docs: community add wechat miniprogram

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
 
 <br>
 

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)
 
 <br>
 

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。
+
 <br>
 
 # 🔧 準備作業

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.
+
 <br>
 
 # 🔧 준비 작업

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()