Merge pull request #4280 from kinaryml:image-segmenter-python-api-updates
PiperOrigin-RevId: 525204893
This commit is contained in:
Copybara-Service
commit
e15d98298f
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@ -15,7 +15,6 @@
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import enum
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import os
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from typing import List
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from unittest import mock
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from absl.testing import absltest
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@ -30,11 +29,10 @@ from mediapipe.tasks.python.test import test_utils
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from mediapipe.tasks.python.vision import image_segmenter
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from mediapipe.tasks.python.vision.core import vision_task_running_mode
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ImageSegmenterResult = image_segmenter.ImageSegmenterResult
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_BaseOptions = base_options_module.BaseOptions
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_Image = image_module.Image
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_ImageFormat = image_frame.ImageFormat
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_OutputType = image_segmenter.ImageSegmenterOptions.OutputType
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_Activation = image_segmenter.ImageSegmenterOptions.Activation
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_ImageSegmenter = image_segmenter.ImageSegmenter
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_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
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_RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
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@ -42,11 +40,33 @@ _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
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_MODEL_FILE = 'deeplabv3.tflite'
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_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
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_SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
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_CAT_IMAGE = 'cat.jpg'
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_CAT_MASK = 'cat_mask.jpg'
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_MASK_MAGNIFICATION_FACTOR = 10
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_MASK_SIMILARITY_THRESHOLD = 0.98
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
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def _calculate_soft_iou(m1, m2):
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intersection_sum = np.sum(m1 * m2)
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union_sum = np.sum(m1 * m1) + np.sum(m2 * m2) - intersection_sum
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if union_sum > 0:
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return intersection_sum / union_sum
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else:
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return 0
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def _similar_to_float_mask(actual_mask, expected_mask, similarity_threshold):
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actual_mask = actual_mask.numpy_view()
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expected_mask = expected_mask.numpy_view() / 255.0
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return (
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actual_mask.shape == expected_mask.shape
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and _calculate_soft_iou(actual_mask, expected_mask) > similarity_threshold
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)
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def _similar_to_uint8_mask(actual_mask, expected_mask):
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actual_mask_pixels = actual_mask.numpy_view().flatten()
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expected_mask_pixels = expected_mask.numpy_view().flatten()
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@ -56,8 +76,9 @@ def _similar_to_uint8_mask(actual_mask, expected_mask):
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for index in range(num_pixels):
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consistent_pixels += (
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actual_mask_pixels[index] *
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_MASK_MAGNIFICATION_FACTOR == expected_mask_pixels[index])
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actual_mask_pixels[index] * _MASK_MAGNIFICATION_FACTOR
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== expected_mask_pixels[index]
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)
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return consistent_pixels / num_pixels >= _MASK_SIMILARITY_THRESHOLD
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@ -73,16 +94,27 @@ class ImageSegmenterTest(parameterized.TestCase):
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super().setUp()
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# Load the test input image.
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self.test_image = _Image.create_from_file(
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test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _IMAGE_FILE)))
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test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _IMAGE_FILE))
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)
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# Loads ground truth segmentation file.
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gt_segmentation_data = cv2.imread(
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test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _SEGMENTATION_FILE)),
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cv2.IMREAD_GRAYSCALE)
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os.path.join(_TEST_DATA_DIR, _SEGMENTATION_FILE)
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),
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cv2.IMREAD_GRAYSCALE,
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)
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self.test_seg_image = _Image(_ImageFormat.GRAY8, gt_segmentation_data)
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self.model_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _MODEL_FILE))
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os.path.join(_TEST_DATA_DIR, _MODEL_FILE)
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)
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def _load_segmentation_mask(self, file_path: str):
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# Loads ground truth segmentation file.
