import cv2 from numpy import random from collections import deque import numpy as np import math import torch import torch.backends.cudnn as cudnn from utils.google_utils import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import ( check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, strip_optimizer) from utils.plots import plot_one_box from utils.torch_utils import select_device, load_classifier, time_synchronized from models.models import * from utils.datasets import * from utils.general import * from deep_sort_pytorch.utils.parser import get_config from deep_sort_pytorch.deep_sort import DeepSort from byte_track.bytetracker import ByteTrack from yolov7.yolov7_detector import YOLOv7Detector def load_classes(path): # Loads *.names file at 'path' with open(path, 'r') as f: names = f.read().split('\n') return list(filter(None, names)) # filter removes empty strings (such as last line) global names names = load_classes('data/coco.names') colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) data_deque = {} speed_four_line_queue = {} object_counter = {} # line1 = [(250,450), (1000, 450)] line2 = [(200,500), (1050, 500)] def xyxy_to_xywh(*xyxy): """" Calculates the relative bounding box from absolute pixel values. """ bbox_left = min([xyxy[0].item(), xyxy[2].item()]) bbox_top = min([xyxy[1].item(), xyxy[3].item()]) bbox_w = abs(xyxy[0].item() - xyxy[2].item()) bbox_h = abs(xyxy[1].item() - xyxy[3].item()) x_c = (bbox_left + bbox_w / 2) y_c = (bbox_top + bbox_h / 2) w = bbox_w h = bbox_h return x_c, y_c, w, h def xyxy_to_tlwh(bbox_xyxy): tlwh_bboxs = [] for i, box in enumerate(bbox_xyxy): x1, y1, x2, y2 = [int(i) for i in box] top = x1 left = y1 w = int(x2 - x1) h = int(y2 - y1) tlwh_obj = [top, left, w, h] tlwh_bboxs.append(tlwh_obj) return tlwh_bboxs def compute_color_for_labels(label): """ Simple function that adds fixed color depending on the class """ if label == 0: #person #BGR color = (85,45,255) elif label == 2: # Car color = (222,82,175) elif label == 3: # Motobike color = (0, 204, 255) elif label == 5: # Bus color = (0, 149, 255) else: color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette] return tuple(color) def draw_border(img, pt1, pt2, color, thickness, r, d): x1,y1 = pt1 x2,y2 = pt2 # Top left cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness) cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness) cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness) # Top right cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness) cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness) cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness) # Bottom left cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness) cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness) cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness) # Bottom right cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness) cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness) cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness) cv2.rectangle(img, (x1 + r, y1), (x2 - r, y2), color, -1, cv2.LINE_AA) cv2.rectangle(img, (x1, y1 + r), (x2, y2 - r - d), color, -1, cv2.LINE_AA) cv2.circle(img, (x1 +r, y1+r), 2, color, 12) cv2.circle(img, (x2 -r, y1+r), 2, color, 12) cv2.circle(img, (x1 +r, y2-r), 2, color, 12) cv2.circle(img, (x2 -r, y2-r), 2, color, 12) return img def UI_box(x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness color = color or [random.randint(0, 255) for _ in range(3)] c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) # cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) if label: tf = max(tl - 1, 1) # font thickness t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] # c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 img = draw_border(img, (c1[0], c1[1] - t_size[1] -3), (c1[0] + t_size[0], c1[1]+3), color, 1, 8, 2) # cv2.line(img, c1, c2, color, 30) # cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) def estimateSpeed(location1, location2): d_pixels = math.sqrt(math.pow(location2[0] - location1[0], 2) + math.pow(location2[1] - location1[1], 2)) ppm = 8 #Pixels per Meter d_meters = d_pixels / ppm time_constant = 15 * 3.6 speed = d_meters * time_constant return speed # Return true if