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1.py

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+import torch
+from ultralytics import YOLO
+from ultralytics.data import download
+
+# 下载COCO128数据集
+download('coco128')
+
+# 定义训练参数
+epochs = 10  # 训练轮数
+batch_size = 16  # 批次大小
+img_size = 640  # 输入图像尺寸
+
+# 加载YOLOv8模型
+model = YOLO('yolov8s.yaml')  # 创建新的模型实例
+
+# 开始训练
+model.train(data='coco128.yaml', epochs=epochs, batch=batch_size, imgsz=img_size)
+
+# 加载经过训练的模型，假设模型保存在 'best.pt'
+model = YOLO('best.pt')
+
+# 设置要检测的对象类别，这里的例子是只检测行人
+class_names = model.names
+person_class_id = class_names.index('person')
+
+# 加载图片或视频
+img_path = 'path_to_your_image.jpg'
+
+# 进行目标检测
+results = model(img_path)
+
+# 处理结果
+for result in results:
+    boxes = result.boxes
+    for box in boxes:
+        if box.cls == person_class_id:  # 只处理行人检测结果
+            x1, y1, x2, y2 = box.xyxy[0]  # 获取边界框坐标
+            confidence = box.conf.item()  # 获取置信度
+            print(f"Pedestrian detected at ({x1:.2f}, {y1:.2f}) to ({x2:.2f}, {y2:.2f}), Confidence: {confidence:.2f}")

2.py

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+import numpy as np
+import cv2
+from ultralytics import YOLO
+
+def point_to_line_distance(point, line_start, line_end):
+    # 线的方向向量
+    line_dir = np.array(line_end) - np.array(line_start)
+    # 点到线起点的向量
+    point_to_start = np.array(point) - np.array(line_start)
+
+    # 计算叉积（只适用于二维空间）
+    cross_product = line_dir[0] * point_to_start[1] - line_dir[1] * point_to_start[0]
+
+    # 叉积的符号会表明点在直线的哪一侧
+    return cross_product
+
+def check_crossing_detection_box(box, start_point, end_point):
+    x1, y1, x2, y2 = box
+    points = [(x1, y1), (x2, y1), (x1, y2), (x2, y2)]
+    distances = [point_to_line_distance(p, start_point, end_point) for p in points]
+
+    # 检查是否有正负距离，即检测框的点分别位于斜线两侧
+    crossing = any(dist < 0 for dist in distances) and any(dist > 0 for dist in distances)
+    return crossing
+
+def process_frame(frame):
+    height, width = frame.shape[:2]
+    desired_height = 500
+    scale = desired_height / height
+    resized_frame = cv2.resize(frame, (int(width * scale), desired_height))
+    frame = resized_frame
+
+    height, width = frame.shape[:2]
+    start_point = (0, height // 2)
+    end_point = (width, height // 3)
+
+    cv2.line(frame, start_point, end_point, (255, 0, 0), 3)
+
+    results = model(frame, verbose=False)
+    for result in results:
+        boxes = result.boxes
+        classes = model.names
+        for box in boxes:
+            x1, y1, x2, y2 = box.xyxy[0].tolist()
+            class_id = int(box.cls.item())
+            if classes[class_id] == 'person':
+                cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
+                if check_crossing_detection_box((x1, y1, x2, y2), start_point, end_point):
+                    cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 0, 255), 2)
+
+    return frame
+
+def main():
+    global model
+    try:
+        model = YOLO('yolov8s.pt')
+        print("模型加载成功。")
+    except Exception as e:
+        print(f"加载模型时出现错误: {e}")
+        model = None
+
+    if model is not None:
+        video_path = "./image/a.mp4"
+        print("开始视频捕获...")
+        cap = cv2.VideoCapture(video_path)
+        if not cap.isOpened():
+            print("打开视频流或文件时出错")
+        else:
+            print("视频流已成功打开。")
+
+            # 获取视频的帧率和尺寸
+            fps = cap.get(cv2.CAP_PROP_FPS)
+            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+            # 创建VideoWriter对象
+            fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 使用mp4v编码器
+            out = cv2.VideoWriter('output.mp4', fourcc, fps, (width, height))  # 输出文件名为output.mp4
+
+            while cap.isOpened():
+                ret, frame = cap.read()
+                if not ret:
+                    break
+
+                processed_frame = process_frame(frame)
+
+                # 将处理后的帧写入视频文件
+                out.write(processed_frame)
+
+                cv2.imshow('Processed Video', processed_frame)
+                key = cv2.waitKey(1)
+                if key & 0xFF == ord('q'):
+                    break
+
+            # 释放VideoWriter资源
+            out.release()
+
+            cap.release()
+            cv2.destroyAllWindows()
+            print("视频处理完成。")
+    else:
+        print("模型未加载，无法继续处理。")
+
+if __name__ == '__main__':
+    main()

5.py

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+from ultralytics import YOLO
+
+# 加载模型
+model = YOLO('yolov8n.pt')  # 假设你使用的是yolov8n.yaml配置文件
+
+# 开始训练，使用自定义参数
+
+model.train(data='C://Users//86195//Desktop//ultralytics-main (1)//ultralytics-main//data.yaml', epochs=5, batch=20, imgsz=640)

CITATION.cff

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+# This CITATION.cff file was generated with https://bit.ly/cffinit
+
+cff-version: 1.2.0
+title: Ultralytics YOLO
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - given-names: Glenn
+    family-names: Jocher
+    affiliation: Ultralytics
+    orcid: 'https://orcid.org/0000-0001-5950-6979'
+  - given-names: Ayush
+    family-names: Chaurasia
+    affiliation: Ultralytics
+    orcid: 'https://orcid.org/0000-0002-7603-6750'
+  - family-names: Qiu
+    given-names: Jing
+    affiliation: Ultralytics
+    orcid: 'https://orcid.org/0000-0003-3783-7069'
+repository-code: 'https://github.com/ultralytics/ultralytics'
+url: 'https://ultralytics.com'
+license: AGPL-3.0
+version: 8.0.0
+date-released: '2023-01-10'