if opt.save_crop: save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) 其中det是YOLOv5识别出来的结果,例如tensor([[121.00000, 7.00000, 480.00000, 305.00000, 0.67680, 0.00000], [278.00000, 166.00000, 318.00000, 305.00000, 0.66222, 27.00000]])就是...
ifsave_crop:save_one_box(xyxy,imc,file=save_dir/'crops'/names[c]/f'{p.stem}.jpg',BGR=True,xywh=xywh) save_one_box位于utils/general.py,修改如下: 代码语言:javascript 复制 defsave_one_box(xyxy,im,file='image.jpg',gain=1.02,pad=10,square=False,BGR=False,save=True,xywh=[0,0,0,0...
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) # Stream results if view_img: cv2.imshow(str(p), im0) cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(...
pred_cat, pred_cont = inference_emotic(im0, (int(xyxy[0]), int(xyxy[1]), int(xyxy[2]), int(xyxy[3]))) ifsave_imgoropt.save_croporview_img:# Add bbox to image label =Noneifopt.hide_labelselse(names[c]ifopt.hide_confelsef'{names[c]}{conf:.2f}') plot_one_box(xyxy, im...
utils.datasets import *from utils.torch_utils import *from utils.augmentations import *from utils.general import *from models.common import DetectMultiBackendfrom utils.plots import Annotator, colors, save_one_boximport timeimport cv2import torchimport randomimport numpy as npdef detect(save_img=...
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True, xywh=xywh) 1. 2. save_one_box位于utils/general.py,修改如下: def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=Fal...
annotator.box_label(xyxy, label, color=colors(c, True)) if save_crop: save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) # Print time (inference-only) print(f'{s}Done. ({t3 - t2:.3f}s)') ...
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)fromutils.plotsimportAnnotator, colors, save_one_boxfromutils.torch_utilsimportselect_device, time_sync@torch.no_grad()defrun(weights=ROOT /'yolov5s.pt',# model.pt path(s)source=ROOT /'data/images',...
from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import select_device, time_sync @torch.no_grad() def run( weights=ROOT / 'yolov5s.pt', # model.pt path(s) source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam ...
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) # Print time (inference-only) # LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)') # 输出为:image 1/1 F:\github_pro\yolov5\data\images\bus.jpg: 640x480 4 persons, 1 bus, Done. ...