| # 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(): |
| """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 = 6 |
| BURSTS = 2 |
| FRAMES = BURST_LEN * BURSTS |
| |
| DELTA_THRESH = 0.1 |
| |
| with its.device.ItsSession() as cam: |
| |
| # Capture at full resolution. |
| props = cam.get_camera_properties() |
| its.caps.skip_unless(its.caps.manual_sensor(props) and |
| its.caps.awb_lock(props)) |
| w,h = its.objects.get_available_output_sizes("yuv", props)[0] |
| |
| # 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. |
| # Build a 4D array, which is an array of all RGB images after down- |
| # scaling them by a factor of 4x4. |
| imgs = numpy.empty([FRAMES,h/4,w/4,3]) |
| for j in range(BURSTS): |
| caps = cam.do_capture([req]*BURST_LEN) |
| for i,cap in enumerate(caps): |
| n = j*BURST_LEN + i |
| imgs[n] = its.image.downscale_image( |
| its.image.convert_capture_to_rgb_image(cap), 4) |
| |
| # 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)) |
| |
| # Compute the deltas of each image from the mean image; this test |
| # passes if none of the deltas are large. |
| print "Computing frame differences" |
| delta_maxes = [] |
| for i in range(FRAMES): |
| deltas = (imgs[i] - img_mean).reshape(h*w*3/16) |
| delta_max_pos = numpy.max(deltas) |
| delta_max_neg = numpy.min(deltas) |
| delta_maxes.append(max(abs(delta_max_pos), abs(delta_max_neg))) |
| max_delta_max = max(delta_maxes) |
| print "Frame %d has largest diff %f" % ( |
| delta_maxes.index(max_delta_max), max_delta_max) |
| assert(max_delta_max < DELTA_THRESH) |
| |
| if __name__ == '__main__': |
| main() |
| |