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import pandas as pd
import numpy as np
import os
from scipy.io import loadmat, savemat
from sklearn.model_selection import train_test_split
import logging
# cross_dataset
def process_cross_dataset(train_data_name, test_data_name, metadata_path, feature_path, network_name):
metadata_name1 = f"{train_data_name.replace('_all', '').upper()}_metadata.csv"
metadata_name2 = f"{test_data_name.replace('_all', '').upper()}_metadata.csv"
# load CSV data
train_df = pd.read_csv(f'{metadata_path}/{metadata_name1}')
test_df = pd.read_csv(f'{metadata_path}/{metadata_name2}')
# grayscale videos, do not consider them for fair comparison
grey_df_train = pd.read_csv(f"{metadata_path}/greyscale_report/{train_data_name.replace('_all', '').upper()}_greyscale_metadata.csv")
grey_df_test = pd.read_csv(f"{metadata_path}/greyscale_report/{test_data_name.replace('_all', '').upper()}_greyscale_metadata.csv")
grey_indices_train = grey_df_train.iloc[:, 0].tolist()
grey_indices_test = grey_df_test.iloc[:, 0].tolist()
train_df = train_df.drop(index=grey_indices_train).reset_index(drop=True)
test_df = test_df.drop(index=grey_indices_test).reset_index(drop=True)
# split videonames into train and test sets
train_vids = train_df.iloc[:, 0]
test_vids = test_df.iloc[:, 0]
# scores (1-100) map to 1-5
train_scores = train_df['mos'].tolist()
test_scores = test_df['mos'].tolist()
if train_data_name == 'konvid_1k_all' or train_data_name == 'youtube_ugc_all':
train_mos_list = ((np.array(train_scores) - 1) * (99/4) + 1.0).tolist()
else:
train_mos_list = train_scores
if test_data_name == 'konvid_1k_all' or test_data_name == 'youtube_ugc_all':
test_mos_list = ((np.array(test_scores) - 1) * (99/4) + 1.0).tolist()
else:
test_mos_list = test_scores
# reorder columns
sorted_train_df = pd.DataFrame({'vid': train_df['vid'], 'framerate': train_df['framerate'], 'MOS': train_mos_list, 'MOS_raw': train_df['mos']})
sorted_test_df = pd.DataFrame({'vid': test_df['vid'], 'framerate': test_df['framerate'], 'MOS': test_mos_list, 'MOS_raw': test_df['mos']})
# use indices from the train and test DataFrames to split features
train_data = loadmat(f"{feature_path}/{train_data_name.replace('_all', '')}_{network_name}_feats.mat")
test_data = loadmat(f"{feature_path}/{test_data_name.replace('_all', '')}_{network_name}_feats.mat")
train_features = train_data[f"{train_data_name.replace('_all', '')}"]
test_features = test_data[f"{test_data_name.replace('_all', '')}"]
train_features = np.delete(train_features, grey_indices_train, axis=0)
test_features = np.delete(test_features, grey_indices_test, axis=0)
# save the files
sorted_train_df.to_csv(f'{metadata_path}mos_files/{train_data_name}_MOS_train.csv', index=False)
sorted_test_df.to_csv(f'{metadata_path}mos_files/{test_data_name}_MOS_test.csv', index=False)
os.makedirs(os.path.join(feature_path, "split_train_test"), exist_ok=True)
savemat(f'{feature_path}/split_train_test/{train_data_name}_{network_name}_cross_train_features.mat', {f'{train_data_name}_train_features': train_features})
savemat(f'{feature_path}/split_train_test/{test_data_name}_{network_name}_cross_test_features.mat', {f'{test_data_name}_test_features': test_features})
return train_features, test_features, test_vids
#NR: original
def process_lsvq(train_data_name, test_data_name, metadata_path, feature_path, network_name):
train_df = pd.read_csv(f'{metadata_path}/{train_data_name.upper()}_metadata.csv')
test_df = pd.read_csv(f'{metadata_path}/{test_data_name.upper()}_metadata.csv')
# grayscale videos, do not consider them for fair comparison
grey_df_train = pd.read_csv(f'{metadata_path}/greyscale_report/{train_data_name.upper()}_greyscale_metadata.csv')
grey_df_test = pd.read_csv(f'{metadata_path}/greyscale_report/{test_data_name.upper()}_greyscale_metadata.csv')
grey_indices_train = grey_df_train.iloc[:, 0].tolist()
grey_indices_test = grey_df_test.iloc[:, 0].tolist()
train_df = train_df.drop(index=grey_indices_train).reset_index(drop=True)
test_df = test_df.drop(index=grey_indices_test).reset_index(drop=True)
test_vids = test_df['vid']
# mos scores
train_scores = train_df['mos'].tolist()
test_scores = test_df['mos'].tolist()
train_mos_list = train_scores
test_mos_list = test_scores
# reorder columns
sorted_train_df = pd.DataFrame({'vid': train_df['vid'], 'framerate': train_df['framerate'], 'MOS': train_mos_list, 'MOS_raw': train_df['mos']})
sorted_test_df = pd.DataFrame({'vid': test_df['vid'], 'framerate': test_df['framerate'], 'MOS': test_mos_list, 'MOS_raw': test_df['mos']})
