Revised API implementation and added more tests for segment_for_video and segment_async
This commit is contained in:
parent
36ac0689d7
commit
f84e0bc1c6
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@ -47,7 +47,7 @@ py_test(
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deps = [
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_util",
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"//mediapipe/tasks/python/test:test_utils",
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"//mediapipe/tasks/python/components/proto:segmenter_options",
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"//mediapipe/tasks/python/vision:image_segmenter",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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@ -16,6 +16,8 @@
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import enum
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import numpy as np
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import cv2
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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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from absl.testing import parameterized
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@ -24,7 +26,7 @@ from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.python._framework_bindings import image_frame as image_frame_module
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from mediapipe.tasks.python.components.proto import segmenter_options
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_util
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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 as running_mode_module
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@ -42,7 +44,22 @@ _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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_MASK_MAGNIFICATION_FACTOR = 10
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_MATCH_PIXELS_THRESHOLD = 0.01
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_MASK_SIMILARITY_THRESHOLD = 0.98
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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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consistent_pixels = 0
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num_pixels = len(expected_mask_pixels)
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for index in range(num_pixels):
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consistent_pixels += (
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actual_mask_pixels[index] * _MASK_MAGNIFICATION_FACTOR ==
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expected_mask_pixels[index])
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return consistent_pixels / num_pixels >= _MASK_SIMILARITY_THRESHOLD
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class ModelFileType(enum.Enum):
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@ -54,10 +71,14 @@ class ImageSegmenterTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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self.test_image = test_util.read_test_image(
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test_util.get_test_data_path(_IMAGE_FILE))
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self.test_seg_path = test_util.get_test_data_path(_SEGMENTATION_FILE)
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self.model_path = test_util.get_test_data_path(_MODEL_FILE)
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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(_IMAGE_FILE))
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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(_SEGMENTATION_FILE), cv2.IMREAD_GRAYSCALE)
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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(_MODEL_FILE)
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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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@ -76,7 +97,7 @@ class ImageSegmenterTest(parameterized.TestCase):
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with self.assertRaisesRegex(
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ValueError,
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r"ExternalFile must specify at least one of 'file_content', "
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r"'file_name' or 'file_descriptor_meta'."):
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r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."):
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base_options = _BaseOptions(model_asset_path='')
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options = _ImageSegmenterOptions(base_options=base_options)
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_ImageSegmenter.create_from_options(options)
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@ -112,34 +133,16 @@ class ImageSegmenterTest(parameterized.TestCase):
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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.assertEqual(len(category_masks), 1)
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result_pixels = category_masks[0].numpy_view().flatten()
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category_mask = category_masks[0]
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result_pixels = category_mask.numpy_view().flatten()
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# Check if data type of `category_masks` is correct.
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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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# Loads ground truth segmentation file.
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image_data = cv2.imread(self.test_seg_path, cv2.IMREAD_GRAYSCALE)
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gt_segmentation = _Image(_ImageFormat.GRAY8, image_data)
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gt_segmentation_array = gt_segmentation.numpy_view()
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gt_segmentation_shape = gt_segmentation_array.shape
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num_pixels = gt_segmentation_shape[0] * gt_segmentation_shape[1]
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ground_truth_pixels = gt_segmentation_array.flatten()
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self.assertEqual(
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len(result_pixels), len(ground_truth_pixels),
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'Segmentation mask size does not match the ground truth mask size.')
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inconsistent_pixels = 0
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for index in range(num_pixels):
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inconsistent_pixels += (
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result_pixels[index] * _MASK_MAGNIFICATION_FACTOR !=
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ground_truth_pixels[index])
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self.assertLessEqual(
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inconsistent_pixels / num_pixels, _MATCH_PIXELS_THRESHOLD,
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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 {_MATCH_PIXELS_THRESHOLD}.')
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f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
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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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@ -188,6 +191,174 @@ class ImageSegmenterTest(parameterized.TestCase):
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# a context.
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segmenter.close()
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@parameterized.parameters(
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(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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segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
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options = _ImageSegmenterOptions(base_options=base_options,
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segmenter_options=segmenter_options)
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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.assertEqual(len(category_masks), 1)
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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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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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with _ImageSegmenter.create_from_options(options) as unused_segmenter:
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pass
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@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
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def test_illegal_result_callback(self, running_mode):
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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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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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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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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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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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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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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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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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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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segmenter.segment_async(self.test_image, 0)
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def test_detect_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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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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segmenter.segment_for_video(self.test_image, 0)
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def test_segment_for_video(self):
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segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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segmenter_options=segmenter_options,
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running_mode=_RUNNING_MODE.VIDEO)
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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.assertEqual(len(category_masks), 1)
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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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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),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=mock.MagicMock())
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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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segmenter.segment(self.test_image)
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def test_calling_segment_for_video_in_live_stream_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.LIVE_STREAM,
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result_callback=mock.MagicMock())
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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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segmenter.segment_for_video(self.test_image, 0)
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def test_segment_async_calls_with_illegal_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.LIVE_STREAM,
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result_callback=mock.MagicMock())
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with _ImageSegmenter.create_from_options(options) as segmenter:
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segmenter.segment_async(self.test_image, 100)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'):
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segmenter.segment_async(self.test_image, 0)
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def test_segment_async_calls(self):
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observed_timestamp_ms = -1
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def check_result(result: List[image_module.Image],
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output_image: _Image,
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timestamp_ms: int):
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# Get the output category mask.
