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.caps
17import its.device
18import its.objects
19import its.target
20import os.path
21import numpy
22
23def main():
24    """Take long bursts of images and check that they're all identical.
25
26    Assumes a static scene. Can be used to idenfity if there are sporadic
27    frames that are processed differently or have artifacts. Uses manual
28    capture settings.
29    """
30    NAME = os.path.basename(__file__).split(".")[0]
31
32    BURST_LEN = 50
33    BURSTS = 5
34    FRAMES = BURST_LEN * BURSTS
35
36    SPREAD_THRESH = 0.03
37
38    with its.device.ItsSession() as cam:
39
40        # Capture at the smallest resolution.
41        props = cam.get_camera_properties()
42        its.caps.skip_unless(its.caps.manual_sensor(props) and
43                             its.caps.per_frame_control(props))
44
45        _, fmt = its.objects.get_fastest_manual_capture_settings(props)
46        e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
47        req = its.objects.manual_capture_request(s, e)
48        w,h = fmt["width"], fmt["height"]
49
50        # Capture bursts of YUV shots.
51        # Get the mean values of a center patch for each.
52        # Also build a 4D array, which is an array of all RGB images.
53        r_means = []
54        g_means = []
55        b_means = []
56        imgs = numpy.empty([FRAMES,h,w,3])
57        for j in range(BURSTS):
58            caps = cam.do_capture([req]*BURST_LEN, [fmt])
59            for i,cap in enumerate(caps):
60                n = j*BURST_LEN + i
61                imgs[n] = its.image.convert_capture_to_rgb_image(cap)
62                tile = its.image.get_image_patch(imgs[n], 0.45, 0.45, 0.1, 0.1)
63                means = its.image.compute_image_means(tile)
64                r_means.append(means[0])
65                g_means.append(means[1])
66                b_means.append(means[2])
67
68        # Dump all images.
69        print "Dumping images"
70        for i in range(FRAMES):
71            its.image.write_image(imgs[i], "%s_frame%03d.jpg"%(NAME,i))
72
73        # The mean image.
74        img_mean = imgs.mean(0)
75        its.image.write_image(img_mean, "%s_mean.jpg"%(NAME))
76
77        # Pass/fail based on center patch similarity.
78        for means in [r_means, g_means, b_means]:
79            spread = max(means) - min(means)
80            print spread
81            assert(spread < SPREAD_THRESH)
82
83if __name__ == '__main__':
84    main()
85
86