v1

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+import os
+import tempfile
+from ultralytics import YOLO
+
+# 加载YOLOv8模型
+model_path = "docgenome_object_detection_yolov8.pt"
+model = YOLO(model_path)
+
+def detect_and_visualize(image):
+    """
+    对上传的图像进行目标检测并可视化结果
+    
+    Args:
+        image: 上传的图像
+        
+    Returns:
+        annotated_image: 带有检测框的图像
+        yolo_annotations: YOLO格式的标注内容
+    """
+    # 运行检测
+    results = model(image)
+    
+    # 获取第一帧的结果
+    result = results[0]
+    
+    # 创建图像副本用于可视化
+    annotated_image = image.copy()
+    
+    # 准备YOLO格式的标注内容
+    yolo_annotations = []
+    
+    # 获取图像尺寸
+    img_height, img_width = image.shape[:2]
+    
+    # 在原图上绘制检测结果
+    for box in result.boxes:
+        # 获取边界框坐标
+        x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        
+        # 获取置信度
+        conf = float(box.conf[0])
+        
+        # 获取类别ID和名称
+        cls_id = int(box.cls[0])
+        cls_name = result.names[cls_id]
+        
+        # 为每个类别生成不同的颜色
+        color = tuple(np.random.randint(0, 255, 3).tolist())
+        
+        # 绘制边界框
+        cv2.rectangle(annotated_image, (x1, y1), (x2, y2), color, 2)
+        
+        # 准备标签文本
+        label = f'{cls_name} {conf:.2f}'
+        
+        # 计算标签大小
+        (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+        
+        # 绘制标签背景
+        cv2.rectangle(annotated_image, (x1, y1-label_height-5), (x1+label_width, y1), color, -1)
+        
+        # 绘制标签文本
+        cv2.putText(annotated_image, label, (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+        
+        # 转换为YOLO格式 (x_center, y_center, width, height) 归一化到0-1
+        x_center = (x1 + x2) / (2 * img_width)
+        y_center = (y1 + y2) / (2 * img_height)
+        width = (x2 - x1) / img_width
+        height = (y2 - y1) / img_height
+        
+        # 添加到YOLO标注列表
+        yolo_annotations.append(f"{cls_id} {x_center:.6f} {y_center:.6f} {width:.6f} {height:.6f}")
+    
+    # 将YOLO标注转换为字符串
+    yolo_annotations_str = "\n".join(yolo_annotations)
+    
+    return annotated_image, yolo_annotations_str
+
+def save_yolo_annotations(yolo_annotations_str):
+    """
+    保存YOLO标注到临时文件并返回文件路径
+    
+    Args:
+        yolo_annotations_str: YOLO格式的标注字符串
+        
+    Returns:
+        file_path: 保存的标注文件路径
+    """
+    # 创建临时文件
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt")
+    temp_file_path = temp_file.name
+    
+    # 写入标注内容
+    with open(temp_file_path, "w") as f:
+        f.write(yolo_annotations_str)
+    
+    return temp_file_path
+
+# 创建Gradio界面
+with gr.Blocks(title="YOLOv8目标检测可视化") as demo:
+    gr.Markdown("# YOLOv8目标检测可视化")
+    gr.Markdown("上传图像，使用YOLOv8模型进行目标检测，并下载YOLO格式的标注。")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(label="上传图像", type="numpy")
+            detect_btn = gr.Button("开始检测")
+        
+        with gr.Column():
+            output_image = gr.Image(label="检测结果")
+            yolo_annotations = gr.Textbox(label="YOLO标注", lines=10)
+            download_btn = gr.Button("下载YOLO标注")
+            download_file = gr.File(label="下载文件")
+    
+    # 设置点击事件
+    detect_btn.click(
+        fn=detect_and_visualize,
+        inputs=[input_image],
+        outputs=[output_image, yolo_annotations]
+    )
+    
+    download_btn.click(
+        fn=save_yolo_annotations,
+        inputs=[yolo_annotations],
+        outputs=[download_file]
+    )
+
+# 启动应用
+if __name__ == "__main__":
+    demo.launch() 

latex2layout_object_detection_yolov8.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:94d75fc3df499e59b03857a7c2e6c22c88498cc83892c1a2674965160c976fa8
+size 273162929

requirements.txt

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+ultralytics>=8.0.0
+opencv-python>=4.5.0
+numpy>=1.20.0
+gradio>=3.0.0 

test_yolo.py

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+from ultralytics import YOLO
+import cv2
+import numpy as np
+
+def detect_and_visualize(image_path, model_path):
+    # 加载YOLOv8模型
+    model = YOLO(model_path)  # 例如 'yolov8n.pt', 'yolov8s.pt' 等
+    
+    # 读取图片
+    image = cv2.imread(image_path)
+    
+    # 运行检测
+    results = model(image)
+    
+    # 获取第一帧的结果
+    result = results[0]
+    
+    # 在原图上绘制检测结果
+    for box in result.boxes:
+        # 获取边界框坐标
+        x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
+        x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
+        
+        # 获取置信度
+        conf = float(box.conf[0])
+        
+        # 获取类别ID和名称
+        cls_id = int(box.cls[0])
+        cls_name = result.names[cls_id]
+        
+        # 为每个类别生成不同的颜色
+        color = tuple(np.random.randint(0, 255, 3).tolist())
+        
+        # 绘制边界框
+        cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
+        
+        # 准备标签文本
+        label = f'{cls_name} {conf:.2f}'
+        
+        # 计算标签大小
+        (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+        
+        # 绘制标签背景
+        cv2.rectangle(image, (x1, y1-label_height-5), (x1+label_width, y1), color, -1)
+        
+        # 绘制标签文本
+        cv2.putText(image, label, (x1, y1-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+    
+    # 保存结果图片
+    output_path = 'output_detected.jpg'
+    cv2.imwrite(output_path, image)
+    print(f"检测结果已保存至: {output_path}")
+
+# 使用示例
+if __name__ == "__main__":
+    image_path = "./test_math.png"  # 替换为你的图片路径
+    model_path = "docgenome_object_detection_yolov8.pt"  # 替换为你的模型权重路径
+    detect_and_visualize(image_path, model_path)