Added hand landmarker Python API and tests
This commit is contained in:
kinaryml
parent
4a6562d423
commit
0402ee383f
|
|
@ -73,3 +73,26 @@ py_test(
|
|||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
||||
py_test(
|
||||
name = "hand_landmarker_test",
|
||||
srcs = ["hand_landmarker_test.py"],
|
||||
data = [
|
||||
"//mediapipe/tasks/testdata/vision:test_images",
|
||||
"//mediapipe/tasks/testdata/vision:test_models",
|
||||
"//mediapipe/tasks/testdata/vision:test_protos",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2",
|
||||
"//mediapipe/tasks/python/components/containers:rect",
|
||||
"//mediapipe/tasks/python/components/containers:landmark",
|
||||
"//mediapipe/tasks/python/components/containers:landmark_detection_result",
|
||||
"//mediapipe/tasks/python/core:base_options",
|
||||
"//mediapipe/tasks/python/test:test_utils",
|
||||
"//mediapipe/tasks/python/vision:hand_landmarker",
|
||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
"//mediapipe/tasks/python/vision/core:image_processing_options",
|
||||
"@com_google_protobuf//:protobuf_python"
|
||||
],
|
||||
)
|
||||
|
|
|
|||
436
mediapipe/tasks/python/test/vision/hand_landmarker_test.py
Normal file
436
mediapipe/tasks/python/test/vision/hand_landmarker_test.py
Normal file
|
|
@ -0,0 +1,436 @@
|
|||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# 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 hand landmarker."""
|
||||
|
||||
import enum
|
||||
from unittest import mock
|
||||
|
||||
import numpy as np
|
||||
from google.protobuf import text_format
|
||||
from absl.testing import absltest
|
||||
from absl.testing import parameterized
|
||||
|
||||
from mediapipe.python._framework_bindings import image as image_module
|
||||
from mediapipe.tasks.cc.components.containers.proto import landmarks_detection_result_pb2
|
||||
from mediapipe.tasks.python.components.containers import rect as rect_module
|
||||
from mediapipe.tasks.python.components.containers import landmark as landmark_module
|
||||
from mediapipe.tasks.python.components.containers import landmark_detection_result as landmark_detection_result_module
|
||||
from mediapipe.tasks.python.core import base_options as base_options_module
|
||||
from mediapipe.tasks.python.test import test_utils
|
||||
from mediapipe.tasks.python.vision import hand_landmarker
|
||||
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
|
||||
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
|
||||
|
||||
_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_Rect = rect_module.Rect
|
||||
_Landmark = landmark_module.Landmark
|
||||
_NormalizedLandmark = landmark_module.NormalizedLandmark
|
||||
_LandmarksDetectionResult = landmark_detection_result_module.LandmarksDetectionResult
|
||||
_Image = image_module.Image
|
||||
_HandLandmarker = hand_landmarker.HandLandmarker
|
||||
_HandLandmarkerOptions = hand_landmarker.HandLandmarkerOptions
|
||||
_HandLandmarksDetectionResult = hand_landmarker.HandLandmarksDetectionResult
|
||||
_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
|
||||
_HAND_LANDMARKER_BUNDLE_ASSET_FILE = 'hand_landmarker.task'
|
||||
_NO_HANDS_IMAGE = 'cats_and_dogs.jpg'
|
||||
_TWO_HANDS_IMAGE = 'right_hands.jpg'
|
||||
_THUMB_UP_IMAGE = 'thumb_up.jpg'
|
||||
_THUMB_UP_LANDMARKS = 'thumb_up_landmarks.pbtxt'
|
||||
_POINTING_UP_IMAGE = 'pointing_up.jpg'
|
||||
_POINTING_UP_LANDMARKS = 'pointing_up_landmarks.pbtxt'
|
||||
_POINTING_UP_ROTATED_IMAGE = 'pointing_up_rotated.jpg'
|
||||
_POINTING_UP_ROTATED_LANDMARKS = 'pointing_up_rotated_landmarks.pbtxt'
|
||||
_LANDMARKS_ERROR_TOLERANCE = 0.03
|
||||
_HANDEDNESS_MARGIN = 0.05
|
||||
|
||||
|
||||
def _get_expected_hand_landmarks_detection_result(
|
||||
file_path: str) -> _HandLandmarksDetectionResult:
|
||||
landmarks_detection_result_file_path = test_utils.get_test_data_path(
|
||||
file_path)
|
||||
with open(landmarks_detection_result_file_path, "rb") as f:
|
||||
landmarks_detection_result_proto = _LandmarksDetectionResultProto()
