def rtsp_to_mongodb(): with open("/home/asyed/airflow/dags/parameters.json") as f: parms = json.load(f) agnostic_nms = parms["agnostic_nms"] augment = parms["augment"] classes = parms["classes"] conf_thres = parms["conf_thres"] config_deepsort = parms["config_deepsort"] deep_sort_model = parms["deep_sort_model"] device = parms["device"] dnn = False evaluate = parms["evaluate"] exist_ok = parms["exist_ok"] fourcc = parms["fourcc"] half = False print(device) imgsz = parms["imgsz"] iou_thres = parms["iou_thres"] max_det = parms["max_det"] name = parms["name"] # save_vid = parms["save_vid"] #show_vid = parms["show_vid"] source = parms["source"] visualize = parms["visualize"] yolo_model = parms["yolo_model"] webcam = parms["webcam"] save_txt = parms["save_txt"] homography = np.array(parms["homography"]) url = "mongodb://localhost:27017" client = MongoClient(url) db = client.trajectory_database today_date = date.today().strftime("%m-%d-%y") new = "file_image_coordinates_" + today_date collection = db[new] cfg = get_config() cfg.merge_from_file(config_deepsort) deepsort = DeepSort(deep_sort_model, max_dist=cfg.DEEPSORT.MAX_DIST, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, use_cuda=True) device = select_device(device) half &= device.type != 'cpu' # half precision only supported on CUDA # The MOT16 evaluation runs multiple inference streams in parallel, each one writing to # its own .txt file. Hence, in that case, the output folder is not restored # make new output folder # Load model device = select_device(device) model = DetectMultiBackend(yolo_model, device=device, dnn=dnn) stride, names, pt, jit, _ = model.stride, model.names, model.pt, model.jit, model.onnx imgsz = check_img_size(imgsz, s=stride) # check image size # Half half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA if pt: model.model.half() if half else model.model.float() # Set Dataloader vid_path, vid_writer = None, None # Check if environment supports image displays cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit) bs = len(dataset) # batch_size vid_path, vid_writer = [None] * bs, [None] * bs # Get names and colors names = model.module.names if hasattr(model, 'module') else model.names if pt and device.type != 'cpu': model( torch.zeros(1, 3, *imgsz).to(device).type_as( next(model.model.parameters()))) # warmup # global framess_im2 dt, seen = [0.0, 0.0, 0.0, 0.0], 0 # arr = None past = [] for frame_idx, (path, img, im0s, vid_cap, s) in enumerate(dataset): t1 = time_sync() img = torch.from_numpy(img).to(device) # print("raw_frame",img.shape) 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) t2 = time_sync() dt[0] += t2 - t1 pred = model(img, augment=augment, visualize=visualize) t3 = time_sync() dt[1] += t3 - t2 pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) dt[2] += time_sync() - t3 # Process detections # dets_per_img = [] for i, det in enumerate(pred): # detections per image seen += 1 if webcam: # batch_size >= 1 p, im0, _ = path[i], im0s[i].copy(), dataset.count s += f'{i}: ' else: p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0) annotator = Annotator(im0, line_width=2, pil=not ascii) 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 += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string xywhs = xyxy2xywh(det[:, 0:4]) confs = det[:, 4] clss = det[:, 5] # pass detections to deepsort t4 = time_sync() outputs = deepsort.update(xywhs.cpu(), confs.cpu(), clss.cpu(), im0) t5 = time_sync() dt[3] += t5 - t4 if len(outputs) > 0: for j, (output, conf) in enumerate(zip(outputs, confs)): bboxes = output[0:4] id = output[4] cls = output[5] c = int(cls) # integer class label = f'{id} {names[c]} {conf:.2f}' annotator.box_label(bboxes, label, color=colors(c, True)) if save_txt: # to MOT format bbox_left = output[0] bbox_top = output[1] bbox_w = output[2] - output[0] bbox_h = output[3] - output[1] # bbox_left = bbox_left + bbox_h bbox_top = bbox_top + bbox_h agent_data = { 'frame': int(frame_idx + 1), 'agent_id': int(id), "labels": str(names[c]), "x": int(bbox_left), "y": int(bbox_top) } print("agent", agent_data) collection.insert_one(agent_data) #db.object_detection.insert_one(agent_data) #db.pedestrian_detection_15_june.insert_one(agent_data) #db.test_21_july.insert_one(agent_data) LOGGER.info( f'{s}Done. YOLO:({t3 - t2:.3f}s), DeepSort:({t5 - t4:.3f}s)' ) else: deepsort.increment_ages() LOGGER.info('No detections') im0 = annotator.result()
