# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", help = "path to the normal image file")
ap.add_argument("-c", "--class", help = "class for training data")
# ap.add_argument("-s", "--splitimage", help = "path to the spectrum image file")
args = vars(ap.parse_args())
# load the image
image = cv2.imread(args["image"])
orig = image.copy()
# for training data
ID = args["class"]
# find pairs of eyes
contours = find_eye(image)
[con_pairs, pair_det] = find_pairs(image, contours)
num_pairs = len(con_pairs)
fname = os.path.basename(args["image"])
print("------- RESULTS -------")
print("SEARCHED " + str(fname))
print("FOUND " + str(num_pairs) + " PAIR/S")
# set up new databases
# interpupillary distance and colour
fields1 = ['file','ID','distance','hue','r','g','b']
f1 = open("distcolour.csv", 'w')
writer = csv.writer(f1)
writer.writerow(fields1)
f1.close()
# spectrum
def main(filename):
# load the image
image = cv2.imread(filename)
orig = image.copy()
new = image.copy()
# for training data
#ID = args["class"]
# find pairs of eyes
contours = find_eye(image)
[con_pairs, pair_det] = find_pairs(image, contours)
num_pairs = len(con_pairs)
fname = filename
print("------- RESULTS -------")
print("SEARCHED " + str(fname))
print("FOUND " + str(num_pairs) + " PAIR/S")
"""
# set up new databases
# spectrum
fields2 = ['file','ID','L/R']
fields2 = fields2 + list(range(400, 721))
f2 = open("spec.csv", 'w')
writer = csv.writer(f2)
writer.writerow(fields2)
f2.close()
"""
# For each pair
for i in range(0, num_pairs):
# get eye details for the pair
con1, con2 = con_pairs[i][0], con_pairs[i][1]
pair = pair_det[i]
eye1, eye2 = pair[0], pair[1]
### IPD and colour
# interpupillary distance (relative to pupil width)
w1, w2 = eye1[3][0]-eye1[2][0], eye2[3][0]-eye2[2][0]
ave_w = (w1+w2) / 2
dist = math.sqrt((eye2[0]-eye1[0])**2 + (eye2[1]-eye1[1])**2) / ave_w
# get colour
col1 = get_colour(orig, eye1[0:2], ave_w/2)
col2 = get_colour(orig, eye2[0:2], ave_w/2)
ave_col = ave_eye_colours(col1, col2)
r,g,b = ave_col
hue = get_hue(ave_col)
# print results
print("---------")
print("NAE Pair " + str(i+1))
print("Interpupillary distance: " + str(dist))
print("Colour (hue): " + str(hue))
print("Colour (RGB): " + str(ave_col))
### Spectrum
# get spectrum
[spec1, spec2] = get_spectrum(pair, orig)
# graph spectrum
x1, y1 = zip(*spec1)
x2, y2 = zip(*spec2)
plt.plot(x1,y1)
plt.plot(x2,y2, 'red')
plt.title("NAE Spectrum - file: %s" % filename)
plt.xlabel("Wavelength (nm)")
plt.ylabel("Intensity")
plt.savefig('graph.jpg')
plt.show()
"""
def main(filename):
# load the image
image = cv2.imread(filename)
orig = image.copy()
new = image.copy()
# for training data
#ID = args["class"]
# find pairs of eyes
contours = find_eye(image)
[con_pairs, pair_det] = find_pairs(image, contours)
num_pairs = len(con_pairs)
fname = filename
print("\n\n============================================================")
print("========================= RESULTS ==========================")
print("\tSEARCHED " + str(fname))
print("\tFOUND " + str(num_pairs) + " PAIR/S")
colours = [(255, 0, 0), (0, 0, 255), (0, 255, 0), (255, 255, 0),
(255, 0, 255), (0, 255, 255), (100, 200, 100), (100, 0, 200),
(200, 100, 200), (200, 100, 100)]
i = 0
# circle around the pairs found in the image
for pair in con_pairs:
i += 1
col = colours[i % 10]
if len(pair) == 2:
for eye in pair:
(cX, cY), radius = cv2.minEnclosingCircle(eye)
cv2.circle(new, (int(cX), int(cY)), int(radius + 8), col, 5)
else:
pass
# save circled image
cv2.imwrite(fname[0:-4] + "_circled.jpg", new)
# set up new databases
# spectrum
fields2 = ['file', 'ID', 'L/R']
fields2 = fields2 + list(range(400, 700))
f2 = open("data/spectest.csv", 'w')
writer = csv.writer(f2)
writer.writerow(fields2)
f2.close()
# For each pair
for i in range(0, num_pairs):
# get eye details for the pair
con1, con2 = con_pairs[i][0], con_pairs[i][1]
pair = pair_det[i]
eye1, eye2 = pair[0], pair[1]
### IPD and colour
# interpupillary distance (relative to pupil width)
w1, w2 = eye1[3][0] - eye1[2][0], eye2[3][0] - eye2[2][0]
ave_w = (w1 + w2) / 2
dist = math.sqrt((eye2[0] - eye1[0])**2 +
(eye2[1] - eye1[1])**2) / ave_w
# get colour
col1 = get_colour(orig, eye1[0:2], ave_w / 2)
col2 = get_colour(orig, eye2[0:2], ave_w / 2)
ave_col = ave_eye_colours(col1, col2)
r, g, b = ave_col
hue = get_hue(ave_col)
# print results
print("------------------------------------------------------------")
print("\tNAE Pair " + str(i + 1))
print("\tInterpupillary distance: " + str(dist))
print("\tColour (hue): " + str(hue))
print("\tColour (RGB): " + str(ave_col))
### Spectrum
# get spectrum
[spec1, spec2] = get_spectrum(pair, orig)
# graph spectrum
x1, y1 = zip(*spec1)
x2, y2 = zip(*spec2)
plt.plot(x1, y1)
plt.plot(x2, y2, 'red')
plt.title("NAE Spectrum - file: %s" % filename)
plt.xlabel("Wavelength (nm)")
plt.ylabel("Intensity")
plt.savefig(filename[:-4] + 'graph.jpg')
plt.show()
# add spectrum to spec database
f2 = open("data/spectest.csv", 'a')
writer = csv.writer(f2)
writer.writerow([fname, 0, 'L'] + list(y1))
writer.writerow([fname, 0, 'R'] + list(y2))
f2.close()