def symmetryCalculationIntensity(face_boundary_points, input_img,
eye_line_point1_woNP, eye_line_point2_woNP,
x_symmetry, y_symmetry, vx_symmetry,
vy_symmetry):
# We first need to get the points of the symmetry line
symmetry_point1_woNP = [(x_symmetry - 200 * vx_symmetry),
(y_symmetry - 200 * vy_symmetry)]
symmetry_point2_woNP = [(x_symmetry + 200 * vx_symmetry),
(y_symmetry + 200 * vy_symmetry)]
# symmetry_point1_woNP = x_symmetry - 200*vx_symmetry,y_symmetry - 200*vy_symmetry
# symmetry_point2_woNP = x_symmetry + 200*vx_symmetry,y_symmetry + 200*vy_symmetry
# symmetry_point1 = np.array([(x_symmetry - 200*vx_symmetry),(y_symmetry - 200*vy_symmetry)])
# symmetry_point2 = np.array([(x_symmetry + 200*vx_symmetry),(y_symmetry + 200*vy_symmetry)])
# symmetry_point1 = np.array(symmetry_point1_woNP)
# symmetry_point2 = np.array(symmetry_point2_woNP)
symmetry_point1 = symmetry_point1_woNP
symmetry_point2 = symmetry_point2_woNP
# eye_line_point1 = np.array([(eye_line_point1[0]),(eye_line_point1[1])])
# eye_line_point2 = np.array([(eye_line_point2[0]),(eye_line_point2[1])])
# eye_line_point1 = np.array(eye_line_point1_woNP)
# eye_line_point2 = np.array(eye_line_point2_woNP)
#
# # so now a hack - which also didn't work !
# eye_line_point1 = np.transpose(eye_line_point1)
# eye_line_point2 = np.transpose(eye_line_point2)
eye_line_point1 = eye_line_point1_woNP
eye_line_point2 = eye_line_point2_woNP
# we come down in y axis
row, col, depth = input_img.shape
if col < row:
input_img = np.transpose(input_img)
else:
pass
# theta = 0
# pdb.set_trace()
# We know that eye_line_point1 is going to be on the left side of the face
points_count = len(face_boundary_points[0, :])
left_boundary = face_boundary_points[:, 0:((points_count / 2) - 1)]
right_boundary = face_boundary_points[:, (points_count / 2):(points_count -
1)]
# We need to scan all the points on the left boundary which have y value greater than eye_line_point1
sum = 0
symmetry_point1 = np.asarray(symmetry_point1)
symmetry_point2 = np.asarray(symmetry_point2)
symmetry_point1 = symmetry_point1.astype(int)
symmetry_point2 = symmetry_point2.astype(int)
# symmetry_perpendicular_intersection = LineSegmentIntersection([int(symmetry_point1[0]), int(symmetry_point1[1])], [int(symmetry_point2[0]), int(symmetry_point2[1])], eye_line_point1, eye_line_point2)
eye_line_point1 = np.asarray(eye_line_point1)
eye_line_point2 = np.asarray(eye_line_point2)
eye_line_point1 = eye_line_point1.reshape((2, 1))
eye_line_point2 = eye_line_point2.reshape((2, 1))
eye_line_point1 = eye_line_point1.astype(int)
eye_line_point2 = eye_line_point2.astype(int)
# pdb.set_trace()
symmetry_perpendicular_intersection = LineSegmentIntersection(
symmetry_point1, symmetry_point2, eye_line_point1, eye_line_point2)
symmetry_perpendicular_intersection = np.asarray(
symmetry_perpendicular_intersection)
symmetry_perpendicular_intersection = symmetry_perpendicular_intersection.astype(
int)
sum_left = 0
sum_right = 0
for i in range(0, (points_count / 2) - 1):
# The above loop will run if eye_line_point1[1]< eye_line_point2[1]
if (eye_line_point1[1] < eye_line_point2[1]) or (
eye_line_point1[1] == eye_line_point2[1]):
if face_boundary_points[1, i] < eye_line_point1[1]:
print 'pass ', i
pass
elif (face_boundary_points[1, i] <
symmetry_perpendicular_intersection[1]) and (
