2.1.826

extras/preprocessors.py

@@ -0,0 +1,82 @@
+import cv2
+import numpy as np
+import modules.advanced_parameters as advanced_parameters
+
+
+def centered_canny(x: np.ndarray):
+    assert isinstance(x, np.ndarray)
+    assert x.ndim == 2 and x.dtype == np.uint8
+
+    y = cv2.Canny(x, int(advanced_parameters.canny_low_threshold), int(advanced_parameters.canny_high_threshold))
+    y = y.astype(np.float32) / 255.0
+    return y
+
+
+def centered_canny_color(x: np.ndarray):
+    assert isinstance(x, np.ndarray)
+    assert x.ndim == 3 and x.shape[2] == 3
+
+    result = [centered_canny(x[..., i]) for i in range(3)]
+    result = np.stack(result, axis=2)
+    return result
+
+
+def pyramid_canny_color(x: np.ndarray):
+    assert isinstance(x, np.ndarray)
+    assert x.ndim == 3 and x.shape[2] == 3
+
+    H, W, C = x.shape
+    acc_edge = None
+
+    for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
+        Hs, Ws = int(H * k), int(W * k)
+        small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA)
+        edge = centered_canny_color(small)
+        if acc_edge is None:
+            acc_edge = edge
+        else:
+            acc_edge = cv2.resize(acc_edge, (edge.shape[1], edge.shape[0]), interpolation=cv2.INTER_LINEAR)
+            acc_edge = acc_edge * 0.75 + edge * 0.25
+
+    return acc_edge
+
+
+def norm255(x, low=4, high=96):
+    assert isinstance(x, np.ndarray)
+    assert x.ndim == 2 and x.dtype == np.float32
+
+    v_min = np.percentile(x, low)
+    v_max = np.percentile(x, high)
+
+    x -= v_min
+    x /= v_max - v_min
+
+    return x * 255.0
+
+
+def canny_pyramid(x):
+    # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions.
+    # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions.
+
+    color_canny = pyramid_canny_color(x)
+    result = np.sum(color_canny, axis=2)
+
+    return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8)
+
+
+def cpds(x):
+    # cv2.decolor is not "decolor", it is Cewu Lu's method
+    # See http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html
+    # See https://docs.opencv.org/3.0-beta/modules/photo/doc/decolor.html
+
+    raw = cv2.GaussianBlur(x, (0, 0), 0.8)
+    density, boost = cv2.decolor(raw)
+
+    raw = raw.astype(np.float32)
+    density = density.astype(np.float32)
+    boost = boost.astype(np.float32)
+
+    offset = np.sum((raw - boost) ** 2.0, axis=2) ** 0.5
+    result = density + offset
+
+    return norm255(result, low=4, high=96).clip(0, 255).astype(np.uint8)