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bytetrack.py
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bytetrack.py
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import sys
import time
import numpy as np
import cv2
import matplotlib
import ailia
# import original modules
sys.path.append('../../util')
from arg_utils import get_base_parser, update_parser
from model_utils import check_and_download_models # noqa: E402
from image_utils import normalize_image # noqa: E402C
from webcamera_utils import get_capture, get_writer # noqa: E402
# logger
from logging import getLogger # noqa: E402
logger = getLogger(__name__)
from bytetrack_utils import multiclass_nms
# ======================
# Parameters
# ======================
WEIGHT_MOT17_X_PATH = 'bytetrack_x_mot17.onnx'
MODEL_MOT17_X_PATH = 'bytetrack_x_mot17.onnx.prototxt'
WEIGHT_MOT17_S_PATH = 'bytetrack_s_mot17.onnx'
MODEL_MOT17_S_PATH = 'bytetrack_s_mot17.onnx.prototxt'
WEIGHT_MOT17_TINY_PATH = 'bytetrack_tiny_mot17.onnx'
MODEL_MOT17_TINY_PATH = 'bytetrack_tiny_mot17.onnx.prototxt'
WEIGHT_MOT20_X_PATH = 'bytetrack_x_mot20.onnx'
MODEL_MOT20_X_PATH = 'bytetrack_x_mot20.onnx.prototxt'
REMOTE_PATH = \
'https://storage.googleapis.com/ailia-models/bytetrack/'
WEIGHT_YOLOX_S_PATH = 'yolox_s.opt.onnx'
MODEL_YOLOX_S_PATH = 'yolox_s.opt.onnx.prototxt'
WEIGHT_YOLOX_TINY_PATH = 'yolox_tiny.opt.onnx'
MODEL_YOLOX_TINY_PATH = 'yolox_tiny.opt.onnx.prototxt'
REMOTE_YOLOX_PATH = \
'https://storage.googleapis.com/ailia-models/yolox/'
VIDEO_PATH = 'demo.mp4'
IMAGE_MOT17_X_HEIGHT = 800
IMAGE_MOT17_X_WIDTH = 1440
IMAGE_MOT17_S_HEIGHT = 608
IMAGE_MOT17_S_WIDTH = 1088
IMAGE_MOT17_TINY_HEIGHT = 416
IMAGE_MOT17_TINY_WIDTH = 416
IMAGE_MOT20_X_HEIGHT = 896
IMAGE_MOT20_X_WIDTH = 1600
IMAGE_YOLOX_S_HEIGHT = 640
IMAGE_YOLOX_S_WIDTH = 640
IMAGE_YOLOX_TINY_HEIGHT = 416
IMAGE_YOLOX_TINY_WIDTH = 416
# ======================
# Arguemnt Parser Config
# ======================
parser = get_base_parser(
'ByteTrack', VIDEO_PATH, None
)
parser.add_argument(
"--score_thre", type=float, default=0.1,
help="Score threshould to filter the result.",
)
parser.add_argument(
"--nms_thre", type=float, default=0.7,
help="NMS threshould.",
)
parser.add_argument(
'-m', '--model_type', default='mot17_x',
choices=('mot17_x', 'mot20_x', 'mot17_s', 'mot17_tiny', 'yolox_s', 'yolox_tiny'),
help='model type'
)
parser.add_argument(
'--cui',
action='store_true',
help="Don't display preview in GUI."
)
# tracking args
parser.add_argument("--track_thresh", type=float, default=0.5, help="tracking confidence threshold")
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('--min-box-area', type=float, default=10, help='filter out tiny boxes')
args = update_parser(parser)
# ======================
# Secondaty Functions
# ======================
def get_colors(n, colormap="gist_ncar"):