eye_line_point1[1] < face_boundary_points[1, i]):
xf = find_x(face_boundary_points[1, i], eye_line_point1,
eye_line_point2)
sum_left = sum_left + np.sum(
input_img[face_boundary_points[0, i]:xf,
face_boundary_points[1, i]])
print 'cond 1'
elif ((symmetry_perpendicular_intersection[1] <
face_boundary_points[1, i])
and (face_boundary_points[1, i] < eye_line_point2[1])):
xf = find_x(face_boundary_points[1, i], eye_line_point1,
eye_line_point2)
xg = find_x(face_boundary_points[1, i], symmetry_point1,
symmetry_point2)
sum_left = sum_left + np.sum(
input_img[face_boundary_points[0, i]:xg,
face_boundary_points[1, i]])
sum_right = sum_right + np.sum(
input_img[xg:xf, face_boundary_points[1, i]])
print 'cond 2'
elif face_boundary_points[1, i] > eye_line_point2[1]:
xg = find_x(face_boundary_points[1, i], symmetry_point1,
symmetry_point2)
sum_left = sum_left + np.sum(
input_img[face_boundary_points[0, i]:xg,
face_boundary_points[1, i]])
sum_right = sum_right + np.sum(
input_img[xg:right_boundary[0, i],
face_boundary_points[1, i]])
# sum_right = sum_right + np.sum(input_img[xg:face_boundary_points[1,i], face_boundary_points[1,i]])
print 'cond 3'
# total_sum = sum_left + sum_right
# # pdb.set_trace()set_trace
# left_percentage = (sum_left/total_sum)*100
# right_percentage = (sum_right/total_sum)*100
# total_sum = sum_left + sum_right
# # pdb.set_trace()set_trace
# left_percentage = (sum_left/total_sum)*100
# right_percentage = (sum_right/total_sum)*100
# For the case where eye_line_point1[1]>=eye_line_point2[1]:
else:
print 'Case 2 - left tilted face'
if face_boundary_points[1, i] < eye_line_point2[1]:
pass
elif (face_boundary_points[1, i] <
symmetry_perpendicular_intersection[1]) and (
eye_line_point2[1] < face_boundary_points[1, i]):
xf = find_x(face_boundary_points[1, i], eye_line_point1,
eye_line_point2)
sum_right = sum_right + np.sum(
input_img[xf:right_boundary[0, i],
face_boundary_points[1, i]])
#
# xg = find_x(face_boundary_points[1], symmetry_point1, symmetry_point2)
# sum_left = sum_left + np.sum(input_img[face_boundary_points[0,i]:xg, face_boundary_points[1,i]])
# sum_right = sum_right + np.sum(input_img[xg:xf, face_boundary_points[1,i]])
elif ((symmetry_perpendicular_intersection[1] <
face_boundary_points[1, i])
and (face_boundary_points[1, i] < eye_line_point1[1])):
xf = find_x(face_boundary_points[1, i], eye_line_point1,
eye_line_point2)
xg = find_x(face_boundary_points[1, i], symmetry_point1,
symmetry_point2)
sum_left = sum_left + np.sum(
input_img[xf:xg, face_boundary_points[1, i]])
sum_right = sum_right + np.sum(
input_img[xg:right_boundary[0, i],
face_boundary_points[1, i]])
elif face_boundary_points[1, i] > eye_line_point1[1]:
xg = find_x(face_boundary_points[1, i], symmetry_point1,
symmetry_point2)
# pdb.set_trace()
print "face_boundary_points[0,i] = ", face_boundary_points[0,
i]
print "xg = ", xg
sum_left = sum_left + np.sum(
input_img[face_boundary_points[0, i]:xg,
face_boundary_points[1, i]])
sum_right = sum_right + np.sum(
input_img[xg:right_boundary[0, i],
face_boundary_points[1, i]])
total_sum = sum_left + sum_right
# pdb.set_trace()
left_percentage = (sum_left / total_sum) * 100
right_percentage = (sum_right / total_sum) * 100
return left_percentage, right_percentage
def symmetryCalculationIntensity(face_boundary_points,input_img, eye_line_point1_woNP,eye_line_point2_woNP, x_symmetry, y_symmetry, vx_symmetry, vy_symmetry):