|
||||
# Use this if a .pb file is available.
|
||||
# landmarks_detection_result_proto.ParseFromString(f.read())
|
||||
text_format.Parse(f.read(), landmarks_detection_result_proto)
|
||||
landmarks_detection_result = _LandmarksDetectionResult.create_from_pb2(
|
||||
landmarks_detection_result_proto)
|
||||
return _HandLandmarksDetectionResult(
|
||||
handedness=[landmarks_detection_result.categories],
|
||||
hand_landmarks=[landmarks_detection_result.landmarks],
|
||||
hand_world_landmarks=[landmarks_detection_result.world_landmarks])
|
||||
|
||||
|
||||
class ModelFileType(enum.Enum):
|
||||
FILE_CONTENT = 1
|
||||
FILE_NAME = 2
|
||||
|
||||
|
||||
class GestureRecognizerTest(parameterized.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
self.test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(_THUMB_UP_IMAGE))
|
||||
self.model_path = test_utils.get_test_data_path(
|
||||
_HAND_LANDMARKER_BUNDLE_ASSET_FILE)
|
||||
|
||||
def _assert_actual_result_approximately_matches_expected_result(
|
||||
self,
|
||||
actual_result: _HandLandmarksDetectionResult,
|
||||
expected_result: _HandLandmarksDetectionResult
|
||||
):
|
||||
# Expects to have the same number of hands detected.
|
||||
self.assertLen(actual_result.hand_landmarks,
|
||||
len(expected_result.hand_landmarks))
|
||||
self.assertLen(actual_result.hand_world_landmarks,
|
||||
len(expected_result.hand_world_landmarks))
|
||||
self.assertLen(actual_result.handedness, len(expected_result.handedness))
|
||||
# Actual landmarks match expected landmarks.
|
||||
self.assertLen(actual_result.hand_landmarks[0],
|
||||
len(expected_result.hand_landmarks[0]))
|
||||
actual_landmarks = actual_result.hand_landmarks[0]
|
||||
expected_landmarks = expected_result.hand_landmarks[0]
|
||||
for i in range(len(actual_landmarks)):
|
||||
self.assertAlmostEqual(actual_landmarks[i].x, expected_landmarks[i].x,
|
||||
delta=_LANDMARKS_ERROR_TOLERANCE)
|
||||
self.assertAlmostEqual(actual_landmarks[i].y, expected_landmarks[i].y,
|
||||
delta=_LANDMARKS_ERROR_TOLERANCE)
|
||||
# Actual handedness matches expected handedness.
|
||||
actual_top_handedness = actual_result.handedness[0][0]
|
||||
expected_top_handedness = expected_result.handedness[0][0]
|
||||
self.assertEqual(actual_top_handedness.index, expected_top_handedness.index)
|
||||
self.assertEqual(actual_top_handedness.category_name,
|
||||
expected_top_handedness.category_name)
|
||||
self.assertAlmostEqual(actual_top_handedness.score,
|
||||
expected_top_handedness.score,
|
||||
delta=_HANDEDNESS_MARGIN)
|
||||
|
||||
def test_create_from_file_succeeds_with_valid_model_path(self):
|
||||
# Creates with default option and valid model file successfully.
|
||||
with _HandLandmarker.create_from_model_path(self.model_path) as landmarker:
|
||||
self.assertIsInstance(landmarker, _HandLandmarker)
|
||||
|
||||
def test_create_from_options_succeeds_with_valid_model_path(self):
