Update app.py

app.py

@@ -1,23 +1,3 @@
-import gradio as gr
-import torch
-from qai_hub_models.models.detr_resnet50 import Model
-from PIL import Image, ImageDraw
-import numpy as np
-
-# 注册 AVIF 支持（根据所安装的插件选择一种）
-try:
-    from pillow_avif import register_avif_opener
-    register_avif_opener()
-except ImportError:
-    try:
-        import pillow_heif
-        pillow_heif.register_heif_opener()
-    except ImportError:
-        print("AVIF support not available. Please install 'pillow-avif-plugin' or 'pillow-heif'.")
-
-# 加载模型
-torch_model = Model.from_pretrained()
-
 def detect_objects(image):
     if image is None:
         raise ValueError("No image uploaded!")  # 检查图像是否为 None
@@ -53,44 +33,15 @@ def detect_objects(image):
             box[3] *= original_image.height / 800
             
             detections.append({
-                "label": f"Object {i}",  # 根据实际情况替换标签
+                "label": f"Object {i}",  
                 "confidence": round(score, 3),
                 "box": box,
             })
-            # 绘制边界框和标签到原始图像上
-            draw = ImageDraw.Draw(original_image)
-            draw.rectangle(box[:4], outline="red", width=3)  # 绘制矩形框，确保只取前四个坐标
             
-            draw.text((box[0], box[1]), f"{detections[-1]['label']} ({detections[-1]['confidence']})", fill="red")  # 添加标签
+            # 检查并绘制边界框和标签到原始图像上
+            draw = ImageDraw.Draw(original_image)
+            if box[1] < box[3]:  # 确保 y_min < y_max
+                draw.rectangle(box[:4], outline="red", width=3)  
+                draw.text((box[0], box[1]), f"{detections[-1]['label']} ({detections[-1]['confidence']})", fill="red")  
 
     return original_image, detections
-
-# 创建 Gradio 接口，自动处理 AVIF 图像并转换为 PNG 格式以供显示和处理。
-with gr.Blocks() as iface:
-    gr.Markdown("# Object Detection with DETR-ResNet50")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            image_input = gr.Image(type="numpy", label="Upload Image (supports PNG, JPEG, AVIF...)")  
-            submit_button = gr.Button("Submit")
-            clear_button = gr.Button("Clear")
-        with gr.Column(scale=1):
-            output_image = gr.Image(label="Detected Image")
-            output_json = gr.JSON(label="Detections")
-
-    def on_submit(image):
-        try:
-            detected_image, detections = detect_objects(image)
-            return detected_image, detections
-        except Exception as e:
-            return None, {"error": str(e)}
-
-    def on_clear():
-        return None, None, None  # 清空输入和输出
-
-    submit_button.click(on_submit, inputs=image_input, outputs=[output_image, output_json])
-    clear_button.click(on_clear, inputs=None, outputs=[image_input, output_image, output_json])  # 修复清除功能
-
-# 启动应用程序
-if __name__ == "__main__":
-    iface.launch()