计算机设计竞赛 行人雷德识别（人物雷德） - 机器视觉 深度学习 opencv python

import argparse import time from sys import platform from models import * from utils.datasets import * from utils.utils import * from reid.data import make_data_loader from reid.data.transforms import build_transforms from reid.modeling import build_model from reid.config import cfg as reidCfg def detect(cfg, data, weights, images='data/samples', # input folder output='output', # output folder fourcc='mp4v', # video codec img_size=416, conf_thres=0.5, nms_thres=0.5, dist_thres=1.0, save_txt=False, save_images=True): # Initialize device = torch_utils.select_device(force_cpu=False) torch.backends.cudnn.benchmark = False # set False for reproducible results if os.path.exists(output): shutil.rmtree(output) # delete output folder os.makedirs(output) # make new output folder ############# 行人重识别模型初始化 ############# query_loader, num_query = make_data_loader(reidCfg) reidModel = build_model(reidCfg, num_classes=10126) reidModel.load_param(reidCfg.TEST.WEIGHT) reidModel.to(device).eval() query_feats = [] query_pids = [] for i, batch in enumerate(query_loader): with torch.no_grad(): img, pid, camid = batch img = img.to(device) feat = reidModel(img) # 一共2张待查询图片，每张图片特征向量2048 torch.Size([2, 2048]) query_feats.append(feat) query_pids.extend(np.asarray(pid)) # extend() 函数用于在列表末尾一次性追加另一个序列中的多个值（用新列表扩展原来的列表）。 query_feats = torch.cat(query_feats, dim=0) # torch.Size([2, 2048]) print("The query feature is normalized") query_feats = torch.nn.functional.normalize(query_feats, dim=1, p=2) # 计算出查询图片的特征向量 ############# 行人检测模型初始化 ############# model = Darknet(cfg, img_size) # Load weights if weights.endswith('.pt'): # pytorch format model.load_state_dict(torch.load(weights, map_location=device)['model']) else: # darknet format _ = load_darknet_weights(model, weights) # Eval mode model.to(device).eval() # Half precision opt.half = opt.half and device.type != 'cpu' # half precision only supported on CUDA if opt.half: model.half() # Set Dataloader vid_path, vid_writer = None, None if opt.webcam: save_images = False dataloader = LoadWebcam(img_size=img_size, half=opt.half) else: dataloader = LoadImages(images, img_size=img_size, half=opt.half) # Get classes and colors # parse_data_cfg(data)['names']:得到类别名称文件路径 names=data/coco.names classes = load_classes(parse_data_cfg(data)['names']) # 得到类别名列表: ['person', 'bicycle'...] colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))] # 对于每种类别随机使用一种颜色画框 # Run inference t0 = time.time() for i, (path, img, im0, vid_cap) in enumerate(dataloader): t = time.time() # if i < 500 or i % 5 == 0: # continue save_path = str(Path(output) / Path(path).name) # 保存的路径 # Get detections shape: (3, 416, 320) img = torch.from_numpy(img).unsqueeze(0).to(device) # torch.Size([1, 3, 416, 320]) pred, _ = model(img) # 经过处理的网络预测，和原始的 det = non_max_suppression(pred.float(), conf_thres, nms_thres)[0] # torch.Size([5, 7]) if det is not None and len(det) > 0: # Rescale boxes from 416 to true image size 映射到原图 det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() # Print results to screen image 1/3 data\samples\000493.jpg: 288x416 5 persons, Done. (0.869s) print('%gx%g ' % img.shape[2:], end='') # print image size '288x416' for c in det[:, -1].unique(): # 对图片的所有类进行遍历循环 n = (det[:, -1] == c).sum() # 得到了当前类别的个数，也可以用来统计数目 if classes[int(c)] == 'person': print('%g %ss' % (n, classes[int(c)]), end=', ') # 打印个数和类别'5 persons' # Draw bounding boxes and labels of detections # (x1y1x2y2, obj_conf, class_conf, class_pred) count = 0 gallery_img = [] gallery_loc = [] for *xyxy, conf, cls_conf, cls in det: # 对于最后的预测框进行遍历 # *xyxy: 对于原图来说的左上角右下角坐标: [tensor(349.), tensor(26.), tensor(468.), tensor(341.)] if save_txt: # Write to file with open(save_path + '.txt', 'a') as file: file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf)) # Add bbox to