Python object_detection.core.preprocessor.random_crop_image() Examples
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Example #1
Source File: preprocessor_builder_test.py From yolo_v2 with Apache License 2.0 | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
Example #2
Source File: preprocessor_builder_test.py From garbage-object-detection-tensorflow with MIT License | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
Example #3
Source File: preprocessor_builder_test.py From object_detector_app with MIT License | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
Example #4
Source File: preprocessor_builder_test.py From HereIsWally with MIT License | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
Example #5
Source File: preprocessor_builder_test.py From Person-Detection-and-Tracking with MIT License | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
Example #6
Source File: preprocessor_builder_test.py From DOTA_models with Apache License 2.0 | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
Example #7
Source File: preprocessor_builder_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 clip_boxes: False random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'clip_boxes': False, 'random_coef': 0.125, })
Example #8
Source File: preprocessor_builder_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
Example #9
Source File: preprocessor_builder_test.py From ros_people_object_detection_tensorflow with Apache License 2.0 | 6 votes |
def test_build_random_crop_image(self): preprocessor_text_proto = """ random_crop_image { min_object_covered: 0.75 min_aspect_ratio: 0.75 max_aspect_ratio: 1.5 min_area: 0.25 max_area: 0.875 overlap_thresh: 0.5 random_coef: 0.125 } """ preprocessor_proto = preprocessor_pb2.PreprocessingStep() text_format.Merge(preprocessor_text_proto, preprocessor_proto) function, args = preprocessor_builder.build(preprocessor_proto) self.assertEqual(function, preprocessor.random_crop_image) self.assertEqual(args, { 'min_object_covered': 0.75, 'aspect_ratio_range': (0.75, 1.5), 'area_range': (0.25, 0.875), 'overlap_thresh': 0.5, 'random_coef': 0.125, })
Example #10
Source File: preprocessor_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def testRandomCropImageWithCache(self): preprocess_options = [(preprocessor.random_rgb_to_gray, {'probability': 0.5}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] self._testPreprocessorCache(preprocess_options, test_boxes=True, test_masks=False, test_keypoints=False)
Example #11
Source File: preprocessor_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def testRandomCropImageGrayscale(self): preprocessing_options = [(preprocessor.rgb_to_gray, {}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess( tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(1, distorted_images.get_shape()[3]) with self.test_session() as sess: session_results = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = session_results self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #12
Source File: preprocessor_test.py From vehicle_counting_tensorflow with MIT License | 5 votes |
def testRandomCropImageGrayscale(self): preprocessing_options = [(preprocessor.rgb_to_gray, {}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() weights = self.createTestGroundtruthWeights() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, fields.InputDataFields.groundtruth_weights: weights, } distorted_tensor_dict = preprocessor.preprocess( tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(1, distorted_images.get_shape()[3]) with self.test_session() as sess: session_results = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = session_results self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #13
Source File: preprocessor_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def testRandomCropImageWithBoxOutOfImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxesOutOfImage() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run( [boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #14
Source File: preprocessor_test.py From Person-Detection-and-Tracking with MIT License | 5 votes |
def testRandomCropImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(3, distorted_images.get_shape()[3]) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #15
Source File: preprocessor_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
def testRandomCropImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = {fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels} distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(3, distorted_images.get_shape()[3]) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #16
Source File: preprocessor_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
def testRandomCropImageGrayscale(self): preprocessing_options = [(preprocessor.rgb_to_gray, {}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels } distorted_tensor_dict = preprocessor.preprocess( tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(1, distorted_images.get_shape()[3]) with self.test_session() as sess: session_results = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = session_results self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #17
Source File: preprocessor_test.py From garbage-object-detection-tensorflow with MIT License | 5 votes |
def testRandomCropImageWithBoxOutOfImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxesOutOfImage() labels = self.createTestLabels() tensor_dict = {fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels} distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run( [boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #18
Source File: preprocessor_test.py From DOTA_models with Apache License 2.0 | 5 votes |
def testRandomCropImageGrayscale(self): preprocessing_options = [(preprocessor.rgb_to_gray, {}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels } distorted_tensor_dict = preprocessor.preprocess( tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(1, distorted_images.get_shape()[3]) with self.test_session() as sess: session_results = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = session_results self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #19
