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track.py
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track.py
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import argparse
from sys import platform
from yolov3.models import * # set ONNX_EXPORT in models.py
from yolov3.utils.datasets import *
from yolov3.utils.utils import *
from deep_sort import DeepSort
deepsort = DeepSort("deep_sort/deep/checkpoint/ckpt.t7")
palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
def bbox_rel(image_width, image_height, bbox_left, bbox_top, bbox_w, bbox_h):
"""" Calculates the relative bounding box from absolute pixel values. """
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 compute_color_for_labels(label):
"""
Simple function that adds fixed color depending on the class
"""
color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
return tuple(color)
def draw_boxes(img, bbox, identities=None, offset=(0,0)):
for i, box in enumerate(bbox):
x1, y1, x2, y2 = [int(i) for i in box]
x1 += offset[0]
x2 += offset[0]
y1 += offset[1]
y2 += offset[1]
# box text and bar
id = int(identities[i]) if identities is not None else 0
color = compute_color_for_labels(id)
label = '{}{:d}'.format("", id)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
cv2.rectangle(img, (x1, y1),(x2,y2), color, 3)
cv2.rectangle(img, (x1, y1), (x1 + t_size[0] + 3, y1 + t_size[1] + 4), color, -1)
cv2.putText(img, label, (x1, y1 + t_size[1] + 4), cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
return img
def detect(save_img=True):
img_size = (320, 192) if ONNX_EXPORT else opt.img_size # (320, 192) or (416, 256) or (608, 352) for (height, width)
out, source, weights, half, view_img, save_txt = opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt
webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Initialize model
model = Darknet(opt.cfg, img_size)
# Load weights
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format
load_darknet_weights(model, weights)
# Eval mode
model.to(device).eval()
# Half precision
half = half and device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
save_img = False
view_img = True
torch.backends.cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=img_size, half=half)
else:
save_img = True
dataset = LoadImages(source, img_size=img_size, half=half)
# Get names and colors
names = load_classes(opt.names)
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
# Run inference
t0 = time.time()
for path, img, im0s, vid_cap in dataset:
t = time.time()
# Get detections
img = torch.from_numpy(img).to(device)
if img.ndimension() == 3:
img = img.unsqueeze(0)
pred = model(img)[0]
if opt.half:
pred = pred.float()
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
# 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]
else:
p, s, im0 = path, '', im0s
save_path = str(Path(out) / Path(p).name)
s += '%gx%g ' % img.shape[2:] # print string
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(det[:, :5])
# 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
bbox_xywh = []
confs = []
# Write results
for *xyxy, conf, cls in det:
img_h, img_w, _ = im0.shape # get image shape
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, y_c, bbox_w, bbox_h = bbox_rel(img_w, img_h, bbox_left, bbox_top, bbox_w, bbox_h)
#print(x_c, y_c, bbox_w, bbox_h)
obj = [x_c, y_c, bbox_w, bbox_h]
bbox_xywh.append(obj)
confs.append([conf.item()])
label = '%s %.2f' % (names[int(cls)], conf)
#
#print('bboxes')
#print(torch.Tensor(bbox_xywh))
#print('confs')
#print(torch.Tensor(confs))
outputs = deepsort.update((torch.Tensor(bbox_xywh)), (torch.Tensor(confs)) , im0)
if len(outputs) > 0:
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1]
draw_boxes(im0, bbox_xyxy, identities)
#print('\n\n\t\ttracked objects')
#print(outputs)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, time.time() - t))
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# 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
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(*opt.fourcc), fps, (w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % os.getcwd() + os.sep + out)
if platform == 'darwin': # MacOS
os.system('open ' + out + ' ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='yolov3/cfg/yolov3-spp.cfg', help='*.cfg path')
parser.add_argument('--names', type=str, default='yolov3/data/coco.names', help='*.names path')
parser.add_argument('--weights', type=str, default='yolov3/weights/yolov3-spp-ultralytics.pt', help='path to weights file')
parser.add_argument('--source', type=str, default='0', help='source') # input file/folder, 0 for webcam
parser.add_argument('--output', type=str, default='output', help='output folder') # output folder
parser.add_argument('--img-size', type=int, default=608, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.3, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--classes', nargs='+', type=int, default=[0], help='filter by class')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
opt = parser.parse_args()
print(opt)
with torch.no_grad():
detect()