@@ -0,0 +1,154 @@ # # DeepLabCut Toolbox (deeplabcut.org) # © A. & M.W. Mathis Labs # https://github.com/DeepLabCut/DeepLabCut # # Please see AUTHORS for contributors. # https://github.com/DeepLabCut/DeepLabCut/blob/master/AUTHORS # # Licensed under GNU Lesser General Public License v3.0 # import numpy as np import pandas as pd import pytest
import deeplabcut.pose_estimation_tensorflow as pet

def make_single_animal_rmse_df( bodyparts, train_indices, test_indices, error_data=None, ) -> pd.DataFrame: if error_data is None: error_data = np.ones((len(train_indices) + len(test_indices), len(bodyparts))) return pd.DataFrame(error_data, columns=bodyparts)

def make_multi_animal_rmse_df( scorer, individuals, bodyparts, train_indices, test_indices, error_data=None, ) -> pd.DataFrame: columns = pd.MultiIndex.from_product( [[scorer], individuals, bodyparts], names=["scorer", "individuals", "bodyparts"], ) if error_data is None: error_data = np.ones( (len(train_indices) + len(test_indices), len(individuals) * len(bodyparts)) ) return pd.DataFrame(error_data, columns=columns)

KEYPOINT_ERROR_NAMES = [ "Train error (px)", "Test error (px)", "Train error (px) with p-cutoff", "Test error (px) with p-cutoff", ]
KEYPOINT_ERROR_TEST_DATA = [ ( { "df_error": make_single_animal_rmse_df( bodyparts=["leg", "arm", "head"], train_indices=[0, 1, 3], test_indices=[2, 4], ), "train_indices": [0, 1, 3], "test_indices": [2, 4], }, { "leg": [1.0, 1.0], # train, test "arm": [1.0, 1.0], # train, test "head": [1.0, 1.0], # train, test }, ), ( { "df_error": make_single_animal_rmse_df( bodyparts=["leftHand", "rightHand"], train_indices=[0, 2], test_indices=[1, 3], error_data=[ [1.0, np.nan], [1.0, 0.0], [0.0, 10.0], [5.0, 5.0], ], ), "train_indices": [0, 2], "test_indices": [1, 3], }, { "leftHand": [0.5, 3.0], # train, test "rightHand": [10.0, 2.5], # train, test }, ), ( { "df_error": make_single_animal_rmse_df( bodyparts=["leg", "arm", "head"], train_indices=[0, 1, 3], test_indices=[2, 4], ), "train_indices": [0, 1, 3], "test_indices": [2, 4], }, { "leg": [1.0, 1.0], # train, test "arm": [1.0, 1.0], # train, test "head": [1.0, 1.0], # train, test }, ), ( { "df_error": make_multi_animal_rmse_df( scorer="john", individuals=["individual_1", "individual_2"], bodyparts=["leftArm", "rightArm"], train_indices=[0, 1, 3], test_indices=[2], error_data=[ # individual_1, individual2 # leftArm, rightArm, leftArm, rightArm [1.0, np.nan, 1.0, 2.0], [2.0, 0.0, 1.0, np.nan], [3.0, 10.0, 1.0, np.nan], [10.0, 4.0, np.nan, np.nan], ], ), "train_indices": [0, 1, 3], "test_indices": [2], }, { "leftArm": [3.0, 2.0], # train, test "rightArm": [2.0, 10.0], # train, test }, ), ]

@pytest.mark.parametrize("inputs, expected_values", KEYPOINT_ERROR_TEST_DATA) def test_evaluate_keypoint_error(inputs, expected_values): keypoint_error = pet.keypoint_error( inputs["df_error"], inputs["df_error"], inputs["train_indices"], inputs["test_indices"], ) print(inputs["df_error"]) print(keypoint_error) for bodypart, mean_errors in expected_values.items(): for error_name in KEYPOINT_ERROR_NAMES: if "train" in error_name.lower(): mean_error = mean_errors[0] else: mean_error = mean_errors[1]
assert keypoint_error.loc[error_name, bodypart] == mean_error