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"""Tests for tensorflow_model.object_detection.metrics.coco_tools.""" |
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import json |
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import os |
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import re |
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import numpy as np |
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from pycocotools import mask |
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import tensorflow as tf |
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from object_detection.metrics import coco_tools |
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class CocoToolsTest(tf.test.TestCase): |
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def setUp(self): |
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groundtruth_annotations_list = [ |
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{ |
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'id': 1, |
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'image_id': 'first', |
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'category_id': 1, |
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'bbox': [100., 100., 100., 100.], |
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'area': 100.**2, |
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'iscrowd': 0 |
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}, |
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{ |
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'id': 2, |
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'image_id': 'second', |
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'category_id': 1, |
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'bbox': [50., 50., 50., 50.], |
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'area': 50.**2, |
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'iscrowd': 0 |
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}, |
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] |
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image_list = [{'id': 'first'}, {'id': 'second'}] |
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category_list = [{'id': 0, 'name': 'person'}, |
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{'id': 1, 'name': 'cat'}, |
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{'id': 2, 'name': 'dog'}] |
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self._groundtruth_dict = { |
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'annotations': groundtruth_annotations_list, |
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'images': image_list, |
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'categories': category_list |
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} |
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self._detections_list = [ |
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{ |
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'image_id': 'first', |
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'category_id': 1, |
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'bbox': [100., 100., 100., 100.], |
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'score': .8 |
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}, |
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{ |
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'image_id': 'second', |
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'category_id': 1, |
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'bbox': [50., 50., 50., 50.], |
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'score': .7 |
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}, |
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] |
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def testCocoWrappers(self): |
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groundtruth = coco_tools.COCOWrapper(self._groundtruth_dict) |
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detections = groundtruth.LoadAnnotations(self._detections_list) |
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evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections) |
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summary_metrics, _ = evaluator.ComputeMetrics() |
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self.assertAlmostEqual(1.0, summary_metrics['Precision/mAP']) |
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def testExportGroundtruthToCOCO(self): |
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image_ids = ['first', 'second'] |
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groundtruth_boxes = [np.array([[100, 100, 200, 200]], np.float), |
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np.array([[50, 50, 100, 100]], np.float)] |
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groundtruth_classes = [np.array([1], np.int32), np.array([1], np.int32)] |
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categories = [{'id': 0, 'name': 'person'}, |
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{'id': 1, 'name': 'cat'}, |
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{'id': 2, 'name': 'dog'}] |
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output_path = os.path.join(tf.test.get_temp_dir(), 'groundtruth.json') |
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result = coco_tools.ExportGroundtruthToCOCO( |
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image_ids, |
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groundtruth_boxes, |
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groundtruth_classes, |
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categories, |
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output_path=output_path) |
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self.assertDictEqual(result, self._groundtruth_dict) |
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with tf.gfile.GFile(output_path, 'r') as f: |
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written_result = f.read() |
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matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,', re.MULTILINE) |
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self.assertTrue(matcher.findall(written_result)) |
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written_result = json.loads(written_result) |
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self.assertAlmostEqual(result, written_result) |
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def testExportDetectionsToCOCO(self): |
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image_ids = ['first', 'second'] |
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detections_boxes = [np.array([[100, 100, 200, 200]], np.float), |
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np.array([[50, 50, 100, 100]], np.float)] |
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detections_scores = [np.array([.8], np.float), np.array([.7], np.float)] |
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detections_classes = [np.array([1], np.int32), np.array([1], np.int32)] |
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categories = [{'id': 0, 'name': 'person'}, |
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{'id': 1, 'name': 'cat'}, |
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{'id': 2, 'name': 'dog'}] |
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output_path = os.path.join(tf.test.get_temp_dir(), 'detections.json') |
