CameraITS: Add DNG noise model test
Bug: 18401428
Change-Id: If57b46f0cb6671a7ba7e63ab05b2f35fea8e0ee9
diff --git a/apps/CameraITS/tests/scene1/test_dng_noise_model.py b/apps/CameraITS/tests/scene1/test_dng_noise_model.py
new file mode 100644
index 0000000..51270b6
--- /dev/null
+++ b/apps/CameraITS/tests/scene1/test_dng_noise_model.py
@@ -0,0 +1,114 @@
+# Copyright 2014 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.device
+import its.caps
+import its.objects
+import its.image
+import os.path
+import pylab
+import matplotlib
+import matplotlib.pyplot
+
+def main():
+ """Verify that the DNG raw model parameters are correct.
+ """
+ NAME = os.path.basename(__file__).split(".")[0]
+
+ NUM_STEPS = 4
+
+ # Pass if the difference between expected and computed variances is small,
+ # defined as being within an absolute variance delta of 0.0005, or within
+ # 20% of the expected variance, whichever is larger; this is to allow the
+ # test to pass in the presence of some randomness (since this test is
+ # measuring noise of a small patch) and some imperfect scene conditions
+ # (since ITS doesn't require a perfectly uniformly lit scene).
+ DIFF_THRESH = 0.0005
+ FRAC_THRESH = 0.2
+
+ with its.device.ItsSession() as cam:
+
+ props = cam.get_camera_properties()
+ its.caps.skip_unless(its.caps.raw(props) and
+ its.caps.raw16(props) and
+ its.caps.manual_sensor(props) and
+ its.caps.read_3a(props) and
+ its.caps.per_frame_control(props))
+
+ white_level = float(props['android.sensor.info.whiteLevel'])
+ black_levels = props['android.sensor.blackLevelPattern']
+ cfa_idxs = its.image.get_canonical_cfa_order(props)
+ black_levels = [black_levels[i] for i in cfa_idxs]
+
+ # Expose for the scene with min sensitivity
+ sens_min, sens_max = props['android.sensor.info.sensitivityRange']
+ sens_step = (sens_max - sens_min) / NUM_STEPS
+ s_ae,e_ae,_,_,_ = cam.do_3a(get_results=True)
+ s_e_prod = s_ae * e_ae
+ sensitivities = range(sens_min, sens_max, sens_step)
+
+ var_expected = [[],[],[],[]]
+ var_measured = [[],[],[],[]]
+ for sens in sensitivities:
+
+ # Capture a raw frame with the desired sensitivity.
+ exp = int(s_e_prod / float(sens))
+ req = its.objects.manual_capture_request(sens, exp)
+ cap = cam.do_capture(req, cam.CAP_RAW)
+
+ # Test each raw color channel (R, GR, GB, B):
+ noise_profile = cap["metadata"]["android.sensor.noiseProfile"]
+ assert((len(noise_profile)) == 4)
+ for ch in range(4):
+ # Get the noise model parameters for this channel of this shot.
+ s,o = noise_profile[cfa_idxs[ch]]
+
+ # Get a center tile of the raw channel, and compute the mean.
+ # Use a very small patch to ensure gross uniformity (i.e. so
+ # non-uniform lighting or vignetting doesn't affect the variance
+ # calculation).
+ plane = its.image.convert_capture_to_planes(cap, props)[ch]
+ plane = (plane * white_level - black_levels[ch]) / (
+ white_level - black_levels[ch])
+ tile = its.image.get_image_patch(plane, 0.49,0.49,0.02,0.02)
+ mean = tile.mean()
+
+ # Calculate the expected variance based on the model, and the
+ # measured variance from the tile.
+ var_measured[ch].append(
+ its.image.compute_image_variances(tile)[0])
+ var_expected[ch].append(s * mean + o)
+
+ for ch in range(4):
+ pylab.plot(sensitivities, var_expected[ch], "rgkb"[ch],
+ label=["R","GR","GB","B"][ch]+" expected")
+ pylab.plot(sensitivities, var_measured[ch], "rgkb"[ch]+"--",
+ label=["R", "GR", "GB", "B"][ch]+" measured")
+ pylab.xlabel("Sensitivity")
+ pylab.ylabel("Center patch variance")
+ pylab.legend(loc=2)
+ matplotlib.pyplot.savefig("%s_plot.png" % (NAME))
+
+ # Pass/fail check.
+ for ch in range(4):
+ diffs = [var_measured[ch][i] - var_expected[ch][i]
+ for i in range(NUM_STEPS)]
+ print "Diffs (%s):"%(["R","GR","GB","B"][ch]), diffs
+ for i,diff in enumerate(diffs):
+ thresh = max(DIFF_THRESH, FRAC_THRESH * var_expected[ch][i])
+ assert(diff <= thresh)
+
+if __name__ == '__main__':
+ main()
+