1 files changed, 0 insertions, 92 deletions
diff --git a/apps/CameraITS/tests/scene1/test_exposure.py b/apps/CameraITS/tests/scene1/test_exposure.py
deleted file mode 100644
index 8676358..0000000
--- a/apps/CameraITS/tests/scene1/test_exposure.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# 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()
-