apps/CameraITS/tests/scene5/test_lens_shading_and_color_uniformity.py - platform/cts - Git at Google

 # Copyright 2016 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.
 """Test lens shading and color uniformity with diffuser over camera."""


 import logging
 import math
 import os.path

 from mobly import test_runner
 import numpy

 import cv2
 import its_base_test
 import camera_properties_utils
 import capture_request_utils
 import image_processing_utils
 import its_session_utils

 _NAME = os.path.basename(__file__).split('.')[0]
 _NSEC_TO_MSEC = 1E-6

 # List to create NUM-1 blocks around the center block for sampling grid in image
 _NUM_RADIUS = 8
 _BLOCK_R = 1/2/(_NUM_RADIUS*2-1)  # 'radius' of block (x/2 & y/2 in rel values)
 _BLOCK_POSITION_LIST = numpy.arange(_BLOCK_R, 1/2, _BLOCK_R*2)

 # Thresholds for PASS/FAIL
 _THRESH_SHADING_CT = 0.9  # len shading allowance for center
 _THRESH_SHADING_CN = 0.6  # len shading allowance for corner
 _THRESH_SHADING_HIGH = 0.2  # max allowed % for patch to be brighter than center
 _THRESH_UNIFORMITY = 0.2  # uniformity allowance

 # cv2 drawing colors
 _CV2_RED = (1, 0, 0)   # blocks failed the test
 _CV2_GREEN = (0, 0.7, 0.3)   # blocks passed the test


 def _calc_block_lens_shading_thresh_l(
     block_center_x, block_center_y, center_luma, img_w, img_h, dist_max):
   dist_to_img_center = math.sqrt(pow(abs(block_center_x-0.5)*img_w, 2) +
                                  pow(abs(block_center_y-0.5)*img_h, 2))
   return ((_THRESH_SHADING_CT - _THRESH_SHADING_CN) *
           (1 - dist_to_img_center/dist_max) + _THRESH_SHADING_CN) * center_luma


 def _calc_color_plane_ratios(img_rgb):
   """Calculate R/G and B/G ratios."""
   img_g_plus_delta = img_rgb[:, :, 1] + 0.001  # in case G channel has 0 value.
   img_r_g = img_rgb[:, :, 0] / img_g_plus_delta
   img_b_g = img_rgb[:, :, 2] / img_g_plus_delta
   return img_r_g, img_b_g


 def _create_block_center_vals(block_center):
   """Create lists of x and y values for sub-block centers."""
   num_sample = int(((1-block_center*2)/_BLOCK_R/2 + 1).item())
   center_xs = numpy.concatenate(
       (numpy.arange(block_center, 1-block_center+_BLOCK_R, _BLOCK_R*2),
        block_center*numpy.ones((num_sample-1)),
        (1-block_center)*numpy.ones((num_sample-1)),
        numpy.arange(block_center, 1-block_center+_BLOCK_R, _BLOCK_R*2)))
   center_ys = numpy.concatenate(
       (block_center*numpy.ones(num_sample+1),
        numpy.arange(block_center+_BLOCK_R*2, 1-block_center, _BLOCK_R*2),
        numpy.arange(block_center+_BLOCK_R*2, 1-block_center, _BLOCK_R*2),
        (1-block_center)*numpy.ones(num_sample+1)))
   return zip(center_xs, center_ys)


 def _assert_results(ls_test_failed, cu_test_failed, center_luma, ls_thresh_h):
   """Check the lens shading and color uniformity results."""
   if ls_test_failed:
     logging.error('Lens shading test summary')
     logging.error('Center block average Y value: %.3f', center_luma)
     logging.error('Blocks failed in the lens shading test:')
     for block in ls_test_failed:
       top, bottom, left, right = block['position']
       logging.error('Block[top: %d, bottom: %d, left: %d, right: %d]; '
                     'avg Y value: %.3f; valid range: %.3f ~ %.3f', top, bottom,
                     left, right, block['val'], block['thresh_l'], ls_thresh_h)
   if cu_test_failed:
     logging.error('Color uniformity test summary')
     logging.error('Valid color uniformity range: 0 ~ %.2f', _THRESH_UNIFORMITY)
     logging.error('Areas that failed the color uniformity test:')
     for rd in cu_test_failed:
       logging.error('Radius position: %.3f; R/G uniformity: %.3f; B/G '
                     'uniformity: %.3f', rd['position'], rd['uniformity_r_g'],
                     rd['uniformity_b_g'])
   if ls_test_failed:
     raise AssertionError('Lens shading test failed.')
   if cu_test_failed:
     raise AssertionError('Color uniformity test failed.')


 def _draw_legend(img, texts, text_org, font_scale, text_offset, color,
                  line_width):
   """Draw legend on an image.

