apps/CameraITS/tests/scene1/test_crop_region_raw.py - platform/cts - Git at Google

 # 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 numpy
 import os.path

 def main():
     """Test that raw streams are not croppable.
     """
     NAME = os.path.basename(__file__).split(".")[0]

     DIFF_THRESH = 0.05
     CROP_REGION_ERROR_THRESHOLD = 0.01

     with its.device.ItsSession() as cam:
         props = cam.get_camera_properties()
         its.caps.skip_unless(its.caps.compute_target_exposure(props) and
                              its.caps.raw16(props) and
                              its.caps.per_frame_control(props))

         # Calculate the active sensor region for a full (non-cropped) image.
         a = props['android.sensor.info.activeArraySize']
         ax, ay = a["left"], a["top"]
         aw, ah = a["right"] - a["left"], a["bottom"] - a["top"]
         print "Active sensor region: (%d,%d %dx%d)" % (ax, ay, aw, ah)

         full_region = {
             "left": 0,
             "top": 0,
             "right": aw,
             "bottom": ah
         }

         # Calculate a center crop region.
         zoom = min(3.0, its.objects.get_max_digital_zoom(props))
         assert(zoom >= 1)
         cropw = aw / zoom
         croph = ah / zoom

         crop_region = {
             "left": aw / 2 - cropw / 2,
             "top": ah / 2 - croph / 2,
             "right": aw / 2 + cropw / 2,
             "bottom": ah / 2 + croph / 2
         }

         # Capture without a crop region.
         # Use a manual request with a linear tonemap so that the YUV and RAW
         # should look the same (once converted by the its.image module).
         e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
         req = its.objects.manual_capture_request(s,e, True, props)
         cap1_raw, cap1_yuv = cam.do_capture(req, cam.CAP_RAW_YUV)

         # Capture with a crop region.
         req["android.scaler.cropRegion"] = crop_region
         cap2_raw, cap2_yuv = cam.do_capture(req, cam.CAP_RAW_YUV)

         # Check the metadata related to crop regions.
         # When both YUV and RAW are requested, the crop region that's
         # applied to YUV should be reported.
         # Note that the crop region returned by the cropped captures doesn't
         # need to perfectly match the one that was requested.
         imgs = {}
         for s, cap, cr_expected, err_delta in [
                 ("yuv_full",cap1_yuv,full_region,0),
                 ("raw_full",cap1_raw,full_region,0),
                 ("yuv_crop",cap2_yuv,crop_region,CROP_REGION_ERROR_THRESHOLD),
                 ("raw_crop",cap2_raw,crop_region,CROP_REGION_ERROR_THRESHOLD)]:

             # Convert the capture to RGB and dump to a file.
             img = its.image.convert_capture_to_rgb_image(cap, props=props)
             its.image.write_image(img, "%s_%s.jpg" % (NAME, s))
             imgs[s] = img

             # Get the crop region that is reported in the capture result.
             cr_reported = cap["metadata"]["android.scaler.cropRegion"]
             x, y = cr_reported["left"], cr_reported["top"]
             w = cr_reported["right"] - cr_reported["left"]
             h = cr_reported["bottom"] - cr_reported["top"]
             print "Crop reported on %s: (%d,%d %dx%d)" % (s, x, y, w, h)

             # Test that the reported crop region is the same as the expected
             # one, for a non-cropped capture, and is close to the expected one,
             # for a cropped capture.
             ex = aw * err_delta
             ey = ah * err_delta
             assert ((abs(cr_expected["left"] - cr_reported["left"]) <= ex) and
                     (abs(cr_expected["right"] - cr_reported["right"]) <= ex) and
                     (abs(cr_expected["top"] - cr_reported["top"]) <= ey) and
                     (abs(cr_expected["bottom"] - cr_reported["bottom"]) <= ey))

         # Also check the image content; 3 of the 4 shots should match.
         # Note that all the shots are RGB below; the variable names correspond
         # to what was captured.

