This will fix multiple typos in the tasks internal files.
PiperOrigin-RevId: 565055843
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@ -113,8 +113,8 @@ class FaceAlignerTest(parameterized.TestCase):
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aligner = _FaceAligner.create_from_options(options)
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aligner = _FaceAligner.create_from_options(options)
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# Performs face alignment on the input.
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# Performs face alignment on the input.
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alignd_image = aligner.align(self.test_image)
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aligned_image = aligner.align(self.test_image)
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self.assertIsInstance(alignd_image, _Image)
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self.assertIsInstance(aligned_image, _Image)
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# Closes the aligner explicitly when the aligner is not used in
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# Closes the aligner explicitly when the aligner is not used in
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# a context.
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# a context.
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aligner.close()
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aligner.close()
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@ -144,10 +144,10 @@ class FaceAlignerTest(parameterized.TestCase):
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options = _FaceAlignerOptions(base_options=base_options)
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options = _FaceAlignerOptions(base_options=base_options)
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with _FaceAligner.create_from_options(options) as aligner:
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with _FaceAligner.create_from_options(options) as aligner:
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# Performs face alignment on the input.
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# Performs face alignment on the input.
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alignd_image = aligner.align(self.test_image)
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aligned_image = aligner.align(self.test_image)
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self.assertIsInstance(alignd_image, _Image)
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self.assertIsInstance(aligned_image, _Image)
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self.assertEqual(alignd_image.width, _MODEL_IMAGE_SIZE)
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self.assertEqual(aligned_image.width, _MODEL_IMAGE_SIZE)
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self.assertEqual(alignd_image.height, _MODEL_IMAGE_SIZE)
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self.assertEqual(aligned_image.height, _MODEL_IMAGE_SIZE)
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def test_align_succeeds_with_region_of_interest(self):
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def test_align_succeeds_with_region_of_interest(self):
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base_options = _BaseOptions(model_asset_path=self.model_path)
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base_options = _BaseOptions(model_asset_path=self.model_path)
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@ -163,10 +163,10 @@ class FaceAlignerTest(parameterized.TestCase):
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roi = _Rect(left=0.32, top=0.02, right=0.67, bottom=0.32)
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roi = _Rect(left=0.32, top=0.02, right=0.67, bottom=0.32)
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image_processing_options = _ImageProcessingOptions(roi)
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image_processing_options = _ImageProcessingOptions(roi)
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# Performs face alignment on the input.
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# Performs face alignment on the input.
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alignd_image = aligner.align(test_image, image_processing_options)
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aligned_image = aligner.align(test_image, image_processing_options)
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self.assertIsInstance(alignd_image, _Image)
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self.assertIsInstance(aligned_image, _Image)
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self.assertEqual(alignd_image.width, _MODEL_IMAGE_SIZE)
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self.assertEqual(aligned_image.width, _MODEL_IMAGE_SIZE)
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self.assertEqual(alignd_image.height, _MODEL_IMAGE_SIZE)
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self.assertEqual(aligned_image.height, _MODEL_IMAGE_SIZE)
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def test_align_succeeds_with_no_face_detected(self):
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def test_align_succeeds_with_no_face_detected(self):
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base_options = _BaseOptions(model_asset_path=self.model_path)
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base_options = _BaseOptions(model_asset_path=self.model_path)
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@ -182,8 +182,8 @@ class FaceAlignerTest(parameterized.TestCase):
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roi = _Rect(left=0.1, top=0.1, right=0.2, bottom=0.2)
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roi = _Rect(left=0.1, top=0.1, right=0.2, bottom=0.2)
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image_processing_options = _ImageProcessingOptions(roi)
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image_processing_options = _ImageProcessingOptions(roi)
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# Performs face alignment on the input.
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# Performs face alignment on the input.
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alignd_image = aligner.align(test_image, image_processing_options)
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aligned_image = aligner.align(test_image, image_processing_options)
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self.assertIsNone(alignd_image)
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self.assertIsNone(aligned_image)
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if __name__ == '__main__':
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if __name__ == '__main__':
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@ -51,7 +51,7 @@ const POSE_LANDMARKER_GRAPH =
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const DEFAULT_NUM_POSES = 1;
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const DEFAULT_NUM_POSES = 1;
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const DEFAULT_SCORE_THRESHOLD = 0.5;
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const DEFAULT_SCORE_THRESHOLD = 0.5;
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const DEFAULT_OUTPUT_SEGMANTATION_MASKS = false;
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const DEFAULT_OUTPUT_SEGMENTATION_MASKS = false;
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/**
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/**
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* A callback that receives the result from the pose detector. The returned
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* A callback that receives the result from the pose detector. The returned
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@ -201,7 +201,7 @@ export class PoseLandmarker extends VisionTaskRunner {
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if ('outputSegmentationMasks' in options) {
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if ('outputSegmentationMasks' in options) {
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this.outputSegmentationMasks =
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this.outputSegmentationMasks =
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options.outputSegmentationMasks ?? DEFAULT_OUTPUT_SEGMANTATION_MASKS;
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options.outputSegmentationMasks ?? DEFAULT_OUTPUT_SEGMENTATION_MASKS;
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}
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}
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return this.applyOptions(options);
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return this.applyOptions(options);
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