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Copy pathAnalysis_of_the_ratio_of_features.py
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Analysis_of_the_ratio_of_features.py
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from symtable import Symbol
import cv2
import mediapipe as mp
import numpy as np
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles
mp_face_mesh = mp.solutions.face_mesh
# 이미지 파일의 경우을 사용하세요.:
IMAGE_FILES = ["test_image.jpg"]
# 표현되는 랜드마크의 굵기와 반경
drawing_spec = mp_drawing.DrawingSpec(thickness=1, circle_radius=2)
mean = 0
oval = [10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288, 397, 365, 379, 378,
400, 377, 152, 148, 176, 149, 150, 136, 172, 58, 132, 93, 234, 127, 162, 21,
54, 103, 67, 109]
cheek_left = [123, 50, 36, 137, 205, 206, 177, 147, 187, 207, 213, 216, 215, 192, 138,
214, 212, 135]
cheek_right = [266, 280, 352, 366, 425, 426, 411, 427, 376, 401, 436, 433, 435, 416,
434, 367, 364, 432]
face_whole = [10, 338, 297, 332, 284, 251, 389, 356, 454, 323, 361, 288, 397, 365, 379, 378,
400, 377, 152, 148, 176, 149, 150, 136, 172, 58, 132, 93, 234, 127, 162, 21,
54, 103, 67, 109, 123, 50, 36, 137, 205, 206, 177, 147, 187, 207, 213, 216, 215, 192, 138,
214, 212, 135, 266, 280, 352, 366, 425, 426, 411, 427, 376, 401, 436, 433, 435, 416,
434, 367, 364, 432]
x_list = np.linspace(0, 0, len(face_whole))
y_list = np.linspace(0, 0, len(face_whole))
z_list = np.linspace(0, 0, len(face_whole))
with mp_face_mesh.FaceMesh(
static_image_mode=True,
max_num_faces=1,
refine_landmarks=True,
min_detection_confidence=0.5) as face_mesh:
for idx, file in enumerate(IMAGE_FILES):
# 얼굴부분 crop
# haarcascade 불러오기
face_cascade = cv2.CascadeClassifier(
cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
# 이미지 불러오기
image = cv2.imread(file)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 얼굴 찾기
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
# cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
cropped = image[y: y+h+100, x: x+w]
resize = cv2.resize(cropped, (800, 900))
image = resize
# 작업 전에 BGR 이미지를 RGB로 변환합니다.
results = face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
# 이미지에 출력하고 그 위에 얼굴 그물망 경계점을 그립니다.
if not results.multi_face_landmarks:
continue
annotated_image = image.copy()
ih, iw, ic = annotated_image.shape
for face_landmarks in results.multi_face_landmarks:
# 각 랜드마크를 image에 overlay 시켜줌
mp_drawing.draw_landmarks(
image=annotated_image,
landmark_list=face_landmarks,
connections=mp_face_mesh.FACEMESH_CONTOURS,
landmark_drawing_spec=drawing_spec)
