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Configuration error
Configuration error
| import cv2 | |
| import numpy as np | |
| def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold): | |
| assert isinstance(x, np.ndarray) | |
| assert x.ndim == 2 and x.dtype == np.uint8 | |
| y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold)) | |
| y = y.astype(np.float32) / 255.0 | |
| return y | |
| def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): | |
| assert isinstance(x, np.ndarray) | |
| assert x.ndim == 3 and x.shape[2] == 3 | |
| result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)] | |
| result = np.stack(result, axis=2) | |
| return result | |
| def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): | |
| 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, canny_low_threshold, canny_high_threshold) | |
| 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, canny_low_threshold, canny_high_threshold): | |
| # 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, canny_low_threshold, canny_high_threshold) | |
| 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) | |