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人脸关键点检测

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cnn人脸关键点检测/cascades/haarcascade_frontalface_default.xml 0 → 100644
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cnn人脸关键点检测/data/test.csv 0 → 100644
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cnn人脸关键点检测/data/training.csv 0 → 100644
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cnn人脸关键点检测/face.keypoints.py 0 → 100644
import numpy as np
import numpy as np
import time
import os
import cv2
import kmodel as kmodel
from utils import transparentOverlay # 贴片的覆盖层
os.environ['KMP_DUPLICATE_LIB_OK']='True'
# 加载预先训练好的模型
# my_model = kmodel.load_trained_model('yuan_model_mac')
# 加载自己训练好的模型（测试时取消下面行的注释）
my_model = kmodel.load_trained_model('Bai_model')
# 创建人脸检测器
face_cascade = cv2.CascadeClassifier('cascades/haarcascade_frontalface_default.xml')
# 加载摄像头
camera = cv2.VideoCapture(0)
# 加载一个太阳眼镜图像
sunglasses = cv2.imread('sunglass.png', cv2.IMREAD_UNCHANGED)
# 死循环
while True:
time.sleep(0.01)
# 从摄像头获取一张图像
(_, frame) = camera.read()
frame = cv2.flip(frame, 1) # 翻转一下图像
frame2 = np.copy(frame) # 复制一帧图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 转为灰度图像
# 检测所有的人脸
faces = face_cascade.detectMultiScale(gray, 1.25, 6) # 1.25
# 对每一个检测到的人脸。faces得到的是矩形框，多张人脸就是多个矩形框，然后遍历每一张人脸。x，y为起始坐标，w，h为宽度和高度
for (x, y, w, h) in faces:
# 只包含人脸的图像
gray_face = gray[y:y+h, x:x+w]
color_face = frame[y:y+h, x:x+w]
# 将人脸图像的值 normalize 在 [0, 1] 之间。训练数据也做了归一化，所有次人脸也需要。
gray_normalized = gray_face / 255
# 缩放灰度图人脸到 96x96 匹配网络的输入
original_shape = gray_face.shape # A Copy for future reference
face_resized = cv2.resize(gray_normalized, (96, 96), interpolation = cv2.INTER_AREA) #
face_resized = face_resized.reshape(1, 96, 96, 1)
# 预测关键点坐标
keypoints = my_model.predict(face_resized) # （1,30）15个关键点的坐标
# 将关键点坐标的值从 [-1, 1] 之间转换为 [0, 96] 之间
keypoints = keypoints * 48 + 48
# 缩放彩色图人脸到 96x96 匹配关键点
face_resized_color = cv2.resize(color_face, (96, 96), interpolation = cv2.INTER_AREA) # 将要展示的彩色图像
face_resized_color2 = np.copy(face_resized_color)
# 将网络输出的30个值配对为15个tuple对
points = []
for i, co in enumerate(keypoints[0][0::2]): # x坐标
points.append((co, keypoints[0][1::2][i])) # y坐标
# 按照关键点的 left_eyebrow_outer_end_x[7], right_eyebrow_outer_end_x[9]确定眼镜的宽度
sunglass_width = int((points[7][0]-points[9][0])*1.1) # 左眉毛外侧减去右眉毛外侧
# 按照关键点的 nose_tip_y[10], right_eyebrow_inner_end_y[8]确定眼镜的高度
sunglass_height = int((points[10][1]-points[8][1])/1.1)
sunglass_resized = cv2.resize(sunglasses, (sunglass_width, sunglass_height), interpolation = cv2.INTER_CUBIC)
# 戴眼镜
face_resized_color = transparentOverlay(face_resized_color, sunglass_resized , pos=(int(points[9][0]),int(points[9][1])), scale = 1)
# 将覆盖了眼镜的 face_resized_color 图像转为摄像头捕捉到的原始图像中的大小
frame[y:y+h, x:x+w] = cv2.resize(face_resized_color, original_shape, interpolation = cv2.INTER_CUBIC)
# 在人脸图像中显示关键点坐标
for keypoint in points:
cv2.circle(face_resized_color2, keypoint, 1, (0,255,0), 1)
frame2[y:y+h, x:x+w] = cv2.resize(face_resized_color2, original_shape, interpolation = cv2.INTER_CUBIC)
# 显示加了眼镜的图像
cv2.imshow("With Glass", frame)
# 显示添加了关键点的图像
cv2.imshow("With Keypoints", frame2)
# 当 'q' 键被点击, 退出循环
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# 释放摄像头, 关闭窗口
camera.release()
cv2.destroyAllWindows()
