 import argparse import os import shutil import time from pathlib import Path import json import cv2 import torch import torch.backends.cudnn as cudnn from numpy import random 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 def detect(save_img=False): # 获取设置的参数数据 out, source, weights, view_img, save_txt, imgsz = opt.save_dir, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size webcam = source.isnumeric() or source.startswith(('rtsp://', 'rtmp://', 'http://')) or source.endswith('.txt') # Initialize set_logging() device = select_device(opt.device) if os.path.exists(out): # output dir shutil.rmtree(out) # delete dir os.makedirs(out) # make new dir # 如果设备为GPU时， 使用Float16 half = device.type != 'cpu' # half precision only supported on CUDA # Load model 确保用户设定的输入图片分辨率能整除32(如不能则调整为能整除并返回) 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 设置第二次分类，默认不使用 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 通过不同的输入源来设置不同的数据加载方式 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 # 获取类别名字 names = model.module.names if hasattr(model, 'module') else model.names # 设置画框的颜色 colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] # Run inference t0 = time.time() # 进行一次前向推理,测试程序是否正常 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 # 输出json文件 save_json = True content_json = [] # path 图片/视频路径 # img 进行resize+pad之后的图片 # img0 原size图片 # cap 当读取图片时为None，读取视频时为视频源 for path, img, im0s, vid_cap in dataset: img = torch.from_numpy(img).to(device) # 图片也设置为Float16 img = img.half() if half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 # 没有batch_size的话则在最前面添加一个轴 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference t1 = time_synchronized() """ 前向传播 返回pred的shape是(1, num_boxes, 5+num_class) h,w为传入网络图片的长和宽，注意dataset在检测时使用了矩形推理，所以这里h不一定等于w num_boxes = h/32 * w/32 + h/16 * w/16 + h/8 * w/8 pred[..., 0:4]为预测框坐标 预测框坐标为xywh(中心点+宽长)格式 pred[..., 4]为objectness置信度 pred[..., 5:-1]为分类结果 """ pred = model(img, augment=opt.augment)[0] # Apply NMS """ pred:前向传播的输出 conf_thres:置信度阈值 iou_thres:iou阈值 classes:是否只保留特定的类别 agnostic:进行nms是否也去除不同类别之间的框 经过nms之后，预测框格式：xywh-->xyxy(左上角右下角) pred是一个列表list[torch.tensor]，长度为batch_size 每一个torch.tensor的shape为(num_boxes, 6),内容为box+conf+cls """ pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) t2 = time_synchronized() # Apply Classifier # 添加二次分类，默认不使用 if classify: pred = apply_classifier(pred, modelc, img, im0s) # Process detections # 对每一张图片作处理 for i, det in enumerate(pred): # detections per image # 如果输入源是webcam，则batch_size不为1，取出dataset中的一张图片 if webcam: # batch_size >= 1 p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() else: p, s, im0 = path, '', im0s # 设置保存图片/视频的路径 save_path = str(Path(out) / Path(p).name) # 设置保存框坐标txt文件的路径 txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '') # 设置打印信息（图片长宽） 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 # 调整预测框的坐标：基于resize+pad的图片的坐标-->基于原size图片的坐标 # 此时坐标格式为xyxy det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results # 打印检测到的类别数量 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 # 将xyxy(左上角+右下角)格式转为xywh(中心点+宽长)格式，并除上w，h做归一化，转化为列表再保存 xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, conf, *xywh) if opt.save_conf else (cls, *xywh) # label format with open(txt_path + '.txt', 'a') as f: # 在原图上画框 f.write(('%g ' * len(line) + '\n') % line) 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) # 输出 json 文件 if save_json: # windows下使用 file_name = save_path.split('\\') # Linux下使用 # file_name = save_path.split('/') content_dic = { "name": file_name[len(file_name)-1], "category": (names[int(cls)]), "bbox": torch.tensor(xyxy).view(1, 4).view(-1).tolist(), "score": conf.tolist() } content_json.append(content_dic) # Print time (inference + NMS) # 打印前向传播时间 print('%sDone. (%.3fs)' % (s, t2 - t1)) # Stream results # 如果设置展示，则show图片/视频 if view_img: cv2.imshow(p, im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration # Save results (image with detections) # 设置保存图片/视频 # 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 or save_json: print('Results saved to %s' % Path(out)) # 将 json 数据写入文件 with open(os.path.join(Path(out), 'result.json'), 'w') as f: json.dump(content_json, f) # 打印总时间 print('Done. (%.3fs)' % (time.time() - t0)) if __name__ == '__main__': """ weights:训练的权重 source:测试数据，可以是图片/视频路径，也可以是'0'(电脑自带摄像头),也可以是rtsp等视频流 output:网络预测之后的图片/视频的保存路径 img-size:网络输入图片大小 conf-thres:置信度阈值 iou-thres:做nms的iou阈值 device:设置设备 view-img:是否展示预测之后的图片/视频，默认False save-txt:是否将预测的框坐标以txt文件形式保存，默认False classes:设置只保留某一部分类别，形如0或者0 2 3 agnostic-nms:进行nms是否也去除不同类别之间的框，默认False augment:推理的时候进行多尺度，翻转等操作(TTA)推理 update:如果为True，则对所有模型进行strip_optimizer操作，去除pt文件中的优化器等信息，默认为False """ parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='best.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='../tile/testA_imgs', help='source') # file/folder, 0 for webcam parser.add_argument('--img-size', type=int, default=1600, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.1, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, 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('--save-conf', action='store_true', help='save confidences in --save-txt labels') parser.add_argument('--save-dir', type=str, default='detect_img/output', help='directory to save results') 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']: detect() # 去除pt文件中的优化器等信息 strip_optimizer(opt.weights) else: detect() |
|
|