|
||||
# Creates with options containing model file successfully.
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
options = _HandLandmarkerOptions(base_options=base_options)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
self.assertIsInstance(landmarker, _HandLandmarker)
|
||||
|
||||
def test_create_from_options_fails_with_invalid_model_path(self):
|
||||
# Invalid empty model path.
|
||||
with self.assertRaisesRegex(
|
||||
ValueError,
|
||||
r"ExternalFile must specify at least one of 'file_content', "
|
||||
r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."):
|
||||
base_options = _BaseOptions(model_asset_path='')
|
||||
options = _HandLandmarkerOptions(base_options=base_options)
|
||||
_HandLandmarker.create_from_options(options)
|
||||
|
||||
def test_create_from_options_succeeds_with_valid_model_content(self):
|
||||
# Creates with options containing model content successfully.
|
||||
with open(self.model_path, 'rb') as f:
|
||||
base_options = _BaseOptions(model_asset_buffer=f.read())
|
||||
options = _HandLandmarkerOptions(base_options=base_options)
|
||||
landmarker = _HandLandmarker.create_from_options(options)
|
||||
self.assertIsInstance(landmarker, _HandLandmarker)
|
||||
|
||||
@parameterized.parameters(
|
||||
(ModelFileType.FILE_NAME, _get_expected_hand_landmarks_detection_result(
|
||||
_THUMB_UP_LANDMARKS
|
||||
)),
|
||||
(ModelFileType.FILE_CONTENT, _get_expected_hand_landmarks_detection_result(
|
||||
_THUMB_UP_LANDMARKS
|
||||
)))
|
||||
def test_detect(self, model_file_type, expected_detection_result):
|
||||
# Creates hand landmarker.
|
||||
if model_file_type is ModelFileType.FILE_NAME:
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||||
with open(self.model_path, 'rb') as f:
|
||||
model_content = f.read()
|
||||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||||
else:
|
||||
# Should never happen
|
||||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
options = _HandLandmarkerOptions(base_options=base_options)
|
||||
landmarker = _HandLandmarker.create_from_options(options)
|
||||
|
||||
# Performs hand landmarks detection on the input.
|
||||
detection_result = landmarker.detect(self.test_image)
|
||||
# Comparing results.
|
||||
self._assert_actual_result_approximately_matches_expected_result(
|
||||
detection_result, expected_detection_result)
|
||||
# Closes the hand landmarker explicitly when the hand landmarker is not used
|
||||
# in a context.
|
||||
landmarker.close()
|
||||
|
||||
@parameterized.parameters(
|
||||
(ModelFileType.FILE_NAME, _get_expected_hand_landmarks_detection_result(
|
||||
_THUMB_UP_LANDMARKS
|
||||
)),
|
||||
(ModelFileType.FILE_CONTENT, _get_expected_hand_landmarks_detection_result(
|
||||
_THUMB_UP_LANDMARKS
|
||||
)))
|
||||
def test_detect_in_context(self, model_file_type, expected_detection_result):
|
||||
# Creates hand landmarker.
|
||||
if model_file_type is ModelFileType.FILE_NAME:
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||||
with open(self.model_path, 'rb') as f:
|
||||
model_content = f.read()
|
||||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||||
else:
|
||||
# Should never happen
|
||||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
options = _HandLandmarkerOptions(base_options=base_options)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
# Performs hand landmarks detection on the input.
|
||||
detection_result = landmarker.detect(self.test_image)
|
||||
# Comparing results.
|
||||
self._assert_actual_result_approximately_matches_expected_result(
|
||||
detection_result, expected_detection_result)
|
||||
|
||||
def test_detect_succeeds_with_num_hands(self):
|
||||
# Creates hand landmarker.
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
options = _HandLandmarkerOptions(base_options=base_options, num_hands=2)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
# Load the two hands image.
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(_TWO_HANDS_IMAGE))
|
||||
# Performs hand landmarks detection on the input.
|
||||
detection_result = landmarker.detect(test_image)
|
||||
# Comparing results.
|
||||
self.assertLen(detection_result.handedness, 2)
|
||||
|
||||
def test_detect_succeeds_with_rotation(self):
|
||||
# Creates hand landmarker.
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
options = _HandLandmarkerOptions(base_options=base_options)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