# We first need to get the points of the symmetry line
symmetry_point1_woNP = [(x_symmetry - 200*vx_symmetry),(y_symmetry - 200*vy_symmetry)]
symmetry_point2_woNP = [(x_symmetry + 200*vx_symmetry),(y_symmetry + 200*vy_symmetry)]
# symmetry_point1_woNP = x_symmetry - 200*vx_symmetry,y_symmetry - 200*vy_symmetry
# symmetry_point2_woNP = x_symmetry + 200*vx_symmetry,y_symmetry + 200*vy_symmetry
# symmetry_point1 = np.array([(x_symmetry - 200*vx_symmetry),(y_symmetry - 200*vy_symmetry)])
# symmetry_point2 = np.array([(x_symmetry + 200*vx_symmetry),(y_symmetry + 200*vy_symmetry)])
# symmetry_point1 = np.array(symmetry_point1_woNP)
# symmetry_point2 = np.array(symmetry_point2_woNP)
symmetry_point1 = symmetry_point1_woNP
symmetry_point2 = symmetry_point2_woNP
# eye_line_point1 = np.array([(eye_line_point1[0]),(eye_line_point1[1])])
# eye_line_point2 = np.array([(eye_line_point2[0]),(eye_line_point2[1])])
# eye_line_point1 = np.array(eye_line_point1_woNP)
# eye_line_point2 = np.array(eye_line_point2_woNP)
#
# # so now a hack - which also didn't work !
# eye_line_point1 = np.transpose(eye_line_point1)
# eye_line_point2 = np.transpose(eye_line_point2)
eye_line_point1 = eye_line_point1_woNP
eye_line_point2 = eye_line_point2_woNP
# we come down in y axis
row, col, depth = input_img.shape
if col<row:
input_img = np.transpose(input_img)
else:
pass
# theta = 0
# pdb.set_trace()
# We know that eye_line_point1 is going to be on the left side of the face
points_count = len(face_boundary_points[0,:])
left_boundary = face_boundary_points[:,0:((points_count/2)-1)]
right_boundary = face_boundary_points[:, (points_count/2):(points_count-1)]
# We need to scan all the points on the left boundary which have y value greater than eye_line_point1
sum = 0
symmetry_point1 = np.asarray(symmetry_point1)
symmetry_point2 = np.asarray(symmetry_point2)
symmetry_point1 = symmetry_point1.astype(int)
symmetry_point2 = symmetry_point2.astype(int)
# symmetry_perpendicular_intersection = LineSegmentIntersection([int(symmetry_point1[0]), int(symmetry_point1[1])], [int(symmetry_point2[0]), int(symmetry_point2[1])], eye_line_point1, eye_line_point2)
eye_line_point1 = np.asarray(eye_line_point1)
eye_line_point2 = np.asarray(eye_line_point2)
eye_line_point1 = eye_line_point1.reshape((2,1))
eye_line_point2 = eye_line_point2.reshape((2,1))
eye_line_point1 = eye_line_point1.astype(int)
eye_line_point2 = eye_line_point2.astype(int)
# pdb.set_trace()
symmetry_perpendicular_intersection = LineSegmentIntersection(symmetry_point1, symmetry_point2, eye_line_point1, eye_line_point2)
symmetry_perpendicular_intersection = np.asarray(symmetry_perpendicular_intersection)
symmetry_perpendicular_intersection = symmetry_perpendicular_intersection.astype(int)
sum_left = 0
sum_right = 0
for i in range(0, (points_count/2) - 1):
# The above loop will run if eye_line_point1[1]< eye_line_point2[1]
if (eye_line_point1[1]<eye_line_point2[1]) or (eye_line_point1[1] == eye_line_point2[1]):
if face_boundary_points[1, i] < eye_line_point1[1]:
print 'pass ',i
pass
elif (face_boundary_points[1, i] < symmetry_perpendicular_intersection[1]) and (
eye_line_point1[1] < face_boundary_points[1, i]):
xf = find_x(face_boundary_points[1, i], eye_line_point1, eye_line_point2)
