Merge pull request #4241 from kinaryml:interactive-segmenter-python
PiperOrigin-RevId: 522470912
This commit is contained in:
Copybara-Service
commit
6495f8624b
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@ -99,6 +99,7 @@ cc_library(
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"//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
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"//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
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"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
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"//mediapipe/tasks/cc/vision/interactive_segmenter:interactive_segmenter_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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],
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)
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332
mediapipe/tasks/python/test/vision/interactive_segmenter_test.py
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332
mediapipe/tasks/python/test/vision/interactive_segmenter_test.py
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@ -0,0 +1,332 @@
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# Copyright 2023 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 interactive segmenter."""
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import enum
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import os
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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.components.containers import keypoint as keypoint_module
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from mediapipe.tasks.python.components.containers import rect
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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 interactive_segmenter
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from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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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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_NormalizedKeypoint = keypoint_module.NormalizedKeypoint
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_Rect = rect.Rect
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_OutputType = interactive_segmenter.InteractiveSegmenterOptions.OutputType
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_InteractiveSegmenter = interactive_segmenter.InteractiveSegmenter
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_InteractiveSegmenterOptions = interactive_segmenter.InteractiveSegmenterOptions
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_RegionOfInterest = interactive_segmenter.RegionOfInterest
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_Format = interactive_segmenter.RegionOfInterest.Format
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_MODEL_FILE = 'ptm_512_hdt_ptm_woid.tflite'
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_CATS_AND_DOGS = 'cats_and_dogs.jpg'
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_CATS_AND_DOGS_MASK_DOG_1 = 'cats_and_dogs_mask_dog1.png'
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_CATS_AND_DOGS_MASK_DOG_2 = 'cats_and_dogs_mask_dog2.png'
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_MASK_MAGNIFICATION_FACTOR = 255
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_MASK_SIMILARITY_THRESHOLD = 0.97
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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, similarity_threshold):
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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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)
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return consistent_pixels / num_pixels >= 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 InteractiveSegmenterTest(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, _CATS_AND_DOGS)
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)
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)
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# Loads ground truth segmentation file.
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self.test_seg_image = self._load_segmentation_mask(
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_CATS_AND_DOGS_MASK_DOG_1
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)
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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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)
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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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with _InteractiveSegmenter.create_from_model_path(
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self.model_path
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) as segmenter:
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self.assertIsInstance(segmenter, _InteractiveSegmenter)
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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 = _InteractiveSegmenterOptions(base_options=base_options)
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with _InteractiveSegmenter.create_from_options(options) as segmenter:
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self.assertIsInstance(segmenter, _InteractiveSegmenter)
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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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):
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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite'
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)
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options = _InteractiveSegmenterOptions(base_options=base_options)
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_InteractiveSegmenter.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 = _InteractiveSegmenterOptions(base_options=base_options)
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segmenter = _InteractiveSegmenter.create_from_options(options)
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self.assertIsInstance(segmenter, _InteractiveSegmenter)
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@parameterized.parameters(
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(
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ModelFileType.FILE_NAME,
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_RegionOfInterest.Format.KEYPOINT,
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_NormalizedKeypoint(0.44, 0.7),
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_CATS_AND_DOGS_MASK_DOG_1,
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0.84,
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),
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(
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ModelFileType.FILE_CONTENT,
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_RegionOfInterest.Format.KEYPOINT,
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_NormalizedKeypoint(0.44, 0.7),
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_CATS_AND_DOGS_MASK_DOG_1,
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0.84,
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),
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(
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ModelFileType.FILE_NAME,
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_RegionOfInterest.Format.KEYPOINT,
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_NormalizedKeypoint(0.66, 0.66),
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_CATS_AND_DOGS_MASK_DOG_2,
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_MASK_SIMILARITY_THRESHOLD,
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),
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(
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ModelFileType.FILE_CONTENT,
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_RegionOfInterest.Format.KEYPOINT,
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_NormalizedKeypoint(0.66, 0.66),
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_CATS_AND_DOGS_MASK_DOG_2,
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_MASK_SIMILARITY_THRESHOLD,
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),
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)
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def test_segment_succeeds_with_category_mask(
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self,
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model_file_type,
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roi_format,
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keypoint,
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output_mask,
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similarity_threshold,
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):
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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 = _InteractiveSegmenterOptions(
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base_options=base_options, output_type=_OutputType.CATEGORY_MASK
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)
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segmenter = _InteractiveSegmenter.create_from_options(options)
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# Performs image segmentation on the input.
