from ultralytics import YOLO from ultralytics.solutions import object_counter import cv2 model = YOLO("yolov8n.pt") cap = cv2.VideoCapture("car.mp4") assert cap.isOpened(), "Error reading video file" w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PR...
heatmap.py: 用于生成和处理热图数据;这在物体检测和事件定位中很常见。object_counter.py: 用于物体计数的脚本;包含从图像中检测和计数实例的逻辑。 (9)trackers trackers文件夹包含了实现目标跟踪功能的脚本和模块: __init__.py: 指示该文件夹是一个Python包。basetrack.py: 包含跟踪器的基础类或方法。bot_sor...
帮我们将这个代码,复制粘贴到我们YOLOv8的仓库里然后创建一个py文件存放进去即可。 from ultralytics import YOLOfrom ultralytics.solutions import object_counterimport cv2model = YOLO("yolov8n.pt")cap = cv2.VideoCapture("car.mp4")assert cap.isOpened(), "Error reading video file"w, h, fps = (in...
# 遍历向南移动的对象计数器中的条目,并在图像的左上角显示计数信息 for idx, (key, value) in enumerate(object_counter.items()): # 离开的车辆数量 cnt_str1 = str(key) + ":" + str(value) cv2.line(img, (20, 25), (500, 25), [85, 45, 255], 40) cv2.putText(img, f'Numbers of...
deepsort =Noneobject_counter = {} object_counter1 = {} line = [(100,500), (1050,500)]definit_tracker():globaldeepsort cfg_deep = get_config() cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml") deepsort= DeepSort(cfg_deep.DEEPSORT.REID_CKPT, ...
sv.process_video(source_path=self.input_video_path,target_path=self.output_video_path,callback=self.process_frame)if__name__=="__main__":obj=CountObject('demo.mp4','single.mp4')obj.process_video() 运行代码,结果如下。 发现检测效果还不错,目标的置信度都挺高的。
Code Issues Pull requests tracking detection yolo segmentation object-detection object-tracking pose-estimation object-segmentation object-counter object-counting custom-yolo yolo-pose yolov8 yolov8n yolov8-segmentation yolov8-pose yolov8-detection Updated May 26, 2024 Python marco...
classCountObject: def__init__(self, input_video_path, output_video_path)->None: # 加载YOLOv8模型 self.model = YOLO('yolov8s.pt') # 设置颜色 self.colors = sv.ColorPalette.default # 输入视频, 输出视频 self.input_video_path = input_video_path ...
For a comprehensive guide on using YOLOv8 with Object Tracking, please refer toMulti-Object Tracking with Ultralytics YOLO. .\yolov8\examples\YOLOv8-Region-Counter\yolov8_region_counter.py # Ultralytics YOLO 🚀, AGPL-3.0 licenseimportargparse# 导入命令行参数解析模块fromcollectionsimportdefaultdict...
# warmup 30 times for i in range(30): tmp = np.random.randn(1, 3, 640, 640).astype(np.float32) model.infer([tmp]) # calculate infer FPS start = time.perf_counter() outputs = model.infer([img]) end = time.perf_counter() print(f'Inference FPS: {1 / (end - start)}') ...