GPT答：

7i24.cn

以下是使用python和tensorflowAPI实现脸部检测算法的示例：

#引入必要的模块
import numpy as np
import cv2
import tensorflow as tf

# 加载haarcascade分类器
face_cascade = cv2.CascadeClassifier('/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml')

# 读取要检测的图片
img = cv2.imread("face_image.jpg")

# 把图片转换为灰度格式
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 将图像输入CNN
img_rows, img_cols = 64, 64
x_in = cv2.resize(img, (img_rows, img_cols)).reshape((1, img_rows, img_cols, 3))

# 构建CNN并加载保存的参数
inp = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, 3))
conv1 = tf.layers.conv2d(inp, filters=32, kernel_size=3,
                         strides=(1, 1), padding='same',
                         activation='relu')
pool1 = tf.layers.max_pooling2d(conv1, pool_size=2, strides=2)
conv2 = tf.layers.conv2d(pool1, filters=64,kernel_size=3 ,
                         strides=(1,1), padding='same',
                         activation='relu')
pool2 = tf.layers.max_pooling2d(conv2, pool_size=2, strides=2)
flat  = tf.layers.flatten(pool2)
fc1   = tf.layers.dropout(tf.layers.dense(flat, units=64, activation='relu'), rate=0.5)
out   = tf.layers.dense(fc1, units=2, activation='softmax')

saver = tf.train.Saver()
with tf.Session() as sess:
    # 加载CNN参数
    saver.restore(sess, 'saved_params/cnn_params.ckpt')
    # 运行CNN
    p = sess.run(out, feed_dict={inp: x_in})

# 通过CNN检测出的脸部的位置
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

# 将检测出的脸部位置框出来
for (x,y,w,h) in faces:
    cv2.rectangle(img,(x,y),(x+w,y+h),(0,0,255),2)

cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()