def main():
parser = ArgumentParser()
parser.add_argument('--img', help='Image file', default=img_path)
parser.add_argument('--config', help='Config file', default=config)
parser.add_argument('--checkpoint', help='Checkpoint file', default=ckpt)
parser.add_argument(
'--device', default='cuda:1', help='Device used for inference')
parser.add_argument(
'--palette',
default=None,
help='Color palette used for segmentation map')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_segmentor(args.config, args.checkpoint, device=args.device)
if args.img=='':
list_img=get_list_file_in_folder(img_dir)
list_img=sorted(list_img)
for img_ in list_img:
img=os.path.join(img_dir,img_)
print(img)
result = inference_segmentor(model, img)
show_result_pyplot(model, img, result, get_palette(args.palette))
else:
result = inference_segmentor(model, args.img)
show_result_pyplot(model, args.img, result, get_palette(args.palette))
def main():
parser = ArgumentParser()
parser.add_argument('img', help='Image file')
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(
'--opacity',
type=float,
default=0.5,
help='Opacity of painted segmentation map. In (0, 1] range.')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_segmentor(args.config, args.checkpoint, device=args.device)
# test a single image
result = inference_segmentor(model, args.img)
# show the results
show_result_pyplot(
model,
args.img,
result,
get_palette(args.palette),
opacity=args.opacity)
def test_palette():
assert CityscapesDataset.PALETTE == get_palette('cityscapes')
assert PascalVOCDataset.PALETTE == get_palette('voc') == get_palette(
'pascal_voc')
assert ADE20KDataset.PALETTE == get_palette('ade') == get_palette('ade20k')
with pytest.raises(ValueError):
get_palette('unsupported')
def main():
parser = ArgumentParser()
# parser.add_argument('--img', default="Image_20200925100338349.bmp", help='Image file')
parser.add_argument('--img', default="star.png", help='Image file')
# parser.add_argument('--img', default="demo.png", help='Image file')
parser.add_argument(
'--config',
# default="../configs/deeplabv3/deeplabv3_r50-d8_512x1024_40k_cityscapes_custom_binary.py",
default=
"../configs/danet/danet_r50-d8_512x1024_40k_cityscapes_custom.py",
# default="../configs/deeplabv3plus/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes.py",
help='Config file')
parser.add_argument(
'--checkpoint',
# default="../tools/work_dirs/deeplabv3_r50-d8_512x1024_40k_cityscapes_custom_binary/iter_200.pth",
default=
"../tools/work_dirs/danet_r50-d8_512x1024_40k_cityscapes_custom/iter_4000.pth",
# default="../checkpoints/deeplabv3plus_r101-d16-mg124_512x1024_40k_cityscapes_20200908_005644-cf9ce186.pth",
help='Checkpoint file')
parser.add_argument('--device',
default='cuda:0',
help='Device used for inference')
parser.add_argument(
'--palette',
default='cityscapes_custom',
# default='cityscapes',
help='Color palette used for segmentation map')
args = parser.parse_args()
# build the model from a config file and a checkpoint file
model = init_segmentor(args.config, args.checkpoint, device=args.device)
# test a single image
result = inference_segmentor(model, args.img)
# io.imsave("result.png", result[0])
# io.imshow(result[0])
# io.show()
# show the results
show_result_pyplot(model, args.img, result, get_palette(args.palette))
"""
def test_palette():
assert CityscapesDataset.PALETTE == get_palette('cityscapes')
assert PascalVOCDataset.PALETTE == get_palette('voc') == get_palette(
'pascal_voc')
assert ADE20KDataset.PALETTE == get_palette('ade') == get_palette('ade20k')
assert LoveDADataset.PALETTE == get_palette('loveda')
assert PotsdamDataset.PALETTE == get_palette('potsdam')
assert COCOStuffDataset.PALETTE == get_palette('cocostuff')
assert iSAIDDataset.PALETTE == get_palette('isaid')
with pytest.raises(ValueError):
get_palette('unsupported')
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()