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gt_segmentation_data = cv2.imread(
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test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_path)),
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cv2.IMREAD_GRAYSCALE,
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)
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return _Image(_ImageFormat.GRAY8, gt_segmentation_data)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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@ -98,9 +130,11 @@ class ImageSegmenterTest(parameterized.TestCase):
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def test_create_from_options_fails_with_invalid_model_path(self):
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with self.assertRaisesRegex(
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RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
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RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'
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):
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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite')
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model_asset_path='/path/to/invalid/model.tflite'
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)
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options = _ImageSegmenterOptions(base_options=base_options)
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_ImageSegmenter.create_from_options(options)
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@ -112,8 +146,9 @@ class ImageSegmenterTest(parameterized.TestCase):
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segmenter = _ImageSegmenter.create_from_options(options)
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self.assertIsInstance(segmenter, _ImageSegmenter)
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@parameterized.parameters((ModelFileType.FILE_NAME,),
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(ModelFileType.FILE_CONTENT,))
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@parameterized.parameters(
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(ModelFileType.FILE_NAME,), (ModelFileType.FILE_CONTENT,)
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)
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def test_segment_succeeds_with_category_mask(self, model_file_type):
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# Creates segmenter.
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if model_file_type is ModelFileType.FILE_NAME:
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@ -127,22 +162,27 @@ class ImageSegmenterTest(parameterized.TestCase):
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raise ValueError('model_file_type is invalid.')
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options = _ImageSegmenterOptions(
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base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
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base_options=base_options,
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output_category_mask=True,
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output_confidence_masks=False,
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)
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segmenter = _ImageSegmenter.create_from_options(options)
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# Performs image segmentation on the input.
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category_masks = segmenter.segment(self.test_image)
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self.assertLen(category_masks, 1)
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category_mask = category_masks[0]
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segmentation_result = segmenter.segment(self.test_image)
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category_mask = segmentation_result.category_mask
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result_pixels = category_mask.numpy_view().flatten()
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# Check if data type of `category_mask` is correct.
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self.assertEqual(result_pixels.dtype, np.uint8)
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self.assertTrue(
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_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
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f'Number of pixels in the candidate mask differing from that of the '
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f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
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_similar_to_uint8_mask(category_mask, self.test_seg_image),
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(
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'Number of pixels in the candidate mask differing from that of the'
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f' ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
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),
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)
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# Closes the segmenter explicitly when the segmenter is not used in
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# a context.
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@ -152,74 +192,46 @@ class ImageSegmenterTest(parameterized.TestCase):
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# Creates segmenter.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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# Run segmentation on the model in CATEGORY_MASK mode.
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options = _ImageSegmenterOptions(
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base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
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segmenter = _ImageSegmenter.create_from_options(options)
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category_masks = segmenter.segment(self.test_image)
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category_mask = category_masks[0].numpy_view()
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# Load the cat image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
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)
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# Run segmentation on the model in CONFIDENCE_MASK mode.
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options = _ImageSegmenterOptions(
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base_options=base_options,
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output_type=_OutputType.CONFIDENCE_MASK,
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activation=_Activation.SOFTMAX)
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segmenter = _ImageSegmenter.create_from_options(options)
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confidence_masks = segmenter.segment(self.test_image)
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output_category_mask=False,
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output_confidence_masks=True,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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segmentation_result = segmenter.segment(test_image)
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confidence_masks = segmentation_result.confidence_masks
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# Check if confidence mask shape is correct.
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self.assertLen(
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confidence_masks, 21,
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'Number of confidence masks must match with number of categories.')
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confidence_masks,
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21,
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'Number of confidence masks must match with number of categories.',
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)
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# Gather the confidence masks in a single array `confidence_mask_array`.
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confidence_mask_array = np.array(
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[confidence_mask.numpy_view() for confidence_mask in confidence_masks])
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# Check if data type of `confidence_masks` are correct.
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self.assertEqual(confidence_mask_array.dtype, np.float32)
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# Compute the category mask from the created confidence mask.
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calculated_category_mask = np.argmax(confidence_mask_array, axis=0)
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self.assertListEqual(
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calculated_category_mask.tolist(), category_mask.tolist(),
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'Confidence mask does not match with the category mask.')
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# Closes the segmenter explicitly when the segmenter is not used in
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# a context.
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segmenter.close()
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@parameterized.parameters((ModelFileType.FILE_NAME),
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(ModelFileType.FILE_CONTENT))
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def test_segment_in_context(self, model_file_type):
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_contents = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_contents)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _ImageSegmenterOptions(
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base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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# Performs image segmentation on the input.
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category_masks = segmenter.segment(self.test_image)
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self.assertLen(category_masks, 1)
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# Loads ground truth segmentation file.
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expected_mask = self._load_segmentation_mask(_CAT_MASK)
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self.assertTrue(
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_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
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f'Number of pixels in the candidate mask differing from that of the '
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f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
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_similar_to_float_mask(
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confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
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)
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)
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def test_missing_result_callback(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM)
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with self.assertRaisesRegex(ValueError,
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r'result callback must be provided'):
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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)
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with self.assertRaisesRegex(
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ValueError, r'result callback must be provided'
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):
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with _ImageSegmenter.create_from_options(options) as unused_segmenter:
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pass
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@ -228,130 +240,236 @@ class ImageSegmenterTest(parameterized.TestCase):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=running_mode,
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result_callback=mock.MagicMock())
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with self.assertRaisesRegex(ValueError,
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r'result callback should not be provided'):
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result_callback=mock.MagicMock(),
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)
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with self.assertRaisesRegex(
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ValueError, r'result callback should not be provided'
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):
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with _ImageSegmenter.create_from_options(options) as unused_segmenter:
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pass
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def test_calling_segment_for_video_in_image_mode(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE)
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running_mode=_RUNNING_MODE.IMAGE,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the video mode'):
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the video mode'
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):
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segmenter.segment_for_video(self.test_image, 0)
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def test_calling_segment_async_in_image_mode(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE)
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running_mode=_RUNNING_MODE.IMAGE,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the live stream mode'
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):
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segmenter.segment_async(self.test_image, 0)
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def test_calling_segment_in_video_mode(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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running_mode=_RUNNING_MODE.VIDEO,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the image mode'
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):
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segmenter.segment(self.test_image)
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def test_calling_segment_async_in_video_mode(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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running_mode=_RUNNING_MODE.VIDEO,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the live stream mode'
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):
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segmenter.segment_async(self.test_image, 0)
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def test_segment_for_video_with_out_of_order_timestamp(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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running_mode=_RUNNING_MODE.VIDEO,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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unused_result = segmenter.segment_for_video(self.test_image, 1)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'):
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ValueError, r'Input timestamp must be monotonically increasing'
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):
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segmenter.segment_for_video(self.test_image, 0)
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def test_segment_for_video(self):
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def test_segment_for_video_in_category_mask_mode(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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output_type=_OutputType.CATEGORY_MASK,
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running_mode=_RUNNING_MODE.VIDEO)
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output_category_mask=True,
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output_confidence_masks=False,
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running_mode=_RUNNING_MODE.VIDEO,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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for timestamp in range(0, 300, 30):
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category_masks = segmenter.segment_for_video(self.test_image, timestamp)
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self.assertLen(category_masks, 1)
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segmentation_result = segmenter.segment_for_video(
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self.test_image, timestamp
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)
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category_mask = segmentation_result.category_mask
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self.assertTrue(
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_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
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f'Number of pixels in the candidate mask differing from that of the '
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f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
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_similar_to_uint8_mask(category_mask, self.test_seg_image),
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(
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'Number of pixels in the candidate mask differing from that of'
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f' the ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
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),
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)
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def test_segment_for_video_in_confidence_mask_mode(self):
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# Load the cat image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
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)
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO,
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output_category_mask=False,
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output_confidence_masks=True,
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)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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for timestamp in range(0, 300, 30):
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segmentation_result = segmenter.segment_for_video(test_image, timestamp)
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confidence_masks = segmentation_result.confidence_masks
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# Check if confidence mask shape is correct.
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self.assertLen(
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confidence_masks,
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21,
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'Number of confidence masks must match with number of categories.',
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)
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# Loads ground truth segmentation file.
