-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathlandmark_test(long chin).py
167 lines (133 loc) · 7.59 KB
/
landmark_test(long chin).py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
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())
# 랜드마크의 좌표 정보 확인
for id, lm in enumerate(face_landmarks.landmark):
ih, iw, ic = annotated_image.shape
x,y = int(lm.x*iw),int(lm.y*ih)
# print(id,x,y)
#print(face_landmarks.landmark[id].x, face_landmarks.landmark[id].y, face_landmarks.landmark[id].z)
if id == 105 : # 왼쪽 눈썹 위
cv2.putText(annotated_image,str(id),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(0,255,0),2)
elif id == 334 : # 오른쪽 눈썹 위
cv2.putText(annotated_image,str(id),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(0,255,0),2)
elif id == 94 : # 코 끝
cv2.putText(annotated_image,str(id),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(0,255,0),2)
elif id == 152 : # 턱 끝
cv2.putText(annotated_image,str(id),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(0,255,0),2)
elif id == 61 : # 왼입술 끝
cv2.putText(annotated_image,str(id),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(0,0,255),2)
elif id == 291 : # 오른입술 끝
cv2.putText(annotated_image,str(id),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(0,0,255),2)
elif id == 0 : # 입술 위
cv2.putText(annotated_image,str(id),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(0,0,255),2)
elif id == 17 : # 입술 아래
cv2.putText(annotated_image,str(id),(x,y),cv2.FONT_HERSHEY_PLAIN,1,(0,0,255),2)
## 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)
print("Eyebrow to Nose : ", BtN_len)
print("Nose to Chin : ", BtC_len-BtN_len)
# 결과값이 1보다 작으면, 긴 턱
print("Middle-Lower part of the face length ratio : ", middle_lower_length_ratio)
## 얼굴 비율 측정 3, 긴 턱 판단 (인중 길이 대비 턱의 길이가 2배보다 길때)
# 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)
print("Nose to Upper Lip : ", InJung_len)
# 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)
print("Lower Lip to Chin : ", Chin_len)
# 결과값이 1보다 크면, 긴 턱
print("Percentage measured by the above two : ", Chin_len/(2*InJung_len))
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