Source File: preprocessor_test.py From HereIsWally with MIT License | 5 votes |
def testRandomCropImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = {fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels} distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(3, distorted_images.get_shape()[3]) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #20
Source File: preprocessor_test.py From HereIsWally with MIT License | 5 votes |
def testRandomCropImageGrayscale(self): preprocessing_options = [(preprocessor.rgb_to_gray, {}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels } distorted_tensor_dict = preprocessor.preprocess( tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(1, distorted_images.get_shape()[3]) with self.test_session() as sess: session_results = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = session_results self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #21
Source File: preprocessor_test.py From HereIsWally with MIT License | 5 votes |
def testRandomCropImageWithBoxOutOfImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxesOutOfImage() labels = self.createTestLabels() tensor_dict = {fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels} distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run( [boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #22
Source File: preprocessor_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def testRandomCropImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(3, distorted_images.get_shape()[3]) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #23
Source File: preprocessor_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def testRandomCropImageGrayscale(self): preprocessing_options = [(preprocessor.rgb_to_gray, {}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess( tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(1, distorted_images.get_shape()[3]) with self.test_session() as sess: session_results = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = session_results self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #24
Source File: preprocessor_test.py From yolo_v2 with Apache License 2.0 | 5 votes |
def testRandomCropImageWithBoxOutOfImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxesOutOfImage() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run( [boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #25
Source File: preprocessor_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def testRandomCropImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(3, distorted_images.get_shape()[3]) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #26
Source File: preprocessor_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def testRandomCropImageWithCache(self): preprocess_options = [(preprocessor.random_rgb_to_gray, {'probability': 0.5}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] self._testPreprocessorCache(preprocess_options, test_boxes=True, test_masks=False, test_keypoints=False)
Example #27
Source File: preprocessor_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def testRandomCropImageGrayscale(self): preprocessing_options = [(preprocessor.rgb_to_gray, {}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess( tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(1, distorted_images.get_shape()[3]) with self.test_session() as sess: session_results = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = session_results self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #28
Source File: preprocessor_test.py From Traffic-Rule-Violation-Detection-System with MIT License | 5 votes |
def testRandomCropImageWithBoxOutOfImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxesOutOfImage() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels, } distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run( [boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #29
Source File: preprocessor_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 5 votes |
def testRandomCropImage(self): preprocessing_options = [] preprocessing_options.append((preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1 })) preprocessing_options.append((preprocessor.random_crop_image, {})) images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = {fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels} distorted_tensor_dict = preprocessor.preprocess(tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(3, distorted_images.get_shape()[3]) with self.test_session() as sess: (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)
Example #30
Source File: preprocessor_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 5 votes |
def testRandomCropImageGrayscale(self): preprocessing_options = [(preprocessor.rgb_to_gray, {}), (preprocessor.normalize_image, { 'original_minval': 0, 'original_maxval': 255, 'target_minval': 0, 'target_maxval': 1, }), (preprocessor.random_crop_image, {})] images = self.createTestImages() boxes = self.createTestBoxes() labels = self.createTestLabels() tensor_dict = { fields.InputDataFields.image: images, fields.InputDataFields.groundtruth_boxes: boxes, fields.InputDataFields.groundtruth_classes: labels } distorted_tensor_dict = preprocessor.preprocess( tensor_dict, preprocessing_options) distorted_images = distorted_tensor_dict[fields.InputDataFields.image] distorted_boxes = distorted_tensor_dict[ fields.InputDataFields.groundtruth_boxes] boxes_rank = tf.rank(boxes) distorted_boxes_rank = tf.rank(distorted_boxes) images_rank = tf.rank(images) distorted_images_rank = tf.rank(distorted_images) self.assertEqual(1, distorted_images.get_shape()[3]) with self.test_session() as sess: session_results = sess.run([ boxes_rank, distorted_boxes_rank, images_rank, distorted_images_rank ]) (boxes_rank_, distorted_boxes_rank_, images_rank_, distorted_images_rank_) = session_results self.assertAllEqual(boxes_rank_, distorted_boxes_rank_) self.assertAllEqual(images_rank_, distorted_images_rank_)