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101 lines
3.3 KiB
Python
101 lines
3.3 KiB
Python
import warnings
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import random
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import cv2
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import numpy as np
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from PIL import Image
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from modules.control.util import HWC3, img2mask, make_noise_disk, resize_image
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class ContentShuffleDetector:
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def __call__(self, input_image, h=None, w=None, f=None, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
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if "return_pil" in kwargs:
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warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
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output_type = "pil" if kwargs["return_pil"] else "np"
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if type(output_type) is bool:
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warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
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if output_type:
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output_type = "pil"
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if not isinstance(input_image, np.ndarray):
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input_image = np.array(input_image, dtype=np.uint8)
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input_image = HWC3(input_image)
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input_image = resize_image(input_image, detect_resolution)
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H, W, _C = input_image.shape
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if h is None:
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h = H
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if w is None:
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w = W
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if f is None:
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f = 256
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x = make_noise_disk(h, w, 1, f) * float(W - 1)
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y = make_noise_disk(h, w, 1, f) * float(H - 1)
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flow = np.concatenate([x, y], axis=2).astype(np.float32)
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detected_map = cv2.remap(input_image, flow, None, cv2.INTER_LINEAR)
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img = resize_image(input_image, image_resolution)
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H, W, _C = img.shape
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detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
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if output_type == "pil":
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detected_map = Image.fromarray(detected_map)
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return detected_map
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class ColorShuffleDetector:
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def __call__(self, img):
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H, W, C = img.shape
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F = np.random.randint(64, 384) # noqa
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A = make_noise_disk(H, W, 3, F)
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B = make_noise_disk(H, W, 3, F)
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C = (A + B) / 2.0
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A = (C + (A - C) * 3.0).clip(0, 1)
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B = (C + (B - C) * 3.0).clip(0, 1)
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L = img.astype(np.float32) / 255.0
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Y = A * L + B * (1 - L)
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Y -= np.min(Y, axis=(0, 1), keepdims=True)
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Y /= np.maximum(np.max(Y, axis=(0, 1), keepdims=True), 1e-5)
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Y *= 255.0
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return Y.clip(0, 255).astype(np.uint8)
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class GrayDetector:
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def __call__(self, img):
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eps = 1e-5
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X = img.astype(np.float32)
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r, g, b = X[:, :, 0], X[:, :, 1], X[:, :, 2]
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kr, kg, kb = [random.random() + eps for _ in range(3)]
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ks = kr + kg + kb
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kr /= ks
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kg /= ks
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kb /= ks
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Y = r * kr + g * kg + b * kb
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Y = np.stack([Y] * 3, axis=2)
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return Y.clip(0, 255).astype(np.uint8)
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class DownSampleDetector:
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def __call__(self, img, level=3, k=16.0):
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h = img.astype(np.float32)
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for _ in range(level):
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h += np.random.normal(loc=0.0, scale=k, size=h.shape) # noqa
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h = cv2.pyrDown(h)
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for _ in range(level):
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h = cv2.pyrUp(h)
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h += np.random.normal(loc=0.0, scale=k, size=h.shape) # noqa
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return h.clip(0, 255).astype(np.uint8)
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class Image2MaskShuffleDetector:
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def __init__(self, resolution=(640, 512)):
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self.H, self.W = resolution
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def __call__(self, img):
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m = img2mask(img, self.H, self.W)
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m *= 255.0
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return m.clip(0, 255).astype(np.uint8)
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