| # Copyright 2014 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.device |
| import its.objects |
| import os.path |
| import pylab |
| import matplotlib |
| import matplotlib.pyplot |
| import numpy |
| |
| def main(): |
| """Tests that EV compensation is applied. |
| """ |
| NAME = os.path.basename(__file__).split(".")[0] |
| |
| MAX_LUMA_DELTA_THRESH = 0.01 |
| AVG_LUMA_DELTA_THRESH = 0.001 |
| |
| with its.device.ItsSession() as cam: |
| props = cam.get_camera_properties() |
| cam.do_3a() |
| |
| # Capture auto shots, but with a linear tonemap. |
| req = its.objects.auto_capture_request() |
| req["android.tonemap.mode"] = 0 |
| req["android.tonemap.curveRed"] = (0.0, 0.0, 1.0, 1.0) |
| req["android.tonemap.curveGreen"] = (0.0, 0.0, 1.0, 1.0) |
| req["android.tonemap.curveBlue"] = (0.0, 0.0, 1.0, 1.0) |
| |
| evs = range(-4,5) |
| lumas = [] |
| for ev in evs: |
| req['android.control.aeExposureCompensation'] = ev |
| cap = cam.do_capture(req) |
| y = its.image.convert_capture_to_planes(cap)[0] |
| tile = its.image.get_image_patch(y, 0.45,0.45,0.1,0.1) |
| lumas.append(its.image.compute_image_means(tile)[0]) |
| |
| ev_step_size_in_stops = its.objects.rational_to_float( |
| props['android.control.aeCompensationStep']) |
| luma_increase_per_step = pow(2, ev_step_size_in_stops) |
| expected_lumas = [lumas[0] * pow(luma_increase_per_step, i) \ |
| for i in range(len(evs))] |
| |
| pylab.plot(evs, lumas, 'r') |
| pylab.plot(evs, expected_lumas, 'b') |
| matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME)) |
| |
| luma_diffs = [expected_lumas[i] - lumas[i] for i in range(len(evs))] |
| max_diff = max(luma_diffs) |
| avg_diff = sum(luma_diffs) / len(luma_diffs) |
| print "Max delta between modeled and measured lumas:", max_diff |
| print "Avg delta between modeled and measured lumas:", avg_diff |
| assert(max_diff < MAX_LUMA_DELTA_THRESH) |
| assert(avg_diff < AVG_LUMA_DELTA_THRESH) |
| |
| if __name__ == '__main__': |
| main() |