Python preprocessor._strict_random_crop_image方法代碼示例

本文整理匯總了Python中object_detection.core.preprocessor._strict_random_crop_image方法的典型用法代碼示例。如果您正苦於以下問題：Python preprocessor._strict_random_crop_image方法的具體用法？Python preprocessor._strict_random_crop_image怎麽用？Python preprocessor._strict_random_crop_image使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.core.preprocessor的用法示例。

在下文中一共展示了preprocessor._strict_random_crop_image方法的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: testStrictRandomCropImageWithMasks

# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 別名]
def testStrictRandomCropImageWithMasks(self):
    image = self.createColorfulTestImage()[0]
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
    with mock.patch.object(
        tf.image,
        'sample_distorted_bounding_box'
    ) as mock_sample_distorted_bounding_box:
      mock_sample_distorted_bounding_box.return_value = (
          tf.constant([6, 143, 0], dtype=tf.int32),
          tf.constant([190, 237, -1], dtype=tf.int32),
          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
      (new_image, new_boxes, new_labels,
       new_masks) = preprocessor._strict_random_crop_image(
           image, boxes, labels, masks=masks)
      with self.test_session() as sess:
        new_image, new_boxes, new_labels, new_masks = sess.run([
            new_image, new_boxes, new_labels, new_masks])

        expected_boxes = np.array([
            [0.0, 0.0, 0.75789469, 1.0],
            [0.23157893, 0.24050637, 0.75789469, 1.0],
        ], dtype=np.float32)
        self.assertAllEqual(new_image.shape, [190, 237, 3])
        self.assertAllEqual(new_masks.shape, [2, 190, 237])
        self.assertAllClose(
            new_boxes.flatten(), expected_boxes.flatten())

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:30，代碼來源:preprocessor_test.py

示例2: testStrictRandomCropImageWithKeypoints

# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 別名]
def testStrictRandomCropImageWithKeypoints(self):
    image = self.createColorfulTestImage()[0]
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    keypoints = self.createTestKeypoints()
    with mock.patch.object(
        tf.image,
        'sample_distorted_bounding_box'
    ) as mock_sample_distorted_bounding_box:
      mock_sample_distorted_bounding_box.return_value = (
          tf.constant([6, 143, 0], dtype=tf.int32),
          tf.constant([190, 237, -1], dtype=tf.int32),
          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
      (new_image, new_boxes, new_labels,
       new_keypoints) = preprocessor._strict_random_crop_image(
           image, boxes, labels, keypoints=keypoints)
      with self.test_session() as sess:
        new_image, new_boxes, new_labels, new_keypoints = sess.run([
            new_image, new_boxes, new_labels, new_keypoints])

        expected_boxes = np.array([
            [0.0, 0.0, 0.75789469, 1.0],
            [0.23157893, 0.24050637, 0.75789469, 1.0],
        ], dtype=np.float32)
        expected_keypoints = np.array([
            [[np.nan, np.nan],
             [np.nan, np.nan],
             [np.nan, np.nan]],
            [[0.38947368, 0.07173],
             [0.49473682, 0.24050637],
             [0.60000002, 0.40928277]]
        ], dtype=np.float32)
        self.assertAllEqual(new_image.shape, [190, 237, 3])
        self.assertAllClose(
            new_boxes.flatten(), expected_boxes.flatten())
        self.assertAllClose(
            new_keypoints.flatten(), expected_keypoints.flatten())

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:39，代碼來源:preprocessor_test.py

示例3: testStrictRandomCropImageWithGroundtruthWeights

# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 別名]
def testStrictRandomCropImageWithGroundtruthWeights(self):
    image = self.createColorfulTestImage()[0]
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    weights = self.createTestGroundtruthWeights()
    with mock.patch.object(
        tf.image,
        'sample_distorted_bounding_box'
    ) as mock_sample_distorted_bounding_box:
      mock_sample_distorted_bounding_box.return_value = (
          tf.constant([6, 143, 0], dtype=tf.int32),
          tf.constant([190, 237, -1], dtype=tf.int32),
          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
      new_image, new_boxes, new_labels, new_groundtruth_weights = (
          preprocessor._strict_random_crop_image(
              image, boxes, labels, weights))
      with self.test_session() as sess:
        new_image, new_boxes, new_labels, new_groundtruth_weights = (
            sess.run(
                [new_image, new_boxes, new_labels, new_groundtruth_weights])
        )

        expected_boxes = np.array(
            [[0.0, 0.0, 0.75789469, 1.0],
             [0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
        self.assertAllEqual(new_image.shape, [190, 237, 3])
        self.assertAllEqual(new_groundtruth_weights, [1.0, 0.5])
        self.assertAllClose(
            new_boxes.flatten(), expected_boxes.flatten())

