diff options
Diffstat (limited to 'apps/CameraITS/tests/scene1/test_burst_sameness_manual.py')
| -rw-r--r-- | apps/CameraITS/tests/scene1/test_burst_sameness_manual.py | 36 |
1 files changed, 17 insertions, 19 deletions
diff --git a/apps/CameraITS/tests/scene1/test_burst_sameness_manual.py b/apps/CameraITS/tests/scene1/test_burst_sameness_manual.py index 0404e98..e500860 100644 --- a/apps/CameraITS/tests/scene1/test_burst_sameness_manual.py +++ b/apps/CameraITS/tests/scene1/test_burst_sameness_manual.py @@ -32,10 +32,10 @@ def main(): NAME = os.path.basename(__file__).split(".")[0] BURST_LEN = 50 - BURSTS = 10 + BURSTS = 5 FRAMES = BURST_LEN * BURSTS - DELTA_THRESH = 0.1 + SPREAD_THRESH = 0.03 with its.device.ItsSession() as cam: @@ -46,17 +46,23 @@ def main(): req = its.objects.manual_capture_request(s, e) w,h = fmt["width"], fmt["height"] - # Converge 3A prior to capture. - cam.do_3a() - # Capture bursts of YUV shots. - # Build a 4D array, which is an array of all RGB images. + # 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" @@ -67,19 +73,11 @@ def main(): 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) - 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) + # 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() |