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      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 
     15 import os.path
     16 
     17 import its.caps
     18 import its.device
     19 import its.image
     20 import its.objects
     21 import its.target
     22 
     23 import matplotlib
     24 from matplotlib import pylab
     25 
     26 NAME = os.path.basename(__file__).split('.')[0]
     27 NUM_STEPS = 5
     28 
     29 
     30 def main():
     31     """Test that the android.sensor.sensitivity parameter is applied."""
     32 
     33     sensitivities = None
     34     r_means = []
     35     g_means = []
     36     b_means = []
     37 
     38     with its.device.ItsSession() as cam:
     39         props = cam.get_camera_properties()
     40         its.caps.skip_unless(its.caps.compute_target_exposure(props))
     41         sync_latency = its.caps.sync_latency(props)
     42 
     43         debug = its.caps.debug_mode()
     44         largest_yuv = its.objects.get_largest_yuv_format(props)
     45         if debug:
     46             fmt = largest_yuv
     47         else:
     48             match_ar = (largest_yuv['width'], largest_yuv['height'])
     49             fmt = its.objects.get_smallest_yuv_format(props, match_ar=match_ar)
     50 
     51         expt, _ = its.target.get_target_exposure_combos(cam)['midSensitivity']
     52         sens_range = props['android.sensor.info.sensitivityRange']
     53         sens_step = (sens_range[1] - sens_range[0]) / float(NUM_STEPS-1)
     54         sensitivities = [
     55                 sens_range[0] + i * sens_step for i in range(NUM_STEPS)]
     56 
     57         for s in sensitivities:
     58             req = its.objects.manual_capture_request(s, expt)
     59             cap = its.device.do_capture_with_latency(
     60                     cam, req, sync_latency, fmt)
     61             img = its.image.convert_capture_to_rgb_image(cap)
     62             its.image.write_image(img, '%s_iso=%04d.jpg' % (NAME, s))
     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 
     69     # Draw a plot.
     70     pylab.plot(sensitivities, r_means, '-ro')
     71     pylab.plot(sensitivities, g_means, '-go')
     72     pylab.plot(sensitivities, b_means, '-bo')
     73     pylab.ylim([0, 1])
     74     pylab.title(NAME)
     75     pylab.xlabel('Gain (ISO)')
     76     pylab.ylabel('RGB means')
     77     matplotlib.pyplot.savefig('%s_plot_means.png' % (NAME))
     78 
     79     # Test for pass/fail: check that each shot is brighter than the previous.
     80     for means in [r_means, g_means, b_means]:
     81         for i in range(len(means)-1):
     82             assert means[i+1] > means[i]
     83 
     84 if __name__ == '__main__':
     85     main()
     86 
     87