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result = coco_tools.ExportDetectionsToCOCO( |
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image_ids, |
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detections_boxes, |
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detections_scores, |
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detections_classes, |
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categories, |
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output_path=output_path) |
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self.assertListEqual(result, self._detections_list) |
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with tf.gfile.GFile(output_path, 'r') as f: |
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written_result = f.read() |
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matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,', re.MULTILINE) |
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self.assertTrue(matcher.findall(written_result)) |
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written_result = json.loads(written_result) |
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self.assertAlmostEqual(result, written_result) |
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def testExportSegmentsToCOCO(self): |
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image_ids = ['first', 'second'] |
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detection_masks = [np.array( |
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[[[0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1]]], |
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dtype=np.uint8), np.array( |
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[[[0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1]]], |
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dtype=np.uint8)] |
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for i, detection_mask in enumerate(detection_masks): |
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detection_masks[i] = detection_mask[:, :, :, None] |
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detection_scores = [np.array([.8], np.float), np.array([.7], np.float)] |
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detection_classes = [np.array([1], np.int32), np.array([1], np.int32)] |
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categories = [{'id': 0, 'name': 'person'}, |
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{'id': 1, 'name': 'cat'}, |
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{'id': 2, 'name': 'dog'}] |
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output_path = os.path.join(tf.test.get_temp_dir(), 'segments.json') |
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result = coco_tools.ExportSegmentsToCOCO( |
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image_ids, |
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detection_masks, |
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detection_scores, |
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detection_classes, |
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categories, |
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output_path=output_path) |
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with tf.gfile.GFile(output_path, 'r') as f: |
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written_result = f.read() |
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written_result = json.loads(written_result) |
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mask_load = mask.decode([written_result[0]['segmentation']]) |
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self.assertTrue(np.allclose(mask_load, detection_masks[0])) |
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self.assertAlmostEqual(result, written_result) |
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def testExportKeypointsToCOCO(self): |
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image_ids = ['first', 'second'] |
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detection_keypoints = [ |
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np.array( |
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[[[100, 200], [300, 400], [500, 600]], |
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[[50, 150], [250, 350], [450, 550]]], dtype=np.int32), |
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np.array( |
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[[[110, 210], [310, 410], [510, 610]], |
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[[60, 160], [260, 360], [460, 560]]], dtype=np.int32)] |
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detection_scores = [np.array([.8, 0.2], np.float), |
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np.array([.7, 0.3], np.float)] |
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detection_classes = [np.array([1, 1], np.int32), np.array([1, 1], np.int32)] |
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categories = [{'id': 1, 'name': 'person', 'num_keypoints': 3}, |
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{'id': 2, 'name': 'cat'}, |
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{'id': 3, 'name': 'dog'}] |
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output_path = os.path.join(tf.test.get_temp_dir(), 'keypoints.json') |
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result = coco_tools.ExportKeypointsToCOCO( |
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image_ids, |
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detection_keypoints, |
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detection_scores, |
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detection_classes, |
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categories, |
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output_path=output_path) |
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with tf.gfile.GFile(output_path, 'r') as f: |
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written_result = f.read() |
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written_result = json.loads(written_result) |
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self.assertAlmostEqual(result, written_result) |
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def testSingleImageDetectionBoxesExport(self): |
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boxes = np.array([[0, 0, 1, 1], |
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[0, 0, .5, .5], |
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[.5, .5, 1, 1]], dtype=np.float32) |
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classes = np.array([1, 2, 3], dtype=np.int32) |
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scores = np.array([0.8, 0.2, 0.7], dtype=np.float32) |
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coco_boxes = np.array([[0, 0, 1, 1], |
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[0, 0, .5, .5], |
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[.5, .5, .5, .5]], dtype=np.float32) |
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coco_annotations = coco_tools.ExportSingleImageDetectionBoxesToCoco( |
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image_id='first_image', |
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category_id_set=set([1, 2, 3]), |
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detection_boxes=boxes, |
