# 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. # ============================================================================== """Tests for object_detection.utils.visualization_utils.""" import logging import os import numpy as np import PIL.Image as Image import tensorflow as tf from object_detection.core import standard_fields as fields from object_detection.utils import visualization_utils _TESTDATA_PATH = 'object_detection/test_images' class VisualizationUtilsTest(tf.test.TestCase): def create_colorful_test_image(self): """This function creates an image that can be used to test vis functions. It makes an image composed of four colored rectangles. Returns: colorful test numpy array image. """ ch255 = np.full([100, 200, 1], 255, dtype=np.uint8) ch128 = np.full([100, 200, 1], 128, dtype=np.uint8) ch0 = np.full([100, 200, 1], 0, dtype=np.uint8) imr = np.concatenate((ch255, ch128, ch128), axis=2) img = np.concatenate((ch255, ch255, ch0), axis=2) imb = np.concatenate((ch255, ch0, ch255), axis=2) imw = np.concatenate((ch128, ch128, ch128), axis=2) imu = np.concatenate((imr, img), axis=1) imd = np.concatenate((imb, imw), axis=1) image = np.concatenate((imu, imd), axis=0) return image def create_test_image_with_five_channels(self): return np.full([100, 200, 5], 255, dtype=np.uint8) def create_test_grayscale_image(self): return np.full([100, 200, 1], 255, dtype=np.uint8) def test_draw_bounding_box_on_image(self): test_image = self.create_colorful_test_image() test_image = Image.fromarray(test_image) width_original, height_original = test_image.size ymin = 0.25 ymax = 0.75 xmin = 0.4 xmax = 0.6 visualization_utils.draw_bounding_box_on_image(test_image, ymin, xmin, ymax, xmax) width_final, height_final = test_image.size self.assertEqual(width_original, width_final) self.assertEqual(height_original, height_final) def test_draw_bounding_box_on_image_array(self): test_image = self.create_colorful_test_image() width_original = test_image.shape[0] height_original = test_image.shape[1] ymin = 0.25 ymax = 0.75 xmin = 0.4 xmax = 0.6 visualization_utils.draw_bounding_box_on_image_array( test_image, ymin, xmin, ymax, xmax) width_final = test_image.shape[0] height_final = test_image.shape[1] self.assertEqual(width_original, width_final) self.assertEqual(height_original, height_final) def test_draw_bounding_boxes_on_image(self): test_image = self.create_colorful_test_image() test_image = Image.fromarray(test_image) width_original, height_original = test_image.size boxes = np.array([[0.25, 0.75, 0.4, 0.6], [0.1, 0.1, 0.9, 0.9]]) visualization_utils.draw_bounding_boxes_on_image(test_image, boxes) width_final, height_final = test_image.size self.assertEqual(width_original, width_final) self.assertEqual(height_original, height_final) def test_draw_bounding_boxes_on_image_array(self): test_image = self.create_colorful_test_image() width_original = test_image.shape[0] height_original = test_image.shape[1] boxes = np.array([[0.25, 0.75, 0.4, 0.6], [0.1, 0.1, 0.9, 0.9]]) visualization_utils.draw_bounding_boxes_on_image_array(test_image, boxes) width_final = test_image.shape[0] height_final = test_image.shape[1] self.assertEqual(width_original, width_final) self.assertEqual(height_original, height_final) def test_draw_bounding_boxes_on_image_tensors(self): """Tests that bounding box utility produces reasonable results.""" category_index = {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}} fname = os.path.join(_TESTDATA_PATH, 'image1.jpg') image_np = np.array(Image.open(fname)) images_np = np.stack((image_np, image_np), axis=0) original_image_shape = [[636, 512], [636, 512]] with tf.Graph().as_default(): images_tensor = tf.constant(value=images_np, dtype=tf.uint8) image_shape = tf.constant(original_image_shape, dtype=tf.int32) boxes = tf.constant([[[0.4, 0.25, 0.75, 0.75], [0.5, 0.3, 0.6, 0.9]], [[0.25, 0.25, 0.75, 0.75], [0.1, 0.3, 0.6, 1.0]]]) classes = tf.constant([[1, 1], [1, 