# coding=utf-8
# Copyright 2023 The TensorFlow Datasets Authors.
#
# 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.

"""Kitti dataset."""

import collections
import csv
import os

import numpy as np
import tensorflow_datasets.public_api as tfds

_HOMEPAGE_URL = "http://www.cvlibs.net/datasets/kitti/"
_DATA_URL = "https://s3.eu-central-1.amazonaws.com/avg-kitti"
_IMAGES_FNAME = "data_object_image_2.zip"
_LABELS_FNAME = "data_object_label_2.zip"
_DEVKIT_FNAME = "devkit_object.zip"
_OBJECT_LABELS = [
    "Car",
    "Van",
    "Truck",
    "Pedestrian",
    "Person_sitting",
    "Cyclist",
    "Tram",
    "Misc",
]
_OCCLUDED_LABELS = [
    "fully visible",
    "partly occluded",
    "largely occluded",
    "unknown",
]
# The percentage of trainset videos to put into validation and test sets.
# The released test images do not have labels.
_VALIDATION_SPLIT_PERCENT_VIDEOS = 10
_TEST_SPLIT_PERCENT_VIDEOS = 10

# Raw Kitti representation of a bounding box. Coordinates are in pixels,
# measured from the top-left hand corner.
RawBoundingBox = collections.namedtuple(
    "RawBoundingBox", ["top", "bottom", "left", "right"]
)


class Builder(tfds.core.GeneratorBasedBuilder):
  """Kitti dataset."""

  VERSION = tfds.core.Version("3.3.0")
  SUPPORTED_VERSIONS = [
      tfds.core.Version("3.2.0"),
      tfds.core.Version("3.1.0"),
  ]
  RELEASE_NOTES = {
      "3.3.0": "Added labels for the `occluded` feature.",
      "3.2.0": "Devkit updated.",
  }

  def _info(self):
    # Annotation descriptions are in the object development kit.
    annotations = {
        "type": tfds.features.ClassLabel(
            names=_OBJECT_LABELS, doc="The type of object, e.g. 'Car' or 'Van'"
        ),
        "truncated": tfds.features.Tensor(
            shape=(),
            dtype=np.float32,
            doc=(
                "Float from 0 (non-truncated) to 1 (truncated), where"
                "truncated refers to the object leaving image boundaries"
            ),
        ),
        "occluded": tfds.features.ClassLabel(
            names=_OCCLUDED_LABELS,
            doc=(
                "Integer (0,1,2,3) indicating occlusion state: "
                "0 = fully visible, 1 = partly occluded"
                "2 = largely occluded, 3 = unknown"
            ),
        ),
        "alpha": tfds.features.Tensor(
            shape=(),
            dtype=np.float32,
            doc="Observation angle of object, ranging [-pi..pi]",
        ),
        "bbox": tfds.features.BBoxFeature(
            doc="2D bounding box of object in the image"
        ),
        "dimensions": tfds.features.Tensor(
            shape=(3,),
            dtype=np.float32,
            doc="3D object dimensions: height, width, length (in meters)",
        ),
        "location": tfds.features.Tensor(
            shape=(3,),
            dtype=np.float32,
            doc="3D object location x,y,z in camera coordinates (in meters)",
        ),
        "rotation_y": tfds.features.Tensor(
            shape=(),
            dtype=np.float32,
            doc="Rotation ry around Y-axis in camera coordinates [-pi..pi]",
        ),
    }
    return self.dataset_info_from_configs(
        features=tfds.features.FeaturesDict({
            "image": tfds.features.Image(),
            "image/file_name": tfds.features.Text(),  # E.g. "000001.png".
            "objects": tfds.features.Sequence(annotations),
        }),
        homepage=_HOMEPAGE_URL,
    )

  def _split_generators(self, dl_manager):
    filenames = {
        "images": _DATA_URL + "/" + _IMAGES_FNAME,
        "annotations": _DATA_URL + "/" + _LABELS_FNAME,
        "devkit": _DATA_URL + "/" + _DEVKIT_FNAME,
    }
    files = dl_manager.download(filenames)
    train_images, validation_images, test_images = _build_splits(
        dl_manager.iter_archive(files["devkit"])
    )

    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN,
            gen_kwargs={
                "images": dl_manager.iter_archive(files["images"]),
                "annotations": dl_manager.iter_archive(files["annotations"]),
                "subdir": "training",
                "image_ids": train_images,
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.VALIDATION,
            gen_kwargs={
                "images": dl_manager.iter_archive(files["images"]),
                "annotations": dl_manager.iter_archive(files["annotations"]),
                "subdir": "training",
                "image_ids": validation_images,
            },
        ),
        tfds.core.SplitGenerator(
            name=tfds.Split.TEST,
            gen_kwargs={
                "images": dl_manager.iter_archive(files["images"]),
                "annotations": dl_manager.iter_archive(files["annotations"]),
                "subdir": "training",
                "image_ids": test_images,
            },
        ),
    ]

  def _generate_examples(self, images, annotations, subdir, image_ids):
    """Yields images and annotations.

    Args:
      images: object that iterates over the archive of images.
      annotations: object that iterates over the archive of annotations.
      subdir: subdirectory from which to extract images and annotations, e.g.
        training or testing.
      image_ids: file ids for images in this split.