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detection_classes=classes, |
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detection_scores=scores) |
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for i, annotation in enumerate(coco_annotations): |
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self.assertEqual(annotation['image_id'], 'first_image') |
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self.assertEqual(annotation['category_id'], classes[i]) |
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self.assertAlmostEqual(annotation['score'], scores[i]) |
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self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i]))) |
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def testSingleImageDetectionMaskExport(self): |
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masks = np.array( |
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[[[1, 1,], [1, 1]], |
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[[0, 0], [0, 1]], |
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[[0, 0], [0, 0]]], dtype=np.uint8) |
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classes = np.array([1, 2, 3], dtype=np.int32) |
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scores = np.array([0.8, 0.2, 0.7], dtype=np.float32) |
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coco_annotations = coco_tools.ExportSingleImageDetectionMasksToCoco( |
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image_id='first_image', |
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category_id_set=set([1, 2, 3]), |
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detection_classes=classes, |
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detection_scores=scores, |
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detection_masks=masks) |
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expected_counts = ['04', '31', '4'] |
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for i, mask_annotation in enumerate(coco_annotations): |
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self.assertEqual(mask_annotation['segmentation']['counts'], |
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expected_counts[i]) |
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self.assertTrue(np.all(np.equal(mask.decode( |
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mask_annotation['segmentation']), masks[i]))) |
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self.assertEqual(mask_annotation['image_id'], 'first_image') |
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self.assertEqual(mask_annotation['category_id'], classes[i]) |
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self.assertAlmostEqual(mask_annotation['score'], scores[i]) |
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def testSingleImageGroundtruthExport(self): |
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masks = np.array( |
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[[[1, 1,], [1, 1]], |
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[[0, 0], [0, 1]], |
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[[0, 0], [0, 0]]], dtype=np.uint8) |
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boxes = np.array([[0, 0, 1, 1], |
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[0, 0, .5, .5], |
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[.5, .5, 1, 1]], dtype=np.float32) |
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coco_boxes = np.array([[0, 0, 1, 1], |
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[0, 0, .5, .5], |
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[.5, .5, .5, .5]], dtype=np.float32) |
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classes = np.array([1, 2, 3], dtype=np.int32) |
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is_crowd = np.array([0, 1, 0], dtype=np.int32) |
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next_annotation_id = 1 |
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expected_counts = ['04', '31', '4'] |
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coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco( |
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image_id='first_image', |
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category_id_set=set([1, 2, 3]), |
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next_annotation_id=next_annotation_id, |
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groundtruth_boxes=boxes, |
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groundtruth_classes=classes, |
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groundtruth_masks=masks) |
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for i, annotation in enumerate(coco_annotations): |
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self.assertEqual(annotation['segmentation']['counts'], |
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expected_counts[i]) |
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self.assertTrue(np.all(np.equal(mask.decode( |
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annotation['segmentation']), masks[i]))) |
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self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i]))) |
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self.assertEqual(annotation['image_id'], 'first_image') |
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self.assertEqual(annotation['category_id'], classes[i]) |
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self.assertEqual(annotation['id'], i + next_annotation_id) |
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coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco( |
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image_id='first_image', |
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category_id_set=set([1, 2, 3]), |
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next_annotation_id=next_annotation_id, |
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groundtruth_boxes=boxes, |
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groundtruth_classes=classes, |
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groundtruth_masks=masks, |
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groundtruth_is_crowd=is_crowd) |
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for i, annotation in enumerate(coco_annotations): |
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self.assertEqual(annotation['segmentation']['counts'], |
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expected_counts[i]) |
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self.assertTrue(np.all(np.equal(mask.decode( |
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annotation['segmentation']), masks[i]))) |
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self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i]))) |
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self.assertEqual(annotation['image_id'], 'first_image') |
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self.assertEqual(annotation['category_id'], classes[i]) |
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self.assertEqual(annotation['iscrowd'], is_crowd[i]) |
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self.assertEqual(annotation['id'], i + next_annotation_id) |
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if __name__ == '__main__': |
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tf.test.main() |
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