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| # Required Libraries | |
| import cv2 | |
| import numpy as np | |
| import gradio as gr | |
| def annotate_image(image, grind_degree, detail_degree, strength): | |
| """Annotates the image with parameters in the lower-left corner.""" | |
| font = cv2.FONT_HERSHEY_SIMPLEX | |
| font_scale = 0.5 | |
| color = (0, 0, 255) | |
| thickness = 1 | |
| line_type = cv2.LINE_AA | |
| # Text positions | |
| y_offset = 20 | |
| x_offset = 10 | |
| y_base = image.shape[0] - 10 | |
| # Define each line of the annotation | |
| lines = [ | |
| f"Grind Degree: {grind_degree}", | |
| f"Detail Degree: {detail_degree}", | |
| f"Strength: {strength}", | |
| ] | |
| # Draw the text lines on the image | |
| for i, line in enumerate(lines): | |
| y_position = y_base - (i * y_offset) | |
| cv2.putText( | |
| image, | |
| line, | |
| (x_offset, y_position), | |
| font, | |
| font_scale, | |
| color, | |
| thickness, | |
| line_type, | |
| ) | |
| return image | |
| def grindSkin(src, grindDegree: int = 3, detailDegree: int = 1, strength: int = 9): | |
| """ | |
| Dest =(Src * (100 - Opacity) + (Src + 2 * GaussBlur(EPFFilter(Src) - Src)) * Opacity) / 100 | |
| 人像磨皮方案 | |
| Args: | |
| src: 原图 | |
| grindDegree: 磨皮程度调节参数 | |
| detailDegree: 细节程度调节参数 | |
| strength: 融合程度，作为磨皮强度（0 - 10） | |
| Returns: | |
| 磨皮后的图像 | |
| """ | |
| if strength <= 0: | |
| return src | |
| dst = src.copy() | |
| opacity = min(10.0, strength) / 10.0 | |
| dx = grindDegree * 5 | |
| fc = grindDegree * 12.5 | |
| temp1 = cv2.bilateralFilter(src[:, :, :3], dx, fc, fc) | |
| temp2 = cv2.subtract(temp1, src[:, :, :3]) | |
| temp3 = cv2.GaussianBlur(temp2, (2 * detailDegree - 1, 2 * detailDegree - 1), 0) | |
| temp4 = cv2.add(cv2.add(temp3, temp3), src[:, :, :3]) | |
| dst[:, :, :3] = cv2.addWeighted(temp4, opacity, src[:, :, :3], 1 - opacity, 0.0) | |
| return dst | |
| def process_image(input_img, grind_degree, detail_degree, strength): | |
| # Reading the image using OpenCV | |
| img = cv2.cvtColor(input_img, cv2.COLOR_RGB2BGR) | |
| # Processing the image | |
| output_img = grindSkin(img, grind_degree, detail_degree, strength) | |
| # Annotating the processed image with parameters | |
| output_img_annotated = annotate_image( | |
| output_img.copy(), grind_degree, detail_degree, strength | |
| ) | |
| # Horizontal stacking of input and processed images | |
| combined_img = cv2.hconcat([img, output_img_annotated]) | |
| # Convert the combined image back to RGB for display | |
| combined_img_rgb = cv2.cvtColor(combined_img, cv2.COLOR_BGR2RGB) | |
| return combined_img_rgb | |
| with gr.Blocks(title="Skin Grinding") as iface: | |
| gr.Markdown("## Skin Grinding Application") | |
| with gr.Row(): | |
| image_input = gr.Image(type="numpy", label="Input Image") | |
| image_output = gr.Image(label="Output Image") | |
| grind_degree_slider = gr.Slider( | |
| minimum=1, maximum=10, value=3, step=1, label="Grind Degree" | |
| ) | |
| detail_degree_slider = gr.Slider( | |
| minimum=1, maximum=10, value=1, step=1, label="Detail Degree" | |
| ) | |
| strength_slider = gr.Slider( | |
| minimum=0, maximum=10, value=9, step=1, label="Strength" | |
| ) | |
| gr.Button("Process Image").click( | |
| fn=process_image, | |
| inputs=[ | |
| image_input, | |
| grind_degree_slider, | |
| detail_degree_slider, | |
| strength_slider, | |
| ], | |
| outputs=image_output, | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |