1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
|
# Copyright 2013 The Android Open Source Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import its.image
import its.caps
import its.device
import its.objects
import its.target
import numpy
import math
import pylab
import os.path
import matplotlib
import matplotlib.pyplot
def main():
"""Test that device processing can be inverted to linear pixels.
Captures a sequence of shots with the device pointed at a uniform
target. Attempts to invert all the ISP processing to get back to
linear R,G,B pixel data.
"""
NAME = os.path.basename(__file__).split(".")[0]
RESIDUAL_THRESHOLD = 0.00005
# The HAL3.2 spec requires that curves up to 64 control points in length
# must be supported.
L = 64
LM1 = float(L-1)
gamma_lut = numpy.array(
sum([[i/LM1, math.pow(i/LM1, 1/2.2)] for i in xrange(L)], []))
inv_gamma_lut = numpy.array(
sum([[i/LM1, math.pow(i/LM1, 2.2)] for i in xrange(L)], []))
with its.device.ItsSession() as cam:
props = cam.get_camera_properties()
if not its.caps.compute_target_exposure(props):
print "Test skipped"
return
e,s = its.target.get_target_exposure_combos(cam)["midSensitivity"]
s /= 2
sens_range = props['android.sensor.info.sensitivityRange']
sensitivities = [s*1.0/3.0, s*2.0/3.0, s, s*4.0/3.0, s*5.0/3.0]
sensitivities = [s for s in sensitivities
if s > sens_range[0] and s < sens_range[1]]
req = its.objects.manual_capture_request(0, e)
req["android.blackLevel.lock"] = True
req["android.tonemap.mode"] = 0
req["android.tonemap.curveRed"] = gamma_lut.tolist()
req["android.tonemap.curveGreen"] = gamma_lut.tolist()
req["android.tonemap.curveBlue"] = gamma_lut.tolist()
r_means = []
g_means = []
b_means = []
for sens in sensitivities:
req["android.sensor.sensitivity"] = sens
cap = cam.do_capture(req)
img = its.image.convert_capture_to_rgb_image(cap)
its.image.write_image(
img, "%s_sens=%04d.jpg" % (NAME, sens))
img = its.image.apply_lut_to_image(img, inv_gamma_lut[1::2] * LM1)
tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
rgb_means = its.image.compute_image_means(tile)
r_means.append(rgb_means[0])
g_means.append(rgb_means[1])
b_means.append(rgb_means[2])
pylab.plot(sensitivities, r_means, 'r')
pylab.plot(sensitivities, g_means, 'g')
pylab.plot(sensitivities, b_means, 'b')
pylab.ylim([0,1])
matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
# Check that each plot is actually linear.
for means in [r_means, g_means, b_means]:
line,residuals,_,_,_ = numpy.polyfit(range(5),means,1,full=True)
print "Line: m=%f, b=%f, resid=%f"%(line[0], line[1], residuals[0])
assert(residuals[0] < RESIDUAL_THRESHOLD)
if __name__ == '__main__':
main()
|
