# Copyright 2019 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.
# ==============================================================================
"""The COCO-style evaluator.

The following snippet demonstrates the use of interfaces:

  evaluator = COCOEvaluator(...)
  for _ in range(num_evals):
    for _ in range(num_batches_per_eval):
      predictions, groundtruth = predictor.predict(...)  # pop a batch.
      evaluator.update(predictions, groundtruths)  # aggregate internal stats.
    evaluator.evaluate()  # finish one full eval.

See also: https://github.com/cocodataset/cocoapi/
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import atexit
import tempfile
import numpy as np
from absl import logging
from pycocotools import cocoeval
import six
import tensorflow.compat.v2 as tf

from official.vision.detection.evaluation import coco_utils
from official.vision.detection.utils import class_utils


class MetricWrapper(object):
  # This is only a wrapper for COCO metric and works on for numpy array. So it
  # doesn't inherit from tf.keras.layers.Layer or tf.keras.metrics.Metric.

  def __init__(self, evaluator):
    self._evaluator = evaluator

  def update_state(self, y_true, y_pred):
    labels = tf.nest.map_structure(lambda x: x.numpy(), y_true)
    outputs = tf.nest.map_structure(lambda x: x.numpy(), y_pred)
    groundtruths = {}
    predictions = {}
    for key, val in outputs.items():
      if isinstance(val, tuple):
        val = np.concatenate(val)
      predictions[key] = val
    for key, val in labels.items():
      if isinstance(val, tuple):
        val = np.concatenate(val)
      groundtruths[key] = val
    self._evaluator.update(predictions, groundtruths)

  def result(self):
    return self._evaluator.evaluate()

  def reset_states(self):
    return self._evaluator.reset()


class COCOEvaluator(object):
  """COCO evaluation metric class."""

  def __init__(self, annotation_file, include_mask, need_rescale_bboxes=True):
    """Constructs COCO evaluation class.

    The class provides the interface to metrics_fn in TPUEstimator. The
    _update_op() takes detections from each image and push them to
    self.detections. The _evaluate() loads a JSON file in COCO annotation format
    as the groundtruths and runs COCO evaluation.

    Args:
      annotation_file: a JSON file that stores annotations of the eval dataset.
        If `annotation_file` is None, groundtruth annotations will be loaded
        from the dataloader.
      include_mask: a boolean to indicate whether or not to include the mask
        eval.
      need_rescale_bboxes: If true bboxes in `predictions` will be rescaled back
        to absolute values (`image_info` is needed in this case).
    """
    if annotation_file:
      if annotation_file.startswith('gs://'):
        _, local_val_json = tempfile.mkstemp(suffix='.json')
        tf.io.gfile.remove(local_val_json)

        tf.io.gfile.copy(annotation_file, local_val_json)
        atexit.register(tf.io.gfile.remove, local_val_json)
      else:
        local_val_json = annotation_file
      self._coco_gt = coco_utils.COCOWrapper(
          eval_type=('mask' if include_mask else 'box'),
          annotation_file=local_val_json)
    self._annotation_file = annotation_file
    self._include_mask = include_mask
    self._metric_names = [
        'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'ARmax1', 'ARmax10',
        'ARmax100', 'ARs', 'ARm', 'ARl'
    ]
    self._required_prediction_fields = [
        'source_id', 'num_detections', 'detection_classes', 'detection_scores',
        'detection_boxes'
    ]
    self._need_rescale_bboxes = need_rescale_bboxes
    if self._need_rescale_bboxes:
      self._required_prediction_fields.append('image_info')
    self._required_groundtruth_fields = [
        'source_id', 'height', 'width', 'classes', 'boxes'
    ]
    if self._include_mask:
      mask_metric_names = ['mask_' + x for x in self._metric_names]
      self._metric_names.extend(mask_metric_names)
      self._required_prediction_fields.extend(['detection_masks'])
      self._required_groundtruth_fields.extend(['masks'])

    self.reset()

  def reset(self):
    """Resets internal states for a fresh run."""
    self._predictions = {}
    if not self._annotation_file:
      self._groundtruths = {}

  def evaluate(self):
    """Evaluates with detections from all images with COCO API.

