def predict(
predict_data_path,
preprocessed_dir,
feature_data_path,
color_models,
quality_models,
mixed_models,
output_result_path,
):
if b_load_predict_feat:
feat_df = _load_features(feature_data_home, "*prediction*.csv")
else:
feat_df = _calc_predict_features(predict_data_path, preprocessed_dir, feature_data_path)
x_data = feat_df.drop(subjective_column_name, axis=1, level=0)
color_df = pd.DataFrame(color_models.predict(x_data[hue_column_name]))
quality_df = pd.DataFrame(quality_models.predict(x_data))
mixed_df = pd.DataFrame(mixed_models.predict(x_data))
all_df = pd.concat(
[color_df, quality_df, mixed_df, feat_df[subjective_column_name]],
axis=1,
keys=[
color_models.__class__.__name__,
quality_models.__class__.__name__,
mixed_models.__class__.__name__,
subjective_column_name,
],
)
# store prediction result
PublicSupport.save_dataframe(
all_df, os.path.join(output_result_path, "prediction" + datetime.now().strftime("%Y-%m-%d %H.%M.%S"))
)
def __calc_features(images_data, feature_name, feature_data_path):
# feature extraction
feat_df = pd.DataFrame([FeatureManager.compute_feats(img).append(info) for img, info in images_data])
PublicSupport.save_dataframe(
feat_df, os.path.join(feature_data_path, feature_name + datetime.now().strftime("%Y-%m-%d %H.%M.%S"))
)
return feat_df