   Args:
     img: Numpy float image array in RGB, with pixel values in [0,1].
     texts: List of legends. Each element in the list is a line of legend.
     text_org: Tuple of the bottom left corner of the text position in
               pixels, horizontal and vertical.
     font_scale: Float number. Font scale of the basic font size.
     text_offset: Text line width in pixels.
     color: Text color in rgb value.
     line_width: Text line width in pixels.
   """
   for text in texts:
     cv2.putText(img, text, (text_org[0], text_org[1]),
                 cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_width)
     text_org[1] += text_offset


 class LensShadingAndColorUniformityTest(its_base_test.ItsBaseTest):
   """Test lens shading correction and uniform scene is evenly distributed.

   Test runs with a diffuser (manually) placed in front of the camera.
   Performs this test on a YUV frame with auto 3A. Lens shading is evaluated
   based on the Y channel. Measure the average Y value for each sample block
   specified, and then determine PASS/FAIL by comparing with the center Y value.

   Evaluates the color uniformity in R/G and B/G color space. At specified
   radius of the image, the variance of R/G and B/G values need to be less than
   a threshold in order to pass the test.
   """

   def test_lens_shading_and_color_uniformity(self):

     with its_session_utils.ItsSession(
         device_id=self.dut.serial,
         camera_id=self.camera_id,
         hidden_physical_id=self.hidden_physical_id) as cam:
       props = cam.get_camera_properties()
       props = cam.override_with_hidden_physical_camera_props(props)
       log_path = self.log_path

       # Check SKIP conditions.
       camera_properties_utils.skip_unless(
           camera_properties_utils.ae_lock(props) and
           camera_properties_utils.awb_lock(props))

       if camera_properties_utils.read_3a(props):
         # Converge 3A and get the estimates.
         sens, exp, awb_gains, awb_xform, _ = cam.do_3a(
             get_results=True, do_af=False, lock_ae=True, lock_awb=True)
         logging.debug('AE sensitivity: %d, exp: %dms', sens, exp*_NSEC_TO_MSEC)
         logging.debug('AWB gains: %s', str(awb_gains))
         logging.debug('AWB transform: %s', str(awb_xform))

       req = capture_request_utils.auto_capture_request()
       w, h = capture_request_utils.get_available_output_sizes('yuv', props)[0]
       out_surface = {'format': 'yuv', 'width': w, 'height': h}
       cap = cam.do_capture(req, out_surface)
       logging.debug('Captured YUV %dx%d', w, h)
       # Get Y channel
       img_y = image_processing_utils.convert_capture_to_planes(cap)[0]
       image_processing_utils.write_image(img_y, '%s_y_plane.png' %
                                          (os.path.join(log_path, _NAME)), True)
       # Convert RGB image & calculate R/G, R/B ratioed images
       img_rgb = image_processing_utils.convert_capture_to_rgb_image(cap)
       img_r_g, img_b_g = _calc_color_plane_ratios(img_rgb)

       # Make copies for images with legends and set legend parameters.
       img_lens_shading = numpy.copy(img_rgb)
       img_uniformity = numpy.copy(img_rgb)
       line_width = max(2, int(max(h, w)/500))  # line width of legend
       font_scale = line_width / 7.0   # font scale of the basic font size
       font_line_width = int(line_width/2)
       text_height = cv2.getTextSize('gf', cv2.FONT_HERSHEY_SIMPLEX,
                                     font_scale, line_width)[0][1]
       text_offset = int(text_height*1.5)

       # Calculate center block average Y, R/G, and B/G values.
       top = int((0.5-_BLOCK_R)*h)
       bottom = int((0.5+_BLOCK_R)*h)
       left = int((0.5-_BLOCK_R)*w)
       right = int((0.5+_BLOCK_R)*w)
       center_luma = numpy.mean(img_y[top:bottom, left:right])
       center_r_g = numpy.mean(img_r_g[top:bottom, left:right])
       center_b_g = numpy.mean(img_b_g[top:bottom, left:right])

