| import numpy as np | |
| import cv2 | |
| 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 make_noise_disk(H, W, C, F): | |
| noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C)) | |
| noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC) | |
| noise = noise[F: F + H, F: F + W] | |
| noise -= np.min(noise) | |
| noise /= np.max(noise) | |
| if C == 1: | |
| noise = noise[:, :, None] | |
| return noise | |
| def nms(x, t, s): | |
| x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s) | |
| f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8) | |
| f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8) | |
| f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8) | |
| f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8) | |
| y = np.zeros_like(x) | |
| for f in [f1, f2, f3, f4]: | |
| np.putmask(y, cv2.dilate(x, kernel=f) == x, x) | |
| z = np.zeros_like(y, dtype=np.uint8) | |
| z[y > t] = 255 | |
| return z | |
| def min_max_norm(x): | |
| x -= np.min(x) | |
| x /= np.maximum(np.max(x), 1e-5) | |
| return x | |
| def safe_step(x, step=2): | |
| y = x.astype(np.float32) * float(step + 1) | |
| y = y.astype(np.int32).astype(np.float32) / float(step) | |
| return y | |
| def img2mask(img, H, W, low=10, high=90): | |
| assert img.ndim == 3 or img.ndim == 2 | |
| assert img.dtype == np.uint8 | |
| if img.ndim == 3: | |
| y = img[:, :, random.randrange(0, img.shape[2])] | |
| else: | |
| y = img | |
| y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC) | |
| if random.uniform(0, 1) < 0.5: | |
| y = 255 - y | |
| return y < np.percentile(y, random.randrange(low, high)) |