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# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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 os.path
import numpy
import cv2
import math
def main():
""" Test that the lens shading correction is applied appropriately, and
color of a monochrome uniform scene is evenly distributed, for example,
when a diffuser is placed in front of the camera.
Perform 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
The color uniformity test is evaluated in r/g and b/g space. At specified
radius of the image, the variance of r/g and b/g value need to be less than
a threshold in order to pass the test.
NAME = os.path.basename(__file__).split(".")[0]
# Sample block center location and length
Num_radius = 8
spb_r = 1/2./(Num_radius*2-1)
SPB_CT_LIST = numpy.arange(spb_r, 1/2., spb_r*2)
# Threshold for pass/fail
THRES_LS_CT = 0.9 # len shading allowance for center
THRES_LS_CN = 0.6 # len shading allowance for corner
THRES_LS_HIGH = 0.05 # max allowed percentage for a patch to be brighter
# than center
THRES_UFMT = 0.1 # uniformity allowance
# Drawing color
RED = (1, 0, 0) # blocks failed the test
GREEN = (0, 0.7, 0.3) # blocks passed the test
with its.device.ItsSession() as cam:
props = cam.get_camera_properties()
# Converge 3A and get the estimates.
sens, exp, gains, xform, focus = cam.do_3a(get_results=True,
print "AE sensitivity %d, exposure %dms" % (sens, exp / 1000000.0)
print "AWB gains", gains
print "AWB transform", xform
print "AF distance", focus
req = its.objects.auto_capture_request()
img_size = its.objects.get_available_output_sizes("yuv", props)
w = img_size[0][0]
h = img_size[0][1]
out_surface = {"format": "yuv"}
cap = cam.do_capture(req, out_surface)
print "Captured yuv %dx%d" % (w, h)
# rgb image
img_rgb = its.image.convert_capture_to_rgb_image(cap)
img_g_pos = img_rgb[:, :, 1] + 0.001 # in case g channel is zero.
r_g = img_rgb[:, :, 0] / img_g_pos
b_g = img_rgb[:, :, 2] / img_g_pos
# y channel
img_y = its.image.convert_capture_to_planes(cap)[0]
its.image.write_image(img_y, "%s_y_plane.png" % NAME, True)
# Evaluation begins
# image with legend
img_legend_ls = numpy.copy(img_rgb)
img_legend_ufmt = 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
text_height = cv2.getTextSize('gf', cv2.FONT_HERSHEY_SIMPLEX,
font_scale, line_width)[0][1]
text_offset = int(text_height*1.5)
# center block average Y value, r/g, and b/g
top = int((0.5-spb_r)*h)
bottom = int((0.5+spb_r)*h)
left = int((0.5-spb_r)*w)
right = int((0.5+spb_r)*w)
center_y = numpy.mean(img_y[top:bottom, left:right])
center_r_g = numpy.mean(r_g[top:bottom, left:right])
center_b_g = numpy.mean(b_g[top:bottom, left:right])
# add legend to lens Shading figure
cv2.rectangle(img_legend_ls, (left, top), (right, bottom), GREEN,
draw_legend(img_legend_ls, ["Y: %.2f" % center_y],
[left+text_offset, bottom-text_offset],
font_scale, text_offset, GREEN, int(line_width/2))
# add legend to color uniformity figure
cv2.rectangle(img_legend_ufmt, (left, top), (right, bottom), GREEN,
texts = ["r/g: %.2f" % center_r_g,
"b/g: %.2f" % center_b_g]
draw_legend(img_legend_ufmt, texts,
[left+text_offset, bottom-text_offset*2],
font_scale, text_offset, GREEN, int(line_width/2))
# evaluate y and r/g, b/g for each block
ls_test_failed = []
cu_test_failed = []
ls_thres_h = center_y * (1 + THRES_LS_HIGH)
dist_max = math.sqrt(pow(w, 2)+pow(h, 2))/2
for spb_ct in SPB_CT_LIST:
# list sample block center location
num_sample = (1-spb_ct*2)/spb_r/2 + 1
ct_cord_x = numpy.concatenate(
