def test_get_num_frames(self):
video_filename = "79-30-960x720"
expected = len(DataSet.get_targets('test', video_filename))
actual = VideoHelper._extract_num_frames(video_filename)
self.assertEqual(expected, actual)
import numpy as np
import pandas as pd
import tensorflow as tf
from src import metrics
from src.config import PREDICTIONS
from src.data import DataSet
video_filename = "79-30-960x720"
path = os.path.join(PREDICTIONS,
video_filename + '.txt') # numpy will auto-append .npy
pred_df = pd.read_csv(path, sep=",")
pred_df[pred_df['valence'] == -5] = np.nan
# pred_df = pred_df.interpolate(method='linear', axis=0).fillna(-5)
# pred_df = pred_df.interpolate(method='linear', axis=0).fillna(0)
pred_df = pred_df.interpolate(method='linear', axis=0).ffill().bfill()
pred_df = pred_df.ex
pred = pred_df[['valence', 'arousal']].values
true = DataSet.get_targets('test', video_filename)
r = len(true) if len(pred) > len(true) else len(pred)
pred = tf.convert_to_tensor(pred[:r], np.float32)
true = tf.convert_to_tensor(true[:r], np.float32)
ccc_v = metrics.ccc_v(true, pred)
ccc_a = metrics.ccc_a(true, pred)
print(f'ccc_v: {ccc_v}, ccc_a: {ccc_a}')