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roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
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category_masks = segmenter.segment(self.test_image, roi)
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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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# Loads ground truth segmentation file.
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test_seg_image = self._load_segmentation_mask(output_mask)
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self.assertTrue(
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_similar_to_uint8_mask(
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category_masks[0], test_seg_image, similarity_threshold
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),
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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 {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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segmenter.close()
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@parameterized.parameters(
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(
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_RegionOfInterest.Format.KEYPOINT,
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_NormalizedKeypoint(0.44, 0.7),
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_CATS_AND_DOGS_MASK_DOG_1,
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0.84,
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),
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(
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_RegionOfInterest.Format.KEYPOINT,
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_NormalizedKeypoint(0.66, 0.66),
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_CATS_AND_DOGS_MASK_DOG_2,
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_MASK_SIMILARITY_THRESHOLD,
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),
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)
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def test_segment_succeeds_with_confidence_mask(
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self, roi_format, keypoint, output_mask, similarity_threshold
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):
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# Creates segmenter.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
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# Run segmentation on the model in CONFIDENCE_MASK mode.
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options = _InteractiveSegmenterOptions(
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base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
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)
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with _InteractiveSegmenter.create_from_options(options) as segmenter:
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# Perform segmentation
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confidence_masks = segmenter.segment(self.test_image, roi)
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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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2,
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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(output_mask)
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self.assertTrue(
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_similar_to_float_mask(
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confidence_masks[1], expected_mask, similarity_threshold
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)
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)
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def test_segment_succeeds_with_rotation(self):
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# Creates segmenter.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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roi = _RegionOfInterest(
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format=_RegionOfInterest.Format.KEYPOINT,
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keypoint=_NormalizedKeypoint(0.66, 0.66),
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)
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# Run segmentation on the model in CONFIDENCE_MASK mode.
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options = _InteractiveSegmenterOptions(
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base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
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)
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with _InteractiveSegmenter.create_from_options(options) as segmenter:
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# Perform segmentation
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image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
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confidence_masks = segmenter.segment(
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self.test_image, roi, image_processing_options
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)
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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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2,
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'Number of confidence masks must match with number of categories.',
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)
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def test_segment_fails_with_roi_in_image_processing_options(self):
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# Creates segmenter.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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roi = _RegionOfInterest(
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format=_RegionOfInterest.Format.KEYPOINT,
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keypoint=_NormalizedKeypoint(0.66, 0.66),
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)
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# Run segmentation on the model in CONFIDENCE_MASK mode.
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options = _InteractiveSegmenterOptions(
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base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
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)
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with self.assertRaisesRegex(
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ValueError, "This task doesn't support region-of-interest."
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):
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with _InteractiveSegmenter.create_from_options(options) as segmenter:
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# Perform segmentation
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image_processing_options = _ImageProcessingOptions(
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_Rect(left=0.1, top=0, right=0.9, bottom=1)
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)
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segmenter.segment(self.test_image, roi, image_processing_options)
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if __name__ == '__main__':
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absltest.main()
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@ -106,6 +106,28 @@ py_library(
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|||
],
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)
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py_library(
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name = "interactive_segmenter",
|
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srcs = [
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"interactive_segmenter.py",
|
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],
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deps = [
|
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"//mediapipe/python:_framework_bindings",
|
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"//mediapipe/python:packet_creator",
|
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"//mediapipe/python:packet_getter",
|
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"//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_graph_options_py_pb2",
|
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"//mediapipe/tasks/cc/vision/image_segmenter/proto:segmenter_options_py_pb2",
|
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"//mediapipe/tasks/python/components/containers:keypoint",
|
||||
"//mediapipe/tasks/python/core:base_options",
|
||||
"//mediapipe/tasks/python/core:optional_dependencies",
|
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"//mediapipe/tasks/python/core:task_info",
|
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"//mediapipe/tasks/python/vision/core:base_vision_task_api",
|
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"//mediapipe/tasks/python/vision/core:image_processing_options",
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||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
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"//mediapipe/util:render_data_py_pb2",
|
||||
],
|
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)
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py_library(
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name = "gesture_recognizer",
|
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srcs = [
|
||||
|
|
|
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254
mediapipe/tasks/python/vision/interactive_segmenter.py
Normal file
254
mediapipe/tasks/python/vision/interactive_segmenter.py
Normal file
|
|
@ -0,0 +1,254 @@
|
|||
# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""MediaPipe interactive segmenter task."""