(numpy.arange(spb_ct, 1-spb_ct+spb_r, spb_r*2),
numpy.arange(spb_ct, 1-spb_ct+spb_r, spb_r*2)))
ct_cord_y = numpy.concatenate(
numpy.arange(spb_ct+spb_r*2, 1-spb_ct, spb_r*2),
numpy.arange(spb_ct+spb_r*2, 1-spb_ct, spb_r*2),
blocks_info = []
max_r_g = 0
min_r_g = float("inf")
max_b_g = 0
min_b_g = float("inf")
for spb_ctx, spb_cty in zip(ct_cord_x, ct_cord_y):
top = int((spb_cty-spb_r)*h)
bottom = int((spb_cty+spb_r)*h)
left = int((spb_ctx-spb_r)*w)
right = int((spb_ctx+spb_r)*w)
dist_to_img_center = math.sqrt(pow(abs(spb_ctx-0.5)*w, 2)
+ pow(abs(spb_cty-0.5)*h, 2))
ls_thres_l = ((THRES_LS_CT-THRES_LS_CN)*(1-dist_to_img_center
/dist_max)+THRES_LS_CN) * center_y
# compute block average value
block_y = numpy.mean(img_y[top:bottom, left:right])
block_r_g = numpy.mean(r_g[top:bottom, left:right])
block_b_g = numpy.mean(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({"pos": [top, bottom, left, right],
"block_r_g": block_r_g,
"block_b_g": block_b_g})
# check lens shading and draw legend
if block_y > ls_thres_h or block_y < ls_thres_l:
ls_test_failed.append({"pos": [top, bottom, left,
"val": block_y,
"thres_l": ls_thres_l})
legend_color = RED
legend_color = GREEN
text_bottom = bottom - text_offset
cv2.rectangle(img_legend_ls, (left, top), (right, bottom),
legend_color, line_width)
draw_legend(img_legend_ls, ["Y: %.2f" % block_y],
[left+text_offset, text_bottom], font_scale,
text_offset, legend_color, int(line_width/2))
# check color uniformity and draw legend
ufmt_r_g = (max_r_g-min_r_g) / center_r_g
ufmt_b_g = (max_b_g-min_b_g) / center_b_g
if ufmt_r_g > THRES_UFMT or ufmt_b_g > THRES_UFMT:
cu_test_failed.append({"pos": spb_ct,
"ufmt_r_g": ufmt_r_g,
"ufmt_b_g": ufmt_b_g})
legend_color = RED
legend_color = GREEN
for block in blocks_info:
top, bottom, left, right = block["pos"]
cv2.rectangle(img_legend_ufmt, (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_legend_ufmt, texts,
[left+text_offset, text_bottom], font_scale,
text_offset, legend_color, int(line_width/2))
# Save images
"%s_color_uniformity_result.png" % NAME, True)
"%s_lens_shading_result.png" % NAME, True)
# print results
lens_shading_test_passed = True
color_uniformity_test_passed = True
if len(ls_test_failed) > 0:
lens_shading_test_passed = False
print "\nLens shading test summary"
print "Center block average Y value: %.3f" % center_y
print "Blocks failed in the lens shading test:"
for block in ls_test_failed:
top, bottom, left, right = block["pos"]
print "Block position: [top: %d, bottom: %d, left: %d, right: "\
"%d]; average Y value: %.3f; valid value range: %.3f ~ " \
"%.3f" % (top, bottom, left, right, block["val"],
block["thres_l"], ls_thres_h)
if len(cu_test_failed) > 0:
color_uniformity_test_passed = False
print "\nColor uniformity test summary"
print "Valid color uniformity value range: 0 ~ ", THRES_UFMT
print "Areas that failed the color uniformity test:"
for rd in cu_test_failed:
print "Radius position: %.3f; r/g uniformity: %.3f; b/g " \
"uniformity: %.3f" % (rd["pos"], rd["ufmt_r_g"],
assert lens_shading_test_passed
assert color_uniformity_test_passed
def draw_legend(img, texts, text_org, font_scale, text_offset, legend_color,
""" Draw legend on an image.
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.
legend_color: text color in rgb value.
line_width: strokes width in pixels.
for text in texts:
cv2.putText(img, text, (text_org[0], text_org[1]),
cv2.FONT_HERSHEY_SIMPLEX, font_scale,
legend_color, line_width)
text_org[1] += text_offset
if __name__ == '__main__':