| # 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 os.path |
| import its.caps |
| import its.device |
| import its.image |
| import its.objects |
| import numpy |
| |
| MAX_SAME_DELTA = 0.03 # match number in test_burst_sameness_manual |
| MIN_DIFF_DELTA = 0.10 |
| NAME = os.path.basename(__file__).split(".")[0] |
| |
| |
| def main(): |
| """Test a sequence of shots with different tonemap curves. |
| |
| There should be 3 identical frames followed by a different set of |
| 3 identical frames. |
| """ |
| |
| 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 |
| not its.caps.mono_camera(props)) |
| |
| debug = its.caps.debug_mode() |
| largest_yuv = its.objects.get_largest_yuv_format(props) |
| if debug: |
| fmt = largest_yuv |
| else: |
| match_ar = (largest_yuv["width"], largest_yuv["height"]) |
| fmt = its.objects.get_smallest_yuv_format(props, match_ar=match_ar) |
| |
| sens, exp_time, _, _, f_dist = cam.do_3a(do_af=True, get_results=True) |
| |
| means = [] |
| |
| # Capture 3 manual shots with a linear tonemap. |
| req = its.objects.manual_capture_request( |
| sens, exp_time, f_dist, True, props) |
| for i in [0, 1, 2]: |
| cap = cam.do_capture(req, fmt) |
| img = its.image.convert_capture_to_rgb_image(cap) |
| its.image.write_image(img, "%s_i=%d.jpg" % (NAME, i)) |
| tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) |
| means.append(tile.mean(0).mean(0)) |
| |
| # Capture 3 manual shots with the default tonemap. |
| req = its.objects.manual_capture_request(sens, exp_time, f_dist, False) |
| for i in [3, 4, 5]: |
| cap = cam.do_capture(req, fmt) |
| img = its.image.convert_capture_to_rgb_image(cap) |
| its.image.write_image(img, "%s_i=%d.jpg" % (NAME, i)) |
| tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) |
| means.append(tile.mean(0).mean(0)) |
| |
| # Compute the delta between each consecutive frame pair. |
| deltas = [numpy.max(numpy.fabs(means[i+1]-means[i])) \ |
| for i in range(len(means)-1)] |
| print "Deltas between consecutive frames:", deltas |
| |
| msg = "deltas: %s, MAX_SAME_DELTA: %.2f" % ( |
| str(deltas), MAX_SAME_DELTA) |
| assert all([abs(deltas[i]) < MAX_SAME_DELTA for i in [0, 1, 3, 4]]), msg |
| assert abs(deltas[2]) > MIN_DIFF_DELTA, "delta: %.5f, THRESH: %.2f" % ( |
| abs(deltas[2]), MIN_DIFF_DELTA) |
| |
| if __name__ == "__main__": |
| main() |
| |