line segments AB and CD intersect def intersect(A,B,C,D): return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D) def ccw(A,B,C): return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0]) def draw_boxes(img, bbox, object_id, identities=None, offset=(0, 0)): # cv2.line(img, line2[0], line2[1], (0,200,0), 3) height, width, _ = img.shape # remove tracked point from buffer if object is lost for key in list(data_deque): if key not in identities: data_deque.pop(key) for i, box in enumerate(bbox): x1, y1 = int(box[0]), int(box[1]) x2, y2 = x1 + int(box[2]), y1 + int(box[3]) box=[x1, y1, x2, y2] # x1, y1, x2, y2 = [int(i) for i in box] x1 += offset[0] x2 += offset[0] y1 += offset[1] y2 += offset[1] # box_area = (x2-x1) * (y2-y1) box_height = (y2-y1) # code to find center of bottom edge center = (int((x2+x1)/ 2), int((y2+y2)/2)) # get ID of object 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) speed_four_line_queue[id] = [] color = compute_color_for_labels(int(object_id[i])) obj_name = names[int(object_id[i])] label = '%s' % (obj_name) # add center to buffer data_deque[id].appendleft(center) # print("id ", id) # print("data_deque[id] ", data_deque[id]) if len(data_deque[id]) >= 2: # print("data_deque[id][i-1]", data_deque[id][1], data_deque[id][0]) if intersect(data_deque[id][0], data_deque[id][1], line2[0], line2[1]):# or intersect(data_deque[id][0], data_deque[id][1], line1[0], line1[1]) or intersect(data_deque[id][0], data_deque[id][1], line3[0], line3[1]) or intersect(data_deque[id][0], data_deque[id][1], line4[0], line4[1]) : # cv2.line(img, line2[0], line2[1], (0,100,0), 3) obj_speed = estimateSpeed(data_deque[id][1], data_deque[id][0]) speed_four_line_queue[id].append(obj_speed) if obj_name not in object_counter: object_counter[obj_name] = 1 else: object_counter[obj_name] += 1 try: label = label + " " + str(sum(speed_four_line_queue[id])//len(speed_four_line_queue[id])) except : pass UI_box(box, img, label=label, color=color, line_thickness=2) # 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(img, data_deque[id][i - 1], data_deque[id][i], color, thickness) count = 0 for idx, (key, value) in enumerate(object_counter.items()): # print(idx, key, value) cnt_str = str(key) + ": " + str(value) cv2.line(img, (width - 150 ,25+ (idx*40)), (width,25 + (idx*40)), [85,45,255], 30) cv2.putText(img, cnt_str, (width - 150, 35 + (idx*40)), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA) count += value return img, count def load_yolov7_and_process_each_frame(model, vid_name, enable_GPU, save_video, confidence, assigned_class_id, kpi1_text, kpi2_text, kpi3_text, stframe): data_deque.clear() speed_four_line_queue.clear() object_counter.clear() if model == 'yolov7': weights = 'yolov7/weights/yolov7.onnx' elif model == 'yolov7-tiny': weights = 'yolov7/weights/yolov7-tiny.onnx' else: print('Model Not Found!') exit() detector = YOLOv7Detector(weights=weights, use_cuda=enable_GPU, use_onnx=True) tracker = ByteTrack(detector) # dataset = LoadImages(vid_name, img_size=1280, auto_size=64) vdo = cv2.VideoCapture(vid_name) results = [] start = time.time() count = 0 frame_id = 0 prevTime = 0 fourcc = 'mp4v' # output video codec fps = vdo.get(cv2.CAP_PROP_FPS) w = int(vdo.get(cv2.CAP_PROP_FRAME_WIDTH)) h = int(vdo.get(cv2.CAP_PROP_FRAME_HEIGHT)) vid_writer = cv2.VideoWriter('inference/output/results.mp4', cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) while vdo.isOpened(): # for path, img, im0s, vid_cap in dataset: curr_time = time.time() frame_id +=1 _, img = vdo.read() if _ == False: break # img = cv2.cvtColor(ori_im, cv2.COLOR_BGR2RGB) # img, count, obj_ids = tracker.inference(img, conf_thresh=confidence, classes=assigned_class_id) bboxes, ids, scores, obj_ids = tracker.inference(img, conf_thresh=confidence, classes=assigned_class_id) # print(bboxes[0].shape if len(bboxes)>0 else None) img, count = draw_boxes(img, bboxes, obj_ids, identities=ids) currTime = time.time() fps = 1 / (currTime - prevTime) prevTime = currTime # Save results (image with detections) cv2.line(img, (20,25), (127,25), [85,45,255], 30) cv2.putText(img, f'FPS: {int(fps)}', (11, 35), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA) if save_video: vid_writer.write(img) kpi1_text.write(f"

