Create app.py

app.py

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+from transformers import pipeline
+import matplotlib.pyplot as plt
+import streamlit as st
+from PIL import Image
+
+
+
+
+pipe_yolos = pipeline("object-detection", model="hustvl/yolos-tiny")
+pipe_emotions = pipeline("image-classification", model="dima806/facial_emotions_image_detection")
+
+
+
+st.title("Online Teaching Effect Monitor")
+
+file_name = st.file_uploader("Upload a image or a video")
+
+if file_name is not None:
+    image = Image.open(file_name)
+    output = pipe_yolos(face_image)
+
+    data = output
+    # 过滤出所有标签为 "person" 的项
+    persons = [item for item in data if item['label'] == 'person']
+    
+    # 打印结果
+    print(persons)
+    
+    # 假设有一张原始图片，加载图片并截取出每个 "person" 的部分
+    # original_image_path = 'input_image.jpg'  # 替换为实际图片路径
+    # original_image = Image.open(original_image_path)
+    original_image = Image.open(face_image)
+    persons_image_list = []
+    
+    # 截取每个 "person" 的部分并保存
+    for idx, person in enumerate(persons):
+        box = person['box']
+        cropped_image = original_image.crop((box['xmin'], box['ymin'], box['xmax'], box['ymax']))
+        cropped_image.save(f'person_{idx}.jpg')
+        cropped_image.show()
+        persons_image_list.append(cropped_image)
+    
+    
+    # 创建一个新的画布来拼接所有的person图像
+    fig, axes = plt.subplots(1, len(persons), figsize=(15, 5))
+    
+    # 截取每个 "person" 的部分并绘制到一张图上
+    for idx, person in enumerate(persons):
+        box = person['box']
+        cropped_image = original_image.crop((box['xmin'], box['ymin'], box['xmax'], box['ymax']))
+        axes[idx].imshow(cropped_image)
+        axes[idx].axis('off')
+        axes[idx].set_title(f'Person {idx}')
+    
+    # 显示拼接的图像
+    plt.tight_layout()
+    plt.show()
+
+    # 识别每个人的表情
+    output_list = []
+    
+    for idx, face in enumerate(persons_image_list):
+      print(f"processing {idx}")
+      output = pipe_emotions(face)
+      output_list.append(output[0])
+    
+    print(output_list)
+
+    
+    # 统计各种标签的数量
+    label_counts = {}
+    for item in output_list:
+        label = item['label']
+        if label in label_counts:
+            label_counts[label] += 1
+        else:
+            label_counts[label] = 1
+    
+    # 绘制饼状图
+    labels = list(label_counts.keys())
+    sizes = list(label_counts.values())
+    
+    plt.figure(figsize=(8, 8))
+    plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=140)
+    plt.title('Distribution of Labels')
+    plt.axis('equal')  # 确保饼状图为圆形
+    plt.show()