# Copyright 2020 The MediaPipe Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Tests for mediapipe.python.solutions.hands."""

import os
import tempfile  # pylint: disable=unused-import
from typing import NamedTuple

from absl.testing import absltest
from absl.testing import parameterized
import cv2
import numpy as np
import numpy.testing as npt

# resources dependency
# undeclared dependency
from mediapipe.python.solutions import drawing_utils as mp_drawing
from mediapipe.python.solutions import hands as mp_hands

TEST_IMAGE_PATH = 'mediapipe/python/solutions/testdata'
DIFF_THRESHOLD = 15  # pixels
EXPECTED_HAND_COORDINATES_PREDICTION = [[[345, 144], [323, 211], [286, 257],
                                         [237, 289], [203, 322], [216, 219],
                                         [138, 238], [90, 249], [51, 253],
                                         [204, 177], [115, 184], [60, 187],
                                         [19, 185], [208, 138], [127, 131],
                                         [77, 124], [36, 117], [222, 106],
                                         [159, 92], [124, 79], [93, 68]],
                                        [[40, 577], [56, 504], [94, 459],
                                         [146, 429], [182, 397], [167, 496],
                                         [245, 479], [292, 469], [330, 464],
                                         [177, 540], [265, 534], [319, 533],
                                         [360, 536], [172, 581], [252, 587],
                                         [304, 593], [346, 599], [157, 615],
                                         [223, 628], [258, 638], [288, 648]]]


class HandsTest(parameterized.TestCase):

  def _annotate(self, frame: np.ndarray, results: NamedTuple, idx: int):
    for hand_landmarks in results.multi_hand_landmarks:
      mp_drawing.draw_landmarks(frame, hand_landmarks,
                                mp_hands.HAND_CONNECTIONS)
    path = os.path.join(tempfile.gettempdir(), self.id().split('.')[-1] +
                                              '_frame_{}.png'.format(idx))
    cv2.imwrite(path, frame)

  def test_invalid_image_shape(self):
    with mp_hands.Hands() as hands:
      with self.assertRaisesRegex(
          ValueError, 'Input image must contain three channel rgb data.'):
        hands.process(np.arange(36, dtype=np.uint8).reshape(3, 3, 4))

  def test_blank_image(self):
    with mp_hands.Hands() as hands:
      image = np.zeros([100, 100, 3], dtype=np.uint8)
      image.fill(255)
      results = hands.process(image)
      self.assertIsNone(results.multi_hand_landmarks)
      self.assertIsNone(results.multi_handedness)

  @parameterized.named_parameters(('static_image_mode', True, 1),
                                  ('video_mode', False, 5))
  def test_multi_hands(self, static_image_mode, num_frames):
    image_path = os.path.join(os.path.dirname(__file__), 'testdata/hands.jpg')
    image = cv2.imread(image_path)
    with mp_hands.Hands(
        static_image_mode=static_image_mode,
        max_num_hands=2,
        min_detection_confidence=0.5) as hands:
      for idx in range(num_frames):
        results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
        self._annotate(image.copy(), results, idx)
        handedness = [
            handedness.classification[0].label
            for handedness in results.multi_handedness
        ]
        multi_hand_coordinates = []
        for landmarks in results.multi_hand_landmarks:
          self.assertLen(landmarks.landmark, 21)
          x = [landmark.x for landmark in landmarks.landmark]
          y = [landmark.y for landmark in landmarks.landmark]
          hand_coordinates = np.transpose(np.stack((y, x))) * image.shape[0:2]
          multi_hand_coordinates.append(hand_coordinates)
        self.assertLen(handedness, 2)
        self.assertLen(multi_hand_coordinates, 2)
        prediction_error = np.abs(
            np.asarray(multi_hand_coordinates) -
            np.asarray(EXPECTED_HAND_COORDINATES_PREDICTION))
        npt.assert_array_less(prediction_error, DIFF_THRESHOLD)


if __name__ == '__main__':
  absltest.main()