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track_yolov7.py
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track_yolov7.py
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import argparse
import time
from pathlib import Path
import sys
import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
from yolov7.models.experimental import attempt_load
from yolov7.utils.datasets import LoadStreams, LoadImages
from yolov7.utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, \
apply_classifier, \
scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path
from yolov7.utils.plots import plot_one_box
from yolov7.utils.torch_utils import select_device, load_classifier, time_synchronized, TracedModel
from tracker.mc_bot_sort import BoTSORT
from tracker.tracking_utils.timer import Timer
sys.path.insert(0, './yolov7')
sys.path.append('.')
# Global
trackerTimer = Timer()
timer = Timer()
def write_results(filename, results):
save_format = '{frame},{id},{x1},{y1},{w},{h},{s},-1,-1,-1\n'
with open(filename, 'w') as f:
for frame_id, tlwhs, track_ids, scores in results:
for tlwh, track_id, score in zip(tlwhs, track_ids, scores):
if track_id < 0:
continue
x1, y1, w, h = tlwh
line = save_format.format(frame=frame_id, id=track_id, x1=round(x1, 1), y1=round(y1, 1), w=round(w, 1),
h=round(h, 1), s=round(score, 2))
f.write(line)
print('save results to {}'.format(filename))
def detect():
source, weights, view_img, imgsz, trace = opt.source, opt.weights, opt.view_img, opt.img_size, opt.trace
save_img = opt.save_frames and not source.endswith('.txt') # save inference images
webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
if save_img:
save_dir.mkdir(parents=True, exist_ok=True) # make dir
# Initialize
set_logging()
device = select_device(opt.device)
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check img_size
if trace:
model = TracedModel(model, device, opt.img_size)
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']).to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
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)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride)
if opt.ablation:
dataset.files = dataset.files[len(dataset.files) // 2 + 1:]
dataset.nf = len(dataset.files)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(100)]
# Create tracker
tracker = BoTSORT(opt, frame_rate=30.0)
# Run inference
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time()
results = []
fn = 0
for path, img, im0s, vid_cap in dataset:
fn += 1
timer.tic()
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()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
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, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
# Run tracker
detections = []
if len(det):
boxes = scale_coords(img.shape[2:], det[:, :4], im0.shape)
boxes = boxes.cpu().numpy()
detections = det.cpu().numpy()
detections[:, :4] = boxes
trackerTimer.tic()
online_targets = tracker.update(detections, im0)
trackerTimer.toc()
timer.toc()
online_tlwhs = []
online_ids = []
online_scores = []
online_cls = []
for t in online_targets:
tlwh = t.tlwh
tlbr = t.tlbr
tid = t.track_id
tcls = t.cls
vertical = tlwh[2] / tlwh[3] > opt.aspect_ratio_thresh
if tlwh[2] * tlwh[3] > opt.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
online_scores.append(t.score)
online_cls.append(t.cls)
# save results
results.append(
f"{fn},{tid},{tlwh[0]:.2f},{tlwh[1]:.2f},{tlwh[2]:.2f},{tlwh[3]:.2f},{t.score:.2f},-1,-1,-1\n"
)
if save_img or view_img: # Add bbox to image
if opt.hide_labels_name:
label = f'{tid}, {int(tcls)}'
else:
label = f'{tid}, {names[int(tcls)]}'
plot_one_box(tlbr, im0, label=label, color=colors[int(tid) % len(colors)], line_thickness=2)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
# Stream results
if view_img:
cv2.imshow('BoT-SORT', 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)
res_file = opt.project + '/' + opt.name + ".txt"
with open(res_file, 'w') as f:
f.writelines(results)
print(f"save results to {res_file}")
print(f'Done. ({time.time() - t0:.3f}s)')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--weights', nargs='+', type=str, default='yolov7.pt', help='model.pt path(s)')
parser.add_argument("--benchmark", dest="benchmark", type=str, default='MOT17', help="benchmark to evaluate: MOT17 | MOT20")
parser.add_argument("--eval", dest="split_to_eval", type=str, default='test', help="split to evaluate: train | val | test")
parser.add_argument('--img-size', type=int, default=1280, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.09, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.7, help='IOU threshold for NMS')
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='display results')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 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("--fp16", dest="fp16", default=False, action="store_true", help="Adopting mix precision evaluating.")
parser.add_argument("--fuse", dest="fuse", default=False, action="store_true", help="Fuse conv and bn for testing.")
parser.add_argument('--project', default='runs/track', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--trace', action='store_true', help='trace model')
parser.add_argument('--hide-labels-name', default=False, action='store_true', help='hide labels')
parser.add_argument("--default-parameters", dest="default_parameters", default=False, action="store_true", help="use the default parameters as in the paper")
parser.add_argument("--save-frames", dest="save_frames", default=False, action="store_true", help="save sequences with tracks.")
