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yolov8 区域多类别计数
1. 基础
本项目是在 Windows+YOLOV8环境配置 的基础上实现的，测距原理可见上边文章
2. 计数功能
2.1 计数模块
在指定区域内计数模块
region_points为指定区域，可设置为任意形状region_points = [(20, 20), (20, 600), (1200, 600), (1200, 20)] count_class1 = 0 count_class2 = 0 for i, box in enumerate(boxes): x1, y1, x2, y2 = box[:4] x_center = (x1 + x2) / 2 y_center = (y1 + y2) / 2 center_point = (int(x_center), int(y_center)) if is_inside_region(center_point, region_points): if box[-1] == 0: # 类别1的标签.人 count_class1 += 1 elif box[-1] == 2: # 类别2的标签，车 count_class2 += 1- 1
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2.2 判断模块
def is_inside_region(point, region_points): """ 判断点是否在指定区域内 """ return cv2.pointPolygonTest(np.array(region_points), point, False) >= 0- 1
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3. 初始代码
import cv2 import numpy as np from ultralytics import YOLO def is_inside_region(point, region_points): """ 判断点是否在指定区域内 """ return cv2.pointPolygonTest(np.array(region_points), point, False) >= 0 def detect(): model = YOLO("yolov8n.pt") cv2.namedWindow('region couter', cv2.WINDOW_NORMAL) cv2.resizeWindow('region couter', 1280, 360) # 设置宽高 cap = cv2.VideoCapture('ultralytics/assets/a3.mp4') out_video = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'XVID'), 30, (2560, 720)) region_points = [(20, 20), (20, 600), (1200, 600), (1200, 20)] region_points_np = np.array(region_points) assert cap.isOpened(), "Error reading video file" while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break tracks = model.track(im0, persist=True) annotated_frame = tracks[0].plot() boxes = tracks[0].boxes.data count_class1 = 0 count_class2 = 0 for i, box in enumerate(boxes): x1, y1, x2, y2 = box[:4] x_center = (x1 + x2) / 2 y_center = (y1 + y2) / 2 center_point = (int(x_center), int(y_center)) if is_inside_region(center_point, region_points): if box[-1] == 0: # 类别1的标签.人 count_class1 += 1 elif box[-1] == 2: # 类别2的标签，车 count_class2 += 1 cv2.polylines(annotated_frame, [region_points_np], isClosed=True, color=(255, 0, 0), thickness=2) print("Number of objects in class 1:", count_class1) print("Number of objects in class 2:", count_class2) cv2.imshow("region couter", annotated_frame) out_video.write(annotated_frame) if cv2.waitKey(1) == ord('q'): break out_video.release() cap.release() cv2.destroyAllWindows() if __name__ == '__main__': detect()- 1
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4. 实验结果
本实验可以计数多种类别
（1）区域计数实验
（2）如需把区域背景换成填充色，可将上边代码里的cv2.polylines(annotated_frame, [region_points_np], isClosed=True, color=(255, 0, 0), thickness=2)- 1
替换为
region_mask = np.zeros_like(annotated_frame) cv2.fillPoly(region_mask, [region_points_np], color=(255, 0, 0)) # 使用透明度将填充后的区域与原始帧混合 alpha = 0.2 # 调整透明度 annotated_frame = cv2.addWeighted(annotated_frame, 1 - alpha, region_mask, alpha, 0)- 1
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（3）计数在图上显示
在annotated_frame = cv2.addWeighted(annotated_frame, 1 - alpha, region_mask, alpha, 0)添加text1 = "count_class1:%d" % count_class1 text2 = "count_class2:%d" % count_class2 cv2.putText(annotated_frame, text1, (20, 30), cv2.FONT_ITALIC, 1.0, (0, 255, 255), 2) cv2.putText(annotated_frame, text2, (20, 60), cv2.FONT_ITALIC, 1.0, (0, 255, 255), 2)- 1
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5. 完整代码
import cv2 import numpy as np from ultralytics import YOLO def is_inside_region(point, region_points): """ 判断点是否在指定区域内 """ return cv2.pointPolygonTest(np.array(region_points), point, False) >= 0 def detect(): model = YOLO("yolov8n.pt") cv2.namedWindow('region couter', cv2.WINDOW_NORMAL) cv2.resizeWindow('region couter', 1280, 360) # 设置宽高 cap = cv2.VideoCapture('ultralytics/assets/a3.mp4') out_video = cv2.VideoWriter('output.avi', cv2.VideoWriter_fourcc(*'XVID'), 30, (2560, 720)) region_points = [(20, 20), (20, 600), (1200, 600), (1200, 20)] region_points_np = np.array(region_points) assert cap.isOpened(), "Error reading video file" while cap.isOpened(): success, im0 = cap.read() if not success: print("Video frame is empty or video processing has been successfully completed.") break tracks = model.track(im0, persist=True) annotated_frame = tracks[0].plot() boxes = tracks[0].boxes.data count_class1 = 0 count_class2 = 0 for i, box in enumerate(boxes): x1, y1, x2, y2 = box[:4] x_center = (x1 + x2) / 2 y_center = (y1 + y2) / 2 center_point = (int(x_center), int(y_center)) if is_inside_region(center_point, region_points): if box[-1] == 0: # 类别1的标签.人 count_class1 += 1 elif box[-1] == 2: # 类别2的标签，车 count_class2 += 1 # cv2.polylines(annotated_frame, [region_points_np], isClosed=True, color=(255, 0, 0), thickness=2) region_mask = np.zeros_like(annotated_frame) cv2.fillPoly(region_mask, [region_points_np], color=(255, 0, 0)) # 使用透明度将填充后的区域与原始帧混合 alpha = 0.2 # 调整透明度 annotated_frame = cv2.addWeighted(annotated_frame, 1 - alpha, region_mask, alpha, 0) text1 = "count_class1:%d" % count_class1 text2 = "count_class2:%d" % count_class2 cv2.putText(annotated_frame, text1, (20, 30), cv2.FONT_ITALIC, 1.0, (0, 255, 255), 2) cv2.putText(annotated_frame, text2, (20, 60), cv2.FONT_ITALIC, 1.0, (0, 255, 255), 2) print("Number of objects in class 1:", count_class1) print("Number of objects in class 2:", count_class2) cv2.imshow("region couter", annotated_frame) out_video.write(annotated_frame) if cv2.waitKey(1) == ord('q'): break out_video.release() cap.release() cv2.destroyAllWindows() if __name__ == '__main__': detect()- 1
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6. 源码
可以去 Windows+YOLOV8环境配置 下载源码，然后把上边主代码贴进去运行即可
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- 原文地址：https://blog.csdn.net/qq_45077760/article/details/137972314