Upload 2 files

app2.py

@@ -0,0 +1,57 @@
+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import io
+
+# 加载 YOLOv8 模型
+model = YOLO('/mnt/project/yolo_view/best.pt')  # 替换为您的模型文件路径
+
+def detect_objects(image):
+    # 使用 YOLOv8 模型进行预测
+    results = model(image)
+
+    # 处理预测结果
+    class_counts = {}
+    output_text = ''
+    annotated_image = Image.fromarray(results[0].plot())  # 将 NumPy 数组转换为 PIL 图像
+
+    # 将 PIL 图像转换为 bytes 以供 Gradio 显示
+    buffered = io.BytesIO()
+    annotated_image.save(buffered, format="PNG")
+    annotated_image_bytes = buffered.getvalue()
+
+    # 将 bytes 转换为 PIL 图像对象
+    annotated_image = Image.open(io.BytesIO(annotated_image_bytes))
+
+    for result in results[0].boxes.data:
+        class_id = int(result[5])
+        class_name = model.names[class_id]
+        confidence = result[4]
+        x1, y1, x2, y2 = result[:4].cpu().numpy().astype(int)
+
+        # 统计类别数量
+        if class_name in class_counts:
+            class_counts[class_name] += 1
+        else:
+            class_counts[class_name] = 1
+
+        # 构建输出文本
+        output_text += f'类别: {class_name}, 置信度: {confidence:.2f}, 坐标: ({x1}, {y1}), ({x2}, {y2})\n'
+
+    # 构建每个类别数量的输出
+    class_count_text = '\n'.join([f'{class_name}: {count}' for class_name, count in class_counts.items()])
+    output_text += f'\n每个类别的数量:\n{class_count_text}'
+
+    return annotated_image, output_text
+
+# 创建 Gradio 界面
+demo = gr.Interface(
+    fn=detect_objects,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Image(type="pil"), "text"],
+    title="细胞检测",
+    description="上传图片,进行物体检测并显示结果"
+)
+
+# 启动应用
+demo.launch()

best.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5a801f9379af97c95d555246f1b684b49f9464fafa106096e817164fe0a2ff4
+size 6250521