|
||||
|
||||
import dataclasses
|
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import enum
|
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from typing import List, Optional
|
||||
|
||||
from mediapipe.python import packet_creator
|
||||
from mediapipe.python import packet_getter
|
||||
from mediapipe.python._framework_bindings import image as image_module
|
||||
from mediapipe.tasks.cc.vision.image_segmenter.proto import image_segmenter_graph_options_pb2
|
||||
from mediapipe.tasks.cc.vision.image_segmenter.proto import segmenter_options_pb2
|
||||
from mediapipe.tasks.python.components.containers import keypoint as keypoint_module
|
||||
from mediapipe.tasks.python.core import base_options as base_options_module
|
||||
from mediapipe.tasks.python.core import task_info as task_info_module
|
||||
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||
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
|
||||
from mediapipe.util import render_data_pb2
|
||||
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_RenderDataProto = render_data_pb2.RenderData
|
||||
_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
|
||||
_ImageSegmenterGraphOptionsProto = (
|
||||
image_segmenter_graph_options_pb2.ImageSegmenterGraphOptions
|
||||
)
|
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_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'
|
||||
_IMAGE_IN_STREAM_NAME = 'image_in'
|
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_IMAGE_OUT_STREAM_NAME = 'image_out'
|
||||
_ROI_STREAM_NAME = 'roi_in'
|
||||
_ROI_TAG = 'ROI'
|
||||
_NORM_RECT_STREAM_NAME = 'norm_rect_in'
|
||||
_NORM_RECT_TAG = 'NORM_RECT'
|
||||
_IMAGE_TAG = 'IMAGE'
|
||||
_TASK_GRAPH_NAME = (
|
||||
'mediapipe.tasks.vision.interactive_segmenter.InteractiveSegmenterGraph'
|
||||
)
|
||||
|
||||
|
||||
@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.
|
||||
"""
|
||||
|
||||
class OutputType(enum.Enum):
|
||||
UNSPECIFIED = 0
|
||||
CATEGORY_MASK = 1
|
||||
CONFIDENCE_MASK = 2
|
||||
|
||||
base_options: _BaseOptions
|
||||
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
|
||||
|
||||
@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
|
||||
)
|
||||
return _ImageSegmenterGraphOptionsProto(
|
||||
base_options=base_options_proto,
|
||||
segmenter_options=segmenter_options_proto,
|
||||
)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class RegionOfInterest:
|
||||
"""The Region-Of-Interest (ROI) to interact with."""
|
||||
|
||||
class Format(enum.Enum):
|
||||
UNSPECIFIED = 0
|
||||
KEYPOINT = 1
|
||||
|
||||
format: Format
|
||||
keypoint: Optional[keypoint_module.NormalizedKeypoint] = None
|
||||
|
||||
|
||||
def _convert_roi_to_render_data(roi: RegionOfInterest) -> _RenderDataProto:
|
||||
"""Converts region of interest to render data proto."""
|
||||
result = _RenderDataProto()
|
||||
|
||||
if roi is not None:
|
||||
if roi.format == RegionOfInterest.Format.UNSPECIFIED:
|
||||
raise ValueError('RegionOfInterest format not specified.')
|
||||
|
||||
elif roi.format == RegionOfInterest.Format.KEYPOINT:
|
||||
if roi.keypoint is not None:
|
||||
annotation = result.render_annotations.add()
|
||||
annotation.color.r = 255
|
||||
point = annotation.point
|
||||
point.normalized = True
|
||||
point.x = roi.keypoint.x
|
||||
point.y = roi.keypoint.y
|
||||
return result
|
||||
else:
|
||||
raise ValueError('Please specify the Region-of-interest for segmentation.')
