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)