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:31，代碼來源:preprocessor_test.py

示例4: testStrictRandomCropImageWithMasks

# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 別名]
def testStrictRandomCropImageWithMasks(self):
    image = self.createColorfulTestImage()[0]
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    weights = self.createTestGroundtruthWeights()
    masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
    with mock.patch.object(
        tf.image,
        'sample_distorted_bounding_box'
    ) as mock_sample_distorted_bounding_box:
      mock_sample_distorted_bounding_box.return_value = (
          tf.constant([6, 143, 0], dtype=tf.int32),
          tf.constant([190, 237, -1], dtype=tf.int32),
          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
      new_image, new_boxes, new_labels, new_weights, new_masks = (
          preprocessor._strict_random_crop_image(
              image, boxes, labels, weights, masks=masks))
      with self.test_session() as sess:
        new_image, new_boxes, new_labels, new_weights, new_masks = sess.run(
            [new_image, new_boxes, new_labels, new_weights, new_masks])
        expected_boxes = np.array(
            [[0.0, 0.0, 0.75789469, 1.0],
             [0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
        self.assertAllEqual(new_image.shape, [190, 237, 3])
        self.assertAllEqual(new_masks.shape, [2, 190, 237])
        self.assertAllClose(
            new_boxes.flatten(), expected_boxes.flatten())

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:29，代碼來源:preprocessor_test.py

示例5: testStrictRandomCropImageWithKeypoints

# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 別名]
def testStrictRandomCropImageWithKeypoints(self):
    image = self.createColorfulTestImage()[0]
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    weights = self.createTestGroundtruthWeights()
    keypoints = self.createTestKeypoints()
    with mock.patch.object(
        tf.image,
        'sample_distorted_bounding_box'
    ) as mock_sample_distorted_bounding_box:
      mock_sample_distorted_bounding_box.return_value = (
          tf.constant([6, 143, 0], dtype=tf.int32),
          tf.constant([190, 237, -1], dtype=tf.int32),
          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
      new_image, new_boxes, new_labels, new_weights, new_keypoints = (
          preprocessor._strict_random_crop_image(
              image, boxes, labels, weights, keypoints=keypoints))
      with self.test_session() as sess:
        new_image, new_boxes, new_labels, new_weights, new_keypoints = sess.run(
            [new_image, new_boxes, new_labels, new_weights, new_keypoints])

        expected_boxes = np.array([
            [0.0, 0.0, 0.75789469, 1.0],
            [0.23157893, 0.24050637, 0.75789469, 1.0],], dtype=np.float32)
        expected_keypoints = np.array([
            [[np.nan, np.nan],
             [np.nan, np.nan],
             [np.nan, np.nan]],
            [[0.38947368, 0.07173],
             [0.49473682, 0.24050637],
             [0.60000002, 0.40928277]]
        ], dtype=np.float32)
        self.assertAllEqual(new_image.shape, [190, 237, 3])
        self.assertAllClose(
            new_boxes.flatten(), expected_boxes.flatten())
        self.assertAllClose(
            new_keypoints.flatten(), expected_keypoints.flatten())

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:39，代碼來源:preprocessor_test.py

