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