# Load the pointing up rotated image.
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(_POINTING_UP_ROTATED_IMAGE))
|
||||
# Set rotation parameters using ImageProcessingOptions.
|
||||
image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
|
||||
# Performs hand landmarks detection on the input.
|
||||
detection_result = landmarker.detect(test_image,
|
||||
image_processing_options)
|
||||
expected_detection_result = _get_expected_hand_landmarks_detection_result(
|
||||
_POINTING_UP_ROTATED_LANDMARKS)
|
||||
# Comparing results.
|
||||
self._assert_actual_result_approximately_matches_expected_result(
|
||||
detection_result, expected_detection_result)
|
||||
|
||||
def test_detect_fails_with_region_of_interest(self):
|
||||
# Creates hand landmarker.
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
options = _HandLandmarkerOptions(base_options=base_options)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, "This task doesn't support region-of-interest."):
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
# Set the `region_of_interest` parameter using `ImageProcessingOptions`.
|
||||
image_processing_options = _ImageProcessingOptions(
|
||||
region_of_interest=_Rect(0, 0, 1, 1))
|
||||
# Attempt to perform hand landmarks detection on the cropped input.
|
||||
landmarker.detect(self.test_image, image_processing_options)
|
||||
|
||||
def test_empty_detection_outputs(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path))
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
# Load the image with no hands.
|
||||
no_hands_test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(_NO_HANDS_IMAGE))
|
||||
# Performs hand landmarks detection on the input.
|
||||
detection_result = landmarker.detect(no_hands_test_image)
|
||||
self.assertEmpty(detection_result.hand_landmarks)
|
||||
self.assertEmpty(detection_result.hand_world_landmarks)
|
||||
self.assertEmpty(detection_result.handedness)
|
||||
|
||||
def test_missing_result_callback(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM)
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'result callback must be provided'):
|
||||
with _HandLandmarker.create_from_options(options) as unused_landmarker:
|
||||
pass
|
||||
|
||||
@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
|
||||
def test_illegal_result_callback(self, running_mode):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=running_mode,
|
||||
result_callback=mock.MagicMock())
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'result callback should not be provided'):
|
||||
with _HandLandmarker.create_from_options(options) as unused_landmarker:
|
||||
pass
|
||||
|
||||
def test_calling_detect_for_video_in_image_mode(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the video mode'):
|
||||
landmarker.detect_for_video(self.test_image, 0)
|
||||
|
||||
def test_calling_detect_async_in_image_mode(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the live stream mode'):
|
||||
landmarker.detect_async(self.test_image, 0)
|
||||
|
||||
def test_calling_detect_in_video_mode(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the image mode'):
|
||||
landmarker.detect(self.test_image)
|
||||
|
||||
def test_calling_detect_async_in_video_mode(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the live stream mode'):
|
||||
landmarker.detect_async(self.test_image, 0)
|
||||
|
||||
def test_detect_for_video_with_out_of_order_timestamp(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
unused_result = landmarker.detect_for_video(self.test_image, 1)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'):
|
||||
landmarker.detect_for_video(self.test_image, 0)
|
||||
|
||||
@parameterized.parameters(
|
||||
(_THUMB_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result(
|
||||
_THUMB_UP_LANDMARKS)),
|
||||
(_POINTING_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result(
|
||||
_POINTING_UP_LANDMARKS)),
|
||||
(_POINTING_UP_ROTATED_IMAGE, -90,
|
||||
_get_expected_hand_landmarks_detection_result(
|
||||
_POINTING_UP_ROTATED_LANDMARKS)),
|
||||
(_NO_HANDS_IMAGE, 0, _HandLandmarksDetectionResult([], [], [])))
|
||||
def test_detect_for_video(self, image_path, rotation, expected_result):
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(image_path))
