import argparse import os import platform import shutil import time from pathlib import Path import cv2 import torch import torch.backends.cudnn as cudnn from numpy import random from utils.utils import * from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import ( 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 utils.general import ( check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, plot_one_box, strip_optimizer, set_logging) # def detect_image(source,out,imgsz = 640,save_img=False,save_txt = False,weights = "./weights/yolov5s.pt"): # # out, source, weights, view_img, save_txt, imgsz = \ # # opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size # # webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt') # webcam = source =='0' # # Initialize # set_logging() # device = select_device('') # # if os.path.exists(out): # # shutil.rmtree(out) # delete output folder # # os.mkdir(out) # make new output folder # half = device.type != 'cpu' # half precision only supported on CUDA # # # Load model # model = attempt_load(weights, map_location=device) # load FP32 model # imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size # if half: # model.half() # to FP16 # # # Second-stage classifier # # classify = False # # if classify: # # modelc = load_classifier(name='resnet101', n=2) # initialize # # modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights # # modelc.to(device).eval() # # # Set Dataloader # vid_path, vid_writer = None, None # if webcam: # view_img = True # cudnn.benchmark = True # set True to speed up constant image size inference # dataset = LoadStreams(source, img_size=imgsz) # else: # save_img = True # view_img = False # dataset = LoadImages(source, img_size=imgsz) # # # Get names and colors # names = model.module.names if hasattr(model, 'module') else model.names # colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] # # # Run inference # t0 = time.time() # img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img # _ = model(img.half() if half else img) if device.type != 'cpu' else None # list_file = open("detection.txt", 'w')# run once # for path, img, im0s, vid_cap in dataset: # img = torch.from_numpy(img).to(device) # img = img.half() if half else img.float() # uint8 to fp16/32 # img /= 255.0 # 0 - 255 to 0.0 - 1.0 # if img.ndimension() == 3: # img = img.unsqueeze(0) # # # Inference # t1 = time_synchronized() # pred = model(img, augment='store_true')[0] # # # Apply NMS # pred = non_max_suppression(pred, 0.4,0.5, agnostic='store_true') # t2 = time_synchronized() # # # # Apply Classifier # # if classify: # # pred = apply_classifier(pred, modelc, img, im0s) # # # Process detections # for i, det in enumerate(pred): # detections per image # if webcam: # batch_size >= 1 # p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() # else: # p, s, im0 = path, '', im0s # # save_path = str(Path(out) / Path(p).name) # txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '') # s += '%gx%g ' % img.shape[2:] # print string # gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh # if det is not None and len(det): # # Rescale boxes from img_size to im0 size # det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # # # Print results # for c in det[:, -1].unique(): # n = (det[:, -1] == c).sum() # detections per class # s += '%g %ss, ' % (n, names[int(c)]) # add to string # # # Write results # # # for *xyxy, conf, cls in reversed(det): # if save_txt: # Write to file # xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh # with open(txt_path + '.txt', 'a') as f: # f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # # # label format # # if save_img or view_img: # Add bbox to image # label = '%s %.2f' % (names[int(cls)], conf) # plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) # # # Print time (inference + NMS) # # with open(os.getcwd()+'output.txt','w') as f: # # f.write('%sDone. (%.3fs)' % (s, t2 - t1)) # # list_file.write('%sDone. (%.3fs)' % (s, t2 - t1)) # list_file.write('\n') # print('%sDone. (%.3fs)' % (s, t2 - t1)) # # # Stream results # if view_img: # cv2.imshow(p, im0) # if cv2.waitKey(1) == ord('q'): # q to quit # raise StopIteration # # # Save results (image with detections) # if save_img: # if dataset.mode == 'images': # cv2.imwrite(save_path, im0) # else: # if vid_path != save_path: # new video # vid_path = save_path # if isinstance(vid_writer, cv2.VideoWriter): # vid_writer.release() # release previous video writer # # fourcc = 'mp4v' # output video codec # fps = vid_cap.get(cv2.CAP_PROP_FPS) # w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) # h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) # vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) # vid_writer.write(im0) # # if save_txt or save_img: # print('Results saved to %s' % Path(out)) # # if platform.system() == 'Darwin' and not opt.update: # MacOS # # os.system('open ' + save_path) # # print('Done. (%.3fs)' % (time.time() - t0)) def detect(save_img=False): out, source, weights, view_img, save_txt, imgsz = \ opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size webcam = source.isnumeric() or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt') print('-----') print(source) print(type(source)) # Initialize set_logging() device = select_device(opt.device) if os.path.exists(out): shutil.rmtree(out) # delete output folder os.makedirs(out) # make new output folder half = device.type != 'cpu' # half precision only supported on CUDA # Load model model = attempt_load(weights, map_location=device) # load FP32 model imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size if half: model.half() # to FP16 # Second-stage classifier classify = False if classify: modelc = load_classifier(name='resnet101', n=2) # initialize modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']) # load weights modelc.to(device).eval() # Set Dataloader vid_path, vid_writer = None, None if webcam: view_img = True cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=imgsz) else: save_img = True dataset = LoadImages(source, img_size=imgsz) # Get names and colors names = model.module.names if hasattr(model, 'module') else model.names colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] # Run inference t0 = time.time() img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once for path, img, im0s, vid_cap in dataset: print('path:{0}'.format(path)) print('im0s:{0}'.format(im0s)) print('im0s类型:{0}'.format(type(im0s))) img = torch.from_numpy(img).to(device) img = img.half() if half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t1 = time_synchronized() pred = model(img, augment=opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t2 = time_synchronized() # Apply Classifier if classify: pred = apply_classifier(pred, modelc, img, im0s) # 用于存储人员边界坐标的列表 ---linjie people_coords = [] # Process detections for i, det in enumerate(pred): # detections per image if webcam: # batch_size >= 1 p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() else: p, s, im0 = path, '', im0s save_path = str(Path(out) / Path(p).name) txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '') s += '%gx%g ' % img.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh if det is not None and len(det): # print('先看看这里能不能进行，再看看im0多少:{0}。再看看im0类型:{1}'.format(im0,type(im0))) # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class s += '%g %ss, ' % (n, names[int(c)]) # add to string # Write results for *xyxy, conf, cls in reversed(det): if save_txt: # Write to file xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format if save_img or view_img: # Add bbox to image label = '%s %.2f' % (names[int(cls)], conf) plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) #判断标签是否为人 --linjie if label is not None: if (label.split())[0] == 'person': print('标签是人') distancing(people_coords, im0, dist_thres_lim=(200, 250)) people_coords.append(xyxy) # plot_one_box(xyxy, im0, line_thickness=3) plot_dots_on_people(xyxy, im0) # 画上人与人的连接线 --linjie distancing(people_coords, im0, dist_thres_lim=(200, 250)) # Print time (inference + NMS) print('%sDone. (%.3fs)' % (s, t2 - t1)) # Stream results if view_img: cv2.imshow(p, im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration # Save results (image with detections) if save_img: if dataset.mode == 'images': cv2.imwrite(save_path, im0) else: if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer fourcc = 'mp4v' # output video codec fps = vid_cap.get(cv2.CAP_PROP_FPS) w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) vid_writer.write(im0) if save_txt or save_img: print('Results saved to %s' % Path(out)) if platform == 'Darwin' and not opt.update: # MacOS os.system('open ' + save_path) print('Done. (%.3fs)' % (time.time() - t0)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='weights/best.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder inference/images, 0 for webcam parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output folder parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true', help='display results') parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') parser.add_argument('--augment', action='store_true', help='augmented inference') parser.add_argument('--update', action='store_true', help='update all models') opt = parser.parse_args() print(opt) with torch.no_grad(): if opt.update: # update all models (to fix SourceChangeWarning) for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: print('model1') detect() strip_optimizer(opt.weights) else: print('model2') detect()