2]], dtype=tf.int64) scores = tf.constant([[0.8, 0.1], [0.6, 0.5]]) images_with_boxes = ( visualization_utils.draw_bounding_boxes_on_image_tensors( images_tensor, boxes, classes, scores, category_index, original_image_spatial_shape=image_shape, true_image_shape=image_shape, min_score_thresh=0.2)) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) # Write output images for visualization. images_with_boxes_np = sess.run(images_with_boxes) self.assertEqual(images_np.shape[0], images_with_boxes_np.shape[0]) self.assertEqual(images_np.shape[3], images_with_boxes_np.shape[3]) self.assertEqual( tuple(original_image_shape[0]), images_with_boxes_np.shape[1:3]) for i in range(images_with_boxes_np.shape[0]): img_name = 'image_' + str(i) + '.png' output_file = os.path.join(self.get_temp_dir(), img_name) logging.info('Writing output image %d to %s', i, output_file) image_pil = Image.fromarray(images_with_boxes_np[i, ...]) image_pil.save(output_file) def test_draw_bounding_boxes_on_image_tensors_with_additional_channels(self): """Tests the case where input image tensor has more than 3 channels.""" category_index = {1: {'id': 1, 'name': 'dog'}} image_np = self.create_test_image_with_five_channels() images_np = np.stack((image_np, image_np), axis=0) with tf.Graph().as_default(): images_tensor = tf.constant(value=images_np, dtype=tf.uint8) boxes = tf.constant(0, dtype=tf.float32, shape=[2, 0, 4]) classes = tf.constant(0, dtype=tf.int64, shape=[2, 0]) scores = tf.constant(0, dtype=tf.float32, shape=[2, 0]) images_with_boxes = ( visualization_utils.draw_bounding_boxes_on_image_tensors( images_tensor, boxes, classes, scores, category_index, min_score_thresh=0.2)) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) final_images_np = sess.run(images_with_boxes) self.assertEqual((2, 100, 200, 3), final_images_np.shape) def test_draw_bounding_boxes_on_image_tensors_grayscale(self): """Tests the case where input image tensor has one channel.""" category_index = {1: {'id': 1, 'name': 'dog'}} image_np = self.create_test_grayscale_image() images_np = np.stack((image_np, image_np), axis=0) with tf.Graph().as_default(): images_tensor = tf.constant(value=images_np, dtype=tf.uint8) image_shape = tf.constant([[100, 200], [100, 200]], dtype=tf.int32) boxes = tf.constant(0, dtype=tf.float32, shape=[2, 0, 4]) classes = tf.constant(0, dtype=tf.int64, shape=[2, 0]) scores = tf.constant(0, dtype=tf.float32, shape=[2, 0]) images_with_boxes = ( visualization_utils.draw_bounding_boxes_on_image_tensors( images_tensor, boxes, classes, scores, category_index, original_image_spatial_shape=image_shape, true_image_shape=image_shape, min_score_thresh=0.2)) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) final_images_np = sess.run(images_with_boxes) self.assertEqual((2, 100, 200, 3), final_images_np.shape) def test_draw_keypoints_on_image(self): test_image = self.create_colorful_test_image() test_image = Image.fromarray(test_image) width_original, height_original = test_image.size keypoints = [[0.25, 0.75], [0.4, 0.6], [0.1, 0.1], [0.9, 0.9]] visualization_utils.draw_keypoints_on_image(test_image, keypoints) width_final, height_final = test_image.size self.assertEqual(width_original, width_final) self.assertEqual(height_original, height_final) def test_draw_keypoints_on_image_array(self): test_image = self.create_colorful_test_image() width_original = test_image.shape[0] height_original = test_image.shape[1] keypoints = [[0.25, 0.75], [0.4, 0.6], [0.1, 0.1], [0.9, 0.9]] visualization_utils.draw_keypoints_on_image_array(test_image, keypoints) width_final = test_image.shape[0] height_final = test_image.shape[1] self.assertEqual(width_original, width_final) self.assertEqual(height_original, height_final) def test_draw_mask_on_image_array(self): test_image = np.asarray([[[0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]], dtype=np.uint8) mask = np.asarray([[0, 1], [1, 1]], dtype=np.uint8) expected_result = np.asarray([[[0, 0, 0], [0, 0, 127]], [[0, 0, 127], [0, 0, 127]]], dtype=np.uint8) visualization_utils.draw_mask_on_image_array(test_image, mask, color='Blue', alpha=.5) self.assertAllEqual(test_image, expected_result) def test_add_cdf_image_summary(self): values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50] visualization_utils.add_cdf_image_summary(values, 'PositiveAnchorLoss') cdf_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0] with self.test_session(): cdf_image_summary.eval() def test_add_hist_image_summary(self): values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50] bins = [0.01 * i for i in range(101)] visualization_utils.add_hist_image_summary(values, bins, 'ScoresDistribution') hist_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0] with self.test_session(): hist_image_summary.eval() def test_eval_metric_ops(self): category_index = {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}} max_examples_to_draw = 4 metric_op_base = 'Detections_Left_Groundtruth_Right' eval_metric_ops = visualization_utils.VisualizeSingleFrameDetections( category_index, max_examples_to_draw=max_examples_to_draw, summary_name_prefix=metric_op_base) original_image = tf.placeholder(tf.uint8, [4, None, None, 3]) original_image_spatial_shape = tf.placeholder(tf.int32, [4, 2]) true_image_shape = tf.placeholder(tf.int32, [4, 3]) detection_boxes = tf.random_uniform([4, 20, 4], minval=0.0, maxval=1.0, dtype=tf.float32) detection_classes = tf.random_uniform([4, 20], minval=1, maxval=3, dtype=tf.int64) detection_scores = tf.random_uniform([4, 20], minval=0., maxval=1., dtype=tf.float32) groundtruth_boxes = tf.random_uniform([4, 8, 4], minval=0.0, maxval=1.0, dtype=tf.float32) groundtruth_classes = tf.random_uniform([4, 8], minval=1, maxval=3, dtype=tf.int64) eval_dict = { fields.DetectionResultFields.detection_boxes: detection_boxes, fields.DetectionResultFields.detection_classes: detection_classes, fields.DetectionResultFields.detection_scores: detection_scores, fields.InputDataFields.original_image: original_image, fields.InputDataFields.original_image_spatial_shape: ( original_image_spatial_shape), fields.InputDataFields.true_image_shape: (true_image_shape), fields.InputDataFields.groundtruth_boxes: groundtruth_boxes, fields.InputDataFields.groundtruth_classes: groundtruth_classes } metric_ops = eval_metric_ops.get_estimator_eval_metric_ops(eval_dict) _, update_op = metric_ops[metric_ops.keys()[0]] with self.test_session() as sess: sess.run(tf.global_variables_initializer()) value_ops = {} for key, (value_op, _) in metric_ops.iteritems(): value_ops[key] = value_op # First run enough update steps to surpass `max_examples_to_draw`. for i in range(max_examples_to_draw): # Use a unique image shape on each eval image. sess.run( update_op, feed_dict={ original_image: np.random.randint( low=0, high=256, size=(4, 6 + i, 7 + i, 3), dtype=np.uint8), original_image_spatial_shape: [[6 + i, 7 + i], [6 + i, 7 + i], [6 + i, 7 + i], [6 + i, 7 + i]], true_image_shape: [[6 + i, 7 + i, 3], [6 + i, 7 + i, 3], [6 + i, 7 + i, 3], [6 + i, 7 + i, 3]] }) value_ops_out = sess.run(value_ops) for key, value_op in value_ops_out.iteritems(): self.assertNotEqual('', value_op) # Now run fewer update steps than `max_examples_to_draw`. A single value # op will be the empty string, since not enough image summaries can be # produced. for i in range(max_examples_to_draw - 1): # Use a unique image shape on each eval image. sess.run( update_op, feed_dict={ original_image: np.random.randint( low=0, high=256, size=(4, 6 + i, 7 + i, 3), dtype=np.uint8), original_image_spatial_shape: [[6 + i, 7 + i], [6 + i, 7 + i], [6 + i, 7 + i], [6 + i, 7 + i]], true_image_shape: [[6 + i, 7 + i, 3], [6 + i, 7 + i, 3], [6 + i, 7 + i, 3], [6 + i, 7 + i, 3]] }) value_ops_out = sess.run(value_ops) self.assertEqual( '', value_ops_out[metric_op_base + '/' + str(max_examples_to_draw - 1)]) if __name__ == '__main__': tf.test.main()