|
||||
|
||||
raise ValueError('Unrecognized format.')
|
||||
|
||||
|
||||
class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
|
||||
"""Class that performs interactive segmentation on images.
|
||||
|
||||
Users can represent user interaction through `RegionOfInterest`, which gives
|
||||
a hint to InteractiveSegmenter to perform segmentation focusing on the given
|
||||
region of interest.
|
||||
|
||||
The API expects a TFLite model with mandatory TFLite Model Metadata.
|
||||
|
||||
Input tensor:
|
||||
(kTfLiteUInt8/kTfLiteFloat32)
|
||||
- image input of size `[batch x height x width x channels]`.
|
||||
- batch inference is not supported (`batch` is required to be 1).
|
||||
- RGB and greyscale inputs are supported (`channels` is required to be
|
||||
1 or 3).
|
||||
- if type is kTfLiteFloat32, NormalizationOptions are required to be
|
||||
attached to the metadata for input normalization.
|
||||
Output tensors:
|
||||
(kTfLiteUInt8/kTfLiteFloat32)
|
||||
- list of segmented masks.
|
||||
- if `output_type` is CATEGORY_MASK, uint8 Image, Image vector of size 1.
|
||||
- if `output_type` is CONFIDENCE_MASK, float32 Image list of size
|
||||
`channels`.
|
||||
- batch is always 1
|
||||
|
||||
An example of such model can be found at:
|
||||
https://tfhub.dev/tensorflow/lite-model/deeplabv3/1/metadata/2
|
||||
"""
|
||||
|
||||
@classmethod
|
||||
def create_from_model_path(cls, model_path: str) -> 'InteractiveSegmenter':
|
||||
"""Creates an `InteractiveSegmenter` object from a TensorFlow Lite model and the default `InteractiveSegmenterOptions`.
|
||||
|
||||
Note that the created `InteractiveSegmenter` instance is in image mode, for
|
||||
performing image segmentation on single image inputs.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`InteractiveSegmenter` object that's created from the model file and the
|
||||
default `InteractiveSegmenterOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `InteractiveSegmenter` object from the
|
||||
provided file such as invalid file path.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
options = InteractiveSegmenterOptions(base_options=base_options)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(
|
||||
cls, options: InteractiveSegmenterOptions
|
||||
) -> 'InteractiveSegmenter':
|
||||
"""Creates the `InteractiveSegmenter` object from interactive segmenter options.
|
||||
|
||||
Args:
|
||||
options: Options for the interactive segmenter task.
|
||||
|
||||
Returns:
|
||||
`InteractiveSegmenter` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `InteractiveSegmenter` object from
|
||||
`InteractiveSegmenterOptions` such as missing the model.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
|
||||
task_info = _TaskInfo(
|
||||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[
|
||||
':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
|
||||
':'.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]),
|
||||
],
|
||||
task_options=options,
|
||||
)
|
||||
return cls(
|
||||
task_info.generate_graph_config(enable_flow_limiting=False),
|
||||
_RunningMode.IMAGE,
|
||||
None,
|
||||
)
|
||||
|
||||
def segment(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
roi: RegionOfInterest,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None,
|
||||
) -> List[image_module.Image]:
|
||||
"""Performs the actual segmentation task on the provided MediaPipe Image.
|
||||
|
||||
The image can be of any size with format RGB.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
roi: Optional user-specified region of interest for segmentation.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
If the output_type is CATEGORY_MASK, the returned vector of images is
|
||||
per-category segmented image mask.
|
||||
If the output_type is CONFIDENCE_MASK, the returned vector of images
|
||||
contains only one confidence image mask. A segmentation result object that
|
||||
contains a list of segmentation masks as images.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If image segmentation failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(
|
||||
image_processing_options, image, roi_allowed=False
|
||||
)
|
||||
render_data_proto = _convert_roi_to_render_data(roi)
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
|
||||
_ROI_STREAM_NAME: packet_creator.create_proto(render_data_proto),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
normalized_rect.to_pb2()
|
||||
),
|
||||
})
|
||||
segmentation_result = packet_getter.get_image_list(
|
||||
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
|
||||
)
|
||||
return segmentation_result
|
||||
Loading…
Reference in New Issue
Block a user