sum_left = sum_left + np.sum(input_img[face_boundary_points[0, i]:xf, face_boundary_points[1, i]])
print 'cond 1'
elif ((symmetry_perpendicular_intersection[1] < face_boundary_points[1, i]) and (
face_boundary_points[1, i] < eye_line_point2[1])):
xf = find_x(face_boundary_points[1, i], eye_line_point1, eye_line_point2)
xg = find_x(face_boundary_points[1, i], symmetry_point1, symmetry_point2)
sum_left = sum_left + np.sum(input_img[face_boundary_points[0, i]:xg, face_boundary_points[1, i]])
sum_right = sum_right + np.sum(input_img[xg:xf, face_boundary_points[1, i]])
print 'cond 2'
elif face_boundary_points[1, i] > eye_line_point2[1]:
xg = find_x(face_boundary_points[1, i], symmetry_point1, symmetry_point2)
sum_left = sum_left + np.sum(input_img[face_boundary_points[0, i]:xg, face_boundary_points[1, i]])
sum_right = sum_right + np.sum(input_img[xg:right_boundary[0, i], face_boundary_points[1, i]])
# sum_right = sum_right + np.sum(input_img[xg:face_boundary_points[1,i], face_boundary_points[1,i]])
print 'cond 3'
# total_sum = sum_left + sum_right
# # pdb.set_trace()set_trace
# left_percentage = (sum_left/total_sum)*100
# right_percentage = (sum_right/total_sum)*100
# total_sum = sum_left + sum_right
# # pdb.set_trace()set_trace
# left_percentage = (sum_left/total_sum)*100
# right_percentage = (sum_right/total_sum)*100
# For the case where eye_line_point1[1]>=eye_line_point2[1]:
else:
print 'Case 2 - left tilted face'
if face_boundary_points[1, i] < eye_line_point2[1]:
pass
elif (face_boundary_points[1, i] < symmetry_perpendicular_intersection[1]) and (
eye_line_point2[1] < face_boundary_points[1, i]):
xf = find_x(face_boundary_points[1, i], eye_line_point1, eye_line_point2)
sum_right = sum_right + np.sum(input_img[xf:right_boundary[0, i], face_boundary_points[1, i]])
#
# xg = find_x(face_boundary_points[1], symmetry_point1, symmetry_point2)
# sum_left = sum_left + np.sum(input_img[face_boundary_points[0,i]:xg, face_boundary_points[1,i]])
# sum_right = sum_right + np.sum(input_img[xg:xf, face_boundary_points[1,i]])
elif ((symmetry_perpendicular_intersection[1] < face_boundary_points[1, i]) and (
face_boundary_points[1, i] < eye_line_point1[1])):
xf = find_x(face_boundary_points[1, i], eye_line_point1, eye_line_point2)
xg = find_x(face_boundary_points[1, i], symmetry_point1, symmetry_point2)
sum_left = sum_left + np.sum(input_img[xf:xg, face_boundary_points[1, i]])
sum_right = sum_right + np.sum(input_img[xg:right_boundary[0, i], face_boundary_points[1, i]])
elif face_boundary_points[1, i] > eye_line_point1[1]:
xg = find_x(face_boundary_points[1, i], symmetry_point1, symmetry_point2)
# pdb.set_trace()
print "face_boundary_points[0,i] = ", face_boundary_points[0,i]
print "xg = ", xg
sum_left = sum_left + np.sum(input_img[face_boundary_points[0,i]:xg, face_boundary_points[1,i]])
sum_right = sum_right + np.sum(input_img[xg:right_boundary[0,i], face_boundary_points[1,i]])
total_sum = sum_left + sum_right
# pdb.set_trace()
left_percentage = (sum_left/total_sum)*100
right_percentage = (sum_right/total_sum)*100
return left_percentage, right_percentage
def symmetryCalculationBoundaryDifference(face_boundary_points, input_img,
eye_line_point1, eye_line_point2,
x_symmetry, y_symmetry, vx_symmetry,
vy_symmetry):
# We first need to get the points of the symmetry line
symmetry_point1 = [(x_symmetry - 200 * vx_symmetry),