# Get n color samples from the colormap, derived from: https://stackoverflow.com/a/25730396/583620
# gist_ncar is the default colormap as it appears to have the highest number of color transitions.
# tab20 also seems like it would be a good option but it can only show a max of 20 distinct colors.
# For more options see:
# https://matplotlib.org/examples/color/colormaps_reference.html
# and https://matplotlib.org/users/colormaps.html
if hasattr(matplotlib, "colormaps"):
cm = matplotlib.colormaps[colormap]
else:
cm = matplotlib.cm.get_cmap(colormap)
colors = cm(np.linspace(0, 1, n))
# Randomly shuffle the colors
np.random.shuffle(colors)
# Opencv expects bgr while cm returns rgb, so we swap to match the colormap (though it also works fine without)
# Also multiply by 255 since cm returns values in the range [0, 1]
colors = colors[:, (2, 1, 0)] * 255
return colors
num_colors = 50
vis_colors = get_colors(num_colors)
def frame_vis_generator(frame, bboxes, ids):
for i, entity_id in enumerate(ids):
color = vis_colors[int(entity_id) % num_colors]
x1, y1, w, h = np.round(bboxes[i]).astype(int)
x2 = x1 + w
y2 = y1 + h
cv2.rectangle(frame, (x1, y1), (x2, y2), color=color, thickness=3)
cv2.putText(frame, str(entity_id), (x1 + 5, y1 + 40),
cv2.FONT_HERSHEY_SIMPLEX, 1.5, color, thickness=3)
return frame
# ======================
# Main functions
# ======================
def preprocess(img, img_size, normalize=True):
h, w = img_size
im_h, im_w, _ = img.shape
r = min(h / im_h, w / im_w)
oh, ow = int(im_h * r), int(im_w * r)
resized_img = cv2.resize(
img,
(ow, oh),
interpolation=cv2.INTER_LINEAR,
)
img = np.ones((h, w, 3)) * 114.0
img[: oh, : ow] = resized_img
if normalize:
img = img[:, :, ::-1] # BGR -> RGB
img = normalize_image(img, 'ImageNet')
img = img.transpose((2, 0, 1))
img = np.expand_dims(img, axis=0)
img = img.astype(np.float32)
return img, r
def postprocess(output, ratio, img_size, p6=False, nms_thre=0.7, score_thre=0.1):
grids = []
expanded_strides = []
if not p6:
strides = [8, 16, 32]
else:
strides = [8, 16, 32, 64]
hsizes = [img_size[0] // stride for stride in strides]
wsizes = [img_size[1] // stride for stride in strides]
for hsize, wsize, stride in zip(hsizes, wsizes, strides):
xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
grids.append(grid)
shape = grid.shape[:2]
expanded_strides.append(np.full((*shape, 1), stride))
grids = np.concatenate(grids, 1)
expanded_strides = np.concatenate(expanded_strides, 1)
output[..., :2] = (output[..., :2] + grids) * expanded_strides
output[..., 2:4] = np.exp(output[..., 2:4]) * expanded_strides
predictions = output[0]
boxes = predictions[:, :4]
scores = predictions[:, 4:5] * predictions[:, 5:]
boxes_xyxy = np.ones_like(boxes)
boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.
boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.
boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.
boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.
