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1# Copyright 2014 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
15import its.image
16import its.device
17import its.objects
18import os.path
19import pylab
20import matplotlib
21import matplotlib.pyplot
22import numpy
23
24def main():
25    """Tests that EV compensation is applied.
26    """
27    NAME = os.path.basename(__file__).split(".")[0]
28
29    MAX_LUMA_DELTA_THRESH = 0.01
30    AVG_LUMA_DELTA_THRESH = 0.001
31
32    with its.device.ItsSession() as cam:
33        props = cam.get_camera_properties()
34        cam.do_3a()
35
36        # Capture auto shots, but with a linear tonemap.
37        req = its.objects.auto_capture_request()
38        req["android.tonemap.mode"] = 0
39        req["android.tonemap.curveRed"] = (0.0, 0.0, 1.0, 1.0)
40        req["android.tonemap.curveGreen"] = (0.0, 0.0, 1.0, 1.0)
41        req["android.tonemap.curveBlue"] = (0.0, 0.0, 1.0, 1.0)
42
43        evs = range(-4,5)
44        lumas = []
45        for ev in evs:
46            req['android.control.aeExposureCompensation'] = ev
47            cap = cam.do_capture(req)
48            y = its.image.convert_capture_to_planes(cap)[0]
49            tile = its.image.get_image_patch(y, 0.45,0.45,0.1,0.1)
50            lumas.append(its.image.compute_image_means(tile)[0])
51
52        ev_step_size_in_stops = its.objects.rational_to_float(
53                props['android.control.aeCompensationStep'])
54        luma_increase_per_step = pow(2, ev_step_size_in_stops)
55        expected_lumas = [lumas[0] * pow(luma_increase_per_step, i) \
56                for i in range(len(evs))]
57
58        pylab.plot(evs, lumas, 'r')
59        pylab.plot(evs, expected_lumas, 'b')
60        matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
61
62        luma_diffs = [expected_lumas[i] - lumas[i] for i in range(len(evs))]
63        max_diff = max(luma_diffs)
64        avg_diff = sum(luma_diffs) / len(luma_diffs)
65        print "Max delta between modeled and measured lumas:", max_diff
66        print "Avg delta between modeled and measured lumas:", avg_diff
67        assert(max_diff < MAX_LUMA_DELTA_THRESH)
68        assert(avg_diff < AVG_LUMA_DELTA_THRESH)
69
70if __name__ == '__main__':
71    main()
72