cnn人脸关键点检测/kmodel.py 0 → 100644
from keras.models import Sequential
from keras.models import Sequential
from keras.models import load_model
from keras.layers import Convolution2D, MaxPooling2D, Dropout
from keras.layers import Flatten, Dense
from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Adamax, Nadam
def create_model():
"""
网络的输入为96x96的单通道灰阶图像, 输出30个值, 代表的15个关键点的横坐标和纵坐标
"""
model = Sequential()
model.add(Convolution2D(32, (5, 5), input_shape=(96, 96, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.1))
model.add(Convolution2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.2))
model.add(Convolution2D(30, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(256, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(30))
return model
def compile_model(model):
optimizer = 'adam' # 优化器的选择
loss = 'mean_squared_error' # 回归问题
metrics = ['mean_squared_error']
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
def train_model(model, X_train, y_train):
return model.fit(X_train, y_train, epochs=100, batch_size=200, verbose=1, validation_split=0.2)
def save_model(model, fileName):
model.save(fileName + '.h5')
def load_trained_model(fileName):
return load_model(fileName + '.h5')
cnn人脸关键点检测/model_builder.py 0 → 100644
from utils import load_data
from utils import load_data
import kmodel as kmodel
# 加载训练数据
X_train, y_train = load_data()
# 创建网络结构
my_model = kmodel.create_model()
# 编译网络模型
kmodel.compile_model(my_model)
# 训练网络模型
kmodel.train_model(my_model, X_train, y_train)
# 保存网络模型
kmodel.save_model(my_model, 'Bai_model')
cnn人脸关键点检测/utils.py 0 → 100644
import numpy as np
import numpy as np
from pandas.io.parsers import read_csv
from sklearn.utils import shuffle
import cv2
def load_data(test=False):
"""
当 test 为真, 加载测试数据, 否则加载训练数据
"""
FTRAIN = './data/training.csv'
FTEST = './data/test.csv'
fname = FTEST if test else FTRAIN
df = read_csv(fname)
# 将'Image' 列中 '空白键' 分割的数字们转换为一个 numpy array
df['Image'] = df['Image'].apply(lambda im: np.fromstring(im, sep=' '))
# 丢弃有缺失值的数据
df = df.dropna()
# 将图像的数字从 0 到 255 的整数转换为 0 到 1 的实数
X = np.vstack(df['Image'].values) / 255.
X = X.astype(np.float32)
# 将 X 的每一行转换为一个 96 * 96 * 1 的三维数组
X = X.reshape(-1, 96, 96, 1)
# 只有 FTRAIN 包含关键点的数据 (target value)
if not test:
y = df[df.columns[:-1]].values
# 将关键点的值 normalize 到 [-1, 1] 之间
y = (y - 48) / 48
# 置乱训练数据
X, y = shuffle(X, y, random_state=42)
y = y.astype(np.float32)
else:
y = None
return X, y
def transparentOverlay(src , overlay , pos=(0,0), scale = 1):
"""
将带透明通道(png图像)的图像 overlay 叠放在src图像上方
:param src: 背景图像
:param overlay: 带透明通道的图像 (BGRA)
:param pos: 叠放的起始位置
:param scale : overlay图像的缩放因子
:return: Resultant Image
"""
if scale != 1:
overlay = cv2.resize(overlay,(0,0),fx=scale,fy=scale)
# overlay图像的高和宽
h,w,_ = overlay.shape
# 叠放的起始坐标
y,x = pos[0],pos[1]
# 以下被注释的代码是没有优化的版本, 便于理解, 与如下没有注释的版本的功能一样
"""
# src图像的高和款
rows,cols,_ = src.shape
for i in range(h):
for j in range(w):
if x+i >= rows or y+j >= cols:
continue
alpha = float(overlay[i][j][3]/255.0) # 读取alpha通道的值
src[x+i][y+j] = alpha*overlay[i][j][:3]+(1-alpha)*src[x+i][y+j]
return src """
alpha = overlay[:,:,3]/255.0
alpha = alpha[..., np.newaxis]
src[x:x+h,y:y+w,:] = alpha * overlay[:,:,:3] + (1-alpha)*src[x:x+h,y:y+w,:] # 贴眼睛
return src
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