Datasets:
File size: 1,930 Bytes
23d0d42 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
import json
from collections import defaultdict
# Function to process the input JSON into desired structure
# Now groups by video_id with a single caption per video and list of frame entries
def process_ego4d_annotations(json_file_path, output_json_path=None):
# Load the JSON data from file
with open(json_file_path, 'r') as f:
data = json.load(f)
grouped = {}
for video in data:
video_id = video['video_id']
caption = video['frames'][0]['caption'] # All frames have the same caption
frames = []
for frame in video['frames']:
print(frame)
frame_data = {
"frame_path": frame['image_path'].replace('/home/shehan/workspace_grounding_lmm/Hanan/segment-anything-2/VideoGUI-High-Plan/video_gui_frames', 'videogui/frames/'),
"mask_path": frame['mask'].replace('/home/shehan/workspace_grounding_lmm/Hanan/segment-anything-2/VideoGUI-High-Plan/', 'videogui/') \
if frame['mask'] is not None else frame['mask'],
"points": [point[:2] for point in frame['clicked_points']] # Remove the third element from clicked points
}
frames.append(frame_data)
grouped[video_id] = {
"caption": caption,
"frames": frames
}
# Optionally save to output JSON file
if output_json_path:
with open(output_json_path, 'w') as outf:
json.dump(grouped, outf, indent=2)
return grouped
# Example usage
if __name__ == '__main__':
input_json = '/share/data/drive_1/heakl/benchmark/annotated/videogui_data/VideoGUI_final.json'
output_json = '/share/data/drive_1/heakl/benchmark/annotated/videogui_data/annotations.json'
grouped_dict = process_ego4d_annotations(input_json, output_json)
# Print nicely for verification
import pprint
pprint.pprint(grouped_dict)
|