# Copyright 2020 The Android Open Source Project
#
# 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.
"""Verifies android.jpeg.quality increases JPEG image quality."""


import logging
import math
import os.path

from matplotlib import pylab
import matplotlib.pyplot
from mobly import test_runner
import numpy as np

import its_base_test
import camera_properties_utils
import capture_request_utils
import image_processing_utils
import its_session_utils

JPEG_APPN_MARKERS = [[255, 224], [255, 225], [255, 226], [255, 227], [255, 228],
                     [255, 229], [255, 230], [255, 231], [255, 232], [255, 235]]
JPEG_DHT_MARKER = [255, 196]  # JPEG Define Huffman Table
JPEG_DQT_MARKER = [255, 219]  # JPEG Define Quantization Table
JPEG_DQT_TOL = 0.8  # -20% for each +20 in jpeg.quality (empirical number)
JPEG_EOI_MARKER = [255, 217]  # JPEG End of Image
JPEG_SOI_MARKER = [255, 216]  # JPEG Start of Image
JPEG_SOS_MARKER = [255, 218]  # JPEG Start of Scan
NAME = os.path.splitext(os.path.basename(__file__))[0]
QUALITIES = [25, 45, 65, 85]
SYMBOLS = ['o', 's', 'v', '^', '<', '>']


def is_square(integer):
  root = math.sqrt(integer)
  return integer == int(root + 0.5)**2


def strip_soi_marker(jpeg):
  """Strip off start of image marker.

  SOI is of form [xFF xD8] and JPEG needs to start with marker.

  Args:
   jpeg: 1-D numpy int [0:255] array; values from JPEG capture

  Returns:
    jpeg with SOI marker stripped off.
  """

  soi = jpeg[0:2]
  if list(soi) != JPEG_SOI_MARKER:
    raise AssertionError('JPEG has no Start Of Image marker')
  return jpeg[2:]


def strip_appn_data(jpeg):
  """Strip off application specific data at beginning of JPEG.

  APPN markers are of form [xFF, xE*, size_msb, size_lsb] and should follow
  SOI marker.

  Args:
   jpeg: 1-D numpy int [0:255] array; values from JPEG capture

  Returns:
    jpeg with APPN marker(s) and data stripped off.
  """

  length = 0
  i = 0
  # find APPN markers and strip off payloads at beginning of jpeg
  while i < len(jpeg) - 1:
    if [jpeg[i], jpeg[i + 1]] in JPEG_APPN_MARKERS:
      length = jpeg[i + 2] * 256 + jpeg[i + 3] + 2
      logging.debug('stripped APPN length:%d', length)
      jpeg = np.concatenate((jpeg[0:i], jpeg[length:]), axis=None)
    elif ([jpeg[i], jpeg[i + 1]] == JPEG_DQT_MARKER or
          [jpeg[i], jpeg[i + 1]] == JPEG_DHT_MARKER):
      break
    else:
      i += 1

  return jpeg


def find_dqt_markers(marker, jpeg):
  """Find location(s) of marker list in jpeg.

  DQT marker is of form [xFF, xDB].

  Args:
    marker: list; marker values
    jpeg: 1-D numpy int [0:255] array; JPEG capture w/ SOI & APPN stripped

  Returns:
    locs: list; marker locations in jpeg
  """
  locs = []
  marker_len = len(marker)
  for i in range(len(jpeg) - marker_len + 1):
    if list(jpeg[i:i + marker_len]) == marker:
      locs.append(i)
  return locs


def extract_dqts(jpeg, debug=False):
  """Find and extract the DQT info in the JPEG.

  SOI marker and APPN markers plus data are stripped off front of JPEG.
  DQT marker is of form [xFF, xDB] followed by [size_msb, size_lsb].
  Size includes the size values, but not the marker values.
  Luma DQT is prefixed by 0, Chroma DQT by 1.
  DQTs can have both luma & chroma or each individually.
  There can be more than one DQT table for luma and chroma.

  Args:
   jpeg: 1-D numpy int [0:255] array; values from JPEG capture
   debug: bool; command line flag to print debug data

  Returns:
    lumas,chromas: lists of numpy means of luma & chroma DQT matrices.
    Higher values represent higher compression.
  """

