362 lines
17 KiB
Python
362 lines
17 KiB
Python
import os
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import cv2
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import time
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import torch
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import argparse
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from pathlib import Path
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from numpy import random
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from random import randint
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import torch.backends.cudnn as cudnn
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import os
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os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
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from models.experimental import attempt_load
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from utils.datasets import LoadStreams, LoadImages
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from utils.general import check_img_size, check_requirements, \
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check_imshow, non_max_suppression, apply_classifier, \
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scale_coords, xyxy2xywh, strip_optimizer, set_logging, \
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increment_path
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from utils.plots import plot_one_box
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from utils.torch_utils import select_device, load_classifier, \
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time_synchronized, TracedModel
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from utils.download_weights import download
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#For SORT tracking
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import skimage
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from sort import *
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#............................... Bounding Boxes Drawing ............................
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"""Function to Draw Bounding boxes"""
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def draw_boxes(img, bbox, identities=None, categories=None, names=None, save_with_object_id=False, path=None,offset=(0, 0)):
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for i, box in enumerate(bbox):
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x1, y1, x2, y2 = [int(i) for i in box]
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x1 += offset[0]
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x2 += offset[0]
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y1 += offset[1]
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y2 += offset[1]
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cat = int(categories[i]) if categories is not None else 0
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id = int(identities[i]) if identities is not None else 0
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data = (int((box[0]+box[2])/2),(int((box[1]+box[3])/2)))
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label = str(id) + ":"+ names[cat]
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(w, h), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 1)
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cv2.rectangle(img, (x1, y1), (x2, y2), (255,0,20), 2)
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cv2.rectangle(img, (x1, y1 - 20), (x1 + w, y1), (255,144,30), -1)
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cv2.putText(img, label, (x1, y1 - 5),cv2.FONT_HERSHEY_SIMPLEX,
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0.6, [255, 255, 255], 1)
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# cv2.circle(img, data, 6, color,-1) #centroid of box
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txt_str = ""
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if save_with_object_id:
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txt_str += "%i %i %f %f %f %f %f %f" % (
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id, cat, int(box[0])/img.shape[1], int(box[1])/img.shape[0] , int(box[2])/img.shape[1], int(box[3])/img.shape[0] ,int(box[0] + (box[2] * 0.5))/img.shape[1] ,
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int(box[1] + (
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box[3]* 0.5))/img.shape[0])
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txt_str += "\n"
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with open(path + '.txt', 'a') as f:
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f.write(txt_str)
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return img
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#..............................................................................
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def detect(save_img=False):
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source, weights, view_img, save_txt, imgsz, trace, colored_trk, save_bbox_dim, save_with_object_id= opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size, opt.no_trace, opt.colored_trk, opt.save_bbox_dim, opt.save_with_object_id
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save_img = not opt.nosave and not source.endswith('.txt') # save inference images
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webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
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('rtsp://', 'rtmp://', 'http://', 'https://'))
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# opt.no_trace 这里控制是否转模型： no opt.no_trace
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#.... Initialize SORT ....
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#.........................
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sort_max_age = 2
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# sort_min_hits = 2
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sort_min_hits = 3
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sort_iou_thresh = 0.2
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# sort_iou_thresh = 0.1
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sort_tracker = Sort(max_age=sort_max_age,
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min_hits=sort_min_hits,
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iou_threshold=sort_iou_thresh)
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#.........................
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#........Rand Color for every trk.......
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rand_color_list = []
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for i in range(0,5005):
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r = randint(0, 255)
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g = randint(0, 255)
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b = randint(0, 255)
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rand_color = (r, g, b)
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rand_color_list.append(rand_color)
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#......................................