示例6: testStrictRandomCropImageWithLabelScores

# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 別名]
def testStrictRandomCropImageWithLabelScores(self):
    image = self.createColorfulTestImage()[0]
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    label_scores = self.createTestLabelScores()
    with mock.patch.object(
        tf.image,
        'sample_distorted_bounding_box'
    ) as mock_sample_distorted_bounding_box:
      mock_sample_distorted_bounding_box.return_value = (
          tf.constant([6, 143, 0], dtype=tf.int32),
          tf.constant([190, 237, -1], dtype=tf.int32),
          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
      new_image, new_boxes, new_labels, new_label_scores = (
          preprocessor._strict_random_crop_image(
              image, boxes, labels, label_scores))
      with self.test_session() as sess:
        new_image, new_boxes, new_labels, new_label_scores = (
            sess.run(
                [new_image, new_boxes, new_labels, new_label_scores])
        )

        expected_boxes = np.array(
            [[0.0, 0.0, 0.75789469, 1.0],
             [0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
        self.assertAllEqual(new_image.shape, [190, 237, 3])
        self.assertAllEqual(new_label_scores, [1.0, 0.5])
        self.assertAllClose(
            new_boxes.flatten(), expected_boxes.flatten())

開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:31，代碼來源:preprocessor_test.py

示例7: testStrictRandomCropImageWithMasks

# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 別名]
def testStrictRandomCropImageWithMasks(self):
    image = self.createColorfulTestImage()[0]
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    masks = tf.random_uniform([2, 200, 400], dtype=tf.float32)
    with mock.patch.object(
        tf.image,
        'sample_distorted_bounding_box'
    ) as mock_sample_distorted_bounding_box:
      mock_sample_distorted_bounding_box.return_value = (
          tf.constant([6, 143, 0], dtype=tf.int32),
          tf.constant([190, 237, -1], dtype=tf.int32),
          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
      new_image, new_boxes, new_labels, new_masks = (
          preprocessor._strict_random_crop_image(
              image, boxes, labels, masks=masks))
      with self.test_session() as sess:
        new_image, new_boxes, new_labels, new_masks = sess.run(
            [new_image, new_boxes, new_labels, new_masks])
        expected_boxes = np.array(
            [[0.0, 0.0, 0.75789469, 1.0],
             [0.23157893, 0.24050637, 0.75789469, 1.0]], dtype=np.float32)
        self.assertAllEqual(new_image.shape, [190, 237, 3])
        self.assertAllEqual(new_masks.shape, [2, 190, 237])
        self.assertAllClose(
            new_boxes.flatten(), expected_boxes.flatten())

開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:28，代碼來源:preprocessor_test.py

示例8: testStrictRandomCropImageWithKeypoints

# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import _strict_random_crop_image [as 別名]
def testStrictRandomCropImageWithKeypoints(self):
    image = self.createColorfulTestImage()[0]
    boxes = self.createTestBoxes()
    labels = self.createTestLabels()
    keypoints = self.createTestKeypoints()
    with mock.patch.object(
        tf.image,
        'sample_distorted_bounding_box'
    ) as mock_sample_distorted_bounding_box:
      mock_sample_distorted_bounding_box.return_value = (
          tf.constant([6, 143, 0], dtype=tf.int32),
          tf.constant([190, 237, -1], dtype=tf.int32),
          tf.constant([[[0.03, 0.3575, 0.98, 0.95]]], dtype=tf.float32))
      new_image, new_boxes, new_labels, new_keypoints = (
          preprocessor._strict_random_crop_image(
              image, boxes, labels, keypoints=keypoints))
      with self.test_session() as sess:
        new_image, new_boxes, new_labels, new_keypoints = sess.run(
            [new_image, new_boxes, new_labels, new_keypoints])

        expected_boxes = np.array([
            [0.0, 0.0, 0.75789469, 1.0],
            [0.23157893, 0.24050637, 0.75789469, 1.0],], dtype=np.float32)
        expected_keypoints = np.array([
            [[np.nan, np.nan],
             [np.nan, np.nan],
             [np.nan, np.nan]],
            [[0.38947368, 0.07173],
             [0.49473682, 0.24050637],
             [0.60000002, 0.40928277]]
        ], dtype=np.float32)
        self.assertAllEqual(new_image.shape, [190, 237, 3])
        self.assertAllClose(
            new_boxes.flatten(), expected_boxes.flatten())
        self.assertAllClose(
            new_keypoints.flatten(), expected_keypoints.flatten())

開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:38，代碼來源:preprocessor_test.py

注：本文中的object_detection.core.preprocessor._strict_random_crop_image方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。