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on
Zero
Running
on
Zero
| import warnings | |
| import cv2 | |
| import numpy as np | |
| from PIL import Image | |
| from ..util import HWC3, img2mask, make_noise_disk, resize_image | |
| class ContentShuffleDetector: | |
| def __call__(self, input_image, h=None, w=None, f=None, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs): | |
| if "return_pil" in kwargs: | |
| warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning) | |
| output_type = "pil" if kwargs["return_pil"] else "np" | |
| if type(output_type) is bool: | |
| warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions") | |
| if output_type: | |
| output_type = "pil" | |
| if not isinstance(input_image, np.ndarray): | |
| input_image = np.array(input_image, dtype=np.uint8) | |
| input_image = HWC3(input_image) | |
| input_image = resize_image(input_image, detect_resolution) | |
| H, W, C = input_image.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) | |
| detected_map = cv2.remap(input_image, flow, None, cv2.INTER_LINEAR) | |
| img = resize_image(input_image, image_resolution) | |
| H, W, C = img.shape | |
| detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR) | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map | |
| 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) | |