{fps:.1f}

", unsafe_allow_html=True) kpi2_text.write(f"

{len(data_deque)}

", unsafe_allow_html=True) kpi3_text.write(f"

{count}

", unsafe_allow_html=True) # if frame_id%3==0: # stframe.image(img, channels = 'BGR',use_column_width=True) stframe.image(img, channels = 'BGR',use_column_width=True) end = time.time() print('Done. (%.3fs)' % (end - start)) cv2.destroyAllWindows() vdo.release() vid_writer.release() def load_yolor_and_process_each_frame(vid_name, enable_GPU, confidence, assigned_class_id, kpi1_text, kpi2_text, kpi3_text, stframe): data_deque.clear() speed_four_line_queue.clear() object_counter.clear() out, source, weights, save_txt, imgsz, cfg = \ 'inference/output', vid_name, 'yolor_p6.pt', False, 1280, 'cfg/yolor_p6.cfg' #webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt') webcam = source == 0 or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt') # initialize deepsort cfg_deep = get_config() cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml") # attempt_download("deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7", repo='mikel-brostrom/Yolov5_DeepSort_Pytorch') deepsort = DeepSort(cfg_deep.DEEPSORT.REID_CKPT, max_dist=cfg_deep.DEEPSORT.MAX_DIST, min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE, nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE, max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, nn_budget=cfg_deep.DEEPSORT.NN_BUDGET, use_cuda=True) # Initialize GPU if enable_GPU: device = select_device('gpu') else: device = select_device('cpu') if os.path.exists(out): shutil.rmtree(out) # delete output folder os.makedirs(out) # make new output folder half = device.type != 'cpu' # half precision only supported on CUDA # Load model model = Darknet(cfg, imgsz)#.cuda() model.load_state_dict(torch.load(weights, map_location=device)['model']) model.to(device).eval() 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: save_img = True print("HEREHERER") # 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, auto_size=64) # 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 prevTime = 0 count = 0 if webcam: # code for only webcam vid = cv2.VideoCapture(0) while vid.isOpened(): ret, img = vid.read() if not ret: continue im0s = img.copy() print(im0s.shape) img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to bsx3x416x416 print(img.shape) img = torch.from_numpy(img.copy()).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) print(img.shape) # Inference t1 = time_synchronized() pred = model(img)[0] # Apply NMS pred = non_max_suppression(pred, confidence, 0.5, classes=assigned_class_id, agnostic=False) t2 = time_synchronized() # Apply Classifier if classify: pred = apply_classifier(pred, modelc, img, im0s) print("HERE") # Process detections for i, det in enumerate(pred): # detections per image p, s, im0 = "webcam_out.mp4", '', 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 xywh_bboxs = [] confs = [] oids = [] # Write results for *xyxy, conf, cls in det: # to deep sort format x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy) xywh_obj = [x_c, y_c, bbox_w, bbox_h] xywh_bboxs.append(xywh_obj) confs.append([conf.item()]) oids.append(int(cls)) # 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 xywhs = torch.Tensor(xywh_bboxs) confss = torch.Tensor(confs) outputs = deepsort.update(xywhs, confss, oids, im0) if len(outputs) > 0: bbox_xyxy = outputs[:, :4] identities = outputs[:, -2] object_id = outputs[:, -1] im0, count = draw_boxes(im0, bbox_xyxy, object_id,identities) # Print time (inference + NMS) print('%sDone. (%.3fs)' % (s, t2 - t1)) currTime = time.time() fps = 1 / (currTime - prevTime) prevTime = currTime cv2.line(im0, (20,25), (127,25), [85,45,255], 30) cv2.putText(im0, f'FPS: {int(fps)}', (11, 35), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA) kpi1_text.write(f"

{'{:.1f}'.format(fps)}

", unsafe_allow_html=True) # # Save results (image with detections) # if save_img: # if dataset.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 # fourcc = 'mp4v' # output video codec # 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)) # vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) # vid_writer.write(im0) # data_deque assign inside yolor.py kpi2_text.write(f"

{len(data_deque)}

", unsafe_allow_html=True) kpi3_text.write(f"

{count}

", unsafe_allow_html=True) stframe.image(im0,channels = 'BGR',use_column_width=True) else: # without webcam for path, img, im0s, vid_cap in dataset: # print(path) # print(img.shape) # print(im0s.shape) # print(vid_cap) 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() print(img.shape) pred = model(img)[0] # Apply NMS pred = non_max_suppression(pred, confidence, 0.5, classes=assigned_class_id, agnostic=False) 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 xywh_bboxs = [] confs = [] oids = [] # Write results for *xyxy, conf, cls in det: # to deep sort format x_c, y_c, bbox_w, bbox_h = xyxy_to_xywh(*xyxy) xywh_obj = [x_c, y_c, bbox_w, bbox_h] xywh_bboxs.append(xywh_obj) confs.append([conf.item()]) oids.append(int(cls)) 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 xywhs = torch.Tensor(xywh_bboxs) confss = torch.Tensor(confs) outputs = deepsort.update(xywhs, confss, oids, im0) if len(outputs) > 0: bbox_xyxy = outputs[:, :4] identities = outputs[:, -2] object_id = outputs[:, -1] im0, count = draw_boxes(im0, bbox_xyxy, object_id,identities) # Print time (inference + NMS) print('%sDone. (%.3fs)' % (s, t2 - t1)) currTime = time.time() fps = 1 / (currTime - prevTime) prevTime = currTime cv2.line(im0, (20,25), (127,25), [85,45,255], 30) cv2.putText(im0, f'FPS: {int(fps)}', (11, 35), 0, 1, [225, 255, 255], thickness=2, lineType=cv2.LINE_AA) kpi1_text.write(f"

{'{:.1f}'.format(fps)}

", unsafe_allow_html=True) # Save results (image with detections) if save_img: if dataset.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 fourcc = 'mp4v' # output video codec 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)) vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) vid_writer.write(im0) # data_deque assign inside yolor.py kpi2_text.write(f"

{len(data_deque)}

", unsafe_allow_html=True) kpi3_text.write(f"

{count}

", unsafe_allow_html=True) stframe.image(im0,channels = 'BGR',use_column_width=True) if save_txt or save_img: print('Results saved to %s' % Path(out)) if platform == 'darwin': # MacOS os.system('open ' + save_path) print('Done. (%.3fs)' % (time.time() - t0)) cv2.destroyAllWindows() vid.release()