import argparse import os import platform import sys from pathlib import Path import math import torch import numpy as np from deep_sort_pytorch.utils.parser import get_config from deep_sort_pytorch.deep_sort import DeepSort from collections import deque FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLO root directory if str(ROOT) not in sys.path: sys.path.append(str(ROOT)) # add ROOT to PATH ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative from models.common import DetectMultiBackend from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import select_device, smart_inference_mode # def initialize_deepsort(): # # Create the Deep SORT configuration object and load settings from the YAML file # cfg_deep = get_config() # cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml") # # Initialize the DeepSort tracker # deepsort = DeepSort(cfg_deep.DEEPSORT.REID_CKPT, # max_dist=cfg_deep.DEEPSORT.MAX_DIST, # # min_confidence parameter sets the minimum tracking confidence required for an object detection to be considered in the tracking process # min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE, # #nms_max_overlap specifies the maximum allowed overlap between bounding boxes during non-maximum suppression (NMS) # nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, # #max_iou_distance parameter defines the maximum intersection-over-union (IoU) distance between object detections # max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE, # # Max_age: If an object's tracking ID is lost (i.e., the object is no longer detected), this parameter determines how many frames the tracker should wait before assigning a new id # max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, # #nn_budget: It sets the budget for the nearest-neighbor search. # nn_budget=cfg_deep.DEEPSORT.NN_BUDGET, # use_cuda=False # ) # return deepsort #deepsort = initialize_deepsort() data_deque = {} def classNames(): cocoClassNames = ["Bus", "Bike", "Car", "Pedestrian", "Truck" ] return cocoClassNames className = classNames() def colorLabels(classid): if classid == 0: #Bus color = (0, 0, 255) elif classid == 1: #Bike 250, 247, 0 color = (0,148,255) elif classid == 2: #Car color = (0, 255, 10) elif classid == 3: #Pedestrian color = (250,247,0) else: #Truck color = (235,0,255) return tuple(color) def draw_boxes(frame, bbox_xyxy, draw_trails, identities=None, categories=None, offset=(0,0)): height, width, _ = frame.shape for key in list(data_deque): if key not in identities: data_deque.pop(key) for i, box in enumerate(bbox_xyxy): x1, y1, x2, y2 = [int(i) for i in box] x1 += offset[0] y1 += offset[0] x2 += offset[0] y2 += offset[0] #Find the center point of the bounding box center = int((x1+x2)/2), int((y1+y2)/2) cat = int(categories[i]) if categories is not None else 0 color = colorLabels(cat) #color = [255,0,0]#compute_color_labels(cat) id = int(identities[i]) if identities is not None else 0 # create new buffer for new object if id not in data_deque: data_deque[id] = deque(maxlen= 64) data_deque[id].appendleft(center) cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2) name = className[cat] label = str(id) + ":" + name text_size = cv2.getTextSize(label, 0, fontScale=0.5, thickness=2)[0] c2 = x1 + text_size[0], y1 - text_size[1] - 3 cv2.rectangle(frame, (x1, y1), c2, color, -1) cv2.putText(frame, label, (x1, y1 - 2), 0, 0.5, [255, 255, 255], thickness=1, lineType=cv2.LINE_AA) cv2.circle(frame,center, 2, (0,255,0), cv2.FILLED) if draw_trails: # draw trail for i in range(1, len(data_deque[id])): # check if on buffer value is none if data_deque[id][i - 1] is None or data_deque[id][i] is None: continue # generate dynamic thickness of trails thickness = int(np.sqrt(64 / float(i + i)) * 1.5) # draw trails cv2.line(frame, data_deque[id][i - 1], data_deque[id][i], color, thickness) return frame @smart_inference_mode() def run_deepsort( weights=ROOT / 'yolo.pt', # model path or triton URL source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) data=ROOT / 'data/coco.yaml', # dataset.yaml path imgsz=(640, 640), # inference size (height, width) conf_thres=0.25, # confidence threshold iou_thres=0.45, # NMS IOU threshold max_det=1000, # maximum detections per image device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu view_img=False, # show results nosave=False, # do not save images/videos classes=None, # filter by class: --class 0, or --class 0 2 3 agnostic_nms=False, # class-agnostic NMS augment=False, # augmented inference visualize=False, # visualize features update=False, # update all models project=ROOT / 'runs/detect', # save results to project/name name='exp', # save results to project/name exist_ok=False, # existing project/name ok, do not increment half=False, # use FP16 half-precision inference dnn=False, # use OpenCV DNN for ONNX inference vid_stride=1, # video frame-rate stride draw_trails = False, ): source = str(source) save_img = not nosave and not source.endswith('.txt') # save inference images is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file) screenshot = source.lower().startswith('screen') if is_url and is_file: source = check_file(source) # download # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run save_dir.mkdir(parents=True, exist_ok=True) # make dir #Initalize deepsort # Create the Deep SORT configuration object and load settings from the YAML file cfg_deep = get_config() cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml") # Initialize