def detect(opt): memory = {} counter = 0 out, source, yolo_model, deep_sort_model, show_vid, save_vid, save_txt, imgsz, evaluate, half, project, name, exist_ok= \ opt.output, opt.source, opt.yolo_model, opt.deep_sort_model, opt.show_vid, opt.save_vid, \ opt.save_txt, opt.imgsz, opt.evaluate, opt.half, opt.project, opt.name, opt.exist_ok webcam = source == '0' or source.startswith('rtsp') or source.startswith( 'http') or source.endswith('.txt') # initialize deepsort cfg = get_config() cfg.merge_from_file(opt.config_deepsort) deepsort = DeepSort(deep_sort_model, torch.device("cpu"), max_dist=cfg.DEEPSORT.MAX_DIST, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET) # Initialize device = select_device(opt.device) half &= device.type != 'cpu' # half precision only supported on CUDA # The MOT16 evaluation runs multiple inference streams in parallel, each one writing to # its own .txt file. Hence, in that case, the output folder is not restored if not evaluate: if os.path.exists(out): pass shutil.rmtree(out) # delete output folder os.makedirs(out) # make new output folder # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run save_dir.mkdir(parents=True, exist_ok=True) # make dir # Load model device = select_device(device) model = DetectMultiBackend(yolo_model, device=device, dnn=opt.dnn) stride, names, pt, jit, _ = model.stride, model.names, model.pt, model.jit, model.onnx imgsz = check_img_size(imgsz, s=stride) # check image size # Half half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA if pt: model.model.half() if half else model.model.float() # Set Dataloader vid_path, vid_writer = None, None # Check if environment supports image displays if show_vid: show_vid = check_imshow() # Dataloader if webcam: show_vid = check_imshow() cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit) bs = len(dataset) # batch_size else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit) bs = 1 # batch_size vid_path, vid_writer = [None] * bs, [None] * bs # Get names and colors names = model.module.names if hasattr(model, 'module') else model.names # extract what is in between the last '/' and last '.' txt_file_name = source.split('/')[-1].split('.')[0] txt_path = str(Path(save_dir)) + '/' + txt_file_name + '.txt' if pt and device.type != 'cpu': model( torch.zeros(1, 3, *imgsz).to(device).type_as( next(model.model.parameters()))) # warmup dt, seen = [0.0, 0.0, 0.0, 0.0], 0 regionid = set() for frame_idx, (path, img, im0s, vid_cap, s) in enumerate(dataset): t1 = time_sync() 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) t2 = time_sync() dt[0] += t2 - t1 # Inference visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if opt.visualize else False pred = model(img, augment=opt.augment, visualize=visualize) t3 = time_sync() dt[1] += t3 - t2 # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms, max_det=opt.max_det) dt[2] += time_sync() - t3 # Process detections for i, det in enumerate(pred): # detections per image seen += 1 if webcam: # batch_size >= 1 p, im0, _ = path[i], im0s[i].copy(), dataset.count s += f'{i}: ' else: p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg, vid.mp4, ... s += '%gx%g ' % img.shape[2:] # print string annotator = Annotator(im0, line_width=2, font='Arial.ttf', pil=not ascii) if det is not None and len(det): tboxes = [] indexIDs = [] previous = memory.copy() memory = {} # 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 += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string xywhs = xyxy2xywh(det[:, 0:4]) confs = det[:, 4] clss = det[:, 5] # pass detections to deepsort t4 = time_sync() outputs = deepsort.update(xywhs.cpu(), confs.cpu(), clss.cpu(), im0) t5 = time_sync() dt[3] += t5 - t4 # draw boxes for visualization if len(outputs) > 0: for j, (output, conf) in enumerate(zip(outputs, confs)): bboxes = output[0:4] id = output[4] cls = output[5] roi = [(0, 0), (640, 0), (640, 380), (0, 380)] (x, y) = (int(bboxes[0]), int(bboxes[1])) (w, h) = (int(bboxes[2]), int(bboxes[3])) inside = cv2.pointPolygonTest(np.array(roi), (x, h), False) if inside > 0: regionid.add(id) c = int(cls) # integer class label = f' {names[c]} {conf:.2f}' cv2.putText(im0, "count =" + str(len(regionid)), (20, 50), 0, 1, (100, 200, 0), 2) annotator.box_label(bboxes, label, color=colors(c, True)) if save_txt: # to MOT format bbox_left = output[0] bbox_top = output[1] bbox_w = output[2] - output[0] bbox_h = output[3] - output[1] # Write MOT compliant results to file with open(txt_path, 'a') as f: f.write(('%g ' * 10 + '\n') % ( frame_idx + 1, id, bbox_left, # MOT format bbox_top, bbox_w, bbox_h, -1, -1, -1, -1)) LOGGER.info( f'{s}Done. YOLO:({t3 - t2:.3f}s), DeepSort:({t5 - t4:.3f}s)' ) LOGGER.info(f'counter = {len(regionid)}') else: deepsort.increment_ages() LOGGER.info('No detections') # Stream results im0 = annotator.result() if show_vid: cv2.imshow(str(p), im0) if cv2.waitKey(1) == ord('q'): # q to quit raise StopIteration # Save results (image with detections) if save_vid: if vid_path != save_path: # new video vid_path = save_path if isinstance(vid_writer, cv2.VideoWriter): vid_writer.release() # release previous video writer if vid_cap: # video 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)) else: # stream fps, w, h = 30, im0.shape[1], im0.shape[0] vid_writer = cv2.VideoWriter( save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer.write(im0) # Print results t = tuple(x / seen * 1E3 for x in dt) # speeds per image LOGGER.info( f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS, %.1fms deep sort update \ per image at shape {(1, 3, *imgsz)}' % t) if save_txt or save_vid: print('Results saved to %s' % save_path) if platform == 'darwin': # MacOS os.system('open ' + save_path)