       # Add center patch legend to lens shading and color uniformity images
       cv2.rectangle(img_lens_shading, (left, top), (right, bottom), _CV2_GREEN,
                     line_width)
       _draw_legend(img_lens_shading, [f'Y: {center_luma}:.2f'],
                    [left+text_offset, bottom-text_offset],
                    font_scale, text_offset, _CV2_GREEN, font_line_width)

       cv2.rectangle(img_uniformity, (left, top), (right, bottom), _CV2_GREEN,
                     line_width)
       _draw_legend(img_uniformity,
                    [f'R/G: {center_r_g}:.2f', f'B/G: {center_b_g}:.2f'],
                    [left+text_offset, bottom-text_offset*2],
                    font_scale, text_offset, _CV2_GREEN, font_line_width)

       # Evaluate Y, R/G, and B/G for each block
       ls_test_failed = []
       cu_test_failed = []
       ls_thresh_h = center_luma * (1 + _THRESH_SHADING_HIGH)
       dist_max = math.sqrt(pow(w, 2)+pow(h, 2))/2
       for position in _BLOCK_POSITION_LIST:
         # Create sample block centers' positions in all directions around center
         block_centers = _create_block_center_vals(position)

         blocks_info = []
         max_r_g = 0
         min_r_g = float('inf')
         max_b_g = 0
         min_b_g = float('inf')
         for block_center_x, block_center_y in block_centers:
           top = int((block_center_y-_BLOCK_R)*h)
           bottom = int((block_center_y+_BLOCK_R)*h)
           left = int((block_center_x-_BLOCK_R)*w)
           right = int((block_center_x+_BLOCK_R)*w)

           # Compute block average values and running mins and maxes
           block_y = numpy.mean(img_y[top:bottom, left:right])
           block_r_g = numpy.mean(img_r_g[top:bottom, left:right])
           block_b_g = numpy.mean(img_b_g[top:bottom, left:right])
           max_r_g = max(max_r_g, block_r_g)
           min_r_g = min(min_r_g, block_r_g)
           max_b_g = max(max_b_g, block_b_g)
           min_b_g = min(min_b_g, block_b_g)
           blocks_info.append({'position': [top, bottom, left, right],
                               'block_r_g': block_r_g,
                               'block_b_g': block_b_g})
           # Check lens shading
           ls_thresh_l = _calc_block_lens_shading_thresh_l(
               block_center_x, block_center_y, center_luma, w, h, dist_max)

           if not ls_thresh_h > block_y > ls_thresh_l:
             ls_test_failed.append({'position': [top, bottom, left, right],
                                    'val': block_y,
                                    'thresh_l': ls_thresh_l})
             legend_color = _CV2_RED
           else:
             legend_color = _CV2_GREEN

           # Overlay legend rectangle on lens shading image.
           text_bottom = bottom - text_offset
           cv2.rectangle(img_lens_shading, (left, top), (right, bottom),
                         legend_color, line_width)
           _draw_legend(img_lens_shading, ['Y: %.2f' % block_y],
                        [left+text_offset, text_bottom], font_scale,
                        text_offset, legend_color, int(line_width/2))

         # Check color uniformity
         uniformity_r_g = (max_r_g-min_r_g) / center_r_g
         uniformity_b_g = (max_b_g-min_b_g) / center_b_g
         if (uniformity_r_g > _THRESH_UNIFORMITY or
             uniformity_b_g > _THRESH_UNIFORMITY):
           cu_test_failed.append({'position': position,
                                  'uniformity_r_g': uniformity_r_g,
                                  'uniformity_b_g': uniformity_b_g})
           legend_color = _CV2_RED
         else:
           legend_color = _CV2_GREEN

         # Overlay legend blocks on uniformity image based on PASS/FAIL above.
         for block in blocks_info:
           top, bottom, left, right = block['position']
           cv2.rectangle(img_uniformity, (left, top), (right, bottom),
                         legend_color, line_width)
           texts = ['R/G: %.2f' % block['block_r_g'],
                    'B/G: %.2f' % block['block_b_g']]
           text_bottom = bottom - text_offset * 2
           _draw_legend(img_uniformity, texts,
                        [left+text_offset, text_bottom], font_scale,
                        text_offset, legend_color, font_line_width)

       # Save images
       image_processing_utils.write_image(
           img_uniformity, '%s_color_uniformity_result.png' %
           (os.path.join(log_path, _NAME)), True)
       image_processing_utils.write_image(
           img_lens_shading, '%s_lens_shading_result.png' %
           (os.path.join(log_path, _NAME)), True)