|
||||
# Set rotation parameters using ImageProcessingOptions.
|
||||
image_processing_options = _ImageProcessingOptions(rotation_degrees=rotation)
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
for timestamp in range(0, 300, 30):
|
||||
result = landmarker.detect_for_video(test_image,
|
||||
timestamp,
|
||||
image_processing_options)
|
||||
if result.hand_landmarks and result.hand_world_landmarks and \
|
||||
result.handedness:
|
||||
self._assert_actual_result_approximately_matches_expected_result(
|
||||
result, expected_result)
|
||||
else:
|
||||
self.assertEqual(result, expected_result)
|
||||
|
||||
def test_calling_detect_in_live_stream_mode(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the image mode'):
|
||||
landmarker.detect(self.test_image)
|
||||
|
||||
def test_calling_detect_for_video_in_live_stream_mode(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the video mode'):
|
||||
landmarker.detect_for_video(self.test_image, 0)
|
||||
|
||||
def test_detect_async_calls_with_illegal_timestamp(self):
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
landmarker.detect_async(self.test_image, 100)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'):
|
||||
landmarker.detect_async(self.test_image, 0)
|
||||
|
||||
@parameterized.parameters(
|
||||
(_THUMB_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result(
|
||||
_THUMB_UP_LANDMARKS)),
|
||||
(_POINTING_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result(
|
||||
_POINTING_UP_LANDMARKS)),
|
||||
(_POINTING_UP_ROTATED_IMAGE, -90,
|
||||
_get_expected_hand_landmarks_detection_result(
|
||||
_POINTING_UP_ROTATED_LANDMARKS)),
|
||||
(_NO_HANDS_IMAGE, 0, _HandLandmarksDetectionResult([], [], [])))
|
||||
def test_detect_async_calls(self, image_path, rotation, expected_result):
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(image_path))
|
||||
# Set rotation parameters using ImageProcessingOptions.
|
||||
image_processing_options = _ImageProcessingOptions(rotation_degrees=rotation)
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(result: _HandLandmarksDetectionResult,
|
||||
output_image: _Image,
|
||||
timestamp_ms: int):
|
||||
if result.hand_landmarks and result.hand_world_landmarks and \
|
||||
result.handedness:
|
||||
self._assert_actual_result_approximately_matches_expected_result(
|
||||
result, expected_result)
|
||||
else:
|
||||
self.assertEqual(result, expected_result)
|
||||
self.assertTrue(
|
||||
np.array_equal(output_image.numpy_view(),
|
||||
test_image.numpy_view()))
|
||||
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
self.observed_timestamp_ms = timestamp_ms
|
||||
|
||||
options = _HandLandmarkerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=check_result)
|
||||
with _HandLandmarker.create_from_options(options) as landmarker:
|
||||
for timestamp in range(0, 300, 30):
|
||||
landmarker.detect_async(test_image, timestamp, image_processing_options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
||||
|
|
@ -102,3 +102,26 @@ py_library(
|
|||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "hand_landmarker",
|
||||
srcs = [
|
||||
"hand_landmarker.py",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework/formats:classification_py_pb2",
|
||||
"//mediapipe/framework/formats:landmark_py_pb2",
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/python:packet_creator",
|
||||
"//mediapipe/python:packet_getter",
|
||||
"//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/python/components/containers:category",
|
||||
"//mediapipe/tasks/python/components/containers:landmark",
|
||||
"//mediapipe/tasks/python/core:base_options",
|
||||
"//mediapipe/tasks/python/core:optional_dependencies",
|
||||
"//mediapipe/tasks/python/core:task_info",
|
||||
"//mediapipe/tasks/python/vision/core:base_vision_task_api",
|
||||
"//mediapipe/tasks/python/vision/core:image_processing_options",
|
||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
381
mediapipe/tasks/python/vision/hand_landmarker.py
Normal file
381
mediapipe/tasks/python/vision/hand_landmarker.py
Normal file
|
|
@ -0,0 +1,381 @@
|
|||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
"""MediaPipe hand landmarker task."""
