1# Copyright 2013 The Android Open Source Project
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7#      http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14"""Verifies settings latch on the correct frame."""
15
16
17import logging
18import os.path
19import matplotlib
20from matplotlib import pylab
21from mobly import test_runner
22
23import its_base_test
24import camera_properties_utils
25import capture_request_utils
26import image_processing_utils
27import its_session_utils
28import target_exposure_utils
29
30_EXP_GAIN_FACTOR = 2
31_NAME = os.path.splitext(os.path.basename(__file__))[0]
32_PATCH_H = 0.1  # center 10%
33_PATCH_W = 0.1
34_PATCH_X = 0.5 - _PATCH_W/2
35_PATCH_Y = 0.5 - _PATCH_H/2
36_REQ_PATTERN = ['base', 'base', 'iso', 'iso', 'base', 'base', 'exp',
37                'base', 'iso', 'base', 'exp', 'base', 'exp', 'exp']
38_PATTERN_CHECK = [r != 'base' for r in _REQ_PATTERN]
39
40
41class LatchingTest(its_base_test.ItsBaseTest):
42  """Test that settings latch on the right frame.
43
44  Takes a sequence of 14 shots using back-to-back requests, varying the capture
45  request gain and exp parameters between shots. Check images that come back
46  have the properties.
47
48  Pattern is described in EXP_GAIN_HIGH_PATTERN where False is NOM, True is High
49  """
50
51  def test_latching(self):
52    logging.debug('Starting %s', _NAME)
53    with its_session_utils.ItsSession(
54        device_id=self.dut.serial,
55        camera_id=self.camera_id,
56        hidden_physical_id=self.hidden_physical_id) as cam:
57      props = cam.get_camera_properties()
58      props = cam.override_with_hidden_physical_camera_props(props)
59      log_path = self.log_path
60      name_with_log_path = os.path.join(log_path, _NAME)
61
62      # check SKIP conditions
63      camera_properties_utils.skip_unless(
64          camera_properties_utils.full_or_better(props))
65
66      # Load chart for scene
67      its_session_utils.load_scene(
68          cam, props, self.scene, self.tablet,
69          its_session_utils.CHART_DISTANCE_NO_SCALING)
70
71      # Create requests, do captures and extract means for each image
72      _, fmt = capture_request_utils.get_fastest_manual_capture_settings(props)
73      e, s = target_exposure_utils.get_target_exposure_combos(
74          log_path, cam)['midExposureTime']
75
76      e /= _EXP_GAIN_FACTOR
77      r_means = []
78      g_means = []
79      b_means = []
80      reqs = []
81      base_req = capture_request_utils.manual_capture_request(
82          s, e, 0.0, True, props)
83      iso_mult_req = capture_request_utils.manual_capture_request(
84          s * _EXP_GAIN_FACTOR, e, 0.0, True, props)
85      exp_mult_req = capture_request_utils.manual_capture_request(
86          s, e * _EXP_GAIN_FACTOR, 0.0, True, props)
87      for req_type in _REQ_PATTERN:
88        if req_type == 'base':
89          reqs.append(base_req)
90        elif req_type == 'exp':
91          reqs.append(exp_mult_req)
92        elif req_type == 'iso':
93          reqs.append(iso_mult_req)
94        else:
95          raise AssertionError(f'Incorrect capture request! {req_type}')
96
97      caps = cam.do_capture(reqs, fmt)
98      for i, cap in enumerate(caps):
99        img = image_processing_utils.convert_capture_to_rgb_image(cap)
100        image_processing_utils.write_image(
101            img, f'{name_with_log_path}_i={i:02d}.jpg')
102        patch = image_processing_utils.get_image_patch(
103            img, _PATCH_X, _PATCH_Y, _PATCH_W, _PATCH_H)
104        rgb_means = image_processing_utils.compute_image_means(patch)
105        r_means.append(rgb_means[0])
106        g_means.append(rgb_means[1])
107        b_means.append(rgb_means[2])
108      logging.debug('G means: %s', str(g_means))
109
110      # Plot results
111      idxs = range(len(r_means))
112      pylab.figure(_NAME)
113      pylab.plot(idxs, r_means, '-ro')
114      pylab.plot(idxs, g_means, '-go')
115      pylab.plot(idxs, b_means, '-bo')
116      pylab.ylim([0, 1])
117      pylab.title(_NAME)
118      pylab.xlabel('capture')
119      pylab.ylabel('RGB means')
120      matplotlib.pyplot.savefig(f'{name_with_log_path}_plot_means.png')
121
122      # check G mean pattern for correctness
123      g_avg_for_caps = sum(g_means) / len(g_means)
124      g_high = [g / g_avg_for_caps > 1 for g in g_means]
125      if g_high != _PATTERN_CHECK:
126        raise AssertionError(f'G means: {g_means}, TEMPLATE: {_REQ_PATTERN}')
127
128if __name__ == '__main__':
129  test_runner.main()
130
131