Merge pull request #2 from tensorflow/master

Sync to tensorflow-master

object_detection/utils/test_utils.py

+# Copyright 2017 The TensorFlow 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.
+# ==============================================================================
+
+"""Contains functions which are convenient for unit testing."""
+import numpy as np
+import tensorflow as tf
+
+from object_detection.core import anchor_generator
+from object_detection.core import box_coder
+from object_detection.core import box_list
+from object_detection.core import box_predictor
+from object_detection.core import matcher
+
+
+class MockBoxCoder(box_coder.BoxCoder):
+  """Simple `difference` BoxCoder."""
+
+  @property
+  def code_size(self):
+    return 4
+
+  def _encode(self, boxes, anchors):
+    return boxes.get() - anchors.get()
+
+  def _decode(self, rel_codes, anchors):
+    return box_list.BoxList(rel_codes + anchors.get())
+
+
+class MockBoxPredictor(box_predictor.BoxPredictor):
+  """Simple box predictor that ignores inputs and outputs all zeros."""
+
+  def __init__(self, is_training, num_classes):
+    super(MockBoxPredictor, self).__init__(is_training, num_classes)
+
+  def _predict(self, image_features, num_predictions_per_location):
+    batch_size = image_features.get_shape().as_list()[0]
+    num_anchors = (image_features.get_shape().as_list()[1]
+                   * image_features.get_shape().as_list()[2])
+    code_size = 4
+    zero = tf.reduce_sum(0 * image_features)
+    box_encodings = zero + tf.zeros(
+        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
+    class_predictions_with_background = zero + tf.zeros(
+        (batch_size, num_anchors, self.num_classes + 1), dtype=tf.float32)
+    return {box_predictor.BOX_ENCODINGS: box_encodings,
+            box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
+            class_predictions_with_background}
+
+
+class MockAnchorGenerator(anchor_generator.AnchorGenerator):
+  """Mock anchor generator."""
+
+  def name_scope(self):
+    return 'MockAnchorGenerator'
+
+  def num_anchors_per_location(self):
+    return [1]
+
+  def _generate(self, feature_map_shape_list):
+    num_anchors = sum([shape[0] * shape[1] for shape in feature_map_shape_list])
+    return box_list.BoxList(tf.zeros((num_anchors, 4), dtype=tf.float32))
+
+
+class MockMatcher(matcher.Matcher):
+  """Simple matcher that matches first anchor to first groundtruth box."""
+
+  def _match(self, similarity_matrix):
+    return tf.constant([0, -1, -1, -1], dtype=tf.int32)
+
+
+def create_diagonal_gradient_image(height, width, depth):
+  """Creates pyramid image. Useful for testing.
+
+  For example, pyramid_image(5, 6, 1) looks like:
+  # [[[ 5.  4.  3.  2.  1.  0.]
+  #   [ 6.  5.  4.  3.  2.  1.]
+  #   [ 7.  6.  5.  4.  3.  2.]
+  #   [ 8.  7.  6.  5.  4.  3.]
+  #   [ 9.  8.  7.  6.  5.  4.]]]
+
+  Args:
+    height: height of image
+    width: width of image
+    depth: depth of image
+
+  Returns:
+    pyramid image
+  """
+  row = np.arange(height)
+  col = np.arange(width)[::-1]
+  image_layer = np.expand_dims(row, 1) + col
+  image_layer = np.expand_dims(image_layer, 2)
+
+  image = image_layer
+  for i in range(1, depth):
+    image = np.concatenate((image, image_layer * pow(10, i)), 2)
+
+  return image.astype(np.float32)
+
+
+def create_random_boxes(num_boxes, max_height, max_width):
+  """Creates random bounding boxes of specific maximum height and width.
+
+  Args:
+    num_boxes: number of boxes.
+    max_height: maximum height of boxes.
+    max_width: maximum width of boxes.
+
+  Returns:
+    boxes: numpy array of shape [num_boxes, 4]. Each row is in form
+        [y_min, x_min, y_max, x_max].
+  """
+
+  y_1 = np.random.uniform(size=(1, num_boxes)) * max_height
+  y_2 = np.random.uniform(size=(1, num_boxes)) * max_height
+  x_1 = np.random.uniform(size=(1, num_boxes)) * max_width
+  x_2 = np.random.uniform(size=(1, num_boxes)) * max_width
+
+  boxes = np.zeros(shape=(num_boxes, 4))
+  boxes[:, 0] = np.minimum(y_1, y_2)
+  boxes[:, 1] = np.minimum(x_1, x_2)
+  boxes[:, 2] = np.maximum(y_1, y_2)
+  boxes[:, 3] = np.maximum(x_1, x_2)
+
+  return boxes.astype(np.float32)