|
||||
|
||||
import dataclasses
|
||||
from typing import Callable, Mapping, Optional, List
|
||||
|
||||
from mediapipe.framework.formats import classification_pb2
|
||||
from mediapipe.framework.formats import landmark_pb2
|
||||
from mediapipe.python import packet_creator
|
||||
from mediapipe.python import packet_getter
|
||||
from mediapipe.python._framework_bindings import image as image_module
|
||||
from mediapipe.python._framework_bindings import packet as packet_module
|
||||
from mediapipe.tasks.cc.vision.hand_landmarker.proto import hand_landmarker_graph_options_pb2
|
||||
from mediapipe.tasks.python.components.containers import category as category_module
|
||||
from mediapipe.tasks.python.components.containers import landmark as landmark_module
|
||||
from mediapipe.tasks.python.core import base_options as base_options_module
|
||||
from mediapipe.tasks.python.core import task_info as task_info_module
|
||||
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||
from mediapipe.tasks.python.vision.core import base_vision_task_api
|
||||
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
|
||||
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
|
||||
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_HandLandmarkerGraphOptionsProto = hand_landmarker_graph_options_pb2.HandLandmarkerGraphOptions
|
||||
_RunningMode = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
_TaskInfo = task_info_module.TaskInfo
|
||||
|
||||
_IMAGE_IN_STREAM_NAME = 'image_in'
|
||||
_IMAGE_OUT_STREAM_NAME = 'image_out'
|
||||
_IMAGE_TAG = 'IMAGE'
|
||||
_NORM_RECT_STREAM_NAME = 'norm_rect_in'
|
||||
_NORM_RECT_TAG = 'NORM_RECT'
|
||||
_HANDEDNESS_STREAM_NAME = 'handedness'
|
||||
_HANDEDNESS_TAG = 'HANDEDNESS'
|
||||
_HAND_LANDMARKS_STREAM_NAME = 'landmarks'
|
||||
_HAND_LANDMARKS_TAG = 'LANDMARKS'
|
||||
_HAND_WORLD_LANDMARKS_STREAM_NAME = 'world_landmarks'
|
||||
_HAND_WORLD_LANDMARKS_TAG = 'WORLD_LANDMARKS'
|
||||
_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.hand_landmarker.HandLandmarkerGraph'
|
||||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class HandLandmarksDetectionResult:
|
||||
"""The hand landmarks detection result from HandLandmarker, where each vector
|
||||
element represents a single hand detected in the image.
|
||||
|
||||
Attributes:
|
||||
handedness: Classification of handedness.
|
||||
hand_landmarks: Detected hand landmarks in normalized image coordinates.
|
||||
hand_world_landmarks: Detected hand landmarks in world coordinates.
|
||||
"""
|
||||
|
||||
handedness: List[List[category_module.Category]]
|
||||
hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
|
||||
hand_world_landmarks: List[List[landmark_module.Landmark]]
|
||||
|
||||
|
||||
def _build_detection_result(
|
||||
output_packets: Mapping[str,packet_module.Packet]
|
||||
) -> HandLandmarksDetectionResult:
|
||||
"""Constructs a `HandLandmarksDetectionResult` from output packets."""
|
||||
handedness_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_HANDEDNESS_STREAM_NAME])
|
||||
hand_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_HAND_LANDMARKS_STREAM_NAME])
|
||||
hand_world_landmarks_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_HAND_WORLD_LANDMARKS_STREAM_NAME])
|
||||
|
||||
handedness_results = []
|
||||
for proto in handedness_proto_list:
|
||||
handedness_categories = []
|
||||
handedness_classifications = classification_pb2.ClassificationList()
|
||||
handedness_classifications.MergeFrom(proto)
|
||||
for handedness in handedness_classifications.classification:
|
||||
handedness_categories.append(
|
||||
category_module.Category(
|
||||
index=handedness.index,
|
||||
score=handedness.score,
|
||||
display_name=handedness.display_name,
|
||||
category_name=handedness.label))
|
||||
handedness_results.append(handedness_categories)
|
||||
|
||||
hand_landmarks_results = []
|
||||
for proto in hand_landmarks_proto_list:
|
||||
hand_landmarks = landmark_pb2.NormalizedLandmarkList()
|
||||
hand_landmarks.MergeFrom(proto)
|
||||
hand_landmarks_results.append([
|
||||
landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)
|
||||
for hand_landmark in hand_landmarks.landmark
|
||||
])
|
||||
|
||||
hand_world_landmarks_results = []
|
||||
for proto in hand_world_landmarks_proto_list:
|
||||
hand_world_landmarks = landmark_pb2.LandmarkList()
|
||||
hand_world_landmarks.MergeFrom(proto)
|
||||
hand_world_landmarks_results.append([
|
||||
landmark_module.Landmark.create_from_pb2(hand_world_landmark)
|
||||
for hand_world_landmark in hand_world_landmarks.landmark
|
||||
])
|
||||
|
||||
return HandLandmarksDetectionResult(handedness_results,
|
||||
hand_landmarks_results,
|
||||
hand_world_landmarks_results)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class HandLandmarkerOptions:
|
||||
"""Options for the hand landmarker task.
