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GitOrigin-RevId: bbbbcb4f5174dea33525729ede47c770069157cd
This commit is contained in:
MediaPipe Team
2021-10-18 17:00:29 -04:00
committed by chuoling
parent 33d683c671
commit 1faeaae7e5
75 changed files with 1944 additions and 560 deletions
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mediapipe/python/solutions/hands.py
+6
View File
@@ -89,6 +89,7 @@ class Hands(SolutionBase):
def __init__(self,
static_image_mode=False,
max_num_hands=2,
model_complexity=1,
min_detection_confidence=0.5,
min_tracking_confidence=0.5):
"""Initializes a MediaPipe Hand object.
@@ -99,6 +100,10 @@ class Hands(SolutionBase):
https://solutions.mediapipe.dev/hands#static_image_mode.
max_num_hands: Maximum number of hands to detect. See details in
https://solutions.mediapipe.dev/hands#max_num_hands.
model_complexity: Complexity of the hand landmark model: 0 or 1.
Landmark accuracy as well as inference latency generally go up with the
model complexity. See details in
https://solutions.mediapipe.dev/hands#model_complexity.
min_detection_confidence: Minimum confidence value ([0.0, 1.0]) for hand
detection to be considered successful. See details in
https://solutions.mediapipe.dev/hands#min_detection_confidence.
@@ -109,6 +114,7 @@ class Hands(SolutionBase):
super().__init__(
binary_graph_path=_BINARYPB_FILE_PATH,
side_inputs={
'model_complexity': model_complexity,
'num_hands': max_num_hands,
'use_prev_landmarks': not static_image_mode,
},
mediapipe/python/solutions/hands_test.py
+12 -6
View File
@@ -32,7 +32,8 @@ from mediapipe.python.solutions import hands as mp_hands
TEST_IMAGE_PATH = 'mediapipe/python/solutions/testdata'
DIFF_THRESHOLD = 20 # pixels
LITE_MODEL_DIFF_THRESHOLD = 25 # pixels
FULL_MODEL_DIFF_THRESHOLD = 20 # pixels
EXPECTED_HAND_COORDINATES_PREDICTION = [[[138, 343], [211, 330], [257, 286],
[289, 237], [322, 203], [219, 216],
[238, 138], [249, 90], [253, 51],
@@ -40,7 +41,7 @@ EXPECTED_HAND_COORDINATES_PREDICTION = [[[138, 343], [211, 330], [257, 286],
[185, 19], [138, 208], [131, 127],
[124, 77], [117, 36], [106, 222],
[92, 159], [79, 124], [68, 93]],
[[580, 36], [504, 50], [459, 94],
[[580, 34], [504, 50], [459, 94],
[429, 146], [397, 182], [507, 167],
[479, 245], [469, 292], [464, 330],
[545, 180], [534, 265], [533, 319],
@@ -75,14 +76,18 @@ class HandsTest(parameterized.TestCase):
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):
@parameterized.named_parameters(
('static_image_mode_with_lite_model', True, 0, 5),
('video_mode_with_lite_model', False, 0, 10),
('static_image_mode_with_full_model', True, 1, 5),
('video_mode_with_full_model', False, 1, 10))
def test_multi_hands(self, static_image_mode, model_complexity, 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,
model_complexity=model_complexity,
min_detection_confidence=0.5) as hands:
for idx in range(num_frames):
results = hands.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
@@ -104,7 +109,8 @@ class HandsTest(parameterized.TestCase):
prediction_error = np.abs(
np.asarray(multi_hand_coordinates) -
np.asarray(EXPECTED_HAND_COORDINATES_PREDICTION))
npt.assert_array_less(prediction_error, DIFF_THRESHOLD)
diff_threshold = LITE_MODEL_DIFF_THRESHOLD if model_complexity == 0 else FULL_MODEL_DIFF_THRESHOLD
npt.assert_array_less(prediction_error, diff_threshold)
if __name__ == '__main__':