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category_mask = result[0]
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self.assertEqual(output_image.width, self.test_image.width)
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self.assertEqual(output_image.height, self.test_image.height)
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self.assertEqual(output_image.width, self.test_seg_image.width)
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self.assertEqual(output_image.height, self.test_seg_image.height)
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self.assertTrue(
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_similar_to_uint8_mask(category_mask, 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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self.assertLess(observed_timestamp_ms, timestamp_ms)
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self.observed_timestamp_ms = timestamp_ms
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segmenter_options = _SegmenterOptions(output_type=_OutputType.CATEGORY_MASK)
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options = _ImageSegmenterOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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segmenter_options=segmenter_options,
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=check_result)
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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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segmenter.segment_async(self.test_image, timestamp)
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if __name__ == '__main__':
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absltest.main()
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@ -42,6 +42,7 @@ _IMAGE_IN_STREAM_NAME = 'image_in'
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_IMAGE_OUT_STREAM_NAME = 'image_out'
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_IMAGE_TAG = 'IMAGE'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageSegmenterGraph'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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@dataclasses.dataclass
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@ -52,9 +53,9 @@ class ImageSegmenterOptions:
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base_options: Base options for the image segmenter task.
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running_mode: The running mode of the task. Default to the image mode.
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Image segmenter task has three running modes:
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1) The image mode for detecting objects on single image inputs.
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2) The video mode for detecting objects on the decoded frames of a video.
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3) The live stream mode for detecting objects on a live stream of input
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1) The image mode for segmenting objects on single image inputs.
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2) The video mode for segmenting objects on the decoded frames of a video.
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3) The live stream mode for segmenting objects on a live stream of input
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data, such as from camera.
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segmenter_options: Options for the image segmenter task.
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result_callback: The user-defined result callback for processing live stream
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@ -86,7 +87,8 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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@classmethod
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def create_from_model_path(cls, model_path: str) -> 'ImageSegmenter':
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"""Creates an `ImageSegmenter` object from a TensorFlow Lite model and the default `ImageSegmenterOptions`.
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"""Creates an `ImageSegmenter` object from a TensorFlow Lite model and the
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default `ImageSegmenterOptions`.
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Note that the created `ImageSegmenter` instance is in image mode, for
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performing image segmentation on single image inputs.
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@ -131,8 +133,9 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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segmentation_result = packet_getter.get_image_list(
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output_packets[_SEGMENTATION_OUT_STREAM_NAME])
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image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
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timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
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options.result_callback(segmentation_result, image, timestamp)
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timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
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options.result_callback(segmentation_result, image,
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timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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@ -148,7 +151,6 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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_RunningMode.LIVE_STREAM), options.running_mode,
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packets_callback if options.result_callback else None)
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# TODO: Create an Image class for MediaPipe Tasks.
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def segment(self,
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image: image_module.Image) -> List[image_module.Image]:
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"""Performs the actual segmentation task on the provided MediaPipe Image.
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@ -162,10 +164,74 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If object detection failed to run.
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RuntimeError: If image segmentation failed to run.
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"""
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output_packets = self._process_image_data(
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{_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
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segmentation_result = packet_getter.get_image_list(
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output_packets[_SEGMENTATION_OUT_STREAM_NAME])
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return segmentation_result
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def segment_for_video(self, image: image_module.Image,
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timestamp_ms: int) -> List[image_module.Image]:
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"""Performs segmentation on the provided video frames.
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Only use this method when the ImageSegmenter is created with the video
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running mode. It's required to provide the video frame's timestamp (in
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milliseconds) along with the video frame. The input timestamps should be
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monotonically increasing for adjacent calls of this method.
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Args:
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image: MediaPipe Image.
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timestamp_ms: The timestamp of the input video frame in milliseconds.
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Returns:
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A segmentation result object that contains a list of segmentation masks
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as images.
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Raises:
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ValueError: If any of the input arguments is invalid.
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RuntimeError: If image segmentation failed to run.
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"""
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output_packets = self._process_video_data({
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_IMAGE_IN_STREAM_NAME:
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packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
segmentation_result = packet_getter.get_image_list(
|
||||
output_packets[_SEGMENTATION_OUT_STREAM_NAME])
|
||||
return segmentation_result
|
||||
|
||||
def segment_async(self, image: image_module.Image, timestamp_ms: int) -> None:
|
||||
"""Sends live image data (an Image with a unique timestamp) to perform
|
||||
image segmentation.
|
||||
|
||||
Only use this method when the ImageSegmenter 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 `ImageSegmenterOptions`. The
|
||||
`segment_async` method is designed to process live stream data such as
|
||||
camera input. To lower the overall latency, image segmenter may drop the
|
||||
input images if needed. In other words, it's not guaranteed to have output
|
||||
per input image.
|
||||
|
||||
The `result_callback` prvoides:
|
||||
- A segmentation result object that contains a list of segmentation masks
|
||||
as images.
|
||||
- The input image that the image segmenter runs on.
|
||||
- The input timestamp in milliseconds.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input image in milliseconds.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the image
|
||||
segmenter has already processed.
|
||||
"""
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
|
|
Loading…
Reference in New Issue
Block a user