boxes_xyxy /= ratio
dets = multiclass_nms(boxes_xyxy, scores, nms_thr=nms_thre, score_thr=score_thre)
return dets[:, :-1] if dets is not None else np.zeros((0, 5))
def predict(net, img):
dic_model = {
'mot17_x': (IMAGE_MOT17_X_HEIGHT, IMAGE_MOT17_X_WIDTH),
'mot17_s': (IMAGE_MOT17_S_HEIGHT, IMAGE_MOT17_S_WIDTH),
'mot17_tiny': (IMAGE_MOT17_TINY_HEIGHT, IMAGE_MOT17_TINY_WIDTH),
'mot20_x': (IMAGE_MOT20_X_HEIGHT, IMAGE_MOT20_X_WIDTH),
'yolox_s': (IMAGE_YOLOX_S_HEIGHT, IMAGE_YOLOX_S_WIDTH),
'yolox_tiny': (IMAGE_YOLOX_TINY_HEIGHT, IMAGE_YOLOX_TINY_WIDTH),
}
model_type = args.model_type
img_size = dic_model[model_type]
img, ratio = preprocess(img, img_size, normalize=model_type.startswith('mot'))
# feedforward
output = net.predict([img])
output = output[0]
# For yolox, retrieve only the person class
output = output[..., :6]
score_thre = args.score_thre
nms_thre = args.nms_thre
dets = postprocess(output, ratio, img_size, nms_thre=nms_thre, score_thre=score_thre)
return dets
def benchmarking(net):
video_file = args.video if args.video else args.input[0]
capture = get_capture(video_file)
assert capture.isOpened(), 'Cannot capture source'
_, frame = capture.read()
logger.info('BENCHMARK mode')
total_time_estimation = 0
for i in range(args.benchmark_count):
start = int(round(time.time() * 1000))
predict(net, frame)
end = int(round(time.time() * 1000))
estimation_time = (end - start)
# Loggin
logger.info(f'\tailia processing estimation time {estimation_time} ms')
if i != 0:
total_time_estimation = total_time_estimation + estimation_time
logger.info(f'\taverage time estimation {total_time_estimation / (args.benchmark_count - 1)} ms')
def recognize_from_video(net):
min_box_area = args.min_box_area
mot20 = args.model_type == 'mot20'
video_file = args.video if args.video else args.input[0]
capture = get_capture(video_file)
assert capture.isOpened(), 'Cannot capture source'
# create video writer if savepath is specified as video format
f_h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
f_w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
if args.savepath != None:
logger.warning(
'currently, video results cannot be output correctly...'
)
writer = get_writer(args.savepath, f_h, f_w)
else:
writer = None
tracker = BYTETracker(
track_thresh=args.track_thresh, track_buffer=args.track_buffer,
match_thresh=args.match_thresh, frame_rate=30,
mot20=mot20)
frame_shown = False
while True:
ret, frame = capture.read()
if (cv2.waitKey(1) & 0xFF == ord('q')) or not ret:
break
if frame_shown and cv2.getWindowProperty('frame', cv2.WND_PROP_VISIBLE) == 0:
break
# inference
output = predict(net, frame)
# run tracking
online_targets = tracker.update(output)
online_tlwhs = []
online_ids = []
online_scores = []
for t in online_targets:
tlwh = t.tlwh
tid = t.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(tid)
online_scores.append(t.score)
res_img = frame_vis_generator(frame, online_tlwhs, online_ids)
# show
if not args.cui or args.video:
cv2.imshow('frame', res_img)
frame_shown = True
else:
print("Online ids", online_ids)
# save results
if writer is not None:
writer.write(res_img.astype(np.uint8))
capture.release()
cv2.destroyAllWindows()
if writer is not None:
writer.release()
logger.info('Script finished successfully.')
def main():
dic_model = {
'mot17_x': (WEIGHT_MOT17_X_PATH, MODEL_MOT17_X_PATH),
'mot17_s': (WEIGHT_MOT17_S_PATH, MODEL_MOT17_S_PATH),
'mot17_tiny': (WEIGHT_MOT17_TINY_PATH, MODEL_MOT17_TINY_PATH),
'mot20_x': (WEIGHT_MOT20_X_PATH, MODEL_MOT20_X_PATH),
'yolox_s': (WEIGHT_YOLOX_S_PATH, MODEL_YOLOX_S_PATH),
'yolox_tiny': (WEIGHT_YOLOX_TINY_PATH, MODEL_YOLOX_TINY_PATH),
}
model_type = args.model_type
weight_path, model_path = dic_model[model_type]
# model files check and download
check_and_download_models(
weight_path, model_path,
REMOTE_PATH if model_type.startswith('mot') else REMOTE_YOLOX_PATH)
env_id = args.env_id
# initialize
mem_mode = ailia.get_memory_mode(reduce_constant=True, reuse_interstage=True)
net = ailia.Net(model_path, weight_path, env_id=env_id, memory_mode=mem_mode)
if args.benchmark:
benchmarking(net)
else:
recognize_from_video(net)
if __name__ == '__main__':
from tracker.byte_tracker import BYTETracker
main()