       # Assert results
       _assert_results(ls_test_failed, cu_test_failed, center_luma, ls_thresh_h)


 if __name__ == '__main__':
   test_runner.main()
	# Copyright 2016 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.
	"""Test lens shading and color uniformity with diffuser over camera."""


	import logging
	import math
	import os.path

	from mobly import test_runner
	import numpy

	import cv2
	import its_base_test
	import camera_properties_utils
	import capture_request_utils
	import image_processing_utils
	import its_session_utils

	_NAME = os.path.basename(__file__).split('.')[0]
	_NSEC_TO_MSEC = 1E-6

	# List to create NUM-1 blocks around the center block for sampling grid in image
	_NUM_RADIUS = 8
	_BLOCK_R = 1/2/(_NUM_RADIUS*2-1) # 'radius' of block (x/2 & y/2 in rel values)
	_BLOCK_POSITION_LIST = numpy.arange(_BLOCK_R, 1/2, _BLOCK_R*2)

	# Thresholds for PASS/FAIL
	_THRESH_SHADING_CT = 0.9 # len shading allowance for center
	_THRESH_SHADING_CN = 0.6 # len shading allowance for corner
	_THRESH_SHADING_HIGH = 0.2 # max allowed % for patch to be brighter than center
	_THRESH_UNIFORMITY = 0.2 # uniformity allowance

	# cv2 drawing colors
	_CV2_RED = (1, 0, 0) # blocks failed the test
	_CV2_GREEN = (0, 0.7, 0.3) # blocks passed the test


	def _calc_block_lens_shading_thresh_l(
	block_center_x, block_center_y, center_luma, img_w, img_h, dist_max):
	dist_to_img_center = math.sqrt(pow(abs(block_center_x-0.5)*img_w, 2) +
	pow(abs(block_center_y-0.5)*img_h, 2))
	return ((_THRESH_SHADING_CT - _THRESH_SHADING_CN) *
	(1 - dist_to_img_center/dist_max) + _THRESH_SHADING_CN) * center_luma


	def _calc_color_plane_ratios(img_rgb):
	"""Calculate R/G and B/G ratios."""
	img_g_plus_delta = img_rgb[:, :, 1] + 0.001 # in case G channel has 0 value.
	img_r_g = img_rgb[:, :, 0] / img_g_plus_delta
	img_b_g = img_rgb[:, :, 2] / img_g_plus_delta
	return img_r_g, img_b_g


	def _create_block_center_vals(block_center):
	"""Create lists of x and y values for sub-block centers."""
	num_sample = int(((1-block_center*2)/_BLOCK_R/2 + 1).item())
	center_xs = numpy.concatenate(
	(numpy.arange(block_center, 1-block_center+_BLOCK_R, _BLOCK_R*2),
	block_center*numpy.ones((num_sample-1)),
	(1-block_center)*numpy.ones((num_sample-1)),
	numpy.arange(block_center, 1-block_center+_BLOCK_R, _BLOCK_R*2)))
	center_ys = numpy.concatenate(
	(block_center*numpy.ones(num_sample+1),
	numpy.arange(block_center+_BLOCK_R2, 1-block_center, _BLOCK_R2),
	numpy.arange(block_center+_BLOCK_R2, 1-block_center, _BLOCK_R2),
	(1-block_center)*numpy.ones(num_sample+1)))
	return zip(center_xs, center_ys)


	def _assert_results(ls_test_failed, cu_test_failed, center_luma, ls_thresh_h):
	"""Check the lens shading and color uniformity results."""
	if ls_test_failed:
	logging.error('Lens shading test summary')
	logging.error('Center block average Y value: %.3f', center_luma)
	logging.error('Blocks failed in the lens shading test:')
	for block in ls_test_failed:
	top, bottom, left, right = block['position']
	logging.error('Block[top: %d, bottom: %d, left: %d, right: %d]; '
	'avg Y value: %.3f; valid range: %.3f ~ %.3f', top, bottom,
	left, right, block['val'], block['thresh_l'], ls_thresh_h)
	if cu_test_failed:
	logging.error('Color uniformity test summary')
	logging.error('Valid color uniformity range: 0 ~ %.2f', _THRESH_UNIFORMITY)
	logging.error('Areas that failed the color uniformity test:')
	for rd in cu_test_failed:
	logging.error('Radius position: %.3f; R/G uniformity: %.3f; B/G '
	'uniformity: %.3f', rd['position'], rd['uniformity_r_g'],
	rd['uniformity_b_g'])
	if ls_test_failed:
	raise AssertionError('Lens shading test failed.')
	if cu_test_failed:
	raise AssertionError('Color uniformity test failed.')


	def _draw_legend(img, texts, text_org, font_scale, text_offset, color,
	line_width):
	"""Draw legend on an image.

