| # 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.caps |
| 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 = 0.02 |
| |
| with its.device.ItsSession() as cam: |
| props = cam.get_camera_properties() |
| its.caps.skip_unless(its.caps.manual_sensor(props) and |
| its.caps.manual_post_proc(props) and |
| its.caps.per_frame_control(props) and |
| its.caps.ev_compensation(props)) |
| |
| ev_compensation_range = props['android.control.aeCompensationRange'] |
| range_min = ev_compensation_range[0] |
| range_max = ev_compensation_range[1] |
| ev_per_step = its.objects.rational_to_float( |
| props['android.control.aeCompensationStep']) |
| steps_per_ev = int(round(1.0 / ev_per_step)) |
| ev_steps = range(range_min, range_max + 1, steps_per_ev) |
| imid = len(ev_steps) / 2 |
| ev_shifts = [pow(2, step * ev_per_step) for step in ev_steps] |
| lumas = [] |
| for ev in ev_steps: |
| # Re-converge 3A, and lock AE once converged. skip AF trigger as |
| # dark/bright scene could make AF convergence fail and this test |
| # doesn't care the image sharpness. |
| cam.do_3a(ev_comp=ev, lock_ae=True, do_af=False) |
| |
| # Capture a single shot with the same EV comp and locked AE. |
| req = its.objects.auto_capture_request() |
| req['android.control.aeExposureCompensation'] = ev |
| req["android.control.aeLock"] = True |
| # Use linear tone curve to avoid brightness being impacted |
| # by tone curves. |
| 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] |
| 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]) |
| |
| print "ev_step_size_in_stops", ev_per_step |
| shift_mid = ev_shifts[imid] |
| luma_normal = lumas[imid] / shift_mid |
| expected_lumas = [luma_normal * ev_shift for ev_shift in ev_shifts] |
| |
| pylab.plot(ev_steps, lumas, 'r') |
| pylab.plot(ev_steps, expected_lumas, 'b') |
| matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME)) |
| |
| luma_diffs = [expected_lumas[i] - lumas[i] for i in range(len(ev_steps))] |
| max_diff = max(abs(i) for i in luma_diffs) |
| avg_diff = abs(numpy.array(luma_diffs)).mean() |
| 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) |
| |
| if __name__ == '__main__': |
| main() |