def imagetestplt(thetainput, doubleopponencyinput):
"""Summary
Display function that generates the final output images using MatplotLib windows
Args:
thetainput (float): a threshold value for perception
doubleopponencyinput (bool): A boolean toggle for changing the opponency mode
"""
theta = thetainput
rgcMode = doubleopponencyinput
C = retina.sample(img, x, y, coeff[i], loc[i],
rgb=True) # CENTRE(sharp retina)
S = retina.sample(img, x, y, dcoeff[i], dloc[i],
rgb=True) # SURROUND(blurred retina)
if rgcMode == 0:
pV, nV = rgc.opponency(C, S, theta)
else:
pV, nV = rgc.doubleopponency(C, S, theta)
rIntensity, cIntensity = showNonOpponency(C, theta)
# Construct window plots
plt.subplot(3, 1, 1), plt.imshow(cv2.cvtColor(
img, cv2.COLOR_BGR2RGB)), plt.title('Original test image')
plt.xticks([]), plt.yticks([])
plt.subplot(3, 1, 2), plt.imshow(
cv2.cvtColor(rIntensity, cv2.COLOR_BGR2RGB)), plt.title(
'Backprojected R+G Intensity Response')
plt.xticks([]), plt.yticks([])
plt.subplot(3, 1, 3), plt.imshow(
cv2.cvtColor(
cIntensity,
cv2.COLOR_BGR2RGB)), plt.title('Cortical R+G Intensity Response')
plt.xticks([]), plt.yticks([])
# format float to string
thetastring = "%.2f" % theta
plt.suptitle('Rectified DoG Intensity Images. Threshold:' + thetastring,
fontsize=16)
plt.show()
#Generate backprojected images
if showInverse:
rOpponent = showBPImg(pV, nV)
plt.imshow(cv2.cvtColor(rOpponent, cv2.COLOR_BGR2RGB)), plt.title(
'Backprojected Opponent Cells Output')
plt.xticks([]), plt.yticks([])
plt.show()
# Cortex
if showCortex:
cOpponent = showCortexImg(pV, nV)
plt.imshow(cv2.cvtColor(
cOpponent,
cv2.COLOR_BGR2RGB)), plt.title('Cortex Opponent Cells Output')
plt.xticks([]), plt.yticks([])
plt.show()
def imagetest(thetainput, doubleopponencyinput):
"""Summary
Display function that generates the final output images using opencv windows
Args:
thetainput (float): a threshold value for perception
doubleopponencyinput (bool): A boolean toggle for changing the opponency mode
"""
theta = thetainput
rgcMode = doubleopponencyinput
C = retina.sample(img, x, y, coeff[i], loc[i], rgb=True) # CENTRE
S = retina.sample(img, x, y, dcoeff[i], dloc[i], rgb=True) # SURROUND
if rgcMode == 0:
pV, nV = rgc.opponency(C, S, theta)
else:
pV, nV = rgc.doubleopponency(C, S, theta)
cv2.namedWindow("Input", cv2.WINDOW_NORMAL)
cv2.imshow("Input", img)
rIntensity, cIntensity = showNonOpponency(C, theta)
cv2.namedWindow("Intensity Responses", cv2.WINDOW_NORMAL)
cv2.imshow("Intensity Responses", rIntensity)
cv2.namedWindow("Intensity Responses Cortex", cv2.WINDOW_NORMAL)
cv2.imshow("Intensity Responses Cortex", cIntensity)
cv2.waitKey(0)
#Generate backprojected images
if showInverse:
rOpponent = showBPImg(pV, nV)
cv2.namedWindow("Backprojected Opponent Cells Output",
cv2.WINDOW_NORMAL)
cv2.imshow("Backprojected Opponent Cells Output", rOpponent)
cv2.waitKey(0)
# Cortex
if showCortex:
cOpponent = showCortexImg(pV, nV)
cv2.namedWindow("Cortex Opponent Cells Output", cv2.WINDOW_NORMAL)
cv2.imshow("Cortex Opponent Cells Output", cOpponent)
cv2.waitKey(0)
imgsize = (img.shape[0], img.shape[1])
theta = cv2.getTrackbarPos('theta', 'Input') / 100.0
rgcMode = cv2.getTrackbarPos(switch, 'Input')
# sample images
C = retina.sample(img, x, y, coeff[i], loc[i],
rgb=True) # CENTRE(sharp retina)
S = retina.sample(img, x, y, dcoeff[i], dloc[i],
rgb=True) # SURROUND(blurred retina)
# generate rectified imagevectors based on the type of opponency
if rgcMode == 0:
pV, nV = rgc.opponency(C, S, theta)
else:
pV, nV = rgc.doubleopponency(C, S, theta)
# Display functions are called
cv2.imshow("Input", img)
rIntensity, cIntensity = showNonOpponency(C, theta)
cv2.imshow("Intensity Responses", rIntensity)
cv2.namedWindow("Intensity Responses Cortex", cv2.WINDOW_NORMAL)
cv2.imshow("Intensity Responses Cortex", cIntensity)
#Generate backprojected images
if showInverse:
rOpponent = showBPImg(pV, nV)
cv2.namedWindow("Backprojected Opponent Cells Output",
cv2.WINDOW_NORMAL)
cv2.imshow("Backprojected Opponent Cells Output", rOpponent)
# Cortex