358 lines
15 KiB
Python
358 lines
15 KiB
Python
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for image segmenter."""
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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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from absl.testing import parameterized
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import cv2
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import numpy as np
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.python._framework_bindings import image_frame
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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_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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_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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_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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_MASK_SIMILARITY_THRESHOLD = 0.98
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
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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] *
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_MASK_MAGNIFICATION_FACTOR == 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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FILE_CONTENT = 1
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FILE_NAME = 2
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class ImageSegmenterTest(parameterized.TestCase):
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def setUp(self):
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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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# 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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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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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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with _ImageSegmenter.create_from_model_path(self.model_path) as segmenter:
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self.assertIsInstance(segmenter, _ImageSegmenter)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _ImageSegmenterOptions(base_options=base_options)
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with _ImageSegmenter.create_from_options(options) as segmenter:
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self.assertIsInstance(segmenter, _ImageSegmenter)
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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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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite')
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options = _ImageSegmenterOptions(base_options=base_options)
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_ImageSegmenter.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.model_path, 'rb') as f:
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base_options = _BaseOptions(model_asset_buffer=f.read())
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options = _ImageSegmenterOptions(base_options=base_options)
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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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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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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_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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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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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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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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# 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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def test_segment_succeeds_with_confidence_mask(self):
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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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# 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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# 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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# 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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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_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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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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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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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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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], 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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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.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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