the DeepSort tracker deepsort = DeepSort(cfg_deep.DEEPSORT.REID_CKPT, max_dist=cfg_deep.DEEPSORT.MAX_DIST, # min_confidence parameter sets the minimum tracking confidence required for an object detection to be considered in the tracking process min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE, #nms_max_overlap specifies the maximum allowed overlap between bounding boxes during non-maximum suppression (NMS) nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, #max_iou_distance parameter defines the maximum intersection-over-union (IoU) distance between object detections max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE, # Max_age: If an object's tracking ID is lost (i.e., the object is no longer detected), this parameter determines how many frames the tracker should wait before assigning a new id max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, #nn_budget: It sets the budget for the nearest-neighbor search. nn_budget=cfg_deep.DEEPSORT.NN_BUDGET, use_cuda=True ) # Load model device = select_device(device) model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) stride, names, pt = model.stride, model.names, model.pt imgsz = check_img_size(imgsz, s=stride) # check image size # Dataloader bs = 1 # batch_size if webcam: view_img = check_imshow(warn=True) dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) bs = len(dataset) elif screenshot: dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) vid_path, vid_writer = [None] * bs, [None] * bs # Run inference model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) for path, im, im0s, vid_cap, s in dataset: with dt[0]: im = torch.from_numpy(im).to(model.device) im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 if len(im.shape) == 3: im = im[None] # expand for batch dim # Inference with dt[1]: visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False pred = model(im, augment=augment, visualize=visualize) # pred = pred[0][1] # NMS with dt[2]: pred = pred[0][1] if isinstance(pred[0], list) else pred[0] # single model or ensemble pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) # Second-stage classifier (optional) # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) # Process predictions for i, det in enumerate(pred): # per image seen += 1 if webcam: # batch_size >= 1 p, im0, frame = path[i], im0s[i].copy(), dataset.count s += f'{i}: ' else: p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # im.jpg txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt s += '%gx%g ' % im.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh ims = im0.copy() if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # Print results for c in det[:, 5].unique(): n = (det[:, 5] == c).sum() # detections per class s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string xywh_bboxs = [] confs = [] oids = [] outputs = [] # Write results for *xyxy, conf, cls in reversed(det): x1, y1, x2, y2 = xyxy x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) #Find the Center Coordinates for each of the detected object cx, cy = int((x1+x2)/2), int((y1+y2)/2) #Find the Width and Height of the Boundng box bbox_width = abs(x1-x2) bbox_height = abs(y1-y2) xcycwh = [cx, cy, bbox_width, bbox_height] xywh_bboxs.append(xcycwh) conf = math.ceil(conf*100)/100 confs.append(conf) classNameInt = int(cls) oids.append(classNameInt) xywhs = torch.tensor(xywh_bboxs) confss = torch.tensor(confs) outputs = deepsort.update(xywhs, confss, oids, ims) if len(outputs) > 0: bbox_xyxy = outputs[:, :4] identities = outputs[:, -2] object_id = outputs[:, -1] draw_boxes(ims, bbox_xyxy, draw_trails, identities, object_id) # Stream results if view_img: if platform.system() == 'Linux' and p not in windows: windows.append(p) cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) cv2.resizeWindow(str(p), ims.shape[1], ims.shape[0]) cv2.imshow(str(p), ims) cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if vid_path[i] != save_path: # new video vid_path[i] = save_path if isinstance(vid_writer[i], cv2.VideoWriter): vid_writer[i].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, ims.shape[1], ims.shape[0] save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc('m','p','4','v'), fps, (w, h)) vid_writer[i].write(ims) # Print time (inference-only) LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") if update: strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) return save_path def parse_opt(): parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolo.pt', help='model path or triton URL') parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') 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='show results') parser.add_argument('--nosave', action='store_true', help='do not save images/videos') parser.add_argument('--draw-trails', action='store_true', help='do not drawtrails') parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features') parser.add_argument('--update', action='store_true', help='update all models') parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name') parser.add_argument('--name', default='exp', help='save results to project/name') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') opt = parser.parse_args() opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand print_args(vars(opt)) return opt def main(opt): # check_requirements(exclude=('tensorboard', 'thop')) run_deepsort(**vars(opt)) if __name__ == "__main__": opt = parse_opt() main(opt)