import os import sys import time from pathlib import Path import streamlit as st import cv2 import torch import torch.backends.cudnn as cudnn sys.path.insert(0, './yolov5') # Path for internal module without changing base from yolov5.models.common import DetectMultiBackend from yolov5.utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams from yolov5.utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh) from yolov5.utils.general import set_logging from yolov5.utils.plots import Annotator, colors, save_one_box, plot_one_box from yolov5.utils.torch_utils import select_device, time_sync from deep_sort.utils.parser import get_config from deep_sort.deep_sort import DeepSort from graphs import bbox_rel,draw_boxes from collections import Counter import psutil import subprocess FILE = Path(__file__).resolve() ROOT = FILE.parents[0] # YOLOv5 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 def get_gpu_memory(): result = subprocess.check_output( [ 'nvidia-smi', '--query-gpu=memory.used', '--format=csv,nounits,noheader' ], encoding='utf-8') gpu_memory = [int(x) for x in result.strip().split('\n')] return gpu_memory[0] @torch.no_grad() def detect(weights=ROOT / 'yolov5s.pt', # model.pt path(s) source=ROOT / 'yolov5/data/images', # file/dir/URL/glob, 0 for webcam data=ROOT / 'yolov5/data/coco128.yaml', # dataset.yaml path stframe=None, #stgraph=None, kpi1_text="", kpi2_text="", kpi3_text="", js1_text="",js2_text="",js3_text="", 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 save_txt=False, # save results to *.txt save_conf=False, # save confidences in --save-txt labels save_crop=False, # save cropped prediction boxes 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 line_thickness=1, # bounding box thickness (pixels) hide_labels=False, # hide labels hide_conf=False, # hide confidences half=False, # use FP16 half-precision inference dnn=False, display_labels=False, config_deepsort="deep_sort/configs/deep_sort.yaml", #Deep Sort configuration conf_thres_drift = 0.75, save_poor_frame__ = False, inf_ov_1_text="", inf_ov_2_text="",inf_ov_3_text="", inf_ov_4_text="", fps_warn="",fps_drop_warn_thresh=8 ): save_img = not nosave and not source.endswith('.txt') # save inference images webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( ('rtsp://', 'rtmp://', 'http://', 'https://')) ## initialize deepsort cfg = get_config() cfg.merge_from_file(config_deepsort) deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT, max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, 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) # Directories save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir if save_poor_frame__: try: os.mkdir("drift_frames") except: print("Folder exists, overwriting...") # Initialize set_logging() device = select_device(device) half &= device.type != 'cpu' # half precision only supported on CUDA # Load model device = select_device(device) model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data) stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine imgsz = check_img_size(imgsz, s=stride) # check image size # Half half &= (pt or jit or onnx or engine) and device.type != 'cpu' # FP16 supported on limited backends with CUDA if pt or jit: model.model.half() if half else model.model.float() # 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']).to(device).eval() # Dataloader if webcam: #view_img = check_imshow() cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt) bs = len(dataset) # batch_size else: dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt) bs = 1 # batch_size vid_path, vid_writer = [None] * bs, [None] * bs # Run inference t0 = time.time() dt, seen = [0.0, 0.0, 0.0], 0 prev_time = time.time() selected_names = names.copy() global_graph_dict = dict() global_drift_dict = dict() test_drift = [] frame_num = -1 poor_perf_frame_counter=0 mapped_ = dict() min_FPS = 10000 max_FPS = -1 for path, im, im0s, vid_cap, s in dataset: frame_num = frame_num+1 t1 = time_sync() im = torch.from_numpy(im).to(device) im = im.half() if half 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 t2 = time_sync() dt[0] += t2 - t1 # Inference visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False pred = model(im, augment=augment, visualize=visualize) t3 = time_sync() dt[1] += t3 - t2 # NMS pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) dt[2] += time_sync() - t3 # Process predictions class_count = 0 drift_dict = dict() 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 imc = im0.copy() if save_crop else im0 # for save_crop annotator = Annotator(im0, line_width=line_thickness, example=str(names)) if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round() # Print results names_ = [] cnt = [] 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 names_.append(names[int(c)]) cnt.append(int(n.detach().cpu().numpy())) mapped_.update(dict(zip(names_, cnt))) global_graph_dict = Counter(global_graph_dict) + Counter(mapped_) bbox_xywh = [] confs = [] # Adapt detections to deep sort input format for *xyxy, conf, cls in det: x_c, y_c, bbox_w, bbox_h = bbox_rel(*xyxy) obj = [x_c, y_c, bbox_w, bbox_h] bbox_xywh.append(obj) confs.append([conf.item()]) # print("conf : {0}, conf_t : {1}".format(conf, conf_thres)) if conf 0: # print("Outputs :", outputs) bbox_xyxy = outputs[:, :4] identities = outputs[:, -1] draw_boxes(im0, bbox_xyxy, identities) # Write MOT compliant results to file if save_txt and len(outputs) != 0: for j, output in enumerate(outputs): bbox_left = output[0] bbox_top = output[1] bbox_w = output[2] bbox_h = output[3] identity = output[-1] with open(txt_path, 'a') as f: f.write(('%g ' * 10 + '\n') % (frame_idx, identity, bbox_left, bbox_top, bbox_w, bbox_h, -1, -1, -1, -1)) # label format # Write results Label 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 line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') if save_img or save_crop or view_img or display_labels: # Add bbox to image c = int(cls) # integer class label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness) if save_crop: save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) else: deepsort.increment_ages() # Stream results if view_img: cv2.imshow(str(p), im0) cv2.waitKey(1) # 1 millisecond # Save results (image with detections) if save_img: if dataset.mode == 'image': cv2.imwrite(save_path, im0) else: # 'video' or 'stream' 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] save_path += '.mp4' vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) vid_writer.write(im0) curr_time = time.time() fps_ = curr_time - prev_time fps_ = round(1/round(fps_, 3),1) prev_time = curr_time js1_text.write(str(psutil.virtual_memory()[2])+"%") js2_text.write(str(psutil.cpu_percent())+'%') try: js3_text.write(str(get_gpu_memory())+' MB') except: js3_text.write(str('NA')) kpi1_text.write(str(fps_)+' FPS') if fps_ < fps_drop_warn_thresh: fps_warn.warning(f"FPS dropped below {fps_drop_warn_thresh}") kpi2_text.write(mapped_) kpi3_text.write(global_graph_dict) inf_ov_1_text.write(test_drift) inf_ov_2_text.write(poor_perf_frame_counter) if fps_max_FPS: inf_ov_4_text.write(fps_) max_FPS = fps_ stframe.image(im0, channels="BGR", use_column_width=True) if save_txt or save_img: s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' print(f"Results saved to {save_dir}{s}") if update: strip_optimizer(weights) # update model (to fix SourceChangeWarning) if vid_cap: vid_cap.release()