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# Directories
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save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
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(save_dir / 'labels' if save_txt or save_with_object_id else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Initialize
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set_logging()
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device = select_device(opt.device)
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half = device.type != 'cpu' # half precision only supported on CUDA
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# Load model
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model = attempt_load(weights, map_location=device) # load FP32 model
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stride = int(model.stride.max()) # model stride
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imgsz = check_img_size(imgsz, s=stride) # check img_size
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if trace:
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model = TracedModel(model, device, opt.img_size)
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if half:
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model.half() # to FP16
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# Second-stage classifier
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classify = False
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if classify:
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modelc = load_classifier(name='resnet101', n=2) # initialize
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modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
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# Set Dataloader
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vid_path, vid_writer = None, None
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if webcam:
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view_img = check_imshow()
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cudnn.benchmark = True # set True to speed up constant image size inference
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dataset = LoadStreams(source, img_size=imgsz, stride=stride)
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride)
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# Get names and colors
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names = model.module.names if hasattr(model, 'module') else model.names
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colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
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# Run inference
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if device.type != 'cpu':
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model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
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old_img_w = old_img_h = imgsz
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old_img_b = 1
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t0 = time.time()
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for path, img, im0s, vid_cap in dataset:
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img = torch.from_numpy(img).to(device)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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# Warmup
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if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
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old_img_b = img.shape[0]
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old_img_h = img.shape[2]
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old_img_w = img.shape[3]
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for i in range(3):
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model(img, augment=opt.augment)[0]
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# Inference
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t1 = time_synchronized()
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pred = model(img, augment=opt.augment)[0]
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t2 = time_synchronized()
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# Apply NMS
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pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
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t3 = time_synchronized()
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# Apply Classifier
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if classify:
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pred = apply_classifier(pred, modelc, img, im0s)
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# 增加去除非需要追踪和检测类别的过滤器
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# Process detections
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for i, det in enumerate(pred): # detections per image
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if webcam: # batch_size >= 1
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p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
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else:
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p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # img.jpg
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
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gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
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if len(det):
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# Rescale boxes from img_size to im0 size
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det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
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det_boxes = []
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for *x, conf, cls_id in det:
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lbl = names[int(cls_id)]
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# if lbl not in ['freighter']: #只输出boat这个标签的label、坐标及置信度
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# continue
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# pass
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x1, y1 = float(x[0]), float(x[1])
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x2, y2 = float(x[2]), float(x[3])
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conf=float(conf.cpu().numpy())
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cls_id=float(cls_id)
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det_boxes.append(
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(x1, y1, x2, y2, conf,cls_id))
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# det_boxes.numpy()
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###这里有结果
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# Print results
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for c in det[:, -1].unique():
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n = (det[:, -1] == c).sum() # detections per class
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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#s '2 airplanes, 1 kite, '
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#在这里增加设置调用追踪器的频率
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#..................USE TRACK FUNCTION....................
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#pass an empty array to sort
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dets_to_sort = np.empty((0,6))
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# NOTE: We send in detected object class too
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for x1,y1,x2,y2,conf,detclass in det_boxes:
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dets_to_sort = np.vstack((dets_to_sort,
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np.array([x1, y1, x2, y2, conf, detclass])))
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# Run SORT
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tracked_dets = sort_tracker.update(dets_to_sort)
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tracks =sort_tracker.getTrackers()
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txt_str = ""
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#loop over tracks
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for track in tracks:
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# color = compute_color_for_labels(id)
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#draw colored tracks
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if colored_trk:
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[cv2.line(im0, (int(track.centroidarr[i][0]),
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int(track.centroidarr[i][1])),
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(int(track.centroidarr[i+1][0]),
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int(track.centroidarr[i+1][1])),
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rand_color_list[track.id], thickness=2)
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for i,_ in enumerate(track.centroidarr)
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if i < len(track.centroidarr)-1 ]
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#draw same color tracks
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else:
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[cv2.line(im0, (int(track.centroidarr[i][0]),
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int(track.centroidarr[i][1])),
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(int(track.centroidarr[i+1][0]),
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int(track.centroidarr[i+1][1])),
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(255,0,0), thickness=2)
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for i,_ in enumerate(track.centroidarr)
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if i < len(track.centroidarr)-1 ]
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if save_txt and not save_with_object_id:
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# Normalize coordinates
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txt_str += "%i %i %f %f" % (track.id, track.detclass, track.centroidarr[-1][0] / im0.shape[1], track.centroidarr[-1][1] / im0.shape[0])
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if save_bbox_dim:
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txt_str += " %f %f" % (np.abs(track.bbox_history[-1][0] - track.bbox_history[-1][2]) / im0.shape[0], np.abs(track.bbox_history[-1][1] - track.bbox_history[-1][3]) / im0.shape[1])
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txt_str += "\n"
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if save_txt and not save_with_object_id:
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with open(txt_path + '.txt', 'a') as f:
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f.write(txt_str)
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# draw boxes for visualization
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if len(tracked_dets)>0:
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bbox_xyxy = tracked_dets[:,:4]
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identities = tracked_dets[:, 8]
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categories = tracked_dets[:, 4]
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draw_boxes(im0, bbox_xyxy, identities, categories, names, save_with_object_id, txt_path)
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#........................................................
