained("lllyasviel/Annotators"),
@@ -30,3 +34,59 @@ PREPROCCES_DICT = {
     "ContentShuffle": ContentShuffleDetector(),
     "MediapipeFace": MediapipeFaceDetector(),
 }
+
+def pad64(x):
+    return int(np.ceil(float(x) / 64.0) * 64 - x)
+
+def HWC3(x):
+    assert x.dtype == np.uint8
+    if x.ndim == 2:
+        x = x[:, :, None]
+    assert x.ndim == 3
+    H, W, C = x.shape
+    assert C == 1 or C == 3 or C == 4
+    if C == 3:
+        return x
+    if C == 1:
+        return np.concatenate([x, x, x], axis=2)
+    if C == 4:
+        color = x[:, :, 0:3].astype(np.float32)
+        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
+        y = color * alpha + 255.0 * (1.0 - alpha)
+        y = y.clip(0, 255).astype(np.uint8)
+        return y
+        
+def safer_memory(x):
+    return np.ascontiguousarray(x.copy()).copy()
+
+
+def resize_image_with_pad(input_image, resolution, skip_hwc3=False):
+    if skip_hwc3:
+        img = input_image
+    else:
+        img = HWC3(input_image)
+        
+    H_raw, W_raw, _ = img.shape
+    k = float(resolution) / float(min(H_raw, W_raw))
+    interpolation = cv2.INTER_CUBIC if k > 1 else cv2.INTER_AREA
+    H_target = int(np.round(float(H_raw) * k))
+    W_target = int(np.round(float(W_raw) * k))
+    img = cv2.resize(img, (W_target, H_target), interpolation=interpolation)
+    H_pad, W_pad = pad64(H_target), pad64(W_target)
+    img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode='edge')
+
+    def remove_pad(x):
+        return safer_memory(x[:H_target, :W_target])
+
+    return safer_memory(img_padded), remove_pad
+
+
+def scribble_xdog(img, res=512, thr_a=32, **kwargs):
+    img, remove_pad = resize_image_with_pad(img, res)
+    g1 = cv2.GaussianBlur(img.astype(np.float32), (0, 0), 0.5)
+    g2 = cv2.GaussianBlur(img.astype(np.float32), (0, 0), 5.0)
+    dog = (255 - np.min(g2 - g1, axis=2)).clip(0, 255).astype(np.uint8)
+    result = np.zeros_like(img, dtype=np.uint8)
+    result[2 * (255 - dog) > thr_a] = 255
+    return remove_pad(result), True
+