def plot_one_box(x, img, color=None, label=None, line_thickness=None): """ description: Plots one bounding box on image img, this function comes from YoLov5 project. param: x: a box likes [x1,y1,x2,y2] img: a opencv image object color: color to draw rectangle, such as (0,2...
def plot_one_box(x, img, color=None, label=None, line_thickness=None): """ description: Plots one bounding box on image img, this function comes from YoLov5 project. param: x: a box likes [x1,y1,x2,y2] img: a opencv image object color: color to draw rectangle, such as (0,2...
import torch from utilss.plots import colors, plot_one_box import cv2 from utilss.general import xywh2xyxy # 思路 #将box框的xywh格式转化为xyxy格式并乘上相对应的宽高 #将kpts姿态数据乘对应的宽高 # 姿态数据中,每3位有一个2,这个是官方coco annotator标注软件会自动插入的值,在yolopose算法detect检...
f.write(('%g ' * len(line)).rstrip() % line + '\n') if save_img or view_img: # Add bbox to image label = f'{names[int(cls)]} {conf:.2f}' plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=1) # Print time (inference + NMS) print(f'{s...
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) ''' # 改为 # Process detections det=pred[0] #原来的情况是要保持图片,因此多了很多关于保持路径上的处理。另外,pred # 其实是个列表。元素个数为batch_size。由于对于我这个api,每次只处理一个图片, ...
from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, time_synchronized import runOpenpose import YOLO def detect(save_img=False): global ip source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt...
# Show the resultsforrinresults:im_array=r.plot()# plot aBGRnumpy arrayofpredictions im=Image.fromarray(im_array[...,::-1])#RGBPILimage im.show()# show image im.save('results.jpg')# save image 运行结果如下,除了检测到了人还检测到了瓶子和座椅,准确率还是挺高的。
close() # 关闭绘图窗口 if on_plot: on_plot(fname) # 如果指定了回调函数,则调用回调函数 # 根据传入的边界框信息 xyxy,裁剪输入图像 im,并返回裁剪后的图像。 def save_one_box(xyxy, im, file='im.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True): """ Args: xyxy (torch....
```python import torch import torchvision.datasets as datasets import torchvision.transforms as transforms from yolov8.utils.datasets import LoadImages, LoadStreams from yolov8.utils.general import check_img_size, non_max_suppression, scale_coords from yolov8.utils.plots import plot_one_box ...
im = plot_one_box(box,im,color,text) output_video.write(im.astype(np.uint8)) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 总结 基于yolov5+deepsort+slowfast算法的视频实时行为检测就介绍到这里了!!!