| import random |
|
|
| import cv2 |
| import numpy as np |
| from annotator.util import make_noise_disk, img2mask |
|
|
|
|
| class ContentShuffleDetector: |
| def __call__(self, img, h=None, w=None, f=None): |
| H, W, C = img.shape |
| if h is None: |
| h = H |
| if w is None: |
| w = W |
| if f is None: |
| f = 256 |
| x = make_noise_disk(h, w, 1, f) * float(W - 1) |
| y = make_noise_disk(h, w, 1, f) * float(H - 1) |
| flow = np.concatenate([x, y], axis=2).astype(np.float32) |
| return cv2.remap(img, flow, None, cv2.INTER_LINEAR) |
|
|
|
|
| class ColorShuffleDetector: |
| def __call__(self, img): |
| H, W, C = img.shape |
| F = np.random.randint(64, 384) |
| A = make_noise_disk(H, W, 3, F) |
| B = make_noise_disk(H, W, 3, F) |
| C = (A + B) / 2.0 |
| A = (C + (A - C) * 3.0).clip(0, 1) |
| B = (C + (B - C) * 3.0).clip(0, 1) |
| L = img.astype(np.float32) / 255.0 |
| Y = A * L + B * (1 - L) |
| Y -= np.min(Y, axis=(0, 1), keepdims=True) |
| Y /= np.maximum(np.max(Y, axis=(0, 1), keepdims=True), 1e-5) |
| Y *= 255.0 |
| return Y.clip(0, 255).astype(np.uint8) |
|
|
|
|
| class GrayDetector: |
| def __call__(self, img): |
| eps = 1e-5 |
| X = img.astype(np.float32) |
| r, g, b = X[:, :, 0], X[:, :, 1], X[:, :, 2] |
| kr, kg, kb = [random.random() + eps for _ in range(3)] |
| ks = kr + kg + kb |
| kr /= ks |
| kg /= ks |
| kb /= ks |
| Y = r * kr + g * kg + b * kb |
| Y = np.stack([Y] * 3, axis=2) |
| return Y.clip(0, 255).astype(np.uint8) |
|
|
|
|
| class DownSampleDetector: |
| def __call__(self, img, level=3, k=16.0): |
| h = img.astype(np.float32) |
| for _ in range(level): |
| h += np.random.normal(loc=0.0, scale=k, size=h.shape) |
| h = cv2.pyrDown(h) |
| for _ in range(level): |
| h = cv2.pyrUp(h) |
| h += np.random.normal(loc=0.0, scale=k, size=h.shape) |
| return h.clip(0, 255).astype(np.uint8) |
|
|
|
|
| class Image2MaskShuffleDetector: |
| def __init__(self, resolution=(640, 512)): |
| self.H, self.W = resolution |
|
|
| def __call__(self, img): |
| m = img2mask(img, self.H, self.W) |
| m *= 255.0 |
| return m.clip(0, 255).astype(np.uint8) |
|
|
