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# 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()
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