parser.add_argument("-v", help="visualise?", action="store_true")
parser.add_argument("-s", help="save?", action="store_true")
args = parser.parse_args()
# Read image and kernel.
#
im = cv2.imread(args.i, cv2.IMREAD_GRAYSCALE)
# Instantiate Demosaic_NN with the given bayer pattern code [args.p],
# and run on the input bayer image.
#
dem = Demosaic_NN(args.p)
im_BGR = dem.run(im)
if args.m:
im_mono = Convert.bgr2mono(
im_BGR, R_weight=args.rw, G_weight=args.gw, B_weight=args.bw)
# Display?
#
if args.v:
cv2.imshow("input", im)
cv2.imshow("demosaiced", im_BGR)
if args.m:
cv2.imshow("mono", im_mono)
cv2.waitKey(0)
# Save?
#
if args.s:
cv2.imwrite("demosaiced.png", im_BGR)
outputs_titles.append("demosaiced")
else:
im_BGR = im_in
# Perform AWB, if requested.
#
if options.a:
awb = AWB_GrayWorld(options.aws, options.ail, options.aat)
im_BGR = awb.run(im_BGR)
outputs.append(np.copy(im_BGR))
outputs_titles.append("auto white balanced")
# Convert to mono by taking a weighting of the channels.
#
im_mono = Convert.bgr2mono(im_BGR,
R_weight=options.drw,
G_weight=options.dgw,
B_weight=options.dbw)
outputs.append(np.copy(im_mono))
outputs_titles.append("monochromed")
# Remove s&p noise from image, if requested.
#
if options.f:
kernel = np.loadtxt(options.fk, delimiter=',')
wm = Filter_WM(kernel)
im_filtered = wm.run(im_mono)
outputs.append(np.copy(im_filtered))
outputs_titles.append("filtered")
# Display?
#