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expected_mask = self._load_segmentation_mask(_CAT_MASK)
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self.assertTrue(
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_similar_to_float_mask(
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confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
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)
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)
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def test_calling_segment_in_live_stream_mode(self):
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _ImageSegmenter.create_from_options(options) as segmenter:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the image mode'):
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'not initialized with the image mode'
|
||||
):
|
||||
segmenter.segment(self.test_image)
|
||||
|
||||
def test_calling_segment_for_video_in_live_stream_mode(self):
|
||||
options = _ImageSegmenterOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _ImageSegmenter.create_from_options(options) as segmenter:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the video mode'):
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'not initialized with the video mode'
|
||||
):
|
||||
segmenter.segment_for_video(self.test_image, 0)
|
||||
|
||||
def test_segment_async_calls_with_illegal_timestamp(self):
|
||||
options = _ImageSegmenterOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
result_callback=mock.MagicMock(),
|
||||
)
|
||||
with _ImageSegmenter.create_from_options(options) as segmenter:
|
||||
segmenter.segment_async(self.test_image, 100)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'):
|
||||
ValueError, r'Input timestamp must be monotonically increasing'
|
||||
):
|
||||
segmenter.segment_async(self.test_image, 0)
|
||||
|
||||
def test_segment_async_calls(self):
|
||||
def test_segment_async_calls_in_category_mask_mode(self):
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(result: List[image_module.Image], output_image: _Image,
|
||||
timestamp_ms: int):
|
||||
def check_result(
|
||||
result: ImageSegmenterResult, output_image: _Image, timestamp_ms: int
|
||||
):
|
||||
# Get the output category mask.
|
||||
category_mask = result[0]
|
||||
category_mask = result.category_mask
|
||||
self.assertEqual(output_image.width, self.test_image.width)
|
||||
self.assertEqual(output_image.height, self.test_image.height)
|
||||
self.assertEqual(output_image.width, self.test_seg_image.width)
|
||||
self.assertEqual(output_image.height, self.test_seg_image.height)
|
||||
self.assertTrue(
|
||||
_similar_to_uint8_mask(category_mask, self.test_seg_image),
|
||||
f'Number of pixels in the candidate mask differing from that of the '
|
||||
f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
|
||||
(
|
||||
'Number of pixels in the candidate mask differing from that of'
|
||||
f' the ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
|
||||
),
|
||||
)
|
||||
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
self.observed_timestamp_ms = timestamp_ms
|
||||
|
||||
options = _ImageSegmenterOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
output_type=_OutputType.CATEGORY_MASK,
|
||||
output_category_mask=True,
|
||||
output_confidence_masks=False,
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=check_result)
|
||||
result_callback=check_result,
|
||||
)
|
||||
with _ImageSegmenter.create_from_options(options) as segmenter:
|
||||
for timestamp in range(0, 300, 30):
|
||||
segmenter.segment_async(self.test_image, timestamp)
|
||||
|
||||
def test_segment_async_calls_in_confidence_mask_mode(self):
|
||||
# Load the cat image.
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
|
||||
)
|
||||
|
||||
# Loads ground truth segmentation file.
|
||||
expected_mask = self._load_segmentation_mask(_CAT_MASK)
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(
|
||||
result: ImageSegmenterResult, output_image: _Image, timestamp_ms: int
|
||||
):