the image label = '%s %.2f' % (classes[int(cls)], conf) # 'person 1.00' if classes[int(cls)] == 'person': #plot_one_bo x(xyxy, im0, label=label, color=colors[int(cls)]) xmin = int(xyxy[0]) ymin = int(xyxy[1]) xmax = int(xyxy[2]) ymax = int(xyxy[3]) w = xmax - xmin # 233 h = ymax - ymin # 602 # 如果检测到的行人太小了，感觉意义也不大 # 这里需要根据实际情况稍微设置下 if w*h > 500: gallery_loc.append((xmin, ymin, xmax, ymax)) crop_img = im0[ymin:ymax, xmin:xmax] # HWC (602, 233, 3) crop_img = Image.fromarray(cv2.cvtColor(crop_img, cv2.COLOR_BGR2RGB)) # PIL: (233, 602) crop_img = build_transforms(reidCfg)(crop_img).unsqueeze(0) # torch.Size([1, 3, 256, 128]) gallery_img.append(crop_img) if gallery_img: gallery_img = torch.cat(gallery_img, dim=0) # torch.Size([7, 3, 256, 128]) gallery_img = gallery_img.to(device) gallery_feats = reidModel(gallery_img) # torch.Size([7, 2048]) print("The gallery feature is normalized") gallery_feats = torch.nn.functional.normalize(gallery_feats, dim=1, p=2) # 计算出查询图片的特征向量 # m: 2 # n: 7 m, n = query_feats.shape[0], gallery_feats.shape[0] distmat = torch.pow(query_feats, 2).sum(dim=1, keepdim=True).expand(m, n) + \ torch.pow(gallery_feats, 2).sum(dim=1, keepdim=True).expand(n, m).t() # out=(beta∗M)+(alpha∗mat1@mat2) # qf^2 + gf^2 - 2 * qf@gf.t() # distmat - 2 * qf@gf.t() # distmat: qf^2 + gf^2 # qf: torch.Size([2, 2048]) # gf: torch.Size([7, 2048]) distmat.addmm_(1, -2, query_feats, gallery_feats.t()) # distmat = (qf - gf)^2 # distmat = np.array([[1.79536, 2.00926, 0.52790, 1.98851, 2.15138, 1.75929, 1.99410], # [1.78843, 1.96036, 0.53674, 1.98929, 1.99490, 1.84878, 1.98575]]) distmat = distmat.cpu().numpy() # : (3, 12) distmat = distmat.sum(axis=0) / len(query_feats) # 平均一下query中同一行人的多个结果 index = distmat.argmin() if distmat[index] < dist_thres: print('距离：%s'%distmat[index]) plot_one_box(gallery_loc[index], im0, label='find!', color=colors[int(cls)]) # cv2.imshow('person search', im0) # cv2.waitKey() print('Done. (%.3fs)' % (time.time() - t)) if opt.webcam: # Show live webcam cv2.imshow(weights, im0) if save_images: # Save image with detections if dataloader.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 fps = vid_cap.get(cv2.CAP_PROP_FPS) width = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (width, height)) vid_writer.write(im0) if save_images: print('Results saved to %s' % os.getcwd() + os.sep + output) if platform == 'darwin': # macos os.system('open ' + output + ' ' + save_path) print('Done. (%.3fs)' % (time.time() - t0)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--cfg', type=str, default='cfg/yolov3.cfg', help="模型配置文件路径") parser.add_argument('--data', type=str, default='data/coco.data', help="数据集配置文件所在路径") parser.add_argument('--weights', type=str, default='weights/yolov3.weights', help='模型权重文件路径') parser.add_argument('--images', type=str, default='data/samples', help='需要进行检测的图片文件夹') parser.add_argument('-q', '--query', default=r'query', help='查询图片的读取路径.') parser.add_argument('--img-size', type=int, default=416, help='输入分辨率大小') parser.add_argument('--conf-thres', type=float, default=0.1, help='物体置信度阈值') parser.add_argument('--nms-thres', type=float, default=0.4, help='NMS阈值') parser.add_argument('--dist_thres', type=float, default=1.0, help='行人图片距离阈值，小于这个距离，就认为是该行人') parser.add_argument('--fourcc', type=str, default='mp4v', help='fourcc output video codec (verify ffmpeg support)') parser.add_argument('--output', type=str, default='output', help='检测后的图片或视频保存的路径') parser.add_argument('--half', default=False, help='是否采用半精度FP16进行推理') parser.add_argument('--webcam', default=False, help='是否使用摄像头进行检测') opt = parser.parse_args() print(opt) with torch.no_grad(): detect(opt.cfg, opt.data, opt.weights, images=opt.images, img_size=opt.img_size, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres, dist_thres=opt.dist_thres, fourcc=opt.fourcc, output=opt.output)