def showNonOpponency(C, theta):
"""Summary
This function encapsulates the routine to generate backprojected and cortical views for
the magnocellular pathway retinal ganglion cells
Args:
C (vector): The sharp retina is passed to the function
theta (float): A threshold value is passed to the function
Returns:
merged: Return a merged image of the backprojected view as a numpy image array
mergecort: Return a merged image of the cortical view as a numpy image array
"""
GI = retina.gauss_norm_img(x,
y,
dcoeff[i],
dloc[i],
imsize=imgsize,
rgb=False)
# Sample using the other recepetive field, note there is no temporal response with still images
S = retina.sample(img, x, y, dcoeff[i], dloc[i], rgb=True)
#backproject the imagevectors
ncentreV, nsurrV = rgc.nonopponency(C, S, theta)
ninverse = retina.inverse(ncentreV,
x,
y,
dcoeff[i],
dloc[i],
GI,
imsize=imgsize,
rgb=True)
ninv_crop = retina.crop(ninverse, x, y, dloc[i])
ninverse2 = retina.inverse(nsurrV,
x,
y,
dcoeff[i],
dloc[i],
GI,
imsize=imgsize,
rgb=True)
ninv_crop2 = retina.crop(ninverse2, x, y, dloc[i])
# place descriptive text onto generated images
cv2.putText(ninv_crop, "R+G + ", (xx, yy), font, 1, (255, 255, 255), 2)
cv2.putText(ninv_crop2, "R+G - ", (xx, yy), font, 1, (255, 255, 255), 2)
merged = np.concatenate((ninv_crop, ninv_crop2), axis=1)
# create cortical maps of the imagevectors
lposnon, rposnon = cortex.cort_img(ncentreV, L, L_loc, R, R_loc, cort_size,
G)
lnegnon, rnegnon = cortex.cort_img(nsurrV, L, L_loc, R, R_loc, cort_size,
G)
pos_cort_img = np.concatenate((np.rot90(lposnon), np.rot90(rposnon, k=3)),
axis=1)
neg_cort_img = np.concatenate((np.rot90(lnegnon), np.rot90(rnegnon, k=3)),
axis=1)
mergecort = np.concatenate((pos_cort_img, neg_cort_img), axis=1)
return merged, mergecort
def showNonOpponency(C, theta):
"""Summary
This function encapsulates the routine to generate backprojected and cortical views for
the magnocellular pathway retinal ganglion cells
Args:
C (vector): The sharp retina is passed to the function
theta (float): A threshold value is passed to the function
Returns:
merged: Return a merged image of the backprojected view as a numpy image array
mergecort: Return a merged image of the cortical view as a numpy image array
"""
# Sample using accelerated retina function
S = ret1.sample(lateimg) # SURROUND
S = retina_cuda.convert_to_Piotr(S)
#showretina:
#return the modified,rectified imagevectors
ncentreV, nsurrV = rgc.nonopponency(C, S, theta)
# generate packprojected images
ninverse = ret0.inverse(
retina_cuda.convert_from_Piotr(ncentreV.astype(float)))
ninv_crop = retina.crop(ninverse, int(img.shape[1] / 2),
int(img.shape[0] / 2), loc[0])
ninverse2 = ret1.inverse(
retina_cuda.convert_from_Piotr(nsurrV.astype(float)))
ninv_crop2 = retina.crop(ninverse2, int(img.shape[1] / 2),
int(img.shape[0] / 2), dloc[0])
# place descriptive text onto generated images
cv2.putText(ninv_crop, "R+G + ", (1, 270), font, 1, (0, 255, 255), 2)
cv2.putText(ninv_crop2, "R+G - ", (1, 270), font, 1, (0, 255, 255), 2)
merged = np.concatenate((ninv_crop, ninv_crop2), axis=1)
#showcortex
## create cortical maps of the imagevectors using accelerated functions
lposnon = cort0.cort_image_left(
retina_cuda.convert_from_Piotr(ncentreV.astype(float)))
rposnon = cort0.cort_image_right(
retina_cuda.convert_from_Piotr(ncentreV.astype(float)))
lnegnon = cort1.cort_image_left(
retina_cuda.convert_from_Piotr(nsurrV.astype(float)))
rnegnon = cort1.cort_image_right(
retina_cuda.convert_from_Piotr(nsurrV.astype(float)))
pos_cort_img_non = np.concatenate(
(np.rot90(lposnon), np.rot90(rposnon, k=3)), axis=1)
neg_cort_img_non = np.concatenate(
(np.rot90(lnegnon), np.rot90(rnegnon, k=3)), axis=1)
# merge left and right hemispheres
mergedcortex = np.concatenate((pos_cort_img_non, neg_cort_img_non), axis=1)
return merged, mergedcortex
def showNonOpponency(C, theta):
"""Summary
This function encapsulates the routine to generate backprojected and cortical views for
the magnocellular pathway retinal ganglion cells
Args:
C (vector): The sharp retina is passed to the function
theta (float): A threshold value is passed to the function
Returns:
merged: Return a merged image of the backprojected view as a numpy image array
mergecort: Return a merged image of the cortical view as a numpy image array
"""
# Sample using the other recepetive field, but with a temporally different image, lateimg
S = retina.sample(lateimg, x, y, dcoeff[i], dloc[i], rgb=True)
ncentreV, nsurrV = rgc.nonopponency(C, S, theta)
ninverse = retina.inverse(ncentreV,
x,
y,
dcoeff[i],
dloc[i],
GI,
imsize=imgsize,
rgb=False)
ninv_crop = retina.crop(ninverse, x, y, dloc[i])
ninverse2 = retina.inverse(nsurrV,
x,
y,
dcoeff[i],
dloc[i],
GI,
imsize=imgsize,
rgb=False)
ninv_crop2 = retina.crop(ninverse2, x, y, dloc[i])
merged = np.concatenate((ninv_crop, ninv_crop2), axis=1)
lposnon, rposnon = cortex.cort_img(ncentreV, L, L_loc, R, R_loc, cort_size,
G)
lnegnon, rnegnon = cortex.cort_img(nsurrV, L, L_loc, R, R_loc, cort_size,
G)
pos_cort_img = np.concatenate((np.rot90(lposnon), np.rot90(rposnon, k=3)),
axis=1)
neg_cort_img = np.concatenate((np.rot90(lnegnon), np.rot90(rnegnon, k=3)),
axis=1)
mergecort = np.concatenate((pos_cort_img, neg_cort_img), axis=1)
return merged, mergecort