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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 = 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)
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