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import os |
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os.environ['CUDA_VISIBLE_DEVICES'] = '0' |
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import argparse |
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import multiprocessing as mp |
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import sys |
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sys.path.insert(1, os.path.join(sys.path[0], '..')) |
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import tempfile |
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import time |
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import cv2 |
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import numpy as np |
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import re |
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from torch.cuda.amp import autocast |
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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.projects.deeplab import add_deeplab_config |
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from detectron2.utils.logger import setup_logger |
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from mask2former import add_maskformer2_config |
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from avism import add_avism_config |
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from predictor import VisualizationDemo |
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WINDOW_NAME = "avism video demo" |
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def setup_cfg(args): |
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cfg = get_cfg() |
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add_deeplab_config(cfg) |
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add_maskformer2_config(cfg) |
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add_avism_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.freeze() |
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return cfg |
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def get_parser(): |
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parser = argparse.ArgumentParser(description="avism demo for builtin configs") |
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parser.add_argument( |
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"--config-file", |
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default="configs/avism/avis/avism_R50_IN.yaml", |
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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("--video-input", help="Path to video file.") |
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parser.add_argument( |
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"--input", |
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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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"this will be treated as frames of a video", |
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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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"--save-frames", |
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default=True, |
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help="Save frame level image outputs.", |
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) |
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parser.add_argument( |
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"--confidence", |
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type=float, |
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default=0.3, |
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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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"--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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def test_opencv_video_format(codec, file_ext): |
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with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: |
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filename = os.path.join(dir, "test_file" + file_ext) |
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writer = cv2.VideoWriter( |
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filename=filename, |
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fourcc=cv2.VideoWriter_fourcc(*codec), |
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fps=float(30), |
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frameSize=(10, 10), |
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isColor=True, |
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) |
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[writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] |
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writer.release() |
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if os.path.isfile(filename): |
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return True |
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return False |
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def extract_number(filename): |
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return int(re.search(r'(\d+).jpg$', filename).group(1)) |
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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.output: |
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os.makedirs(args.output, exist_ok=True) |
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input_dir = "datasets/test/JPEGImages/" |
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output_dir = "results/avism_R50_IN/" |
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for video_name in os.listdir(input_dir): |
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print(video_name) |
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vid_frames = [] |
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for path in sorted(os.listdir(os.path.join(input_dir, video_name)), key=extract_number): |
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img = read_image(os.path.join(input_dir, video_name, path), format="BGR") |
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vid_frames.append(img) |
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audio_pth = os.path.join("datasets/test/FEATAudios", video_name + ".npy") |
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audio_feats = np.load(audio_pth) |
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start_time = time.time() |
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with autocast(): |
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predictions, visualized_output = demo.run_on_video(vid_frames, audio_feats) |
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os.makedirs(os.path.join(output_dir, video_name), exist_ok=True) |
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for path, _vis_output in zip(sorted(os.listdir(os.path.join(input_dir, video_name)), key=extract_number), visualized_output): |
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out_filename = os.path.join(output_dir, video_name, path) |
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_vis_output.save(out_filename) |
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