def keras(self, img): return keras.apply_affine_transform(img, theta=45, channel_axis=2, fill_mode='reflect')
def keras(self, img): return keras.apply_affine_transform(img, theta=45, tx=50, ty=50, zx=0.5, zy=0.5, fill_mode='reflect')
def random_rotate(img, mask, rotate_limit=(-20, 20), u=0.5): if np.random.random() < u: theta = np.random.uniform(rotate_limit[0], rotate_limit[1]) img = image.apply_affine_transform(img, theta=theta) mask = image.apply_affine_transform(mask, theta=theta) return img, mask
def shift(x, wshift, hshift, row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest', cval=0.): h, w = x.shape[row_axis], x.shape[col_axis] tx = hshift * h ty = wshift * w x = image.apply_affine_transform(x, ty=ty, tx=tx) return x
def keras(self, img): img = keras.apply_affine_transform(img, theta=45, channel_axis=2, fill_mode="reflect") return np.ascontiguousarray(img)
def keras(self, img): return keras.apply_affine_transform(img, theta=45, channel_axis=2, fill_mode='reflect')
def random_shear(img, intensity_range=(-0.5, 0.5), u=0.5): if np.random.random() < u: sh = np.random.uniform(-intensity_range[0], intensity_range[1]) img = image.apply_affine_transform(img, shear=sh) return img
def keras(self, img): return keras.apply_affine_transform(img, theta=45, tx=50, ty=50, zx=0.5, zy=0.5, fill_mode='reflect')
def random_zoom(img, mask, zoom_range=(0.8, 1), u=0.5): if np.random.random() < u: zx, zy = np.random.uniform(zoom_range[0], zoom_range[1], 2) img = image.apply_affine_transform(img, zx=zx, zy=zy) mask = image.apply_affine_transform(mask, zx=zx, zy=zy) return img, mask
def keras(self, img): img = keras.apply_affine_transform(img, theta=45, tx=50, ty=50, zx=0.5, zy=0.5, fill_mode="reflect") return np.ascontiguousarray(img)
] if len(arr.shape) != 3 or arr.shape[2] != 3: continue transform_parameters = img_gen.get_random_transform( arr.shape, seed=attr_count_crop[key]) res = img_gen.random_transform(arr, seed=attr_count_crop[key]) x = np.zeros((h, w, 3)) x[int(bbox[1] * h):int(bbox[3] * h) - 1, int(bbox[0] * w):int(bbox[2] * w) - 1, 0] = 100 x = apply_affine_transform( x, transform_parameters.get('theta', 0), transform_parameters.get('tx', 0), transform_parameters.get('ty', 0), transform_parameters.get('shear', 0), transform_parameters.get('zx', 1), transform_parameters.get('zy', 1), row_axis=0, col_axis=1, channel_axis=2, fill_mode='nearest', cval=0.) if transform_parameters.get('flip_horizontal', False): x = flip_axis(x, 1) if transform_parameters.get('flip_vertical', False): x = flip_axis(x, 0) x = x[:, :, 0] arr_h = np.max(x, axis=1) arr_w = np.max(x, axis=0) i = 0 while (i < h and arr_h[i] < 1e-14):