|
||||
|
||||
Attributes:
|
||||
base_options: Base options for the hand landmarker task.
|
||||
running_mode: The running mode of the task. Default to the image mode.
|
||||
HandLandmarker has three running modes: 1) The image mode for detecting
|
||||
hand landmarks on single image inputs. 2) The video mode for detecting
|
||||
hand landmarks on the decoded frames of a video. 3) The live stream mode
|
||||
for detecting hand landmarks on the live stream of input data, such as
|
||||
from camera. In this mode, the "result_callback" below must be specified
|
||||
to receive the detection results asynchronously.
|
||||
num_hands: The maximum number of hands can be detected by the hand
|
||||
landmarker.
|
||||
min_hand_detection_confidence: The minimum confidence score for the hand
|
||||
detection to be considered successful.
|
||||
min_hand_presence_confidence: The minimum confidence score of hand presence
|
||||
score in the hand landmark detection.
|
||||
min_tracking_confidence: The minimum confidence score for the hand tracking
|
||||
to be considered successful.
|
||||
result_callback: The user-defined result callback for processing live stream
|
||||
data. The result callback should only be specified when the running mode
|
||||
is set to the live stream mode.
|
||||
"""
|
||||
base_options: _BaseOptions
|
||||
running_mode: _RunningMode = _RunningMode.IMAGE
|
||||
num_hands: Optional[int] = 1
|
||||
min_hand_detection_confidence: Optional[float] = 0.5
|
||||
min_hand_presence_confidence: Optional[float] = 0.5
|
||||
min_tracking_confidence: Optional[float] = 0.5
|
||||
result_callback: Optional[Callable[
|
||||
[HandLandmarksDetectionResult, image_module.Image, int], None]] = None
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _HandLandmarkerGraphOptionsProto:
|
||||
"""Generates an HandLandmarkerGraphOptions protobuf object."""
|
||||
base_options_proto = self.base_options.to_pb2()
|
||||
base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
|
||||
|
||||
# Initialize the hand landmarker options from base options.
|
||||
hand_landmarker_options_proto = _HandLandmarkerGraphOptionsProto(
|
||||
base_options=base_options_proto)
|
||||
hand_landmarker_options_proto.min_tracking_confidence = self.min_tracking_confidence
|
||||
hand_landmarker_options_proto.hand_detector_graph_options.num_hands = self.num_hands
|
||||
hand_landmarker_options_proto.hand_detector_graph_options.min_detection_confidence = self.min_hand_detection_confidence
|
||||
hand_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = self.min_hand_presence_confidence
|
||||
return hand_landmarker_options_proto
|
||||
|
||||
|
||||
class HandLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
||||
"""Class that performs hand landmarks detection on images."""
|
||||
|
||||
@classmethod
|
||||
def create_from_model_path(cls, model_path: str) -> 'HandLandmarker':
|
||||
"""Creates an `HandLandmarker` object from a TensorFlow Lite model and the default `HandLandmarkerOptions`.
|
||||
|
||||
Note that the created `HandLandmarker` instance is in image mode, for
|
||||
detecting hand landmarks on single image inputs.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`HandLandmarker` object that's created from the model file and the
|
||||
default `HandLandmarkerOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `HandLandmarker` object from the
|
||||
provided file such as invalid file path.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
options = HandLandmarkerOptions(
|
||||
base_options=base_options, running_mode=_RunningMode.IMAGE)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(
|
||||
cls, options: HandLandmarkerOptions) -> 'HandLandmarker':
|
||||
"""Creates the `HandLandmarker` object from hand landmarker options.
|
||||
|
||||
Args:
|
||||
options: Options for the hand landmarker task.
