app update

.gitignore

@@ -59,7 +59,6 @@
 !.pre-commit-config.yaml
 
 
-app.py
 test.py
 test*.py
 

app.py

@@ -0,0 +1,177 @@
+import os
+import sys
+from pathlib import Path
+
+import face_recognition
+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+
+from util.fonts_opt import is_fonts
+
+ROOT_PATH = sys.path[0]  # 项目根目录
+
+IMG_PATH_Test = "./img_examples/unknown"
+
+FONTSIZE = 15
+
+OCR_TR_DESCRIPTION = '''# Face Recognition
+<div id="content_align">https://github.com/ageitgey/face_recognition demo</div>'''
+
+def str_intercept(img_path):
+    img_path_ = img_path[::-1]
+    point_index = 0  # 记录反转后第一个点的位置
+    slash_index = 0  # 记录反转后第一个斜杠的位置
+
+    flag_pi = 0
+    flag_si = 0
+
+    for i in range(len(img_path_)):
+        if (img_path_[i] == "." and flag_pi == 0):
+            point_index = i
+            flag_pi = 1
+
+        if (img_path_[i] == "/" and flag_si == 0):
+            slash_index = i
+            flag_si = 1
+
+    point_index = len(img_path) - 1 - point_index
+    slash_index = len(img_path) - 1 - slash_index
+
+    return point_index, slash_index
+
+
+# 人脸录入
+def face_entry(img_path, name_text):
+
+    point_index, slash_index = str_intercept(img_path)
+    img_renamePath = f"{img_path[:slash_index+1]}{name_text}{img_path[point_index:]}"
+    os.rename(img_path, img_renamePath)
+    img_ = Image.open(img_renamePath)
+    print(img_renamePath)
+
+    return img_, img_renamePath, name_text
+
+
+# 设置示例
+def set_example_image(example: list):
+    return gr.Image.update(value=example[0])
+
+
+def face_recognition_(img_srcPath, img_tagPath, img_personName):
+    image_of_person = face_recognition.load_image_file(img_srcPath)
+    person_face_encoding = face_recognition.face_encodings(image_of_person)[0]
+
+    known_face_encodings = [
+        person_face_encoding,]
+
+    known_face_names = [
+        img_personName,]
+
+    test_image = face_recognition.load_image_file(img_tagPath)
+
+    face_locations = face_recognition.face_locations(test_image)
+    face_encodings = face_recognition.face_encodings(test_image, face_locations)
+
+    pil_image = Image.fromarray(test_image)
+    img_pil = ImageDraw.Draw(pil_image)
+    textFont = ImageFont.truetype(str(f"{ROOT_PATH}/fonts/SimSun.ttf"), size=FONTSIZE)
+    #     ymin, xmax, ymax, xmin
+    for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
+        matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+
+        name = "Unknown Person"
+
+        if True in matches:
+            first_matches_index = matches.index(True)
+            name = known_face_names[first_matches_index]
+
+        img_pil.rectangle([left, top, right, bottom], fill=None, outline=(255, 228, 181), width=2)  # 边界框
+        text_w, text_h = textFont.getsize(name)  # 标签尺寸
+        # 标签背景
+        img_pil.rectangle(
+            (left, top, left + text_w, top + text_h),
+            fill=(255, 255, 255),
+            outline=(255, 255, 255),
+        )
+
+        # 标签
+        img_pil.multiline_text(
+            (left, top),
+            name,
+            fill=(0, 0, 0),
+            font=textFont,
+            align="center",
+        )
+
+    del img_pil
+    return pil_image
+
+
+def main():
+    is_fonts(f"{ROOT_PATH}/fonts")  # 检查字体文件
+
+    with gr.Blocks(css='style.css') as demo:
+        gr.Markdown(OCR_TR_DESCRIPTION)
+
+        # -------------- 人脸识别 录入 --------------
+        with gr.Row():
+            gr.Markdown("### Step 01: Face Entry")
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    input_img = gr.Image(image_mode="RGB", source="upload", type="filepath", label="face entry")
+                with gr.Row():
+                    input_name = gr.Textbox(label="Name")
+                with gr.Row():
+                    btn = gr.Button(value="Entry")
+
+            with gr.Column():
+                with gr.Row():
+                    output_ = gr.Image(image_mode="RGB", source="upload", type="pil", label="entry image")
+                    input_srcImg = gr.Variable(value="")
+                    input_srcName = gr.Variable(value="")
+        with gr.Row():
+            example_list = [["./img_examples/known/ChengLong.jpg", "成龙"],
+                            ["./img_examples/known/VinDiesel.jpg", "VinDiesel"],
+                            ["./img_examples/known/JasonStatham.jpg", "JasonStatham"],
+                            ["./img_examples/known/ZhenZidan.jpg", "甄子丹"]]
+            gr.Examples(example_list,
+                        [input_img, input_name],
+                        output_,
+                        set_example_image,
+                        cache_examples=False)
+
+
+        # -------------- 人脸识别 测试 --------------
+        with gr.Row():
+                gr.Markdown("### Step 02: Face Test")
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    input_img_test = gr.Image(image_mode="RGB", source="upload", type="filepath", label="test image")
+                with gr.Row():
+                    btn_test = gr.Button(value="Test")
+                with gr.Row():
+                    paths = sorted(Path(IMG_PATH_Test).rglob('*.jpg'))
+                    example_images_test = gr.Dataset(components=[input_img],
+                                                     samples=[[path.as_posix()] for path in paths])
+
+            with gr.Column():
+                with gr.Row():
+                    output_test = gr.Image(image_mode="RGB", source="upload", type="pil", label="identify image")
+
+        btn.click(fn=face_entry, inputs=[input_img, input_name], outputs=[output_, input_srcImg, input_srcName])
+
+        btn_test.click(fn=face_recognition_,
+                       inputs=[input_srcImg, input_img_test, input_srcName],
+                       outputs=[output_test])
+        example_images_test.click(fn=set_example_image, inputs=[
+            example_images_test,], outputs=[
+                input_img_test,])
+
+        return demo
+
+
+if __name__ == "__main__":
+    demo = main()
+    demo.launch(inbrowser=True)