(y_symmetry - 200 * vy_symmetry)]
symmetry_point2 = [(x_symmetry + 200 * vx_symmetry),
(y_symmetry + 200 * vy_symmetry)]
# we come down in y axis
# row, col, depth = input_img.shape
# if col < row:
# input_img = np.transpose(input_img)
# else:
# pass
points_count = len(face_boundary_points[0, :])
left_boundary = face_boundary_points[:, 0:((points_count / 2) - 1)]
right_boundary = face_boundary_points[:, (points_count / 2):(points_count -
1)]
# We need to scan all the points on the left boundary which have y value greater than eye_line_point1
sum = 0
symmetry_point1 = np.asarray(symmetry_point1)
symmetry_point2 = np.asarray(symmetry_point2)
symmetry_point1 = symmetry_point1.astype(int)
symmetry_point2 = symmetry_point2.astype(int)
eye_line_point1 = np.asarray(eye_line_point1)
eye_line_point2 = np.asarray(eye_line_point2)
eye_line_point1 = eye_line_point1.reshape((2, 1))
eye_line_point2 = eye_line_point2.reshape((2, 1))
eye_line_point1 = eye_line_point1.astype(int)
eye_line_point2 = eye_line_point2.astype(int)
# pdb.set_trace()
symmetry_perpendicular_intersection = LineSegmentIntersection(
symmetry_point1, symmetry_point2, eye_line_point1, eye_line_point2)
symmetry_perpendicular_intersection = np.asarray(
symmetry_perpendicular_intersection)
symmetry_perpendicular_intersection = symmetry_perpendicular_intersection.astype(
int)
sum_left = 0
sum_right = 0
y_init = symmetry_perpendicular_intersection[1]
y_jump = 1
# from face_boundary_points find out the maximum value of y_max and replace it below
y_max = max(face_boundary_points[1, :])
y_value = y_init
perpendicular_vectors = Perpendicular([vx_symmetry, vy_symmetry])
DistanceDifference = []
# print "Reached symmetryCalculationBoundaryDifference"
for i in range(y_init, y_max, y_jump):
x_value = find_x(y_value, symmetry_point1, symmetry_point2)
perpendicular_point_1 = [(x_value - 200 * perpendicular_vectors[0]),
(y_value - 200 * perpendicular_vectors[1])]
perpendicular_point_2 = [(x_value + 200 * perpendicular_vectors[0]),
(y_value + 200 * perpendicular_vectors[1])]
left_intersection_point = CurveLineIntersection(
left_boundary, perpendicular_point_1, perpendicular_point_2)
right_intersection_point = CurveLineIntersection(
right_boundary, perpendicular_point_1, perpendicular_point_2)
dist_left = np.linalg.norm(
left_intersection_point -
np.asarray([x_value, y_value]).reshape(1, 2))
dist_right = np.linalg.norm(
right_intersection_point -
np.asarray([x_value, y_value]).reshape(1, 2))
leftRightDifference = dist_left - dist_right
DistanceDifference.append(leftRightDifference)
# print "Reached symmetryCalculationBoundaryDifference after for"
pdb.set_trace()
sumNegative = 0
for number in DistanceDifference:
if number < 0:
sumNegative += number
# print "Reached symmetryCalculationBoundaryDifference after second for"
sumPositive = 0
for number in DistanceDifference:
if number >= 0:
sumPositive += number
# sumNegative = sum(number2 for number2 in DistanceDifference if number2 < 0)
positivePercentage = 100 * (sumPositive / (sumPositive + sumNegative))
negativePercentage = 100 * (sumNegative / (sumPositive + sumNegative))
pdb.set_trace()
return negativePercentage, positivePercentage
def symmetryCalculationBoundaryDifference(face_boundary_points, input_img, eye_line_point1, eye_line_point2, x_symmetry,
