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| import argparse | |
| import json | |
| 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 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): | |
| results = [] | |
| 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') | |
| # Initialize | |
| set_logging() | |
| device = select_device(opt.device) | |
| 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: | |
| 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) | |
| # 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 | |
| for *xyxy, conf, cls in reversed(det): | |
| xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist() # ADDED BY FRANCOIS | |
| results.append({ | |
| "boundingBox": { | |
| "x": int(xywh[0] - xywh[2]/2), | |
| "y": int(xywh[1] - xywh[3]/2), | |
| "width": int(xywh[2]), | |
| "height": int(xywh[3]) | |
| }, | |
| "confidence": float(conf) | |
| }) | |
| print("\n$$$") | |
| print(json.dumps(results)) | |
| print("$$$\n") | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') | |
| parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 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']: | |
| detect() | |
| strip_optimizer(opt.weights) | |
| else: | |
| detect() | |