113 lines
3.7 KiB
Python
113 lines
3.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
from argparse import ArgumentParser
|
|
|
|
import cv2
|
|
|
|
from mmseg.apis import inference_segmentor, init_segmentor
|
|
from mmseg.core.evaluation import get_palette
|
|
|
|
|
|
def main():
|
|
parser = ArgumentParser()
|
|
parser.add_argument('video', help='Video file or webcam id')
|
|
parser.add_argument('config', help='Config file')
|
|
parser.add_argument('checkpoint', help='Checkpoint file')
|
|
parser.add_argument(
|
|
'--device', default='cuda:0', help='Device used for inference')
|
|
parser.add_argument(
|
|
'--palette',
|
|
default='cityscapes',
|
|
help='Color palette used for segmentation map')
|
|
parser.add_argument(
|
|
'--show', action='store_true', help='Whether to show draw result')
|
|
parser.add_argument(
|
|
'--show-wait-time', default=1, type=int, help='Wait time after imshow')
|
|
parser.add_argument(
|
|
'--output-file', default=None, type=str, help='Output video file path')
|
|
parser.add_argument(
|
|
'--output-fourcc',
|
|
default='MJPG',
|
|
type=str,
|
|
help='Fourcc of the output video')
|
|
parser.add_argument(
|
|
'--output-fps', default=-1, type=int, help='FPS of the output video')
|
|
parser.add_argument(
|
|
'--output-height',
|
|
default=-1,
|
|
type=int,
|
|
help='Frame height of the output video')
|
|
parser.add_argument(
|
|
'--output-width',
|
|
default=-1,
|
|
type=int,
|
|
help='Frame width of the output video')
|
|
parser.add_argument(
|
|
'--opacity',
|
|
type=float,
|
|
default=0.5,
|
|
help='Opacity of painted segmentation map. In (0, 1] range.')
|
|
args = parser.parse_args()
|
|
|
|
assert args.show or args.output_file, \
|
|
'At least one output should be enabled.'
|
|
|
|
# build the model from a config file and a checkpoint file
|
|
model = init_segmentor(args.config, args.checkpoint, device=args.device)
|
|
|
|
# build input video
|
|
cap = cv2.VideoCapture(args.video)
|
|
assert (cap.isOpened())
|
|
input_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
|
|
input_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
|
|
input_fps = cap.get(cv2.CAP_PROP_FPS)
|
|
|
|
# init output video
|
|
writer = None
|
|
output_height = None
|
|
output_width = None
|
|
if args.output_file is not None:
|
|
fourcc = cv2.VideoWriter_fourcc(*args.output_fourcc)
|
|
output_fps = args.output_fps if args.output_fps > 0 else input_fps
|
|
output_height = args.output_height if args.output_height > 0 else int(
|
|
input_height)
|
|
output_width = args.output_width if args.output_width > 0 else int(
|
|
input_width)
|
|
writer = cv2.VideoWriter(args.output_file, fourcc, output_fps,
|
|
(output_width, output_height), True)
|
|
|
|
# start looping
|
|
try:
|
|
while True:
|
|
flag, frame = cap.read()
|
|
if not flag:
|
|
break
|
|
|
|
# test a single image
|
|
result = inference_segmentor(model, frame)
|
|
|
|
# blend raw image and prediction
|
|
draw_img = model.show_result(
|
|
frame,
|
|
result,
|
|
palette=get_palette(args.palette),
|
|
show=False,
|
|
opacity=args.opacity)
|
|
|
|
if args.show:
|
|
cv2.imshow('video_demo', draw_img)
|
|
cv2.waitKey(args.show_wait_time)
|
|
if writer:
|
|
if draw_img.shape[0] != output_height or draw_img.shape[
|
|
1] != output_width:
|
|
draw_img = cv2.resize(draw_img,
|
|
(output_width, output_height))
|
|
writer.write(draw_img)
|
|
finally:
|
|
if writer:
|
|
writer.release()
|
|
cap.release()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|