camera2: Move ITS tests to CTS verifier.
Bug: 17994909
Change-Id: Ie788b1ae3a6b079e37a3472c46aed3dfdcfffe2c
diff --git a/apps/CameraITS/tests/scene1/test_exposure.py b/apps/CameraITS/tests/scene1/test_exposure.py
new file mode 100644
index 0000000..8676358
--- /dev/null
+++ b/apps/CameraITS/tests/scene1/test_exposure.py
@@ -0,0 +1,92 @@
+# Copyright 2013 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 its.target
+import pylab
+import numpy
+import os.path
+import matplotlib
+import matplotlib.pyplot
+
+def main():
+ """Test that a constant exposure is seen as ISO and exposure time vary.
+
+ Take a series of shots that have ISO and exposure time chosen to balance
+ each other; result should be the same brightness, but over the sequence
+ the images should get noisier.
+ """
+ NAME = os.path.basename(__file__).split(".")[0]
+
+ THRESHOLD_MAX_OUTLIER_DIFF = 0.1
+ THRESHOLD_MIN_LEVEL = 0.1
+ THRESHOLD_MAX_LEVEL = 0.9
+ THRESHOLD_MAX_ABS_GRAD = 0.001
+
+ mults = []
+ r_means = []
+ g_means = []
+ b_means = []
+
+ with its.device.ItsSession() as cam:
+ props = cam.get_camera_properties()
+ if not its.caps.compute_target_exposure(props):
+ print "Test skipped"
+ return
+
+ e,s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
+ expt_range = props['android.sensor.info.exposureTimeRange']
+ sens_range = props['android.sensor.info.sensitivityRange']
+
+ m = 1
+ while s*m < sens_range[1] and e/m > expt_range[0]:
+ mults.append(m)
+ req = its.objects.manual_capture_request(s*m, e/m)
+ cap = cam.do_capture(req)
+ img = its.image.convert_capture_to_rgb_image(cap)
+ its.image.write_image(img, "%s_mult=%02d.jpg" % (NAME, m))
+ tile = its.image.get_image_patch(img, 0.45, 0.45, 0.1, 0.1)
+ rgb_means = its.image.compute_image_means(tile)
+ r_means.append(rgb_means[0])
+ g_means.append(rgb_means[1])
+ b_means.append(rgb_means[2])
+ m = m + 4
+
+ # Draw a plot.
+ pylab.plot(mults, r_means, 'r')
+ pylab.plot(mults, g_means, 'g')
+ pylab.plot(mults, b_means, 'b')
+ 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
+ # (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()
+ print "Channel %d line fit (y = mx+b): m = %f, b = %f" % (chan, m, b)
+ assert(abs(m) < THRESHOLD_MAX_ABS_GRAD)
+ assert(b > THRESHOLD_MIN_LEVEL and b < THRESHOLD_MAX_LEVEL)
+ for v in values:
+ assert(v > THRESHOLD_MIN_LEVEL and v < THRESHOLD_MAX_LEVEL)
+ assert(abs(v - b) < THRESHOLD_MAX_OUTLIER_DIFF)
+
+if __name__ == '__main__':
+ main()
+