|
||||
# Get the output category mask.
|
||||
confidence_masks = result.confidence_masks
|
||||
|
||||
# Check if confidence mask shape is correct.
|
||||
self.assertLen(
|
||||
confidence_masks,
|
||||
21,
|
||||
'Number of confidence masks must match with number of categories.',
|
||||
)
|
||||
self.assertEqual(output_image.width, test_image.width)
|
||||
self.assertEqual(output_image.height, test_image.height)
|
||||
self.assertTrue(
|
||||
_similar_to_float_mask(
|
||||
confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
|
||||
)
|
||||
)
|
||||
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
self.observed_timestamp_ms = timestamp_ms
|
||||
|
||||
options = _ImageSegmenterOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
output_category_mask=False,
|
||||
output_confidence_masks=True,
|
||||
result_callback=check_result,
|
||||
)
|
||||
with _ImageSegmenter.create_from_options(options) as segmenter:
|
||||
for timestamp in range(0, 300, 30):
|
||||
segmenter.segment_async(test_image, timestamp)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
||||
|
|
|
|||
|
|
@ -30,12 +30,12 @@ from mediapipe.tasks.python.test import test_utils
|
|||
from mediapipe.tasks.python.vision import interactive_segmenter
|
||||
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
|
||||
|
||||
InteractiveSegmenterResult = interactive_segmenter.InteractiveSegmenterResult
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_Image = image_module.Image
|
||||
_ImageFormat = image_frame.ImageFormat
|
||||
_NormalizedKeypoint = keypoint_module.NormalizedKeypoint
|
||||
_Rect = rect.Rect
|
||||
_OutputType = interactive_segmenter.InteractiveSegmenterOptions.OutputType
|
||||
_InteractiveSegmenter = interactive_segmenter.InteractiveSegmenter
|
||||
_InteractiveSegmenterOptions = interactive_segmenter.InteractiveSegmenterOptions
|
||||
_RegionOfInterest = interactive_segmenter.RegionOfInterest
|
||||
|
|
@ -200,15 +200,16 @@ class InteractiveSegmenterTest(parameterized.TestCase):
|
|||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
options = _InteractiveSegmenterOptions(
|
||||
base_options=base_options, output_type=_OutputType.CATEGORY_MASK
|
||||
base_options=base_options,
|
||||
output_category_mask=True,
|
||||
output_confidence_masks=False,
|
||||
)
|
||||
segmenter = _InteractiveSegmenter.create_from_options(options)
|
||||
|
||||
# Performs image segmentation on the input.
|
||||
roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
|
||||
category_masks = segmenter.segment(self.test_image, roi)
|
||||
self.assertLen(category_masks, 1)
|
||||
category_mask = category_masks[0]
|
||||
segmentation_result = segmenter.segment(self.test_image, roi)
|
||||
category_mask = segmentation_result.category_mask
|
||||
result_pixels = category_mask.numpy_view().flatten()
|
||||
|
||||
# Check if data type of `category_mask` is correct.
|
||||
|
|
@ -219,7 +220,7 @@ class InteractiveSegmenterTest(parameterized.TestCase):
|
|||
|
||||
self.assertTrue(
|
||||
_similar_to_uint8_mask(
|
||||
category_masks[0], test_seg_image, similarity_threshold
|
||||
category_mask, test_seg_image, similarity_threshold
|
||||
),
|
||||
(
|
||||
'Number of pixels in the candidate mask differing from that of the'
|
||||
|
|
@ -254,12 +255,15 @@ class InteractiveSegmenterTest(parameterized.TestCase):
|
|||
|
||||
# Run segmentation on the model in CONFIDENCE_MASK mode.
|
||||
options = _InteractiveSegmenterOptions(
|
||||
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
|
||||
base_options=base_options,
|
||||
output_category_mask=False,
|
||||
output_confidence_masks=True,
|
||||
)
|
||||
|
||||
with _InteractiveSegmenter.create_from_options(options) as segmenter:
|
||||
# Perform segmentation
|
||||
confidence_masks = segmenter.segment(self.test_image, roi)
|
||||
segmentation_result = segmenter.segment(self.test_image, roi)
|
||||
confidence_masks = segmentation_result.confidence_masks
|
||||
|
||||
# Check if confidence mask shape is correct.
|
||||
self.assertLen(
|
||||
|
|
@ -287,15 +291,18 @@ class InteractiveSegmenterTest(parameterized.TestCase):
|
|||
|
||||
# Run segmentation on the model in CONFIDENCE_MASK mode.
|
||||
options = _InteractiveSegmenterOptions(
|
||||
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
|
||||
base_options=base_options,
|
||||
output_category_mask=False,
|
||||
output_confidence_masks=True,
|
||||
)
|
||||
|
||||
with _InteractiveSegmenter.create_from_options(options) as segmenter:
|
||||
# Perform segmentation
|
||||
image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
|
||||
confidence_masks = segmenter.segment(
|
||||
segmentation_result = segmenter.segment(
|
||||
self.test_image, roi, image_processing_options
|
||||
)
