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import argparse
import multiprocessing as mp
import os
import time
import cv2
import tqdm
import sys
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/')
from centernet.config import add_centernet_config
from grit.config import add_grit_config
from grit.predictor import VisualizationDemo
# constants
WINDOW_NAME = "GRiT"
def setup_cfg(args):
cfg = get_cfg()
if args.cpu:
cfg.MODEL.DEVICE="cpu"
add_centernet_config(cfg)
add_grit_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# Set score_threshold for builtin models
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
if args.test_task:
cfg.MODEL.TEST_TASK = args.test_task
cfg.MODEL.BEAM_SIZE = 1
cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
cfg.USE_ACT_CHECKPOINT = False
cfg.freeze()
return cfg
def get_parser():
parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs")
parser.add_argument(
"--config-file",
default="",
metavar="FILE",
help="path to config file",
)
parser.add_argument("--cpu", action='store_true', help="Use CPU only.")
parser.add_argument(
"--input",
nargs="+",
help="A list of space separated input images; "
"or a single glob pattern such as 'directory/*.jpg'",
)
parser.add_argument(
"--output",
help="A file or directory to save output visualizations. "
"If not given, will show output in an OpenCV window.",
)
parser.add_argument(
"--confidence-threshold",
type=float,
default=0.5,
help="Minimum score for instance predictions to be shown",
)
parser.add_argument(
"--test-task",
type=str,
default='',
help="Choose a task to have GRiT perform",
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
return parser
if __name__ == "__main__":
mp.set_start_method("spawn", force=True)
args = get_parser().parse_args()
setup_logger(name="fvcore")
logger = setup_logger()
logger.info("Arguments: " + str(args))
cfg = setup_cfg(args)
demo = VisualizationDemo(cfg)
if args.input:
for path in tqdm.tqdm(os.listdir(args.input[0]), disable=not args.output):
img = read_image(os.path.join(args.input[0], path), format="BGR")
start_time = time.time()
predictions, visualized_output = demo.run_on_image(img)
logger.info(
"{}: {} in {:.2f}s".format(
path,
"detected {} instances".format(len(predictions["instances"]))
if "instances" in predictions
else "finished",
time.time() - start_time,
)
)
if args.output:
if not os.path.exists(args.output):
os.mkdir(args.output)
if os.path.isdir(args.output):
assert os.path.isdir(args.output), args.output
out_filename = os.path.join(args.output, os.path.basename(path))
else:
assert len(args.input) == 1, "Please specify a directory with args.output"
out_filename = args.output
visualized_output.save(out_filename)
else:
cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])
if cv2.waitKey(0) == 27:
break # esc to quit |