	Args:
	img: Numpy float image array in RGB, with pixel values in [0,1].
	texts: List of legends. Each element in the list is a line of legend.
	text_org: Tuple of the bottom left corner of the text position in
	pixels, horizontal and vertical.
	font_scale: Float number. Font scale of the basic font size.
	text_offset: Text line width in pixels.
	color: Text color in rgb value.
	line_width: Text line width in pixels.
	"""
	for text in texts:
	cv2.putText(img, text, (text_org[0], text_org[1]),
	cv2.FONT_HERSHEY_SIMPLEX, font_scale, color, line_width)
	text_org[1] += text_offset


	class LensShadingAndColorUniformityTest(its_base_test.ItsBaseTest):
	"""Test lens shading correction and uniform scene is evenly distributed.

	Test runs with a diffuser (manually) placed in front of the camera.
	Performs this test on a YUV frame with auto 3A. Lens shading is evaluated
	based on the Y channel. Measure the average Y value for each sample block
	specified, and then determine PASS/FAIL by comparing with the center Y value.

	Evaluates the color uniformity in R/G and B/G color space. At specified
	radius of the image, the variance of R/G and B/G values need to be less than
	a threshold in order to pass the test.
	"""

	def test_lens_shading_and_color_uniformity(self):

	with its_session_utils.ItsSession(
	device_id=self.dut.serial,
	camera_id=self.camera_id,
	hidden_physical_id=self.hidden_physical_id) as cam:
	props = cam.get_camera_properties()
	props = cam.override_with_hidden_physical_camera_props(props)
	log_path = self.log_path

	# Check SKIP conditions.
	camera_properties_utils.skip_unless(
	camera_properties_utils.ae_lock(props) and
	camera_properties_utils.awb_lock(props))

	if camera_properties_utils.read_3a(props):
	# Converge 3A and get the estimates.
	sens, exp, awb_gains, awb_xform, _ = cam.do_3a(
	get_results=True, do_af=False, lock_ae=True, lock_awb=True)
	logging.debug('AE sensitivity: %d, exp: %dms', sens, exp*_NSEC_TO_MSEC)
	logging.debug('AWB gains: %s', str(awb_gains))
	logging.debug('AWB transform: %s', str(awb_xform))

	req = capture_request_utils.auto_capture_request()
	w, h = capture_request_utils.get_available_output_sizes('yuv', props)[0]
	out_surface = {'format': 'yuv', 'width': w, 'height': h}
	cap = cam.do_capture(req, out_surface)
	logging.debug('Captured YUV %dx%d', w, h)
	# Get Y channel
	img_y = image_processing_utils.convert_capture_to_planes(cap)[0]
	image_processing_utils.write_image(img_y, '%s_y_plane.png' %
	(os.path.join(log_path, _NAME)), True)
	# Convert RGB image & calculate R/G, R/B ratioed images
	img_rgb = image_processing_utils.convert_capture_to_rgb_image(cap)
	img_r_g, img_b_g = _calc_color_plane_ratios(img_rgb)

	# Make copies for images with legends and set legend parameters.
	img_lens_shading = numpy.copy(img_rgb)
	img_uniformity = numpy.copy(img_rgb)
	line_width = max(2, int(max(h, w)/500)) # line width of legend
	font_scale = line_width / 7.0 # font scale of the basic font size
	font_line_width = int(line_width/2)
	text_height = cv2.getTextSize('gf', cv2.FONT_HERSHEY_SIMPLEX,
	font_scale, line_width)[0][1]
	text_offset = int(text_height*1.5)

	# Calculate center block average Y, R/G, and B/G values.
	top = int((0.5-_BLOCK_R)*h)
	bottom = int((0.5+_BLOCK_R)*h)
	left = int((0.5-_BLOCK_R)*w)
	right = int((0.5+_BLOCK_R)*w)
	center_luma = numpy.mean(img_y[top:bottom, left:right])
	center_r_g = numpy.mean(img_r_g[top:bottom, left:right])
	center_b_g = numpy.mean(img_b_g[top:bottom, left:right])

