Revised API implementation and added more tests for segment_for_video and segment_async
This commit is contained in:
kinaryml
@@ -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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