| # 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 its.caps |
| import os.path |
| import numpy |
| import pylab |
| import matplotlib |
| import matplotlib.pyplot |
| |
| def main(): |
| """Test 3A lock + YUV burst (using auto settings). |
| |
| This is a test that is designed to pass even on limited devices that |
| don't have MANUAL_SENSOR or PER_FRAME_CONTROLS. The test checks |
| YUV image consistency while the frame rate check is in CTS. |
| """ |
| NAME = os.path.basename(__file__).split(".")[0] |
| |
| BURST_LEN = 8 |
| SPREAD_THRESH_MANUAL_SENSOR = 0.005 |
| SPREAD_THRESH = 0.03 |
| FPS_MAX_DIFF = 2.0 |
| |
| with its.device.ItsSession() as cam: |
| props = cam.get_camera_properties() |
| |
| # Converge 3A prior to capture. |
| cam.do_3a(do_af=True, lock_ae=True, lock_awb=True) |
| |
| # After 3A has converged, lock AE+AWB for the duration of the test. |
| req = its.objects.fastest_auto_capture_request(props) |
| req["android.control.awbLock"] = True |
| req["android.control.aeLock"] = True |
| |
| # Capture bursts of YUV shots. |
| # Get the mean values of a center patch for each. |
| r_means = [] |
| g_means = [] |
| b_means = [] |
| caps = cam.do_capture([req]*BURST_LEN) |
| for i,cap in enumerate(caps): |
| img = its.image.convert_capture_to_rgb_image(cap) |
| its.image.write_image(img, "%s_frame%d.jpg"%(NAME,i)) |
| tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1) |
| means = its.image.compute_image_means(tile) |
| r_means.append(means[0]) |
| g_means.append(means[1]) |
| b_means.append(means[2]) |
| |
| # Pass/fail based on center patch similarity. |
| for means in [r_means, g_means, b_means]: |
| spread = max(means) - min(means) |
| print "Patch mean spread", spread, \ |
| " (min/max: ", min(means), "/", max(means), ")" |
| threshold = SPREAD_THRESH_MANUAL_SENSOR \ |
| if its.caps.manual_sensor(props) else SPREAD_THRESH |
| assert(spread < threshold) |
| |
| if __name__ == '__main__': |
| main() |
| |