         # Shrink the YUV images 2x2 -> 1 to account for the size reduction that
         # the raw images went through in the RGB conversion.
         imgs2 = {}
         for s,img in imgs.iteritems():
             h,w,ch = img.shape
             if s in ["yuv_full", "yuv_crop"]:
                 img = img.reshape(h/2,2,w/2,2,3).mean(3).mean(1)
                 img = img.reshape(h/2,w/2,3)
             imgs2[s] = img

         # Strip any border pixels from the raw shots (since the raw images may
         # be larger than the YUV images). Assume a symmetric padded border.
         xpad = (imgs2["raw_full"].shape[1] - imgs2["yuv_full"].shape[1]) / 2
         ypad = (imgs2["raw_full"].shape[0] - imgs2["yuv_full"].shape[0]) / 2
         wyuv = imgs2["yuv_full"].shape[1]
         hyuv = imgs2["yuv_full"].shape[0]
         imgs2["raw_full"]=imgs2["raw_full"][ypad:ypad+hyuv:,xpad:xpad+wyuv:,::]
         imgs2["raw_crop"]=imgs2["raw_crop"][ypad:ypad+hyuv:,xpad:xpad+wyuv:,::]
         print "Stripping padding before comparison:", xpad, ypad

         for s,img in imgs2.iteritems():
             its.image.write_image(img, "%s_comp_%s.jpg" % (NAME, s))

         # Compute diffs between images of the same type.
         # The raw_crop and raw_full shots should be identical (since the crop
         # doesn't apply to raw images), and the yuv_crop and yuv_full shots
         # should be different.
         diff_yuv = numpy.fabs((imgs2["yuv_full"] - imgs2["yuv_crop"])).mean()
         diff_raw = numpy.fabs((imgs2["raw_full"] - imgs2["raw_crop"])).mean()
         print "YUV diff (crop vs. non-crop):", diff_yuv
         print "RAW diff (crop vs. non-crop):", diff_raw

         assert(diff_yuv > DIFF_THRESH)
         assert(diff_raw < DIFF_THRESH)

 if __name__ == '__main__':
     main()
	# 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 numpy
	import os.path

	def main():
	"""Test that raw streams are not croppable.
	"""
	NAME = os.path.basename(__file__).split(".")[0]

	DIFF_THRESH = 0.05
	CROP_REGION_ERROR_THRESHOLD = 0.01

	with its.device.ItsSession() as cam:
	props = cam.get_camera_properties()
	its.caps.skip_unless(its.caps.compute_target_exposure(props) and
	its.caps.raw16(props) and
	its.caps.per_frame_control(props))

	# Calculate the active sensor region for a full (non-cropped) image.
	a = props['android.sensor.info.activeArraySize']
	ax, ay = a["left"], a["top"]
	aw, ah = a["right"] - a["left"], a["bottom"] - a["top"]
	print "Active sensor region: (%d,%d %dx%d)" % (ax, ay, aw, ah)

	full_region = {
	"left": 0,
	"top": 0,
	"right": aw,
	"bottom": ah
	}

	# Calculate a center crop region.
	zoom = min(3.0, its.objects.get_max_digital_zoom(props))
	assert(zoom >= 1)
	cropw = aw / zoom
	croph = ah / zoom

	crop_region = {
	"left": aw / 2 - cropw / 2,
	"top": ah / 2 - croph / 2,
	"right": aw / 2 + cropw / 2,
	"bottom": ah / 2 + croph / 2
	}

	# Capture without a crop region.
	# Use a manual request with a linear tonemap so that the YUV and RAW
	# should look the same (once converted by the its.image module).
	e, s = its.target.get_target_exposure_combos(cam)["minSensitivity"]
	req = its.objects.manual_capture_request(s,e, True, props)
	cap1_raw, cap1_yuv = cam.do_capture(req, cam.CAP_RAW_YUV)

	# Capture with a crop region.
	req["android.scaler.cropRegion"] = crop_region
	cap2_raw, cap2_yuv = cam.do_capture(req, cam.CAP_RAW_YUV)

