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
15import its.image
16import its.caps
17import its.device
18import its.objects
19import its.target
20import pylab
21import numpy
22import os.path
23import matplotlib
24import matplotlib.pyplot
25
26def main():
27    """Test that a constant exposure is seen as ISO and exposure time vary.
28
29    Take a series of shots that have ISO and exposure time chosen to balance
30    each other; result should be the same brightness, but over the sequence
31    the images should get noisier.
32    """
33    NAME = os.path.basename(__file__).split(".")[0]
34
35    THRESHOLD_MAX_OUTLIER_DIFF = 0.1
36    THRESHOLD_MIN_LEVEL = 0.1
37    THRESHOLD_MAX_LEVEL = 0.9
38    THRESHOLD_MAX_LEVEL_DIFF = 0.025
39    THRESHOLD_MAX_LEVEL_DIFF_WIDE_RANGE = 0.05
40
41    mults = []
42    r_means = []
43    g_means = []
44    b_means = []
45    threshold_max_level_diff = THRESHOLD_MAX_LEVEL_DIFF
46
47    with its.device.ItsSession() as cam:
48        props = cam.get_camera_properties()
49        its.caps.skip_unless(its.caps.compute_target_exposure(props) and
50                             its.caps.per_frame_control(props))
51
52        e,s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
53        expt_range = props['android.sensor.info.exposureTimeRange']
54        sens_range = props['android.sensor.info.sensitivityRange']
55
56        m = 1
57        while s*m < sens_range[1] and e/m > expt_range[0]:
58            mults.append(m)
59            req = its.objects.manual_capture_request(s*m, e/m)
60            cap = cam.do_capture(req)
61            img = its.image.convert_capture_to_rgb_image(cap)
62            its.image.write_image(img, "%s_mult=%3.2f.jpg" % (NAME, m))
63            tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
64            rgb_means = its.image.compute_image_means(tile)
65            r_means.append(rgb_means[0])
66            g_means.append(rgb_means[1])
67            b_means.append(rgb_means[2])
68            # Test 3 steps per 2x gain
69            m = m * pow(2, 1.0 / 3)
70
71        # Allow more threshold for devices with wider exposure range
72        if m >= 64.0:
73            threshold_max_level_diff = THRESHOLD_MAX_LEVEL_DIFF_WIDE_RANGE
74
75    # Draw a plot.
76    pylab.plot(mults, r_means, 'r')
77    pylab.plot(mults, g_means, 'g')
78    pylab.plot(mults, b_means, 'b')
79    pylab.ylim([0,1])
80    matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
81
82    # Check for linearity. Verify sample pixel mean values are close to each
83    # other. Also ensure that the images aren't clamped to 0 or 1
84    # (which would make them look like flat lines).
85    for chan in xrange(3):
86        values = [r_means, g_means, b_means][chan]
87        m, b = numpy.polyfit(mults, values, 1).tolist()
88        max_val = max(values)
89        min_val = min(values)
90        max_diff = max_val - min_val
91        print "Channel %d line fit (y = mx+b): m = %f, b = %f" % (chan, m, b)
92        print "Channel max %f min %f diff %f" % (max_val, min_val, max_diff)
93        assert(max_diff < threshold_max_level_diff)
94        assert(b > THRESHOLD_MIN_LEVEL and b < THRESHOLD_MAX_LEVEL)
95        for v in values:
96            assert(v > THRESHOLD_MIN_LEVEL and v < THRESHOLD_MAX_LEVEL)
97            assert(abs(v - b) < THRESHOLD_MAX_OUTLIER_DIFF)
98
99if __name__ == '__main__':
100    main()
101
102