def get_age_female(draw):
img = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
if age_gender_one_model:
age, gender, _ = imgProcessing.age_gender_fun(img)
else:
age = imgProcessing.age_fun(img)
gender = imgProcessing.gender_fun(img)
is_female = 0 if FacialImageProcessing.is_male(gender) else 1
#print(age,gender,is_female)
return age, is_female
plt.imshow(
cv2.cvtColor(all_facial_images[filtered_clusters[i][j]],
cv2.COLOR_BGR2RGB))
plt.axis('off')
plt_ind += 1
plt.show()
if __name__ == '__main__':
config = ConfigParser()
config.read('config.txt')
default_config = config['DEFAULT']
minDaysDifferenceBetweenPhotoMDates = int(
default_config['MinDaysDifferenceBetweenPhotoMDates'])
minNoPhotos = int(default_config['MinNoPhotos'])
minNoFrames = int(default_config['MinNoFrames'])
distanceThreshold = float(default_config['DistanceThreshold'])
minFaceWidthPercent = float(default_config['MinFaceWidthPercent']) / 100
print('minDaysDifferenceBetweenPhotoMDates:',
minDaysDifferenceBetweenPhotoMDates, ' minNoPhotos:', minNoPhotos,
'minNoFrames:', minNoFrames, ' distanceThreshold:',
distanceThreshold, ' minFaceWidthPercent:', minFaceWidthPercent)
imgProcessing = FacialImageProcessing(print_stat=False, minsize=112)
process_album(imgProcessing, default_config['InputDirectory'])
#video_filepath='D:/datasets/my_photos/iphone/IMG_2220.MOV' #'D:/datasets/my_photos/iphone/video.AVI'
#process_video(imgProcessing,video_filepath,time.gmtime(os.path.getmtime(video_filepath)))
imgProcessing.close()
from tensorflow.keras.models import load_model
## landmark 설정
landmark_model = "../models/shape_predictor_68_face_landmarks.dat"
landmark_detector = dlib.shape_predictor(landmark_model)
## 클래스 정의
idx_to_class = {0: 'Anger', 1: 'Disgust', 2: 'Fear', 3: 'Happiness', 4: 'Neutral', 5: 'Sadness', 6: 'Surprise'}
## 모델 로드
base_model = load_model('../models/affectnet_emotions/mobilenet_7.h5')
# 이미지 전처리
imgProcessing = FacialImageProcessing(False)
INPUT_SIZE = (224, 224)
# landmark detection using dlib
def lanmark(image, face):
# 얼굴에서 68개 점 찾기
landmarks = landmark_detector(image, face)
# create list to contain landmarks
landmark_list = []
# append (x, y) in landmark_list
for p in landmarks.parts():
landmark_list.append([p.x, p.y])
cv2.circle(image, (p.x, p.y), 2, (255, 255, 255), -1)
import os
import cv2
from sklearn import preprocessing, metrics
from sklearn.metrics.pairwise import pairwise_distances
import scipy.cluster.hierarchy as hac
from scipy.spatial.distance import squareform
import csv
import numpy as np
from facial_analysis import FacialImageProcessing
from facerec_test import is_image
imgProcessing = FacialImageProcessing()
subject_ids = []
face_features = []
ages = []
genders = []
all_facial_images = []
def rect_intersection_square(a, b):
x = max(a[0], b[0])
y = max(a[1], b[1])
w = min(a[2], b[2]) - x
h = min(a[3], b[3]) - y
if w < 0 or h < 0:
w = h = 0
return w * h
adience_range = False
adience_age_list = [(0, 2), (4, 6), (8, 12), (15, 20), (25, 32), (38, 43),
(48, 53), (60, 100)]
def get_age_range(real_age):
for ind in range(len(adience_age_list) - 1):
if real_age <= (adience_age_list[ind][1] +
adience_age_list[ind + 1][0]) / 2:
return ind
return len(adience_age_list) - 1
if True:
from facial_analysis import FacialImageProcessing, is_image, age_gender_one_model
imgProcessing = FacialImageProcessing(False)
def get_age_female(draw):
img = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
if age_gender_one_model:
age, gender, _ = imgProcessing.age_gender_fun(img)
else:
age = imgProcessing.age_fun(img)
gender = imgProcessing.gender_fun(img)
is_female = 0 if FacialImageProcessing.is_male(gender) else 1
#print(age,gender,is_female)
return age, is_female
elif False:
import tensorflow as tf
adience_range = True
RESIZE_FINAL = 227
import tensorflow
import pathlib
import tensorflow as tf
print(tf.__version__)
base_model=load_model('../models/affectnet_emotions/mobilenet_7.h5')
feature_extractor_model=Model(base_model.input,[base_model.get_layer('global_pooling').output,base_model.get_layer('feats').output,base_model.output])
feature_extractor_model.summary()
_,w,h,_=feature_extractor_model.input.shape
DATA_DIR='C:/Users/ccaa9/PycharmProjects/dataset/AFEW/'
emotion_to_index = {'Angry':0, 'Disgust':1, 'Fear':2, 'Happy':3, 'Neutral':4, 'Sad':5, 'Surprise':6}
imgProcessing=FacialImageProcessing(False)
print(tf.__version__)
INPUT_SIZE = (224,224)
### extract frames
def get_iou(bb1, bb2):
# determine the coordinates of the intersection rectangle
x_left = max(bb1[0], bb2[0])
y_top = max(bb1[1], bb2[1])
x_right = min(bb1[2], bb2[2])
y_bottom = min(bb1[3], bb2[3])
if x_right < x_left or y_bottom < y_top:
return 0.0
# The intersection of two axis-aligned bounding boxes is always an