# connection_drawing_spec=mp_drawing_styles <---- 이 부분, 눈썹과 눈, 오른쪽 왼쪽 색깔(초록색, 빨강색)
# .get_default_face_mesh_contours_style())
# 눈 양 끝, 아랫입술 가운데의 landmark를 이용해서 삼각형을 그리고 이목구비/전체얼굴 비율을 구한다.
eye_x = face_landmarks.landmark[33].x - face_landmarks.landmark[263].x
eye_y = face_landmarks.landmark[33].y - face_landmarks.landmark[263].y
## 얼굴 비율 측정 1, 긴 중안부 판단
# 두 눈의 길이와 눈-코 길이 비교
A = np.array([[eye_y/eye_x, -1], [-eye_x/eye_y, -1]])
B = np.array([eye_y/eye_x*face_landmarks.landmark[33].x-face_landmarks.landmark[33].y, -eye_x/eye_y*face_landmarks.landmark[94].x-face_landmarks.landmark[94].y])
x,y = np.linalg.solve(A,B)
EtN_vertical_x = face_landmarks.landmark[94].x - x
EtN_vertical_y = face_landmarks.landmark[94].y - y
# Eye to Nose length
EtN_len = np.sqrt(EtN_vertical_x**2 + EtN_vertical_y**2)
Eyes_len = np.sqrt(eye_x**2 + eye_y**2)
# 결과값이 4.7 이상이면 긴 중안부
if ((EtN_len/Eyes_len*10) > 4.7) :
is_long_mid = True
else :
is_long_mid = False
print("is_long_mid = ",is_long_mid)
## 얼굴 비율 측정 3, 긴 턱 판단 (중안부와 하안부의 비율)
eyebrow_x = face_landmarks.landmark[105].x - face_landmarks.landmark[334].x
eyebrow_y = face_landmarks.landmark[105].y - face_landmarks.landmark[334].y
# 중안부 길이 구하기(눈썹 중간 - 코 끝)
A = np.array([[eyebrow_y/eyebrow_x, -1], [-eyebrow_x/eyebrow_y, -1]])
B = np.array([eyebrow_y/eyebrow_x*face_landmarks.landmark[105].x-face_landmarks.landmark[105].y, -eyebrow_x/eyebrow_y*face_landmarks.landmark[94].x-face_landmarks.landmark[94].y])
x,y = np.linalg.solve(A,B)
middle_face_x = face_landmarks.landmark[94].x - x
middle_face_y = face_landmarks.landmark[94].y - y
# Brow to Nose length
BtN_len = np.sqrt(middle_face_x**2 + middle_face_y**2)
# 하안부 길이 구하는 방법, 중안부의 길이를 빼줌
A = np.array([[eyebrow_y/eyebrow_x, -1], [-eyebrow_x/eyebrow_y, -1]])
B = np.array([eyebrow_y/eyebrow_x*face_landmarks.landmark[105].x-face_landmarks.landmark[105].y, -eyebrow_x/eyebrow_y*face_landmarks.landmark[152].x-face_landmarks.landmark[152].y])
x,y = np.linalg.solve(A,B)
middle_lower_face_x = face_landmarks.landmark[152].x - x
middle_lower_face_y = face_landmarks.landmark[152].y - y
# Eyebrow to Chin length
BtC_len = np.sqrt(middle_lower_face_x**2 + middle_lower_face_y**2)
middle_lower_length_ratio = BtN_len/(BtC_len-BtN_len)
# 결과값이 1.1보다 작으면, 긴 턱
if middle_lower_length_ratio < 1.1 :
is_long_chin = True
## 얼굴 비율 측정 3, 긴 턱 판단 (인중 길이 대비 턱의 길이가 2배보다 길때)
else :
# 코끝 - 윗 입술
injung_x = face_landmarks.landmark[94].x - face_landmarks.landmark[0].x
injung_y = face_landmarks.landmark[94].y - face_landmarks.landmark[0].y
InJung_len = np.sqrt(injung_x**2 + injung_y**2)
# 아랫 입술 - 턱 끝
chin_x = face_landmarks.landmark[17].x - face_landmarks.landmark[152].x
chin_y = face_landmarks.landmark[17].y - face_landmarks.landmark[152].y
Chin_len = np.sqrt(chin_x**2 + chin_y**2)
# 결과값이 1보다 크면, 긴 턱
if Chin_len/(2*InJung_len) > 1:
is_long_chin = True
else :
is_long_chin = False
print("is_long_chin : ",is_long_chin)
## 얼굴 비율 측정 2, 하안부 중 긴 인중 판단
nose2lip_x = face_landmarks.landmark[94].x - face_landmarks.landmark[17].x
nose2lip_y = face_landmarks.landmark[94].y - face_landmarks.landmark[17].y
lip2chin_x = face_landmarks.landmark[17].x - face_landmarks.landmark[152].x
lip2chin_y = face_landmarks.landmark[17].y - face_landmarks.landmark[152].y
# Nose to Under-Lip length
NtL_len = np.sqrt(nose2lip_x**2 + nose2lip_y**2)
# Under-Lip to Chin length
LtC_len = np.sqrt(lip2chin_x**2 + lip2chin_y**2)
length_ratio = (NtL_len*0.8)/LtC_len
# 결과값이 0.9 이상이면 긴 중안부
if length_ratio > 0.9:
is_long_philtrum = True
else :
is_long_philtrum = False
print("is_long_philtrum = ",is_long_philtrum)
coodinate_list = np.array([x_list, y_list, z_list])
#print(coodinate_list)
coodinate_list = coodinate_list.reshape((1, -1))
#print(coodinate_list)
coodinate_list = coodinate_list.reshape((3, -1))
#print(coodinate_list)
cv2.imshow("Image_ESEntial",annotated_image)
# esc 입력시 종료
key = cv2.waitKey(50000)
if key == 27:
break