class CorticalMapping:
def __init__(self):
self.R = None
self.C = None
self.fixation = None
def setup_cortex(self, cortex_left, cortex_right):
#Create and prepare cortex
self.C = Cortex()
lp = join(datadir, "cortices", "{0}loc.pkl".format(cortex_left))
rp = join(datadir, "cortices", "{0}loc.pkl".format(cortex_right))
self.C.loadLocs(lp, rp)
self.C.loadCoeffs(
join(datadir, "cortices", "{0}coeff.pkl".format(cortex_left)),
join(datadir, "cortices", "{0}coeff.pkl".format(cortex_right)))
def setup_retina(self, retina_resolution):
#Create and load retina
self.R = Retina()
self.R.info()
self.R.loadLoc(
join(datadir, "retinas", "{0}_loc.pkl".format(retina_resolution)))
self.R.loadCoeff(
join(datadir, "retinas",
"{0}_coeff.pkl".format(retina_resolution)))
# img = cv2.imread("{}/examples/mario.png".format(os.getcwd()), cv2.IMREAD_COLOR)
# print(os.getcwd())
# print(type(img))
#Prepare retina
# x = img.shape[1]/2
# y = img.shape[0]/2
# print("X: ", x)
# print("Y: ", y)
# print("Retina shape: ", img.shape)
x = 120.0
y = 112.0
self.fixation = (y, x)
self.R.prepare((224, 240, 3), self.fixation)
def cortical_transform(self, im_array):
V = self.R.sample(im_array, self.fixation)
cimg = self.C.cort_img(V)
# print("Cimg type: ", type(cimg))
return cimg
def backproject_transform(self, im_array):
V = self.R.sample(im_array, self.fixation)
tight = self.R.backproject_tight_last()
# print("Cimg type: ", type(cimg))
return tight
def arr_to_img(self, img_arr, index):
cv2.imwrite(
"{}/test_images/test_image_{}.jpg".format(os.getcwd(), index),
img_arr)
C = utils.loadPickle(join(pyr_path, "50K_pyr_narrow_coeffs.pkl"))
C2 = utils.loadPickle(join(pyr_path, "50K_pyr_wide_coeffs.pkl"))
narrow = Pyramid(tess=L, coeffs=C, N=N, R=R)
wide = Pyramid(tess=L2, coeffs=C2, N=N2, R=R)
donarrow = False
dowide = False
dodog = True
while True:
ret, img = cap.read()
if ret is True:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
V = R.sample(img, fixation)
tight = R.backproject_tight_last()
narrow_PV = narrow.sample(V)
wide_PV = wide.sample(V)
DoG = wide_PV - narrow_PV
cv2.namedWindow("inverted", cv2.WINDOW_AUTOSIZE)
cv2.imshow("inverted", tight)
cv2.namedWindow("input", cv2.WINDOW_AUTOSIZE)
cv2.imshow("input", img)
if donarrow: narrow_vis = narrow.backproject_last()
if dowide: wide_vis = wide.backproject_last()
if dodog:
DoG_vis = narrow.backproject(DoG, R._imsize, fixation, n=False)