|
||||
confidence_masks = segmentation_result.confidence_masks
|
||||
|
||||
# Check if confidence mask shape is correct.
|
||||
self.assertLen(
|
||||
|
|
@ -314,7 +321,9 @@ class InteractiveSegmenterTest(parameterized.TestCase):
|
|||
|
||||
# Run segmentation on the model in CONFIDENCE_MASK mode.
|
||||
options = _InteractiveSegmenterOptions(
|
||||
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
|
||||
base_options=base_options,
|
||||
output_category_mask=False,
|
||||
output_confidence_masks=True,
|
||||
)
|
||||
|
||||
with self.assertRaisesRegex(
|
||||
|
|
|
|||
|
|
@ -14,7 +14,6 @@
|
|||
"""MediaPipe image segmenter task."""
|
||||
|
||||
import dataclasses
|
||||
import enum
|
||||
from typing import Callable, List, Mapping, Optional
|
||||
|
||||
from mediapipe.python import packet_creator
|
||||
|
|
@ -31,7 +30,6 @@ 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
|
||||
|
||||
ImageSegmenterResult = List[image_module.Image]
|
||||
_NormalizedRect = rect.NormalizedRect
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
|
||||
|
|
@ -42,8 +40,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
|
|||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
_TaskInfo = task_info_module.TaskInfo
|
||||
|
||||
_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
|
||||
_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
|
||||
_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
|
||||
_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
|
||||
_CATEGORY_MASK_STREAM_NAME = 'category_mask'
|
||||
_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
|
||||
_IMAGE_IN_STREAM_NAME = 'image_in'
|
||||
_IMAGE_OUT_STREAM_NAME = 'image_out'
|
||||
_IMAGE_TAG = 'IMAGE'
|
||||
|
|
@ -53,6 +53,21 @@ _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph'
|
|||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class ImageSegmenterResult:
|
||||
"""Output result of ImageSegmenter.
|
||||
|
||||
confidence_masks: multiple masks of float image where, for each mask, each
|
||||
pixel represents the prediction confidence, usually in the [0, 1] range.
|
||||
|
||||
category_mask: a category mask of uint8 image where each pixel represents the
|
||||
class which the pixel in the original image was predicted to belong to.
|
||||
"""
|
||||
|
||||
confidence_masks: Optional[List[image_module.Image]] = None
|
||||
category_mask: Optional[image_module.Image] = None
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class ImageSegmenterOptions:
|
||||
"""Options for the image segmenter task.
|
||||
|
|
@ -64,28 +79,17 @@ class ImageSegmenterOptions:
|
|||
objects on single image inputs. 2) The video mode for segmenting objects
|
||||
on the decoded frames of a video. 3) The live stream mode for segmenting
|
||||
objects on a live stream of input data, such as from camera.
|
||||
output_type: The output mask type allows specifying the type of
|
||||
post-processing to perform on the raw model results.
|
||||
activation: Activation function to apply to input tensor.
|
||||
output_confidence_masks: Whether to output confidence masks.
|
||||
output_category_mask: Whether to output category mask.
|
||||
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.
