# 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 tensorflow_model.object_detection.metrics.coco_tools."""
import json
import os
import re
import numpy as np

from pycocotools import mask

import tensorflow as tf

from object_detection.metrics import coco_tools


class CocoToolsTest(tf.test.TestCase):

  def setUp(self):
    groundtruth_annotations_list = [
        {
            'id': 1,
            'image_id': 'first',
            'category_id': 1,
            'bbox': [100., 100., 100., 100.],
            'area': 100.**2,
            'iscrowd': 0
        },
        {
            'id': 2,
            'image_id': 'second',
            'category_id': 1,
            'bbox': [50., 50., 50., 50.],
            'area': 50.**2,
            'iscrowd': 0
        },
    ]
    image_list = [{'id': 'first'}, {'id': 'second'}]
    category_list = [{'id': 0, 'name': 'person'},
                     {'id': 1, 'name': 'cat'},
                     {'id': 2, 'name': 'dog'}]
    self._groundtruth_dict = {
        'annotations': groundtruth_annotations_list,
        'images': image_list,
        'categories': category_list
    }

    self._detections_list = [
        {
            'image_id': 'first',
            'category_id': 1,
            'bbox': [100., 100., 100., 100.],
            'score': .8
        },
        {
            'image_id': 'second',
            'category_id': 1,
            'bbox': [50., 50., 50., 50.],
            'score': .7
        },
    ]

  def testCocoWrappers(self):
    groundtruth = coco_tools.COCOWrapper(self._groundtruth_dict)
    detections = groundtruth.LoadAnnotations(self._detections_list)
    evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections)
    summary_metrics, _ = evaluator.ComputeMetrics()
    self.assertAlmostEqual(1.0, summary_metrics['Precision/mAP'])

  def testExportGroundtruthToCOCO(self):
    image_ids = ['first', 'second']
    groundtruth_boxes = [np.array([[100, 100, 200, 200]], np.float),
                         np.array([[50, 50, 100, 100]], np.float)]
    groundtruth_classes = [np.array([1], np.int32), np.array([1], np.int32)]
    categories = [{'id': 0, 'name': 'person'},
                  {'id': 1, 'name': 'cat'},
                  {'id': 2, 'name': 'dog'}]
    output_path = os.path.join(tf.test.get_temp_dir(), 'groundtruth.json')
    result = coco_tools.ExportGroundtruthToCOCO(
        image_ids,
        groundtruth_boxes,
        groundtruth_classes,
        categories,
        output_path=output_path)
    self.assertDictEqual(result, self._groundtruth_dict)
    with tf.gfile.GFile(output_path, 'r') as f:
      written_result = f.read()
      # The json output should have floats written to 4 digits of precision.
      matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,', re.MULTILINE)
      self.assertTrue(matcher.findall(written_result))
      written_result = json.loads(written_result)
      self.assertAlmostEqual(result, written_result)

  def testExportDetectionsToCOCO(self):
    image_ids = ['first', 'second']
    detections_boxes = [np.array([[100, 100, 200, 200]], np.float),
                        np.array([[50, 50, 100, 100]], np.float)]
    detections_scores = [np.array([.8], np.float), np.array([.7], np.float)]
    detections_classes = [np.array([1], np.int32), np.array([1], np.int32)]
    categories = [{'id': 0, 'name': 'person'},
                  {'id': 1, 'name': 'cat'},
                  {'id': 2, 'name': 'dog'}]
    output_path = os.path.join(tf.test.get_temp_dir(), 'detections.json')
    result = coco_tools.ExportDetectionsToCOCO(
        image_ids,
        detections_boxes,
        detections_scores,
        detections_classes,
        categories,
        output_path=output_path)
    self.assertListEqual(result, self._detections_list)
    with tf.gfile.GFile(output_path, 'r') as f:
      written_result = f.read()
      # The json output should have floats written to 4 digits of precision.
      matcher = re.compile(r'"bbox":\s+\[\n\s+\d+.\d\d\d\d,', re.MULTILINE)
      self.assertTrue(matcher.findall(written_result))
      written_result = json.loads(written_result)
      self.assertAlmostEqual(result, written_result)

  def testExportSegmentsToCOCO(self):
    image_ids = ['first', 'second']
    detection_masks = [np.array(
        [[[0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1]]],
        dtype=np.uint8), np.array(
            [[[0, 1, 0, 1], [0, 1, 1, 0], [0, 0, 0, 1], [0, 1, 0, 1]]],
            dtype=np.uint8)]

    for i, detection_mask in enumerate(detection_masks):
      detection_masks[i] = detection_mask[:, :, :, None]

    detection_scores = [np.array([.8], np.float), np.array([.7], np.float)]
    detection_classes = [np.array([1], np.int32), np.array([1], np.int32)]

    categories = [{'id': 0, 'name': 'person'},
                  {'id': 1, 'name': 'cat'},
                  {'id': 2, 'name': 'dog'}]
    output_path = os.path.join(tf.test.get_temp_dir(), 'segments.json')
    result = coco_tools.ExportSegmentsToCOCO(
        image_ids,
        detection_masks,
        detection_scores,
        detection_classes,
        categories,
        output_path=output_path)
    with tf.gfile.GFile(output_path, 'r') as f:
      written_result = f.read()
      written_result = json.loads(written_result)
      mask_load = mask.decode([written_result[0]['segmentation']])
      self.assertTrue(np.allclose(mask_load, detection_masks[0]))
      self.assertAlmostEqual(result, written_result)

  def testExportKeypointsToCOCO(self):
    image_ids = ['first', 'second']
    detection_keypoints = [
        np.array(
            [[[100, 200], [300, 400], [500, 600]],
             [[50, 150], [250, 350], [450, 550]]], dtype=np.int32),
        np.array(
            [[[110, 210], [310, 410], [510, 610]],
             [[60, 160], [260, 360], [460, 560]]], dtype=np.int32)]

