CameraITS: fix ev compendation advance test
Fix a bug in expected luma compensation
Bug: 21619252
Change-Id: I6bb3c68053153d397f0a0282f696a37d6f5f9ed3
diff --git a/apps/CameraITS/tests/scene1/test_ev_compensation_advanced.py b/apps/CameraITS/tests/scene1/test_ev_compensation_advanced.py
index 9b43a74..5fd8f73 100644
--- a/apps/CameraITS/tests/scene1/test_ev_compensation_advanced.py
+++ b/apps/CameraITS/tests/scene1/test_ev_compensation_advanced.py
@@ -41,10 +41,12 @@
range_max = ev_compensation_range[1]
ev_per_step = its.objects.rational_to_float(
props['android.control.aeCompensationStep'])
- steps_per_ev = int(1.0 / ev_per_step)
- evs = range(range_min, range_max + 1, steps_per_ev)
+ steps_per_ev = int(round(1.0 / ev_per_step))
+ ev_steps = range(range_min, range_max + 1, steps_per_ev)
+ imid = len(ev_steps) / 2
+ ev_shifts = [pow(2, step * ev_per_step) for step in ev_steps]
lumas = []
- for ev in evs:
+ for ev in ev_steps:
# Re-converge 3A, and lock AE once converged. skip AF trigger as
# dark/bright scene could make AF convergence fail and this test
# doesn't care the image sharpness.
@@ -65,19 +67,16 @@
tile = its.image.get_image_patch(y, 0.45,0.45,0.1,0.1)
lumas.append(its.image.compute_image_means(tile)[0])
- luma_increase_per_step = pow(2, ev_per_step)
print "ev_step_size_in_stops", ev_per_step
- imid = len(lumas) / 2
- expected_lumas = [lumas[imid] / pow(luma_increase_per_step, i)
- for i in range(imid , 0, -1)] + \
- [lumas[imid] * pow(luma_increase_per_step, i-imid)
- for i in range(imid, len(evs))]
+ shift_mid = ev_shifts[imid]
+ luma_normal = lumas[imid] / shift_mid
+ expected_lumas = [luma_normal * ev_shift for ev_shift in ev_shifts]
- pylab.plot(evs, lumas, 'r')
- pylab.plot(evs, expected_lumas, 'b')
+ pylab.plot(ev_steps, lumas, 'r')
+ pylab.plot(ev_steps, expected_lumas, 'b')
matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
- luma_diffs = [expected_lumas[i] - lumas[i] for i in range(len(evs))]
+ luma_diffs = [expected_lumas[i] - lumas[i] for i in range(len(ev_steps))]
max_diff = max(abs(i) for i in luma_diffs)
avg_diff = abs(numpy.array(luma_diffs)).mean()
print "Max delta between modeled and measured lumas:", max_diff
