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 numpy
21import math
22import pylab
23import os.path
24import matplotlib
25import matplotlib.pyplot
26
27def main():
28    """Test that device processing can be inverted to linear pixels.
29
30    Captures a sequence of shots with the device pointed at a uniform
31    target. Attempts to invert all the ISP processing to get back to
32    linear R,G,B pixel data.
33    """
34    NAME = os.path.basename(__file__).split(".")[0]
35
36    RESIDUAL_THRESHOLD = 0.0003 # approximately each sample is off by 2/255
37
38    # The HAL3.2 spec requires that curves up to 64 control points in length
39    # must be supported.
40    L = 64
41    LM1 = float(L-1)
42
43    gamma_lut = numpy.array(
44            sum([[i/LM1, math.pow(i/LM1, 1/2.2)] for i in xrange(L)], []))
45    inv_gamma_lut = numpy.array(
46            sum([[i/LM1, math.pow(i/LM1, 2.2)] for i in xrange(L)], []))
47
48    with its.device.ItsSession() as cam:
49        props = cam.get_camera_properties()
50        its.caps.skip_unless(its.caps.compute_target_exposure(props) and
51                             its.caps.per_frame_control(props))
52
53        e,s = its.target.get_target_exposure_combos(cam)["midSensitivity"]
54        s /= 2
55        sens_range = props['android.sensor.info.sensitivityRange']
56        sensitivities = [s*1.0/3.0, s*2.0/3.0, s, s*4.0/3.0, s*5.0/3.0]
57        sensitivities = [s for s in sensitivities
58                if s > sens_range[0] and s < sens_range[1]]
59
60        req = its.objects.manual_capture_request(0, e)
61        req["android.blackLevel.lock"] = True
62        req["android.tonemap.mode"] = 0
63        req["android.tonemap.curveRed"] = gamma_lut.tolist()
64        req["android.tonemap.curveGreen"] = gamma_lut.tolist()
65        req["android.tonemap.curveBlue"] = gamma_lut.tolist()
66
67        r_means = []
68        g_means = []
69        b_means = []
70
71        for sens in sensitivities:
72            req["android.sensor.sensitivity"] = sens
73            cap = cam.do_capture(req)
74            img = its.image.convert_capture_to_rgb_image(cap)
75            its.image.write_image(
76                    img, "%s_sens=%04d.jpg" % (NAME, sens))
77            img = its.image.apply_lut_to_image(img, inv_gamma_lut[1::2] * LM1)
78            tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
79            rgb_means = its.image.compute_image_means(tile)
80            r_means.append(rgb_means[0])
81            g_means.append(rgb_means[1])
82            b_means.append(rgb_means[2])
83
84        pylab.plot(sensitivities, r_means, 'r')
85        pylab.plot(sensitivities, g_means, 'g')
86        pylab.plot(sensitivities, b_means, 'b')
87        pylab.ylim([0,1])
88        matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
89
90        # Check that each plot is actually linear.
91        for means in [r_means, g_means, b_means]:
92            line,residuals,_,_,_  = numpy.polyfit(range(5),means,1,full=True)
93            print "Line: m=%f, b=%f, resid=%f"%(line[0], line[1], residuals[0])
94            assert(residuals[0] < RESIDUAL_THRESHOLD)
95
96if __name__ == '__main__':
97    main()
98
99