diff --git a/README.md b/README.md index 62addb5c..c5d52f5b 100644 --- a/README.md +++ b/README.md @@ -102,7 +102,7 @@ HivisionIDPhoto 旨在开发一种实用、系统性的证件照智能制作算 [![](assets/comfyui.png)](https://github.com/AIFSH/HivisionIDPhotos-ComfyUI) - [HivisionIDPhotos-NAS](https://github.com/ONG-Leo/HivisionIDPhotos-NAS): 群晖NAS部署中文教程，由 [ONG-Leo](https://github.com/ONG-Leo) 贡献 -- [zjzWx](https://github.com/no1xuan/zjzWx): 精美的开源证件照微信小程序项目，后台基于HivisionIDphotos算法，由 [no1xuan](https://github.com/no1xuan) 贡献 +- [HivisionIDPhotos-wechat-weapp](https://github.com/no1xuan/HivisionIDPhotos-wechat-weapp): 微信证件照小程序，基于HivisionIDphotos算法驱动，由 [no1xuan](https://github.com/no1xuan) 贡献
diff --git a/README_EN.md b/README_EN.md index d41b19ee..d999ac78 100644 --- a/README_EN.md +++ b/README_EN.md @@ -99,7 +99,7 @@ We have shared some interesting applications and extensions of HivisionIDPhotos [![](assets/comfyui.png)](https://github.com/AIFSH/HivisionIDPhotos-ComfyUI) -- [zjzWx](https://github.com/no1xuan/zjzWx): Exquisite open source ID photo WeChat mini program project, contributed by [no1xuan](https://github.com/no1xuan) +- [HivisionIDPhotos-wechat-weapp](https://github.com/no1xuan/HivisionIDPhotos-wechat-weapp): WeChat ID photo mini program, powered by the HivisionIDphotos algorithm, contributed by [no1xuan](https://github.com/no1xuan)
diff --git a/README_JP.md b/README_JP.md index a521dcc0..333f8d07 100644 --- a/README_JP.md +++ b/README_JP.md @@ -96,6 +96,8 @@ HivisionIDPhotoがあなたに役立つ場合は、このリポジトリをス [![](assets/comfyui.png)](https://github.com/AIFSH/HivisionIDPhotos-ComfyUI) +- [HivisionIDPhotos-wechat-weapp](https://github.com/no1xuan/HivisionIDPhotos-wechat-weapp): WeChat ID写真ミニプログラムで、HivisionIDphotosアルゴリズムに基づいており、[no1xuan](https://github.com/no1xuan)が貢献しました。 +
# 🔧 準備作業 diff --git a/README_KO.md b/README_KO.md index 79c8537b..c5b0a93d 100644 --- a/README_KO.md +++ b/README_KO.md @@ -97,6 +97,8 @@ HivisionIDPhoto가 여러분에게 도움이 된다면, 이 리포지토리를 [![](assets/comfyui.png)](https://github.com/AIFSH/HivisionIDPhotos-ComfyUI) +- [HivisionIDPhotos-wechat-weapp](https://github.com/no1xuan/HivisionIDPhotos-wechat-weapp): WeChat ID 사진 미니 프로그램, HivisionIDphotos 알고리즘을 기반으로 하며, [no1xuan](https://github.com/no1xuan)이 기여하였습니다. +
# 🔧 준비 작업 diff --git a/hivision/plugin/beauty/grind_skin.py b/hivision/plugin/beauty/grind_skin.py index 30402842..58431b9f 100644 --- a/hivision/plugin/beauty/grind_skin.py +++ b/hivision/plugin/beauty/grind_skin.py @@ -1,20 +1,50 @@ -""" -@author: cuny -@file: GrindSkin.py -@time: 2022/7/2 14:44 -@description: -磨皮算法 -""" - +# 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，后续会考虑使用一些皮肤区域检测算法来实现仅皮肤区域磨皮，增加算法的精细程度——或者使用人脸关键点 - https://www.cnblogs.com/Imageshop/p/4709710.html + 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 @@ -28,8 +58,8 @@ def grindSkin(src, grindDegree: int = 3, detailDegree: int = 1, strength: int = return src dst = src.copy() opacity = min(10.0, strength) / 10.0 - dx = grindDegree * 5 # 双边滤波参数之一 - fc = grindDegree * 12.5 # 双边滤波参数之一 + 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) @@ -38,7 +68,49 @@ def grindSkin(src, grindDegree: int = 3, detailDegree: int = 1, strength: int = 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__": - input_image = cv2.imread("test_image/7.jpg") - output_image = grindSkin(src=input_image) - cv2.imwrite("grindSkinCompare.png", np.hstack((input_image, output_image))) + iface.launch()