| # 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.caps |
| import its.device |
| import its.objects |
| import os.path |
| import numpy |
| |
| def main(): |
| """Take long bursts of images and check that they're all identical. |
| |
| Assumes a static scene. Can be used to idenfity if there are sporadic |
| frames that are processed differently or have artifacts, or if 3A isn't |
| stable, since this test converges 3A at the start but doesn't lock 3A |
| throughout capture. |
| """ |
| NAME = os.path.basename(__file__).split(".")[0] |
| |
| BURST_LEN = 50 |
| BURSTS = 5 |
| FRAMES = BURST_LEN * BURSTS |
| |
| SPREAD_THRESH = 0.03 |
| |
| with its.device.ItsSession() as cam: |
| |
| # Capture at the smallest resolution. |
| props = cam.get_camera_properties() |
| its.caps.skip_unless(its.caps.manual_sensor(props)) |
| |
| _, fmt = its.objects.get_fastest_manual_capture_settings(props) |
| w,h = fmt["width"], fmt["height"] |
| |
| # Converge 3A prior to capture. |
| cam.do_3a(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.blackLevel.lock"] = True |
| 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. |
| # Also build a 4D array, which is an array of all RGB images. |
| r_means = [] |
| g_means = [] |
| b_means = [] |
| imgs = numpy.empty([FRAMES,h,w,3]) |
| for j in range(BURSTS): |
| caps = cam.do_capture([req]*BURST_LEN, [fmt]) |
| for i,cap in enumerate(caps): |
| n = j*BURST_LEN + i |
| imgs[n] = its.image.convert_capture_to_rgb_image(cap) |
| tile = its.image.get_image_patch(imgs[n], 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]) |
| |
| # Dump all images. |
| print "Dumping images" |
| for i in range(FRAMES): |
| its.image.write_image(imgs[i], "%s_frame%03d.jpg"%(NAME,i)) |
| |
| # The mean image. |
| img_mean = imgs.mean(0) |
| its.image.write_image(img_mean, "%s_mean.jpg"%(NAME)) |
| |
| # Pass/fail based on center patch similarity. |
| for means in [r_means, g_means, b_means]: |
| spread = max(means) - min(means) |
| print spread |
| assert(spread < SPREAD_THRESH) |
| |
| if __name__ == '__main__': |
| main() |
| |