    detection_scores = [np.array([.8, 0.2], np.float),
                        np.array([.7, 0.3], np.float)]
    detection_classes = [np.array([1, 1], np.int32), np.array([1, 1], np.int32)]

    categories = [{'id': 1, 'name': 'person', 'num_keypoints': 3},
                  {'id': 2, 'name': 'cat'},
                  {'id': 3, 'name': 'dog'}]

    output_path = os.path.join(tf.test.get_temp_dir(), 'keypoints.json')
    result = coco_tools.ExportKeypointsToCOCO(
        image_ids,
        detection_keypoints,
        detection_scores,
        detection_classes,
        categories,
        output_path=output_path)

    with tf.gfile.GFile(output_path, 'r') as f:
      written_result = f.read()
      written_result = json.loads(written_result)
      self.assertAlmostEqual(result, written_result)

  def testSingleImageDetectionBoxesExport(self):
    boxes = np.array([[0, 0, 1, 1],
                      [0, 0, .5, .5],
                      [.5, .5, 1, 1]], dtype=np.float32)
    classes = np.array([1, 2, 3], dtype=np.int32)
    scores = np.array([0.8, 0.2, 0.7], dtype=np.float32)
    coco_boxes = np.array([[0, 0, 1, 1],
                           [0, 0, .5, .5],
                           [.5, .5, .5, .5]], dtype=np.float32)
    coco_annotations = coco_tools.ExportSingleImageDetectionBoxesToCoco(
        image_id='first_image',
        category_id_set=set([1, 2, 3]),
        detection_boxes=boxes,
        detection_classes=classes,
        detection_scores=scores)
    for i, annotation in enumerate(coco_annotations):
      self.assertEqual(annotation['image_id'], 'first_image')
      self.assertEqual(annotation['category_id'], classes[i])
      self.assertAlmostEqual(annotation['score'], scores[i])
      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))

  def testSingleImageDetectionMaskExport(self):
    masks = np.array(
        [[[1, 1,], [1, 1]],
         [[0, 0], [0, 1]],
         [[0, 0], [0, 0]]], dtype=np.uint8)
    classes = np.array([1, 2, 3], dtype=np.int32)
    scores = np.array([0.8, 0.2, 0.7], dtype=np.float32)
    coco_annotations = coco_tools.ExportSingleImageDetectionMasksToCoco(
        image_id='first_image',
        category_id_set=set([1, 2, 3]),
        detection_classes=classes,
        detection_scores=scores,
        detection_masks=masks)
    expected_counts = ['04', '31', '4']
    for i, mask_annotation in enumerate(coco_annotations):
      self.assertEqual(mask_annotation['segmentation']['counts'],
                       expected_counts[i])
      self.assertTrue(np.all(np.equal(mask.decode(
          mask_annotation['segmentation']), masks[i])))
      self.assertEqual(mask_annotation['image_id'], 'first_image')
      self.assertEqual(mask_annotation['category_id'], classes[i])
      self.assertAlmostEqual(mask_annotation['score'], scores[i])

  def testSingleImageGroundtruthExport(self):
    masks = np.array(
        [[[1, 1,], [1, 1]],
         [[0, 0], [0, 1]],
         [[0, 0], [0, 0]]], dtype=np.uint8)
    boxes = np.array([[0, 0, 1, 1],
                      [0, 0, .5, .5],
                      [.5, .5, 1, 1]], dtype=np.float32)
    coco_boxes = np.array([[0, 0, 1, 1],
                           [0, 0, .5, .5],
                           [.5, .5, .5, .5]], dtype=np.float32)
    classes = np.array([1, 2, 3], dtype=np.int32)
    is_crowd = np.array([0, 1, 0], dtype=np.int32)
    next_annotation_id = 1
    expected_counts = ['04', '31', '4']

    # Tests exporting without passing in is_crowd (for backward compatibility).
    coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
        image_id='first_image',
        category_id_set=set([1, 2, 3]),
        next_annotation_id=next_annotation_id,
        groundtruth_boxes=boxes,
        groundtruth_classes=classes,
        groundtruth_masks=masks)
    for i, annotation in enumerate(coco_annotations):
      self.assertEqual(annotation['segmentation']['counts'],
                       expected_counts[i])
      self.assertTrue(np.all(np.equal(mask.decode(
          annotation['segmentation']), masks[i])))
      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
      self.assertEqual(annotation['image_id'], 'first_image')
      self.assertEqual(annotation['category_id'], classes[i])
      self.assertEqual(annotation['id'], i + next_annotation_id)

    # Tests exporting with is_crowd.
    coco_annotations = coco_tools.ExportSingleImageGroundtruthToCoco(
        image_id='first_image',
        category_id_set=set([1, 2, 3]),
        next_annotation_id=next_annotation_id,
        groundtruth_boxes=boxes,
        groundtruth_classes=classes,
        groundtruth_masks=masks,
        groundtruth_is_crowd=is_crowd)
    for i, annotation in enumerate(coco_annotations):
      self.assertEqual(annotation['segmentation']['counts'],
                       expected_counts[i])
      self.assertTrue(np.all(np.equal(mask.decode(
          annotation['segmentation']), masks[i])))
      self.assertTrue(np.all(np.isclose(annotation['bbox'], coco_boxes[i])))
      self.assertEqual(annotation['image_id'], 'first_image')
      self.assertEqual(annotation['category_id'], classes[i])
      self.assertEqual(annotation['iscrowd'], is_crowd[i])
      self.assertEqual(annotation['id'], i + next_annotation_id)


if __name__ == '__main__':
  tf.test.main()