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# Print time (inference + NMS)
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t4 = time_synchronized()
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print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS,, ({(1E3 * (t4 - t3)):.1f}ms) Track')
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# print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}ms) NMS')
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# Stream results
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if view_img:
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cv2.imshow(str(p), im0)
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if cv2.waitKey(1) == ord('q'): # q to quit
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cv2.destroyAllWindows()
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raise StopIteration
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# Save results (image with detections)
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if save_img:
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if dataset.mode == 'image':
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cv2.imwrite(save_path, im0)
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print(f" The image with the result is saved in: {save_path}")
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else: # 'video' or 'stream'
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if vid_path != save_path: # new video
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vid_path = save_path
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if isinstance(vid_writer, cv2.VideoWriter):
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vid_writer.release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path += '.mp4'
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vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
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vid_writer.write(im0)
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if save_txt or save_img or save_with_object_id:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
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#print(f"Results saved to {save_dir}{s}")
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# print(f'总耗时. ({time.time() - t0:.3f}s)')
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print('总耗时', time.time() - t0)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', nargs='+', type=str, default='weights/freighter20230113.pt', help='model.pt path(s)')
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# parser.add_argument('--weights', nargs='+', type=str, default='weights/best_vehicle20230210.pt', help='model.pt path(s)')
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# parser.add_argument('--weights', nargs='+', type=str, default='weights/pedestrian20230210.pt', help='model.pt path(s)')
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parser.add_argument('--download', action='store_true', help='download model weights automatically')
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parser.add_argument('--no-download', dest='download', action='store_false',help='not download model weights if already exist')
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# parser.add_argument('--source', type=str, default='inference/video3', help='source') # file/folder, 0 for webcam
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parser.add_argument('--source', type=str, default=r'D:\TH\8_track\yolov5_sort\inference\video', help='source') # file/folder, 0 for webcam
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parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--view-img', action='store_true', help='display results')
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parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
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parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
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parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
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parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
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parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
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parser.add_argument('--augment', action='store_true', help='augmented inference')
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parser.add_argument('--update', action='store_true', help='update all models')
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parser.add_argument('--project', default='runs/detect', help='save results to project/name')
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parser.add_argument('--name', default='object_tracking', help='save results to project/name')
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parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
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parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
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parser.add_argument('--colored-trk', action='store_true', help='assign different color to every track')
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parser.add_argument('--save-bbox-dim', action='store_true', help='save bounding box dimensions with --save-txt tracks')
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parser.add_argument('--save-with-object-id', action='store_true', help='save results with object id to *.txt')
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parser.set_defaults(download=True)
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opt = parser.parse_args()
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print(opt)
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#check_requirements(exclude=('pycocotools', 'thop'))
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if opt.download and not os.path.exists(str(opt.weights)):
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print('Model weights not found. Attempting to download now...')
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download('./')
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with torch.no_grad():
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if opt.update: # update all models (to fix SourceChangeWarning)
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for opt.weights in ['weights/yolov7.pt']:
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detect()
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strip_optimizer(opt.weights)
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else:
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detect()
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# t7 = time.time()
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#
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# print('总耗时', t7 - t1)
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# print("读二值图像耗时:%s 形成轮廓耗时:%s 等距离缩放耗时:%s 读取原图：%s 绘制多段线: %s 保存图像耗时:%s" % (
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# t2 - t1, t3 - t2, t4 - t3, t5 - t4, t6 - t5, t7 - t6))
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