  dqt_markers = find_dqt_markers(JPEG_DQT_MARKER, jpeg)
  logging.debug('DQT header loc(s):%s', dqt_markers)
  lumas = []
  chromas = []
  for i, dqt in enumerate(dqt_markers):
    if debug:
      logging.debug('DQT %d start: %d, marker: %s, length: %s', i, dqt,
                    jpeg[dqt:dqt + 2], jpeg[dqt + 2:dqt + 4])
    dqt_size = jpeg[dqt + 2] * 256 + jpeg[dqt + 3] - 2  # strip off size marker
    if dqt_size % 2 == 0:  # even payload means luma & chroma
      logging.debug(' both luma & chroma DQT matrices in marker')
      dqt_size = (dqt_size - 2) // 2  # subtact off luma/chroma markers
      if not is_square(dqt_size):
        raise AssertionError(f'DQT size: {dqt_size}')
      luma_start = dqt + 5  # skip header, length, & matrix id
      chroma_start = luma_start + dqt_size + 1  # skip lumen &  matrix_id
      luma = np.array(jpeg[luma_start: luma_start + dqt_size])
      chroma = np.array(jpeg[chroma_start: chroma_start + dqt_size])
      lumas.append(np.mean(luma))
      chromas.append(np.mean(chroma))
      if debug:
        h = int(math.sqrt(dqt_size))
        logging.debug(' luma:%s', luma.reshape(h, h))
        logging.debug(' chroma:%s', chroma.reshape(h, h))
    else:  # odd payload means only 1 matrix
      logging.debug(' single DQT matrix in marker')
      dqt_size = dqt_size - 1  # subtract off luma/chroma marker
      if not is_square(dqt_size):
        raise AssertionError(f'DQT size: {dqt_size}')
      start = dqt + 5
      matrix = np.array(jpeg[start:start + dqt_size])
      if jpeg[dqt + 4]:  # chroma == 1
        chromas.append(np.mean(matrix))
        if debug:
          h = int(math.sqrt(dqt_size))
          logging.debug(' chroma:%s', matrix.reshape(h, h))
      else:  # luma == 0
        lumas.append(np.mean(matrix))
        if debug:
          h = int(math.sqrt(dqt_size))
          logging.debug(' luma:%s', matrix.reshape(h, h))

  return lumas, chromas


def plot_data(qualities, lumas, chromas, img_name):
  """Create plot of data."""
  logging.debug('qualities: %s', str(qualities))
  logging.debug('luma DQT avgs: %s', str(lumas))
  logging.debug('chroma DQT avgs: %s', str(chromas))
  pylab.title(NAME)
  for i in range(lumas.shape[1]):
    pylab.plot(
        qualities, lumas[:, i], '-g' + SYMBOLS[i], label='luma_dqt' + str(i))
    pylab.plot(
        qualities,
        chromas[:, i],
        '-r' + SYMBOLS[i],
        label='chroma_dqt' + str(i))
  pylab.xlim([0, 100])
  pylab.ylim([0, None])
  pylab.xlabel('jpeg.quality')
  pylab.ylabel('DQT luma/chroma matrix averages')
  pylab.legend(loc='upper right', numpoints=1, fancybox=True)
  matplotlib.pyplot.savefig('%s_plot.png' % img_name)


class JpegQualityTest(its_base_test.ItsBaseTest):
  """Test the camera JPEG compression quality.

  Step JPEG qualities through android.jpeg.quality. Ensure quanitization
  matrix decreases with quality increase. Matrix should decrease as the
  matrix represents the division factor. Higher numbers --> fewer quantization
  levels.
  """

  def test_jpeg_quality(self):
    logging.debug('Starting %s', NAME)
    # init variables
    lumas = []
    chromas = []

    with its_session_utils.ItsSession(
        device_id=self.dut.serial,
        camera_id=self.camera_id,
        hidden_physical_id=self.hidden_physical_id) as cam:

      props = cam.get_camera_properties()
      props = cam.override_with_hidden_physical_camera_props(props)
      debug = self.debug_mode

      # Load chart for scene
      its_session_utils.load_scene(
          cam, props, self.scene, self.tablet, self.chart_distance)

      # Check skip conditions
      camera_properties_utils.skip_unless(
          camera_properties_utils.jpeg_quality(props))
      cam.do_3a()

      # do captures over jpeg quality range
      req = capture_request_utils.auto_capture_request()
      for q in QUALITIES:
        logging.debug('jpeg.quality: %.d', q)
        req['android.jpeg.quality'] = q
        cap = cam.do_capture(req, cam.CAP_JPEG)
        jpeg = cap['data']

        # strip off start of image
        jpeg = strip_soi_marker(jpeg)

        # strip off application specific data
        jpeg = strip_appn_data(jpeg)
        logging.debug('remaining JPEG header:%s', jpeg[0:4])

        # find and extract DQTs
        lumas_i, chromas_i = extract_dqts(jpeg, debug)
        lumas.append(lumas_i)
        chromas.append(chromas_i)

        # save JPEG image
        img = image_processing_utils.convert_capture_to_rgb_image(
            cap, props=props)
        img_name = os.path.join(self.log_path, NAME)
        image_processing_utils.write_image(img, '%s_%d.jpg' % (img_name, q))

    # turn lumas/chromas into np array to ease multi-dimensional plots/asserts
    lumas = np.array(lumas)
    chromas = np.array(chromas)

    # create plot of luma & chroma averages vs quality
    plot_data(QUALITIES, lumas, chromas, img_name)

    # assert decreasing luma/chroma with improved jpeg quality
    for i in range(lumas.shape[1]):
      l = lumas[:, i]
      c = chromas[:, i]
      if not all(y < x * JPEG_DQT_TOL for x, y in zip(l, l[1:])):
        raise AssertionError(f'luma DQT avgs: {l}, TOL: {JPEG_DQT_TOL}')

      if not all(y < x * JPEG_DQT_TOL for x, y in zip(c, c[1:])):
        raise AssertionError(f'chroma DQT avgs: {c}, TOL: {JPEG_DQT_TOL}')

if __name__ == '__main__':
  test_runner.main()
