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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)
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