22.2、xywh2xyxy 23、xywhn2xyxy、xyxy2xywhn、xyn2xy 23.1、xywhn2xyxy 23.2、xyxy2xywhn 23.3、xyn2xy 24、non_max_suppression 25、strip_optimizer 26、print_mutation 27、apply_classifier 28、increment_path 29、save_one_box 30、resample_segments 31、segment2box 32、segments2boxes 总结 ...
from utils.datasets import LoadImages, LoadStreams from utils.general import apply_classifier, check_img_size, check_imshow, check_requirements, check_suffix, colorstr, \ increment_path, non_max_suppression, print_args, save_one_box, scale_coords, set_logging, \ strip_optimizer, xyxy2xywh from...
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh, set_logging, apply_classifier) from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( weights=ROOT / '...
from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, check_requirements, non_max_suppression, apply_classifier, scale_coords, \ xyxy2xywh, strip_optimizer, set_logging, increment_path from utils.plots import plot_one_box from utils.torch_utils import select...
# Second-stage classifier (optional). # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) frame_idx=frame_idx+1 # Process predictions. for i, det in enumerate(pred): # Per image. seen += 1 if webcam: # batch_size >= 1 ...
# Second-stage classifier (optional) # pred = utils.general.Apply_classifier(pred, classifier_model, im, im0s) # Process predictions for i, det in enumerate(pred): # per image seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count...
()#Apply Classifier#Process detectionsfori, detinenumerate(pred):#detections per image#batch_size >= 1#if webcam:#p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count#else:#p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)##p =...
()pred=model(img,augment=opt.augment)[0]# Apply NMSpred=non_max_suppression(pred,opt.conf_thres,opt.iou_thres,classes=opt.classes,agnostic=opt.agnostic_nms)t2=time_synchronized()# Apply Classifierifclassify:pred=apply_classifier(pred,modelc,img,im0s)# Process detectionsfori,detinenumerate(...
t1=time_synchronized()pred=model(img,augment=opt.augment)[0]# ApplyNMSpred=non_max_suppression(pred,opt.conf_thres,opt.iou_thres,classes=opt.classes,agnostic=opt.agnostic_nms)t2=time_synchronized()# Apply Classifierifclassify:pred=apply_classifier(pred,modelc,img,im0s)# Process detectionsfori...
check_img_size, non_max_suppression, apply_classifier, scale_coords,xyxy2xywh, plot_one_box, strip_optimizer, set_logging)from utils.torch_utils import select_device, load_classifier, time_synchronized from cfg_mAP import Cfg cfg = Cfg def detect(save_img=False):out, source, weights, view_...