	# Add center patch legend to lens shading and color uniformity images
	cv2.rectangle(img_lens_shading, (left, top), (right, bottom), _CV2_GREEN,
	line_width)
	_draw_legend(img_lens_shading, [f'Y: {center_luma}:.2f'],
	[left+text_offset, bottom-text_offset],
	font_scale, text_offset, _CV2_GREEN, font_line_width)

	cv2.rectangle(img_uniformity, (left, top), (right, bottom), _CV2_GREEN,
	line_width)
	_draw_legend(img_uniformity,
	[f'R/G: {center_r_g}:.2f', f'B/G: {center_b_g}:.2f'],
	[left+text_offset, bottom-text_offset*2],
	font_scale, text_offset, _CV2_GREEN, font_line_width)

	# Evaluate Y, R/G, and B/G for each block
	ls_test_failed = []
	cu_test_failed = []
	ls_thresh_h = center_luma * (1 + _THRESH_SHADING_HIGH)
	dist_max = math.sqrt(pow(w, 2)+pow(h, 2))/2
	for position in _BLOCK_POSITION_LIST:
	# Create sample block centers' positions in all directions around center
	block_centers = _create_block_center_vals(position)

	blocks_info = []
	max_r_g = 0
	min_r_g = float('inf')
	max_b_g = 0
	min_b_g = float('inf')
	for block_center_x, block_center_y in block_centers:
	top = int((block_center_y-_BLOCK_R)*h)
	bottom = int((block_center_y+_BLOCK_R)*h)
	left = int((block_center_x-_BLOCK_R)*w)
	right = int((block_center_x+_BLOCK_R)*w)

	# Compute block average values and running mins and maxes
	block_y = numpy.mean(img_y[top:bottom, left:right])
	block_r_g = numpy.mean(img_r_g[top:bottom, left:right])
	block_b_g = numpy.mean(img_b_g[top:bottom, left:right])
	max_r_g = max(max_r_g, block_r_g)
	min_r_g = min(min_r_g, block_r_g)
	max_b_g = max(max_b_g, block_b_g)
	min_b_g = min(min_b_g, block_b_g)
	blocks_info.append({'position': [top, bottom, left, right],
	'block_r_g': block_r_g,
	'block_b_g': block_b_g})
	# Check lens shading
	ls_thresh_l = _calc_block_lens_shading_thresh_l(
	block_center_x, block_center_y, center_luma, w, h, dist_max)

	if not ls_thresh_h > block_y > ls_thresh_l:
	ls_test_failed.append({'position': [top, bottom, left, right],
	'val': block_y,
	'thresh_l': ls_thresh_l})
	legend_color = _CV2_RED
	else:
	legend_color = _CV2_GREEN

	# Overlay legend rectangle on lens shading image.
	text_bottom = bottom - text_offset
	cv2.rectangle(img_lens_shading, (left, top), (right, bottom),
	legend_color, line_width)
	_draw_legend(img_lens_shading, ['Y: %.2f' % block_y],
	[left+text_offset, text_bottom], font_scale,
	text_offset, legend_color, int(line_width/2))

	# Check color uniformity
	uniformity_r_g = (max_r_g-min_r_g) / center_r_g
	uniformity_b_g = (max_b_g-min_b_g) / center_b_g
	if (uniformity_r_g > _THRESH_UNIFORMITY or
	uniformity_b_g > _THRESH_UNIFORMITY):
	cu_test_failed.append({'position': position,
	'uniformity_r_g': uniformity_r_g,
	'uniformity_b_g': uniformity_b_g})
	legend_color = _CV2_RED
	else:
	legend_color = _CV2_GREEN

	# Overlay legend blocks on uniformity image based on PASS/FAIL above.
	for block in blocks_info:
	top, bottom, left, right = block['position']
	cv2.rectangle(img_uniformity, (left, top), (right, bottom),
	legend_color, line_width)
	texts = ['R/G: %.2f' % block['block_r_g'],
	'B/G: %.2f' % block['block_b_g']]
	text_bottom = bottom - text_offset * 2
	_draw_legend(img_uniformity, texts,
	[left+text_offset, text_bottom], font_scale,
	text_offset, legend_color, font_line_width)

	# Save images
	image_processing_utils.write_image(
	img_uniformity, '%s_color_uniformity_result.png' %
	(os.path.join(log_path, _NAME)), True)
	image_processing_utils.write_image(
	img_lens_shading, '%s_lens_shading_result.png' %
	(os.path.join(log_path, _NAME)), True)

	# Assert results
	_assert_results(ls_test_failed, cu_test_failed, center_luma, ls_thresh_h)


	if __name__ == '__main__':
	test_runner.main()