# tracking args
parser.add_argument("--track_high_thresh", type=float, default=0.5, help="tracking confidence threshold")
parser.add_argument("--track_low_thresh", default=0.1, type=float, help="lowest detection threshold")
parser.add_argument("--new_track_thresh", default=0.6, type=float, help="new track thresh")
parser.add_argument("--track_buffer", type=int, default=30, help="the frames for keep lost tracks")
parser.add_argument("--match_thresh", type=float, default=0.8, help="matching threshold for tracking")
parser.add_argument("--aspect_ratio_thresh", type=float, default=1.6,
help="threshold for filtering out boxes of which aspect ratio are above the given value.")
parser.add_argument('--min_box_area', type=float, default=10, help='filter out tiny boxes')
parser.add_argument("--fuse-score", dest="mot20", default=False, action="store_true",
help="fuse score and iou for association")
# CMC
parser.add_argument("--cmc-method", default="file", type=str, help="cmc method: files (Vidstab GMC) | sparseOptFlow |orb | ecc")
parser.add_argument("--ablation", dest="ablation", default=False, action="store_true", help="ablation ")
# ReID
parser.add_argument("--with-reid", dest="with_reid", default=False, action="store_true", help="with ReID module.")
parser.add_argument("--fast-reid-config", dest="fast_reid_config", default=r"fast_reid/configs/MOT17/sbs_S50.yml",
type=str, help="reid config file path")
parser.add_argument("--fast-reid-weights", dest="fast_reid_weights", default=r"pretrained/mot17_sbs_S50.pth",
type=str, help="reid config file path")
parser.add_argument('--proximity_thresh', type=float, default=0.5,
help='threshold for rejecting low overlap reid matches')
parser.add_argument('--appearance_thresh', type=float, default=0.25,
help='threshold for rejecting low appearance similarity reid matches')
opt = parser.parse_args()
opt.jde = False
print(opt)
opt.exist_ok = True
data_path = opt.source
device = opt.device
if opt.benchmark == 'MOT20':
train_seqs = [1, 2, 3, 5]
test_seqs = [4, 6, 7, 8]
seqs_ext = ['']
MOT = 20
elif opt.benchmark == 'MOT17':
train_seqs = [2, 4, 5, 9, 10, 11, 13]
test_seqs = [1, 3, 6, 7, 8, 12, 14]
seqs_ext = ['FRCNN', 'DPM', 'SDP']
MOT = 17
else:
raise ValueError("Error: Unsupported benchmark:" + opt.benchmark)
ablation = False
if opt.split_to_eval == 'train':
seqs = train_seqs
elif opt.split_to_eval == 'val':
seqs = train_seqs
ablation = True
elif opt.split_to_eval == 'test':
seqs = test_seqs
else:
raise ValueError("Error: Unsupported split to evaluate:" + opt.split_to_eval)
mainTimer = Timer()
mainTimer.tic()
for ext in seqs_ext:
for i in seqs:
if i < 10:
seq = 'MOT' + str(MOT) + '-0' + str(i)
else:
seq = 'MOT' + str(MOT) + '-' + str(i)
if ext != '':
seq += '-' + ext
opt.name = seq
opt.ablation = ablation
opt.mot20 = MOT == 20
opt.fps = 30
opt.device = device
opt.batch_size = 1
opt.trt = False
split = 'train' if i in train_seqs else 'test'
opt.source = data_path + '/' + split + '/' + seq + '/' + 'img1'
if opt.default_parameters:
# TODO: Set YOLOv7 models
if MOT == 20: # MOT20
opt.weights = r'./pretrained/yolov7-d6-mot20.pt'
opt.match_thresh = 0.7
else: # MOT17
if ablation:
opt.weights = r'./pretrained/yolov7-d6-mot17-ablation.pt'
else:
opt.weights = r'./pretrained/yolov7-d6-mot17.pt'
# TODO: Set tracking parameters for YOLOv7
opt.track_high_thresh = 0.3
opt.track_low_thresh = 0.1
opt.track_buffer = 30
opt.agnostic_nms = True
opt.new_track_thresh = opt.track_high_thresh + 0.1
opt.test_conf = max(0.001, opt.track_low_thresh - 0.01)
with torch.no_grad():
detect()
mainTimer.toc()
print("TOTAL TIME END-to-END (with loading networks and images): ", mainTimer.total_time)
print("TOTAL TIME (Detector + Tracker): " + str(timer.total_time) + ", FPS: " + str(1.0 /timer.average_time))
print("TOTAL TIME (Tracker only): " + str(trackerTimer.total_time) + ", FPS: " + str(1.0 / trackerTimer.average_time))