271 lines
12 KiB
Python
271 lines
12 KiB
Python
import argparse
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import time
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import os
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from pathlib import Path
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import cv2
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import sklearn
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import torch
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import torch.backends.cudnn as cudnn
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import numpy as np
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from numpy import random
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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, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
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strip_optimizer, set_logging, 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, time_synchronized
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from deep_sort_pytorch.utils.parser import get_config
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from deep_sort_pytorch.deep_sort import DeepSort
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palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
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def bbox_rel(*xyxy):
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"""" Calculates the relative bounding box from absolute pixel values. """
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bbox_left = min([xyxy[0].item(), xyxy[2].item()])
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bbox_top = min([xyxy[1].item(), xyxy[3].item()])
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bbox_w = abs(xyxy[0].item() - xyxy[2].item())
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bbox_h = abs(xyxy[1].item() - xyxy[3].item())
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x_c = (bbox_left + bbox_w / 2)
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y_c = (bbox_top + bbox_h / 2)
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w = bbox_w
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h = bbox_h
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return x_c, y_c, w, h
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def compute_color_for_labels(label):
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"""
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Simple function that adds fixed color depending on the class
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"""
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color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
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return tuple(color)
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def draw_boxes(img, bbox, identities=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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# box text and bar
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id = int(identities[i]) if identities is not None else 0
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color = compute_color_for_labels(id)
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label = '{}{:d}'.format("", id)
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t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
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cv2.rectangle(img, (x1, y1), (x2, y2), color, 3)
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cv2.rectangle(
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img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
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cv2.putText(img, label, (x1, y1 +
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t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
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return img
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def detect(save_img=False):
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source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
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webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
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('rtsp://', 'rtmp://', 'http://'))
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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 else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# DeepSort Initialize
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cfg = get_config()
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cfg.merge_from_file(opt.config_deepsort)
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deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
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max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
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nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
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max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
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use_cuda=True)
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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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imgsz = check_img_size(imgsz, s=model.stride.max()) # check 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 = True
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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)
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else:
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save_img = True
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dataset = LoadImages(source, img_size=imgsz)
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# Get names
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names = model.module.names if hasattr(model, 'module') else model.names
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# Run inference
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t0 = time.time()
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img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
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_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
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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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# Inference
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t1 = time_synchronized()
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pred = model(img, augment=opt.augment)[0]
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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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t2 = 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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# 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(path[i]), '%g: ' % i, im0s[i].copy(), dataset.count
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else:
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p, s, im0, frame = Path(path), '', im0s, getattr(dataset, 'frame', 0)
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save_path = str(save_dir / p.name)
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txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')
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s += '%gx%g ' % img.shape[2:] # print string
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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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# 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, ' # add to string
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bbox_xywh = []
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confs = []
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# Adapt detections to deep sort input format
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for *xyxy, conf, cls in det:
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if cls == 0:
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x_c, y_c, bbox_w, bbox_h = bbox_rel(*xyxy)
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obj = [x_c, y_c, bbox_w, bbox_h]
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bbox_xywh.append(obj)
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confs.append([conf.item()])
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xywhs = torch.Tensor(bbox_xywh)
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confss = torch.Tensor(confs)
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# Pass detections to deepsort
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outputs = deepsort.update(xywhs, confss, im0)
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# draw boxes for visualization
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if len(outputs) > 0:
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bbox_xyxy = outputs[:, :4]
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identities = outputs[:, -1]
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draw_boxes(im0, bbox_xyxy, identities)
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for *xyxy, conf, cls in reversed(det):
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if save_txt: # Write to file
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xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
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with open(txt_path + '.txt', 'a') as f:
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f.write(('%g ' * len(line)).rstrip() % line + '\n')
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if cls == 32 and (save_img or view_img): # Add bbox to ball
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#label = f'{names[int(cls)]}'
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label = 'ball'
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plot_one_box(xyxy, im0, label=label, color=[0,0,0], line_thickness=2)
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# Write MOT compliant results to file
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""" if save_txt and len(outputs) != 0:
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for j, output in enumerate(outputs):
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bbox_left = output[0]
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bbox_top = output[1]
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bbox_w = output[2]
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bbox_h = output[3]
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identity = output[-1]
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with open(txt_path + '.txt', 'a') as f:
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f.write(('%g ' * 10 + '\n') % (j, identity, bbox_left,
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bbox_top, bbox_w, bbox_h, -1, -1, -1, -1)) # label format """
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else:
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deepsort.increment_ages()
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# Print time (inference + NMS)
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print(f'{s}Done. ({t2 - t1:.3f}s)')
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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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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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else: # 'video'
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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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fourcc = 'mp4v' # output video codec
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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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vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
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vid_writer.write(im0)
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if save_txt or save_img:
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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'Done. ({time.time() - t0:.3f}s)')
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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='yolov5s.pt', help='model.pt path(s)')
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parser.add_argument('--source', type=str, default='data/images', 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('--classes', nargs='+', type=int, default=[0, 32], 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='../files/output', help='save results to project/name')
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parser.add_argument('--name', default='exp', 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("--config_deepsort", type=str, default="deep_sort_pytorch/configs/deep_sort.yaml")
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opt = parser.parse_args()
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print(opt)
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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 ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
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detect()
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strip_optimizer(opt.weights)
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else:
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detect() |