image = cv2.imread(file_path) result_image = predict_image(model, image) self.show_image(result_image) def open_video(self): file_path, _ = QFileDialog.getOpenFileName(self, "打开视频", "", "Video Files (*.mp4 *.avi)") if file_path: self.cap = cv2.VideoCapture(file_path) self...
from ultralytics.yolo.engine.results import Results from ultralytics.yolo.utils.plotting import Annotator results: Results = model.predict(frame)[0] keypoints = results.keypoints.squeeze().tolist() ann = Annotator(frame) for i, kp in enumerate(keypoints): x = int(kp[0]) y = int(kp[...
result_image = predict_image(model, image) show_image(result_image) def open_video(): file_path = filedialog.askopenfilename(filetypes=[("Video files", "*.mp4;*.avi")]) if file_path: predict_video(model, file_path) def open_camera(): predict_camera(model) btn_image = tk.Button(...
predict(source=videoPath, save=True) 代码语言:javascript 复制 import subprocess # Convert AVI to MP4 using FFmpeg subprocess.call(['ffmpeg', '-y', '-loglevel', 'panic', '-i', '/content/runs/segment/predict/sample_video.avi', 'output_video.mp4']) from IPython.display import Video # ...
import cv2 fourcc = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter('output_video.avi', fourcc, 20.0, (frame.shape[1], frame.shape[0])) while cap.isOpened(): status, frame = cap.read() if not status: break results = model.predict(source=frame) for result in results: anno_...
cap=cv2.VideoCapture("src.mp4")assert cap.isOpened(),"Error reading video file"w,h,fps=(int(cap.get(x))forxin(cv2.CAP_PROP_FRAME_WIDTH,cv2.CAP_PROP_FRAME_HEIGHT,cv2.CAP_PROP_FPS))video_writer=cv2.VideoWriter("out.avi",cv2.VideoWriter_fourcc(*'mp4v'),fps,(w,h))line_pts=[(0,40...
参考: https://docs.ultralytics.com/modes/predict/#working-with-results """ # 初始化一个空的二维numpy数组,用于存放检测到的目标的位置信息 # 如果视频中没有需要提取的目标类别,如果不初始化,会导致tracker报错 detections = np.empty((0, 4)) confarray = [] # 初始化一个空列表,用于存放检测到...
我们还转换了模型的类别名称,使其对用户更加友好。preprocess方法处理传入的图像,准备好它们被模型预测。predict方法是实际调用YOLO模型进行预测的地方,它利用模型对图像进行分析并给出检测结果。而postprocess方法则处理预测结果,包括将类别ID转换为中文名称,格式化边界框坐标,并整理最终的检测结果。
摘要:本文深入研究了基于YOLOv8/v7/v6/v5的舰船检测与识别系统,核心采用YOLOv8并整合了YOLOv7、YOLOv6、YOLOv5算法,进行性能指标对比;详述了国内外研究现状、数据集处理、算法原理、模型构建与训练代码,及基于Streamlit的交互式Web应用界面设计。在Web网页中可以支持图像、视频和实时摄像头进行舰船检测与识别,可上传不...
Yolov8 源码解析(三十八) .\yolov8\ultralytics\nn\__init__.py # 导入模块中的特定类和函数,包括: # - BaseModel: 基础模型类 # - ClassificationModel: 分类模型类 # - DetectionModel: 目标检测模型