def transform_base_on_eye_pairs(image, region_info, region_skin_image, eye_pairs, m, n, tempX, tempY, file_output): faceBorder = find_longest_border(region_skin_image, m, n, tempX, tempY) downVector = [0, 1] # count = 0 for eye1, eye2 in eye_pairs: centroid1 = [(eye1[1] + eye1[3]) / 2, (eye1[0] + eye1[2]) / 2] centroid2 = [(eye2[1] + eye2[3]) / 2, (eye2[0] + eye2[2]) / 2] pivot = [(centroid1[0] + centroid2[0]) / 2, (centroid1[1] + centroid2[1]) / 2] direction = get_face_direction(region_skin_image, m, n, tempX, tempY, faceBorder, centroid1, centroid2, pivot) angleToRotate = ut.find_angle_between_two_vectors(downVector, direction) # print(count + 1, angleToRotate) # print(direction) if (direction[0] > 0): angleToRotate = -angleToRotate #adjust pivot point to work with original image pivot[0] = pivot[0] + region_info[1] pivot[1] = pivot[1] + region_info[0] pivot = tuple(pivot) tempImage = image.copy() # cv2.rectangle(tempImage, (eye1[1], eye1[0]), (eye1[3], eye1[2]), (0, 255, 0), 1) # cv2.rectangle(tempImage, (eye2[1], eye2[0]), (eye2[3], eye2[2]), (0, 255, 0), 1) mat = cv2.getRotationMatrix2D(pivot, angleToRotate, 1.0) tempImage = cv2.warpAffine(tempImage, mat, tempImage.shape[1::-1]) #print(tempImage[1,1,1]) #count += 1 value = split_to_get_face(tempImage, pivot, ut.distance_between_points(centroid1, centroid2), file_output)
def transform_base_on_eye_pairs(region_image, region_skin_image, eye_pairs, m, n, tempX, tempY, directory): faceBorder = find_longest_border(region_skin_image, m, n, tempX, tempY) downVector = [0, -1] count = 0 for eye1, eye2 in eye_pairs: centroid1 = [(eye1[0] + eye1[2]) / 2, (eye1[1] + eye1[3]) / 2] centroid2 = [(eye2[0] + eye2[2]) / 2, (eye2[1] + eye2[3]) / 2] pivot = [(centroid1[0] + centroid2[0]) / 2, (centroid1[1] + centroid2[1]) / 2] direction = get_face_direction(region_skin_image, m, n, tempX, tempY, faceBorder, centroid1, centroid2, pivot) angleToRotate = ut.find_angle_between_two_vectors(downVector, direction) if (direction[0] < 0): angleToRotate = -angleToRotate tempImage = region_image.copy() tempSkinImage = region_skin_image.copy() mat = cv2.GetRotationMatrix(pivot, angleToRotate, 1.0) tempImage = cv2.warpAffine(tempImage, mat, tempImage.shape[1::-1]) tempSkinImage = cv2.warpAffine(tempSkinImage, mat, tempImage.shape[1::-1]) count += 1 cv2.imwrite((directory + "/hello%s.jpg") % count, tempImage) cv2.imwrite((directory + "/skin%s.jpg") % count, tempSkinImage)
def get_face_direction(region_skin_image, m, n, tempX, tempY, border, eye1, eye2, pivot): '''based on nose and mouth''' line = ut.find_line(eye1, eye2) farthestPoint = ut.find_the_farthest_point(line, border) specialVector = [farthestPoint[0] - point[0], farthestPoint[1] - point[1]] perpendicularVector = [eye1[1] - eye2[1], eye2[0] - eye1[0]] if (ut.find_angle_between_two_vectors(perpendicularVector, specialVector) < 90): return perpendicularVector return [eye2[1] - eye1[1], eye1[0] - eye2[0]]
def get_faces_base_on_eye_pairs(system_data, image, region_info,
region_skin_image, eye_pairs, m, n, tempX,
tempY):
faceBorder = find_longest_border(region_skin_image, m, n, tempX, tempY)
downVector = [0, 1]
# count = 0
# minDist = 100000000
# minImage = None
result = []
for eye1, eye2 in eye_pairs:
centroid1 = [(eye1[1] + eye1[3]) / 2, (eye1[0] + eye1[2]) / 2]
centroid2 = [(eye2[1] + eye2[3]) / 2, (eye2[0] + eye2[2]) / 2]
pivot = [(centroid1[0] + centroid2[0]) / 2,
(centroid1[1] + centroid2[1]) / 2]
direction = get_face_direction(region_skin_image, m, n, tempX, tempY,
faceBorder, centroid1, centroid2, pivot)
angleToRotate = ut.find_angle_between_two_vectors(
downVector, direction)
# print(count + 1, angleToRotate)
# print(direction)
if (direction[0] > 0):
angleToRotate = -angleToRotate
#adjust pivot point to work with original image
pivot[0] = pivot[0] + region_info[1]
pivot[1] = pivot[1] + region_info[0]
pivot = tuple(pivot)
tempImage = image.copy()
# cv2.rectangle(tempImage, (eye1[1], eye1[0]), (eye1[3], eye1[2]), (0, 255, 0), 1)
# cv2.rectangle(tempImage, (eye2[1], eye2[0]), (eye2[3], eye2[2]), (0, 255, 0), 1)
mat = cv2.getRotationMatrix2D(pivot, angleToRotate, 1.0)
tempImage = cv2.warpAffine(tempImage, mat, tempImage.shape[1::-1])
tempImage = split_to_get_face(
tempImage, pivot, ut.distance_between_points(centroid1, centroid2))
if (tempImage is None):
continue
check, dist = pca.detect_face(
tempImage,
system_data.mean,
system_data.eigenfaces,
dist_threshold=system_data.detectionThreshold)
print("DETECTION DIST", dist)
# cv2.imshow("value", tempImage)
# cv2.waitKey(0)
# cv2.destroyWindow("value")
if (check):
# if (dist < minDist):
# minDist = dist
# minImage = tempImage
result.append([dist, tempImage])
# return minDist, minImage
return result