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 os.path
16import its.caps
17import its.device
18import its.image
19import its.objects
20import numpy
21
22MAX_SAME_DELTA = 0.03  # match number in test_burst_sameness_manual
23MIN_DIFF_DELTA = 0.10
24NAME = os.path.basename(__file__).split(".")[0]
25
26
27def main():
28    """Test a sequence of shots with different tonemap curves.
29
30    There should be 3 identical frames followed by a different set of
31    3 identical frames.
32    """
33
34    with its.device.ItsSession() as cam:
35        props = cam.get_camera_properties()
36        its.caps.skip_unless(its.caps.manual_sensor(props) and
37                             its.caps.manual_post_proc(props) and
38                             its.caps.per_frame_control(props) and
39                             not its.caps.mono_camera(props))
40
41        debug = its.caps.debug_mode()
42        largest_yuv = its.objects.get_largest_yuv_format(props)
43        if debug:
44            fmt = largest_yuv
45        else:
46            match_ar = (largest_yuv["width"], largest_yuv["height"])
47            fmt = its.objects.get_smallest_yuv_format(props, match_ar=match_ar)
48
49        sens, exp_time, _, _, f_dist = cam.do_3a(do_af=True, get_results=True)
50
51        means = []
52
53        # Capture 3 manual shots with a linear tonemap.
54        req = its.objects.manual_capture_request(
55                sens, exp_time, f_dist, True, props)
56        for i in [0, 1, 2]:
57            cap = cam.do_capture(req, fmt)
58            img = its.image.convert_capture_to_rgb_image(cap)
59            its.image.write_image(img, "%s_i=%d.jpg" % (NAME, i))
60            tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
61            means.append(tile.mean(0).mean(0))
62
63        # Capture 3 manual shots with the default tonemap.
64        req = its.objects.manual_capture_request(sens, exp_time, f_dist, False)
65        for i in [3, 4, 5]:
66            cap = cam.do_capture(req, fmt)
67            img = its.image.convert_capture_to_rgb_image(cap)
68            its.image.write_image(img, "%s_i=%d.jpg" % (NAME, i))
69            tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
70            means.append(tile.mean(0).mean(0))
71
72        # Compute the delta between each consecutive frame pair.
73        deltas = [numpy.max(numpy.fabs(means[i+1]-means[i])) \
74                  for i in range(len(means)-1)]
75        print "Deltas between consecutive frames:", deltas
76
77        msg = "deltas: %s, MAX_SAME_DELTA: %.2f" % (
78                str(deltas), MAX_SAME_DELTA)
79        assert all([abs(deltas[i]) < MAX_SAME_DELTA for i in [0, 1, 3, 4]]), msg
80        assert abs(deltas[2]) > MIN_DIFF_DELTA, "delta: %.5f, THRESH: %.2f" % (
81                abs(deltas[2]), MIN_DIFF_DELTA)
82
83if __name__ == "__main__":
84    main()
85
86