Camera: adjust test_exposure pass threshold
Bug: 20534817
Change-Id: I9edb63648fb5ddcb8c8789de7887a497545e9fb0
diff --git a/apps/CameraITS/tests/scene1/test_exposure.py b/apps/CameraITS/tests/scene1/test_exposure.py
index c55e7ad..d217bdb 100644
--- a/apps/CameraITS/tests/scene1/test_exposure.py
+++ b/apps/CameraITS/tests/scene1/test_exposure.py
@@ -35,7 +35,7 @@
THRESHOLD_MAX_OUTLIER_DIFF = 0.1
THRESHOLD_MIN_LEVEL = 0.1
THRESHOLD_MAX_LEVEL = 0.9
- THRESHOLD_MAX_ABS_GRAD = 0.001
+ THRESHOLD_MAX_LEVEL_DIFF = 0.025
mults = []
r_means = []
@@ -72,15 +72,18 @@
pylab.ylim([0,1])
matplotlib.pyplot.savefig("%s_plot_means.png" % (NAME))
- # Check for linearity. For each R,G,B channel, fit a line y=mx+b, and
- # assert that the gradient is close to 0 (flat) and that there are no
- # crazy outliers. Also ensure that the images aren't clamped to 0 or 1
+ # Check for linearity. Verify sample pixel mean values are close to each
+ # other. Also ensure that the images aren't clamped to 0 or 1
# (which would make them look like flat lines).
for chan in xrange(3):
values = [r_means, g_means, b_means][chan]
m, b = numpy.polyfit(mults, values, 1).tolist()
+ max_val = max(values)
+ min_val = min(values)
+ max_diff = max_val - min_val
print "Channel %d line fit (y = mx+b): m = %f, b = %f" % (chan, m, b)
- assert(abs(m) < THRESHOLD_MAX_ABS_GRAD)
+ print "Channel max %f min %f diff %f" % (max_val, min_val, max_diff)
+ assert(max_diff < THRESHOLD_MAX_LEVEL_DIFF)
assert(b > THRESHOLD_MIN_LEVEL and b < THRESHOLD_MAX_LEVEL)
for v in values:
assert(v > THRESHOLD_MIN_LEVEL and v < THRESHOLD_MAX_LEVEL)