    Returns:
      coco_metric: float numpy array with shape [24] representing the
        coco-style evaluation metrics (box and mask).
    """
    if not self._annotation_file:
      logging.info('Thre is no annotation_file in COCOEvaluator.')
      gt_dataset = coco_utils.convert_groundtruths_to_coco_dataset(
          self._groundtruths)
      coco_gt = coco_utils.COCOWrapper(
          eval_type=('mask' if self._include_mask else 'box'),
          gt_dataset=gt_dataset)
    else:
      logging.info('Using annotation file: %s', self._annotation_file)
      coco_gt = self._coco_gt
    coco_predictions = coco_utils.convert_predictions_to_coco_annotations(
        self._predictions)
    coco_dt = coco_gt.loadRes(predictions=coco_predictions)
    image_ids = [ann['image_id'] for ann in coco_predictions]

    coco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='bbox')
    coco_eval.params.imgIds = image_ids
    coco_eval.evaluate()
    coco_eval.accumulate()
    coco_eval.summarize()
    coco_metrics = coco_eval.stats

    if self._include_mask:
      mcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='segm')
      mcoco_eval.params.imgIds = image_ids
      mcoco_eval.evaluate()
      mcoco_eval.accumulate()
      mcoco_eval.summarize()
      mask_coco_metrics = mcoco_eval.stats

    if self._include_mask:
      metrics = np.hstack((coco_metrics, mask_coco_metrics))
    else:
      metrics = coco_metrics

    # Cleans up the internal variables in order for a fresh eval next time.
    self.reset()

    metrics_dict = {}
    for i, name in enumerate(self._metric_names):
      metrics_dict[name] = metrics[i].astype(np.float32)
    return metrics_dict

  def _process_predictions(self, predictions):
    image_scale = np.tile(predictions['image_info'][:, 2:3, :], (1, 1, 2))
    predictions['detection_boxes'] = (
        predictions['detection_boxes'].astype(np.float32))
    predictions['detection_boxes'] /= image_scale
    if 'detection_outer_boxes' in predictions:
      predictions['detection_outer_boxes'] = (
          predictions['detection_outer_boxes'].astype(np.float32))
      predictions['detection_outer_boxes'] /= image_scale

  def update(self, predictions, groundtruths=None):
    """Update and aggregate detection results and groundtruth data.

    Args:
      predictions: a dictionary of numpy arrays including the fields below.
        See different parsers under `../dataloader` for more details.
        Required fields:
          - source_id: a numpy array of int or string of shape [batch_size].
          - image_info [if `need_rescale_bboxes` is True]: a numpy array of
            float of shape [batch_size, 4, 2].
          - num_detections: a numpy array of
            int of shape [batch_size].
          - detection_boxes: a numpy array of float of shape [batch_size, K, 4].
          - detection_classes: a numpy array of int of shape [batch_size, K].
          - detection_scores: a numpy array of float of shape [batch_size, K].
        Optional fields:
          - detection_masks: a numpy array of float of shape
              [batch_size, K, mask_height, mask_width].
      groundtruths: a dictionary of numpy arrays including the fields below.
        See also different parsers under `../dataloader` for more details.
        Required fields:
          - source_id: a numpy array of int or string of shape [batch_size].
          - height: a numpy array of int of shape [batch_size].
          - width: a numpy array of int of shape [batch_size].
          - num_detections: a numpy array of int of shape [batch_size].
          - boxes: a numpy array of float of shape [batch_size, K, 4].
          - classes: a numpy array of int of shape [batch_size, K].
        Optional fields:
          - is_crowds: a numpy array of int of shape [batch_size, K]. If the
              field is absent, it is assumed that this instance is not crowd.
          - areas: a numy array of float of shape [batch_size, K]. If the
              field is absent, the area is calculated using either boxes or
              masks depending on which one is available.
          - masks: a numpy array of float of shape
              [batch_size, K, mask_height, mask_width],

