1 files changed, 0 insertions, 86 deletions
diff --git a/apps/CameraITS/tests/scene1/test_burst_sameness_manual.py b/apps/CameraITS/tests/scene1/test_burst_sameness_manual.py
deleted file mode 100644
index 3858c0c..0000000
--- a/apps/CameraITS/tests/scene1/test_burst_sameness_manual.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# 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.image
-import its.caps
-import its.device
-import its.objects
-import its.target
-import os.path
-import numpy
-
-def main():
-    """Take long bursts of images and check that they're all identical.
-
-    Assumes a static scene. Can be used to idenfity if there are sporadic
-    frames that are processed differently or have artifacts. Uses manual
-    capture settings.
-    """
-    NAME = os.path.basename(__file__).split(".")[0]
-
-    BURST_LEN = 50
-    BURSTS = 5
-    FRAMES = BURST_LEN * BURSTS
-
-    SPREAD_THRESH = 0.03
-
-    with its.device.ItsSession() as cam:
-
-        # Capture at the smallest resolution.
-        props = cam.get_camera_properties()
-        if not its.caps.manual_sensor(props):
-            print "Test skipped"
-            return
-
-        _, fmt = its.objects.get_fastest_manual_capture_settings(props)
-        e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
-        req = its.objects.manual_capture_request(s, e)
-        w,h = fmt["width"], fmt["height"]
-
-        # Capture bursts of YUV shots.
-        # Get the mean values of a center patch for each.
-        # Also build a 4D array, which is an array of all RGB images.
-        r_means = []
-        g_means = []
-        b_means = []
-        imgs = numpy.empty([FRAMES,h,w,3])
-        for j in range(BURSTS):
-            caps = cam.do_capture([req]*BURST_LEN, [fmt])
-            for i,cap in enumerate(caps):
-                n = j*BURST_LEN + i
-                imgs[n] = its.image.convert_capture_to_rgb_image(cap)
-                tile = its.image.get_image_patch(imgs[n], 0.45, 0.45, 0.1, 0.1)
-                means = its.image.compute_image_means(tile)
-                r_means.append(means[0])
-                g_means.append(means[1])
-                b_means.append(means[2])
-
-        # Dump all images.
-        print "Dumping images"
-        for i in range(FRAMES):
-            its.image.write_image(imgs[i], "%s_frame%03d.jpg"%(NAME,i))
-
-        # The mean image.
-        img_mean = imgs.mean(0)
-        its.image.write_image(img_mean, "%s_mean.jpg"%(NAME))
-
-        # Pass/fail based on center patch similarity.
-        for means in [r_means, g_means, b_means]:
-            spread = max(means) - min(means)
-            print spread
-            assert(spread < SPREAD_THRESH)
-
-if __name__ == '__main__':
-    main()
-