    Yields:
      A tuple containing the example's key, and the example.
    """
    cv2 = tfds.core.lazy_imports.cv2

    all_annotations = dict()
    for fpath, fobj in annotations:
      prefix, ext = os.path.splitext(fpath)
      if ext != ".txt":
        continue
      if prefix.split(os.path.sep)[0] != subdir:
        continue

      # Key is the datapoint id. E.g. training/label_2/label_000016 -> 16.
      all_annotations[int(prefix[-6:])] = _parse_kitti_annotations(fobj)

    for fpath, fobj in images:
      prefix, ext = os.path.splitext(fpath)
      if ext != ".png":
        continue
      if prefix.split(os.path.sep)[0] != subdir:
        continue
      image_id = int(prefix[-6:])
      if image_id not in image_ids:
        continue
      annotations = all_annotations[image_id]
      img = cv2.imdecode(
          np.frombuffer(fobj.read(), dtype=np.uint8), cv2.IMREAD_COLOR
      )
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
      height, width, _ = img.shape
      for obj in annotations:
        obj["bbox"] = _build_bounding_box(obj["bbox_raw"], height, width)
        del obj["bbox_raw"]
      _, fname = os.path.split(fpath)
      record = {"image": img, "image/file_name": fname, "objects": annotations}
      yield fname, record


def _build_bounding_box(bbox, height, width):
  """Builds and returns TFDS bounding box.

  Args:
    bbox: RawBoundingBox, bounding box in Kitti coordinates (origin top left).
    height: Image height in pixels.
    width: Image width in pixels.

  Returns:
    A TFDS BBox (origin bottom left).
  """
  return tfds.features.BBox(
      ymin=(height - bbox.bottom) / height,
      ymax=(height - bbox.top) / height,
      xmin=bbox.left / width,
      xmax=bbox.right / width,
  )


def _parse_kitti_annotations(annotations_csv):
  """Loads and parses the Kitti object annotations.

  Args:
    annotations_csv: csv file containing annotations for a single image.

  Returns:
    A list of labelled bounding boxes. Each bounding box is stored as a
    dictionary of features.
  """
  annotations = []
  for line in annotations_csv:
    (
        obj_type,
        truncated,
        occluded,
        alpha,
        left,
        top,
        right,
        bottom,
        height,
        width,
        length,
        x,
        y,
        z,
        rotation_y,
    ) = list(csv.reader([line.decode()], delimiter=" "))[0]
    # DontCare objects lack annotations, so skip them.
    if obj_type == "DontCare":
      continue
    annotations.append({
        "type": obj_type,
        "truncated": float(truncated),
        "occluded": int(occluded),
        "alpha": float(alpha),
        "bbox_raw": RawBoundingBox(
            top=float(top),
            bottom=float(bottom),
            left=float(left),
            right=float(right),
        ),
        "dimensions": [float(v) for v in [height, width, length]],
        "location": [float(v) for v in [x, y, z]],
        "rotation_y": float(rotation_y),
    })
  return annotations


def _build_splits(devkit):
  """Splits the train data into train/val/test by video.

  Ensures that images from the same video do not traverse the splits.

  Args:
    devkit: object that iterates over the devkit archive.

  Returns:
    train_images: File ids for the training set images.
    validation_images: File ids for the validation set images.
    test_images: File ids for the test set images.
  """
  mapping_line_ids = None
  mapping_lines = None
  for fpath, fobj in devkit:
    if fpath == os.path.join("mapping", "train_rand.txt"):
      # Converts 1-based line index to 0-based line index.
      mapping_line_ids = [
          int(x.strip()) - 1 for x in fobj.read().decode("utf-8").split(",")
      ]
    elif fpath == os.path.join("mapping", "train_mapping.txt"):
      mapping_lines = fobj.read().splitlines()
      mapping_lines = [x.decode("utf-8") for x in mapping_lines]

  assert mapping_line_ids
  assert mapping_lines

  video_to_image = collections.defaultdict(list)
  for image_id, mapping_lineid in enumerate(mapping_line_ids):
    line = mapping_lines[mapping_lineid]
    video_id = line.split(" ")[1]
    video_to_image[video_id].append(image_id)

  # Sets numpy random state.
  numpy_original_state = np.random.get_state()
  np.random.seed(seed=123)

  # Max 1 for testing.
  num_test_videos = max(
      1, _TEST_SPLIT_PERCENT_VIDEOS * len(video_to_image) // 100
  )
  num_validation_videos = max(
      1, _VALIDATION_SPLIT_PERCENT_VIDEOS * len(video_to_image) // 100
  )
  test_videos = set(
      np.random.choice(
          sorted(list(video_to_image.keys())), num_test_videos, replace=False
      )
  )
  validation_videos = set(
      np.random.choice(
          sorted(list(set(video_to_image.keys()) - set(test_videos))),
          num_validation_videos,
          replace=False,
      )
  )
  test_images = []
  validation_images = []
  train_images = []
  for k, v in video_to_image.items():
    if k in test_videos:
      test_images.extend(v)
    elif k in validation_videos:
      validation_images.extend(v)
    else:
      train_images.extend(v)

  # Resets numpy random state.
  np.random.set_state(numpy_original_state)
  return train_images, validation_images, test_images
