Update app.py

app.py

@@ -1,47 +1,35 @@
-# app.py
-
 import streamlit as st
+import cv2
+import numpy as np
 import torch
+from torchvision import transforms
 from PIL import Image
-import camera  # 引入拍照功能
-import numpy as np
-import cv2
-
-# 物体识别函数
-def detect_objects(image_path):
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # 使用YOLOv5模型
-    img = Image.open(image_path)
-    results = model(img)
-    return results
-
-def main():
-    st.title("摄像头拍照并进行物体识别")
-
-    # 拍照
-    if st.button('拍照'):
-        camera.take_picture()
-        st.write("照片已拍摄并保存")
-
-    # 物体识别
-    if st.button('物体识别'):
-        st.write("正在进行物体识别...")
-        image_path = "captured_image.jpg"
-        
-        results = detect_objects(image_path)
-        # 显示原始图片
-        st.image(image_path, caption='原始图片', use_column_width=True)
-        
-        # 显示识别的结果
-        results.render()  # 在图片上绘制检测到的物体
-        detected_img = Image.fromarray(results.imgs[0])
-
-        st.image(detected_img, caption='物体识别结果', use_column_width=True)
-
-        # 显示从左到右的物体列表
-        st.write("识别到的物体：")
-        objects = results.pandas().xyxy[0]['name'].tolist()
-        objects_sorted_by_x = sorted(objects)
-        st.write(objects_sorted_by_x)
 
-if __name__ == '__main__':
-    main()
+# 这里你需要替换成你训练好的模型的路径和类名
+model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # 加载 YOLOv5 模型
+classes = model.names
+
+def object_detection(img_path):
+    """
+    对图像进行物体识别。
+    """
+    results = model(img_path)
+    return results.pandas().xyxy[0]
+
+def show_results(results):
+    """
+    在 Streamlit 中显示物体识别结果。
+    """
+    for index, row in results.iterrows():
+        left, top, right, bottom = row['xmin'], row['ymin'], row['xmax'], row['ymax']
+        label = classes[int(row['class'])]
+        st.write(f"物体：{label}, 位置：({left}, {top}) - ({right}, {bottom})")
+
+if __name__ == "__main__":
+    if st.button("选择图片"):
+        uploaded_file = st.file_uploader("Choose an image file", type=["png", "jpg", "jpeg"])
+        if uploaded_file is not None:
+            img = Image.open(uploaded_file)
+            img_array = np.array(img)
+            results = object_detection(img_array)
+            show_results(results)