	# Check the metadata related to crop regions.
	# When both YUV and RAW are requested, the crop region that's
	# applied to YUV should be reported.
	# Note that the crop region returned by the cropped captures doesn't
	# need to perfectly match the one that was requested.
	imgs = {}
	for s, cap, cr_expected, err_delta in [
	("yuv_full",cap1_yuv,full_region,0),
	("raw_full",cap1_raw,full_region,0),
	("yuv_crop",cap2_yuv,crop_region,CROP_REGION_ERROR_THRESHOLD),
	("raw_crop",cap2_raw,crop_region,CROP_REGION_ERROR_THRESHOLD)]:

	# Convert the capture to RGB and dump to a file.
	img = its.image.convert_capture_to_rgb_image(cap, props=props)
	its.image.write_image(img, "%s_%s.jpg" % (NAME, s))
	imgs[s] = img

	# Get the crop region that is reported in the capture result.
	cr_reported = cap["metadata"]["android.scaler.cropRegion"]
	x, y = cr_reported["left"], cr_reported["top"]
	w = cr_reported["right"] - cr_reported["left"]
	h = cr_reported["bottom"] - cr_reported["top"]
	print "Crop reported on %s: (%d,%d %dx%d)" % (s, x, y, w, h)

	# Test that the reported crop region is the same as the expected
	# one, for a non-cropped capture, and is close to the expected one,
	# for a cropped capture.
	ex = aw * err_delta
	ey = ah * err_delta
	assert ((abs(cr_expected["left"] - cr_reported["left"]) <= ex) and
	(abs(cr_expected["right"] - cr_reported["right"]) <= ex) and
	(abs(cr_expected["top"] - cr_reported["top"]) <= ey) and
	(abs(cr_expected["bottom"] - cr_reported["bottom"]) <= ey))

	# Also check the image content; 3 of the 4 shots should match.
	# Note that all the shots are RGB below; the variable names correspond
	# to what was captured.

	# Shrink the YUV images 2x2 -> 1 to account for the size reduction that
	# the raw images went through in the RGB conversion.
	imgs2 = {}
	for s,img in imgs.iteritems():
	h,w,ch = img.shape
	if s in ["yuv_full", "yuv_crop"]:
	img = img.reshape(h/2,2,w/2,2,3).mean(3).mean(1)
	img = img.reshape(h/2,w/2,3)
	imgs2[s] = img

	# Strip any border pixels from the raw shots (since the raw images may
	# be larger than the YUV images). Assume a symmetric padded border.
	xpad = (imgs2["raw_full"].shape[1] - imgs2["yuv_full"].shape[1]) / 2
	ypad = (imgs2["raw_full"].shape[0] - imgs2["yuv_full"].shape[0]) / 2
	wyuv = imgs2["yuv_full"].shape[1]
	hyuv = imgs2["yuv_full"].shape[0]
	imgs2["raw_full"]=imgs2["raw_full"][ypad:ypad+hyuv:,xpad:xpad+wyuv:,::]
	imgs2["raw_crop"]=imgs2["raw_crop"][ypad:ypad+hyuv:,xpad:xpad+wyuv:,::]
	print "Stripping padding before comparison:", xpad, ypad

	for s,img in imgs2.iteritems():
	its.image.write_image(img, "%s_comp_%s.jpg" % (NAME, s))

	# Compute diffs between images of the same type.
	# The raw_crop and raw_full shots should be identical (since the crop
	# doesn't apply to raw images), and the yuv_crop and yuv_full shots
	# should be different.
	diff_yuv = numpy.fabs((imgs2["yuv_full"] - imgs2["yuv_crop"])).mean()
	diff_raw = numpy.fabs((imgs2["raw_full"] - imgs2["raw_crop"])).mean()
	print "YUV diff (crop vs. non-crop):", diff_yuv
	print "RAW diff (crop vs. non-crop):", diff_raw

	assert(diff_yuv > DIFF_THRESH)
	assert(diff_raw < DIFF_THRESH)

	if __name__ == '__main__':
	main()