File size: 4,129 Bytes
991f1c6 | 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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import os
import csv
import json
import argparse
import pandas as pd
parser = argparse.ArgumentParser(description='Codes for splitting the train/validation/test data for cross-domain generalization')
parser.add_argument('-domain_name', '-dname', type=str, default="Origami", choices=['Origami', 'Shuffle_Cards', 'Tangram', 'Tying_Knots'])
parser.add_argument('-split_path', '-spath', type=str, default="/media/alexa/WORKSPACE/Shijia-stage-two/new_struggle_dataset/splits/crossdomain_generalization")
parser.add_argument('-save_path', '-save', type=str, default="/media/alexa/WORKSPACE/Shijia-stage-two/new_struggle_dataset/splits/crossdomain_generalization/")
args = parser.parse_args()
def df_rows_to_dict(df, subset, activity, json_data):
for index, row in df.iterrows():
# Extract values from the row
# print(activity, row['video_name'])
video_name = activity + '-' + row['video_name']
duration = row['duration']
fps = row['fps']
# Create a dictionary for the video entry
video_dict = {
"subset": subset, # Assuming all videos are in the Test subset
"duration": duration,
"fps": fps,
"annotations": []
}
# import pdb; pdb.set_trace()
# Create a dictionary for the struggle annotation (adjust if needed)
row['struggle'] = json.loads(row['struggle'])
row['struggle(frames)'] = json.loads(row['struggle(frames)'])
if len(row['struggle']) > 0:
for idx, segment_items in enumerate(row['struggle']):
struggle_annotation = {
"label": "Struggle", # Replace with your desired label
"segment": segment_items, # Replace with actual struggle segment
"segment(frames)": row['struggle(frames)'][idx], # Replace with actual struggle segment in frames
"label_id": 1 # Replace with your desired label ID
}
# Add the struggle annotation to the video's annotations list
video_dict["annotations"].append(struggle_annotation)
# Add the video entry to the database dictionary
json_data["database"][video_name] = video_dict
return json_data
print(f"Currently preparing activity name: {args.domain_name}")
root_path = args.split_path
args.save_path = os.path.join(args.save_path, args.domain_name)
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
domains_list = ['Origami', 'Shuffle_Cards', 'Tangram', 'Tying_Knots']
json_data = {
"version": "crossdomain_generalization_testonvalonly",
"database": {}
}
for domain_name in domains_list:
if domain_name == args.domain_name:
# Only the validation split in this domain is used as held-out test set
csv_file_name = domain_name + '_' + 'val.csv'
df = pd.read_csv(os.path.join(root_path, domain_name, csv_file_name))
json_data = df_rows_to_dict(df, 'test', domain_name, json_data)
else:
# the other domains should be combined and used as trian/validation set
csv_file_name = domain_name + '_' + 'train.csv'
df = pd.read_csv(os.path.join(root_path, domain_name, csv_file_name))
json_data = df_rows_to_dict(df, 'train', domain_name, json_data)
csv_file_name = domain_name + '_' + 'val.csv'
df = pd.read_csv(os.path.join(root_path, domain_name, csv_file_name))
json_data = df_rows_to_dict(df, 'validation', domain_name, json_data)
# import pdb;pdb.set_trace()
# Save the JSON data to a file
with open(os.path.join(args.save_path, f"{args.domain_name}_crossdomain_testonvalonly.json"), 'w') as fp:
json.dump(json_data, fp, indent=4)
print("Done!")
# Run this script with the following command:
# python crossdomain_generalization_split_generator2.py -domain_name Origami
# python crossdomain_generalization_split_generator2.py -domain_name Shuffle_Cards
# python crossdomain_generalization_split_generator2.py -domain_name Tangram
# python crossdomain_generalization_split_generator2.py -domain_name Tying_Knots
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