|
||||
"""
|
||||
|
||||
class OutputType(enum.Enum):
|
||||
UNSPECIFIED = 0
|
||||
CATEGORY_MASK = 1
|
||||
CONFIDENCE_MASK = 2
|
||||
|
||||
class Activation(enum.Enum):
|
||||
NONE = 0
|
||||
SIGMOID = 1
|
||||
SOFTMAX = 2
|
||||
|
||||
base_options: _BaseOptions
|
||||
running_mode: _RunningMode = _RunningMode.IMAGE
|
||||
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
|
||||
activation: Optional[Activation] = Activation.NONE
|
||||
output_confidence_masks: bool = True
|
||||
output_category_mask: bool = False
|
||||
result_callback: Optional[
|
||||
Callable[[ImageSegmenterResult, image_module.Image, int], None]
|
||||
] = None
|
||||
|
|
@ -97,9 +101,7 @@ class ImageSegmenterOptions:
|
|||
base_options_proto.use_stream_mode = (
|
||||
False if self.running_mode == _RunningMode.IMAGE else True
|
||||
)
|
||||
segmenter_options_proto = _SegmenterOptionsProto(
|
||||
output_type=self.output_type.value, activation=self.activation.value
|
||||
)
|
||||
segmenter_options_proto = _SegmenterOptionsProto()
|
||||
return _ImageSegmenterGraphOptionsProto(
|
||||
base_options=base_options_proto,
|
||||
segmenter_options=segmenter_options_proto,
|
||||
|
|
@ -177,27 +179,48 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
|
|||
def packets_callback(output_packets: Mapping[str, packet.Packet]):
|
||||
if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
|
||||
return
|
||||
segmentation_result = packet_getter.get_image_list(
|
||||
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
|
||||
|
||||
segmentation_result = ImageSegmenterResult()
|
||||
|
||||
if options.output_confidence_masks:
|
||||
segmentation_result.confidence_masks = packet_getter.get_image_list(
|
||||
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
if options.output_category_mask:
|
||||
segmentation_result.category_mask = packet_getter.get_image(
|
||||
output_packets[_CATEGORY_MASK_STREAM_NAME]
|
||||
)
|
||||
|
||||
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
|
||||
timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
|
||||
timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
|
||||
options.result_callback(
|
||||
segmentation_result,
|
||||
image,
|
||||
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
|
||||
)
|
||||
|
||||
output_streams = [
|
||||
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
|
||||
]
|
||||
|
||||
if options.output_confidence_masks:
|
||||
output_streams.append(
|
||||
':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
|
||||
)
|
||||
|
||||
if options.output_category_mask:
|
||||
output_streams.append(
|
||||
':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
|
||||
)
|
||||
|
||||
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([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
|
||||
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
|
||||
],
|
||||
output_streams=output_streams,
|
||||
task_options=options,
|
||||
)
|
||||
return cls(
|
||||
|
|
@ -240,9 +263,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
|
|||
normalized_rect.to_pb2()
|
||||
),
|
||||
})
|
||||
segmentation_result = packet_getter.get_image_list(
|
||||
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
|
||||
segmentation_result = ImageSegmenterResult()
|
||||
|
||||
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
|
||||
segmentation_result.confidence_masks = packet_getter.get_image_list(
|
||||
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
if _CATEGORY_MASK_STREAM_NAME in output_packets:
|
||||
segmentation_result.category_mask = packet_getter.get_image(
|
||||
output_packets[_CATEGORY_MASK_STREAM_NAME]
|
||||
)
|
||||
|
||||
return segmentation_result
|
||||
|
||||
def segment_for_video(
|
||||
|
|
@ -285,9 +317,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
|
|||
normalized_rect.to_pb2()
|
||||
).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
})
|
||||
segmentation_result = packet_getter.get_image_list(
|
||||
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
|
||||
segmentation_result = ImageSegmenterResult()
|
||||
|
||||
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
|
||||
segmentation_result.confidence_masks = packet_getter.get_image_list(
|
||||
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
if _CATEGORY_MASK_STREAM_NAME in output_packets:
|
||||
segmentation_result.category_mask = packet_getter.get_image(
|
||||
output_packets[_CATEGORY_MASK_STREAM_NAME]
|
||||
)
|
||||
|
||||
return segmentation_result
|
||||
|
||||
def segment_async(
|
||||
|
|
|
|||
|
|
@ -41,8 +41,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
|
|||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
_TaskInfo = task_info_module.TaskInfo
|
||||
|
||||
_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
|
||||
_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
|
||||
_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
|
||||
_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
|
||||
_CATEGORY_MASK_STREAM_NAME = 'category_mask'
|
||||
_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
|
||||
_IMAGE_IN_STREAM_NAME = 'image_in'
|
||||
_IMAGE_OUT_STREAM_NAME = 'image_out'
|
||||
_ROI_STREAM_NAME = 'roi_in'
|
||||
|
|
@ -55,32 +57,41 @@ _TASK_GRAPH_NAME = (
|
|||
)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class InteractiveSegmenterResult:
|
||||
"""Output result of InteractiveSegmenter.
