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| import cv2 | |
| import numpy as np | |
| import os | |
| import gradio as gr | |
| class LutWhite: | |
| CUBE64_ROWS = 8 | |
| CUBE64_SIZE = 64 | |
| CUBE256_SIZE = 256 | |
| CUBE_SCALE = CUBE256_SIZE // CUBE64_SIZE | |
| def __init__(self, lut_image): | |
| self.lut = self._create_lut(lut_image) | |
| def _create_lut(self, lut_image): | |
| reshape_lut = np.zeros( | |
| (self.CUBE256_SIZE, self.CUBE256_SIZE, self.CUBE256_SIZE, 3), dtype=np.uint8 | |
| ) | |
| for i in range(self.CUBE64_SIZE): | |
| tmp = i // self.CUBE64_ROWS | |
| cx = (i % self.CUBE64_ROWS) * self.CUBE64_SIZE | |
| cy = tmp * self.CUBE64_SIZE | |
| cube64 = lut_image[cy : cy + self.CUBE64_SIZE, cx : cx + self.CUBE64_SIZE] | |
| if cube64.size == 0: | |
| continue | |
| cube256 = cv2.resize(cube64, (self.CUBE256_SIZE, self.CUBE256_SIZE)) | |
| reshape_lut[i * self.CUBE_SCALE : (i + 1) * self.CUBE_SCALE] = cube256 | |
| return reshape_lut | |
| def apply(self, src): | |
| b, g, r = src[:, :, 0], src[:, :, 1], src[:, :, 2] | |
| return self.lut[b, g, r] | |
| class MakeWhiter: | |
| def __init__(self, lut_image): | |
| self.lut_white = LutWhite(lut_image) | |
| def run(self, src: np.ndarray, strength: int) -> np.ndarray: | |
| strength = np.clip(strength / 10.0, 0, 1) | |
| if strength <= 0: | |
| return src | |
| img = self.lut_white.apply(src[:, :, :3]) | |
| return cv2.addWeighted(src[:, :, :3], 1 - strength, img, strength, 0) | |
| base_dir = os.path.dirname(os.path.abspath(__file__)) | |
| default_lut = cv2.imread(os.path.join(base_dir, "lut/lut_origin.png")) | |
| make_whiter = MakeWhiter(default_lut) | |
| def make_whitening(image, strength): | |
| image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) | |
| iteration = strength // 10 | |
| bias = strength % 10 | |
| for i in range(iteration): | |
| image = make_whiter.run(image, 10) | |
| image = make_whiter.run(image, bias) | |
| return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| def make_whitening_png(image, strength): | |
| image = cv2.cvtColor(np.array(image), cv2.COLOR_RGBA2BGRA) | |
| b, g, r, a = cv2.split(image) | |
| bgr_image = cv2.merge((b, g, r)) | |
| b_w, g_w, r_w = cv2.split(make_whiter.run(bgr_image, strength)) | |
| output_image = cv2.merge((b_w, g_w, r_w, a)) | |
| return cv2.cvtColor(output_image, cv2.COLOR_RGBA2BGRA) | |
| # 启动Gradio应用 | |
| if __name__ == "__main__": | |
| demo = gr.Interface( | |
| fn=make_whitening, | |
| inputs=[ | |
| gr.Image(type="pil", image_mode="RGBA", label="Input Image"), | |
| gr.Slider(0, 30, step=1, label="Whitening Strength"), | |
| ], | |
| outputs=gr.Image(type="pil"), | |
| title="Image Whitening Demo", | |
| description="Upload an image and adjust the whitening strength to see the effect.", | |
| ) | |
| demo.launch() | |