|
||||
|
||||
Returns:
|
||||
`HandLandmarker` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `HandLandmarker` object from
|
||||
`HandLandmarkerOptions` such as missing the model.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
|
||||
def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
|
||||
if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
|
||||
return
|
||||
|
||||
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
|
||||
|
||||
if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty():
|
||||
empty_packet = output_packets[_HAND_LANDMARKS_STREAM_NAME]
|
||||
options.result_callback(
|
||||
HandLandmarksDetectionResult([], [], []), image,
|
||||
empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
|
||||
return
|
||||
|
||||
hand_landmarks_detection_result = _build_detection_result(output_packets)
|
||||
timestamp = output_packets[_HAND_LANDMARKS_STREAM_NAME].timestamp
|
||||
options.result_callback(hand_landmarks_detection_result, image,
|
||||
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
|
||||
|
||||
task_info = _TaskInfo(
|
||||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[
|
||||
':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
|
||||
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
|
||||
],
|
||||
output_streams=[
|
||||
':'.join([_HANDEDNESS_TAG, _HANDEDNESS_STREAM_NAME]),
|
||||
':'.join([_HAND_LANDMARKS_TAG,
|
||||
_HAND_LANDMARKS_STREAM_NAME]), ':'.join([
|
||||
_HAND_WORLD_LANDMARKS_TAG,
|
||||
_HAND_WORLD_LANDMARKS_STREAM_NAME
|
||||
]), ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
|
||||
],
|
||||
task_options=options)
|
||||
return cls(
|
||||
task_info.generate_graph_config(
|
||||
enable_flow_limiting=options.running_mode ==
|
||||
_RunningMode.LIVE_STREAM), options.running_mode,
|
||||
packets_callback if options.result_callback else None)
|
||||
|
||||
def detect(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> HandLandmarksDetectionResult:
|
||||
"""Performs hand landmarks detection on the given image.
|
||||
|
||||
Only use this method when the HandLandmarker is created with the image
|
||||
running mode.
|
||||
|
||||
The image can be of any size with format RGB or RGBA.
|
||||
TODO: Describes how the input image will be preprocessed after the yuv
|
||||
support is implemented.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The hand landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If hand landmarker detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, roi_allowed=False)
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2())
|
||||
})
|
||||
|
||||
if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty():
|
||||
return HandLandmarksDetectionResult([], [], [])
|
||||
|
||||
return _build_detection_result(output_packets)
|
||||
|
||||
def detect_for_video(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> HandLandmarksDetectionResult:
|
||||
"""Performs hand landmarks detection on the provided video frame.
|
||||
|
||||
Only use this method when the HandLandmarker is created with the video
|
||||
running mode.
|
||||
|
||||
Only use this method when the HandLandmarker is created with the video
|
||||
running mode. It's required to provide the video frame's timestamp (in
|
||||
milliseconds) along with the video frame. The input timestamps should be
|
||||
monotonically increasing for adjacent calls of this method.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input video frame in milliseconds.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The hand landmarks detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If hand landmarker detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, roi_allowed=False)
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
|
||||
if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty():
|
||||
return HandLandmarksDetectionResult([], [], [])
|
||||
|
||||
return _build_detection_result(output_packets)
|
||||
|
||||
def detect_async(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> None:
|
||||
"""Sends live image data to perform hand landmarks detection.
|
||||
|
||||
The results will be available via the "result_callback" provided in the
|
||||
HandLandmarkerOptions. Only use this method when the HandLandmarker is
|
||||
created with the live stream running mode.
|
||||
|
||||
Only use this method when the HandLandmarker is created with the live
|
||||
stream running mode. The input timestamps should be monotonically increasing
|
||||
for adjacent calls of this method. This method will return immediately after
|
||||
the input image is accepted. The results will be available via the
|
||||
`result_callback` provided in the `HandLandmarkerOptions`. The
|
||||
`detect_async` method is designed to process live stream data such as
|
||||
camera input. To lower the overall latency, hand landmarker may drop the
|
||||
input images if needed. In other words, it's not guaranteed to have output
|
||||
per input image.
|
||||
|
||||
The `result_callback` provides:
|
||||
- The hand landmarks detection results.
|
||||
- The input image that the hand landmarker runs on.
|
||||
- The input timestamp in milliseconds.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input image in milliseconds.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the
|
||||
hand landmarker has already processed.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, roi_allowed=False)
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
Loading…
Reference in New Issue
Block a user