|
||||
|
||||
confidence_masks: multiple masks of float image where, for each mask, each
|
||||
pixel represents the prediction confidence, usually in the [0, 1] range.
|
||||
|
||||
category_mask: a category mask of uint8 image where each pixel represents the
|
||||
class which the pixel in the original image was predicted to belong to.
|
||||
"""
|
||||
|
||||
confidence_masks: Optional[List[image_module.Image]] = None
|
||||
category_mask: Optional[image_module.Image] = None
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class InteractiveSegmenterOptions:
|
||||
"""Options for the interactive segmenter task.
|
||||
|
||||
Attributes:
|
||||
base_options: Base options for the interactive segmenter task.
|
||||
output_type: The output mask type allows specifying the type of
|
||||
post-processing to perform on the raw model results.
|
||||
output_confidence_masks: Whether to output confidence masks.
|
||||
output_category_mask: Whether to output category mask.
|
||||
"""
|
||||
|
||||
class OutputType(enum.Enum):
|
||||
UNSPECIFIED = 0
|
||||
CATEGORY_MASK = 1
|
||||
CONFIDENCE_MASK = 2
|
||||
|
||||
base_options: _BaseOptions
|
||||
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
|
||||
output_confidence_masks: bool = True
|
||||
output_category_mask: bool = False
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _ImageSegmenterGraphOptionsProto:
|
||||
"""Generates an InteractiveSegmenterOptions protobuf object."""
|
||||
base_options_proto = self.base_options.to_pb2()
|
||||
base_options_proto.use_stream_mode = False
|
||||
segmenter_options_proto = _SegmenterOptionsProto(
|
||||
output_type=self.output_type.value
|
||||
)
|
||||
segmenter_options_proto = _SegmenterOptionsProto()
|
||||
return _ImageSegmenterGraphOptionsProto(
|
||||
base_options=base_options_proto,
|
||||
segmenter_options=segmenter_options_proto,
|
||||
|
|
@ -192,6 +203,20 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
|
|||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
|
||||
output_streams = [
|
||||
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
|
||||
]
|
||||
|
||||
if options.output_confidence_masks:
|
||||
output_streams.append(
|
||||
':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
|
||||
)
|
||||
|
||||
if options.output_category_mask:
|
||||
output_streams.append(
|
||||
':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
|
||||
)
|
||||
|
||||
task_info = _TaskInfo(
|
||||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[
|
||||
|
|
@ -199,10 +224,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
|
|||
':'.join([_ROI_TAG, _ROI_STREAM_NAME]),
|
||||
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
|
||||
],
|
||||
output_streams=[
|
||||
':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
|
||||
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
|
||||
],
|
||||
output_streams=output_streams,
|
||||
task_options=options,
|
||||
)
|
||||
return cls(
|
||||
|
|
@ -216,7 +238,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
|
|||
image: image_module.Image,
|
||||
roi: RegionOfInterest,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> List[image_module.Image]:
|
||||
) -> InteractiveSegmenterResult:
|
||||
"""Performs the actual segmentation task on the provided MediaPipe Image.
|
||||
|
||||
The image can be of any size with format RGB.
|
||||
|
|
@ -248,7 +270,16 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
|
|||
normalized_rect.to_pb2()
|
||||
),
|
||||
})
|
||||
segmentation_result = packet_getter.get_image_list(
|
||||
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
|
||||
segmentation_result = InteractiveSegmenterResult()
|
||||
|
||||
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
|
||||
segmentation_result.confidence_masks = packet_getter.get_image_list(
|
||||
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
|
||||
)
|
||||
|
||||
if _CATEGORY_MASK_STREAM_NAME in output_packets:
|
||||
segmentation_result.category_mask = packet_getter.get_image(
|
||||
output_packets[_CATEGORY_MASK_STREAM_NAME]
|
||||
)
|
||||
|
||||
return segmentation_result
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user