y_symmetry, vx_symmetry, vy_symmetry):
# We first need to get the points of the symmetry line
symmetry_point1 = [(x_symmetry - 200 * vx_symmetry), (y_symmetry - 200 * vy_symmetry)]
symmetry_point2 = [(x_symmetry + 200 * vx_symmetry), (y_symmetry + 200 * vy_symmetry)]
# we come down in y axis
# row, col, depth = input_img.shape
# if col < row:
# input_img = np.transpose(input_img)
# else:
# pass
points_count = len(face_boundary_points[0, :])
left_boundary = face_boundary_points[:, 0:((points_count / 2) - 1)]
right_boundary = face_boundary_points[:, (points_count / 2):(points_count - 1)]
# We need to scan all the points on the left boundary which have y value greater than eye_line_point1
sum = 0
symmetry_point1 = np.asarray(symmetry_point1)
symmetry_point2 = np.asarray(symmetry_point2)
symmetry_point1 = symmetry_point1.astype(int)
symmetry_point2 = symmetry_point2.astype(int)
eye_line_point1 = np.asarray(eye_line_point1)
eye_line_point2 = np.asarray(eye_line_point2)
eye_line_point1 = eye_line_point1.reshape((2, 1))
eye_line_point2 = eye_line_point2.reshape((2, 1))
eye_line_point1 = eye_line_point1.astype(int)
eye_line_point2 = eye_line_point2.astype(int)
# pdb.set_trace()
symmetry_perpendicular_intersection = LineSegmentIntersection(symmetry_point1, symmetry_point2, eye_line_point1,
eye_line_point2)
symmetry_perpendicular_intersection = np.asarray(symmetry_perpendicular_intersection)
symmetry_perpendicular_intersection = symmetry_perpendicular_intersection.astype(int)
sum_left = 0
sum_right = 0
y_init = symmetry_perpendicular_intersection[1]
y_jump = 1
# from face_boundary_points find out the maximum value of y_max and replace it below
y_max = max(face_boundary_points[1,:])
y_value = y_init
perpendicular_vectors = Perpendicular([vx_symmetry, vy_symmetry])
DistanceDifference = []
# print "Reached symmetryCalculationBoundaryDifference"
for i in range(y_init, y_max, y_jump):
x_value = find_x(y_value, symmetry_point1, symmetry_point2)
perpendicular_point_1 = [(x_value - 200 * perpendicular_vectors[0]), (y_value - 200 * perpendicular_vectors[1])]
perpendicular_point_2 = [(x_value + 200 * perpendicular_vectors[0]), (y_value + 200 * perpendicular_vectors[1])]
left_intersection_point = CurveLineIntersection(left_boundary, perpendicular_point_1, perpendicular_point_2)
right_intersection_point = CurveLineIntersection(right_boundary, perpendicular_point_1, perpendicular_point_2)
dist_left = np.linalg.norm(left_intersection_point-np.asarray([x_value, y_value]).reshape(1,2))
dist_right = np.linalg.norm(right_intersection_point-np.asarray([x_value, y_value]).reshape(1,2))
leftRightDifference = dist_left - dist_right
DistanceDifference.append(leftRightDifference)
# print "Reached symmetryCalculationBoundaryDifference after for"
pdb.set_trace()
sumNegative = 0
for number in DistanceDifference:
if number < 0:
sumNegative += number
# print "Reached symmetryCalculationBoundaryDifference after second for"
sumPositive = 0
for number in DistanceDifference:
if number >= 0:
sumPositive += number
# sumNegative = sum(number2 for number2 in DistanceDifference if number2 < 0)
positivePercentage = 100*(sumPositive/(sumPositive+sumNegative))
negativePercentage = 100*(sumNegative/(sumPositive+sumNegative))
pdb.set_trace()
return negativePercentage, positivePercentage