    Raises:
      ValueError: if the required prediction or groundtruth fields are not
        present in the incoming `predictions` or `groundtruths`.
    """
    for k in self._required_prediction_fields:
      if k not in predictions:
        raise ValueError(
            'Missing the required key `{}` in predictions!'.format(k))
    if self._need_rescale_bboxes:
      self._process_predictions(predictions)
    for k, v in six.iteritems(predictions):
      if k not in self._predictions:
        self._predictions[k] = [v]
      else:
        self._predictions[k].append(v)

    if not self._annotation_file:
      assert groundtruths
      for k in self._required_groundtruth_fields:
        if k not in groundtruths:
          raise ValueError(
              'Missing the required key `{}` in groundtruths!'.format(k))
      for k, v in six.iteritems(groundtruths):
        if k not in self._groundtruths:
          self._groundtruths[k] = [v]
        else:
          self._groundtruths[k].append(v)


class ShapeMaskCOCOEvaluator(COCOEvaluator):
  """COCO evaluation metric class for ShapeMask."""

  def __init__(self, mask_eval_class, **kwargs):
    """Constructs COCO evaluation class.

    The class provides the interface to metrics_fn in TPUEstimator. The
    _update_op() takes detections from each image and push them to
    self.detections. The _evaluate() loads a JSON file in COCO annotation format
    as the groundtruths and runs COCO evaluation.

    Args:
      mask_eval_class: the set of classes for mask evaluation.
      **kwargs: other keyword arguments passed to the parent class initializer.
    """
    super(ShapeMaskCOCOEvaluator, self).__init__(**kwargs)
    self._mask_eval_class = mask_eval_class
    self._eval_categories = class_utils.coco_split_class_ids(mask_eval_class)
    if mask_eval_class != 'all':
      self._metric_names = [
          x.replace('mask', 'novel_mask') for x in self._metric_names
      ]

  def evaluate(self):
    """Evaluates with detections from all images with COCO API.

    Returns:
      coco_metric: float numpy array with shape [24] representing the
        coco-style evaluation metrics (box and mask).
    """
    if not self._annotation_file:
      gt_dataset = coco_utils.convert_groundtruths_to_coco_dataset(
          self._groundtruths)
      coco_gt = coco_utils.COCOWrapper(
          eval_type=('mask' if self._include_mask else 'box'),
          gt_dataset=gt_dataset)
    else:
      coco_gt = self._coco_gt
    coco_predictions = coco_utils.convert_predictions_to_coco_annotations(
        self._predictions)
    coco_dt = coco_gt.loadRes(predictions=coco_predictions)
    image_ids = [ann['image_id'] for ann in coco_predictions]

    coco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='bbox')
    coco_eval.params.imgIds = image_ids
    coco_eval.evaluate()
    coco_eval.accumulate()
    coco_eval.summarize()
    coco_metrics = coco_eval.stats

    if self._include_mask:
      mcoco_eval = cocoeval.COCOeval(coco_gt, coco_dt, iouType='segm')
      mcoco_eval.params.imgIds = image_ids
      mcoco_eval.evaluate()
      mcoco_eval.accumulate()
      mcoco_eval.summarize()
      if self._mask_eval_class == 'all':
        metrics = np.hstack((coco_metrics, mcoco_eval.stats))
      else:
        mask_coco_metrics = mcoco_eval.category_stats
        val_catg_idx = np.isin(mcoco_eval.params.catIds,
                               self._eval_categories)
        # Gather the valid evaluation of the eval categories.
        if np.any(val_catg_idx):
          mean_val_metrics = []
          for mid in range(len(self._metric_names) // 2):
            mean_val_metrics.append(
                np.nanmean(mask_coco_metrics[mid][val_catg_idx]))

          mean_val_metrics = np.array(mean_val_metrics)
        else:
          mean_val_metrics = np.zeros(len(self._metric_names) // 2)
        metrics = np.hstack((coco_metrics, mean_val_metrics))
    else:
      metrics = coco_metrics

    # Cleans up the internal variables in order for a fresh eval next time.
    self.reset()

    metrics_dict = {}
    for i, name in enumerate(self._metric_names):
      metrics_dict[name] = metrics[i].astype(np.float32)
    return metrics_dict