# use indices from the train and test DataFrames to split features
train_data_chunk_1 = loadmat(f'{feature_path}/{train_data_name}_{network_name}_feats_chunk_1.mat')[f'{train_data_name}']
train_data_chunk_2 = loadmat(f'{feature_path}/{train_data_name}_{network_name}_feats_chunk_2.mat')[f'{train_data_name}']
train_data_chunk_3 = loadmat(f'{feature_path}/{train_data_name}_{network_name}_feats_chunk_3.mat')[f'{train_data_name}']
merged_train_data = np.vstack((train_data_chunk_1, train_data_chunk_2, train_data_chunk_3))
print(f"loaded {train_data_name}: dimensions are {merged_train_data.shape}")
train_features = merged_train_data
test_data = loadmat(f'{feature_path}/{test_data_name}_{network_name}_feats.mat')
test_features = test_data[f'{test_data_name}']
train_features = np.delete(train_features, grey_indices_train, axis=0)
test_features = np.delete(test_features, grey_indices_test, axis=0)
print(len(train_features))
print(len(test_features))
# save the files
sorted_train_df.to_csv(f'{metadata_path}mos_files/{train_data_name}_MOS_train.csv', index=False)
sorted_test_df.to_csv(f'{metadata_path}mos_files/{train_data_name}_MOS_test.csv', index=False)
os.makedirs(os.path.join(feature_path, "split_train_test"), exist_ok=True)
# savemat(f'{feature_path}/split_train_test/{train_data_name}_{network_name}_train_features.mat', {f'{train_data_name}_train_features': train_features})
# savemat(f'{feature_path}/split_train_test/{train_data_name}_{network_name}_test_features.mat', {f'{train_data_name}_test_features': test_features})
return train_features, test_features, test_vids
def process_other(data_name, test_size, random_state, metadata_path, feature_path, network_name):
metadata_name = f'{data_name.upper()}_metadata.csv'
if data_name == 'test':
metadata_name = f'{data_name}_videos.csv'
# load CSV data
df = pd.read_csv(f'{metadata_path}/{metadata_name}')
if data_name == 'youtube_ugc':
# grayscale videos, do not consider them for fair comparison
grey_df = pd.read_csv(f'{metadata_path}/greyscale_report/{data_name.upper()}_greyscale_metadata.csv')
grey_indices = grey_df.iloc[:, 0].tolist()
df = df.drop(index=grey_indices).reset_index(drop=True)
# get unique vids
unique_vids = df['vid'].unique()
# split videonames into train and test sets
train_vids, test_vids = train_test_split(unique_vids, test_size=test_size, random_state=random_state)
# split all_dfs into train and test based on vids
train_df = df[df['vid'].isin(train_vids)]
test_df = df[df['vid'].isin(test_vids)]
# mos scores
train_scores = train_df['mos'].tolist()
test_scores = test_df['mos'].tolist()
train_mos_list = train_scores
test_mos_list = test_scores
# reorder columns
sorted_train_df = pd.DataFrame({'vid': train_df['vid'], 'framerate': train_df['framerate'], 'MOS': train_mos_list, 'MOS_raw': train_df['mos']})
sorted_test_df = pd.DataFrame({'vid': test_df['vid'], 'framerate': test_df['framerate'], 'MOS': test_mos_list, 'MOS_raw': test_df['mos']})
# use indices from the train and test DataFrames to split features
data = loadmat(f'{feature_path}/{data_name}_{network_name}_feats.mat')
features = data[f'{data_name}']
if data_name == 'youtube_ugc':
features = np.delete(features, grey_indices, axis=0)
train_features = features[train_df.index]
test_features = features[test_df.index]
# save the files
sorted_train_df.to_csv(f'{metadata_path}mos_files/{data_name}_MOS_train.csv', index=False)
sorted_test_df.to_csv(f'{metadata_path}mos_files/{data_name}_MOS_test.csv', index=False)
os.makedirs(os.path.join(feature_path, "split_train_test"), exist_ok=True)
savemat(f'{feature_path}/split_train_test/{data_name}_{network_name}_train_features.mat', {f'{data_name}_train_features': train_features})
savemat(f'{feature_path}/split_train_test/{data_name}_{network_name}_test_features.mat', {f'{data_name}_test_features': test_features})
return train_features, test_features, test_vids
if __name__ == '__main__':
network_name = 'relaxvqa'
data_name = "test"
metadata_path = '../../metadata/'
feature_path = '../../features/'
# train test split
test_size = 0.2
random_state = None
if data_name == 'lsvq_train':
test_data_name = 'lsvq_test'
process_lsvq(data_name, test_data_name, metadata_path, feature_path, network_name)
elif data_name == 'cross_dataset':
train_data_name = 'youtube_ugc_all'
test_data_name = 'cvd_2014_all'
_, _, test_vids = process_cross_dataset(train_data_name, test_data_name, metadata_path, feature_path, network_name)
else:
process_other(data_name, test_size, random_state, metadata_path, feature_path, network_name)
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