2019独角兽企业重金招聘Python工程师标准>>> 
一、安装cv2
sudo apt-get install python-opencv opencv-data二、 Haar特征分类器
Haar特征分类器就是一个XML文件，该文件中会描述人体各个部位的Haar特征值。包括人脸、眼睛、嘴唇等等。
Haar特征分类器存放目录：OpenCV安装目录中的data/haarcascades目录下，通过apt安装的Haar特征分类器如下：
$ ll /usr/share/opencv/haarcascades/
总用量 19504
drwxr-xr-x 2 root root 4096 6月 2 22:01 ./
drwxr-xr-x 4 root root 4096 6月 2 22:01 ../
-rw-r--r-- 1 root root 1095035 9月 19 2014 haarcascade_eye_tree_eyeglasses.xml
-rw-r--r-- 1 root root 506314 9月 19 2014 haarcascade_eye.xml
-rw-r--r-- 1 root root 837462 9月 19 2014 haarcascade_frontalface_alt2.xml
-rw-r--r-- 1 root root 3644763 9月 19 2014 haarcascade_frontalface_alt_tree.xml
-rw-r--r-- 1 root root 919871 9月 19 2014 haarcascade_frontalface_alt.xml
-rw-r--r-- 1 root root 1254733 9月 19 2014 haarcascade_frontalface_default.xml
-rw-r--r-- 1 root root 636650 9月 19 2014 haarcascade_fullbody.xml
-rw-r--r-- 1 root root 323227 9月 19 2014 haarcascade_lefteye_2splits.xml
-rw-r--r-- 1 root root 531497 9月 19 2014 haarcascade_lowerbody.xml
-rw-r--r-- 1 root root 358385 9月 19 2014 haarcascade_mcs_eyepair_big.xml
-rw-r--r-- 1 root root 410204 9月 19 2014 haarcascade_mcs_eyepair_small.xml
-rw-r--r-- 1 root root 312877 9月 19 2014 haarcascade_mcs_leftear.xml
-rw-r--r-- 1 root root 777721 9月 19 2014 haarcascade_mcs_lefteye.xml
-rw-r--r-- 1 root root 719806 9月 19 2014 haarcascade_mcs_mouth.xml
-rw-r--r-- 1 root root 1585210 9月 19 2014 haarcascade_mcs_nose.xml
-rw-r--r-- 1 root root 324727 9月 19 2014 haarcascade_mcs_rightear.xml
-rw-r--r-- 1 root root 1383113 9月 19 2014 haarcascade_mcs_righteye.xml
-rw-r--r-- 1 root root 1522737 9月 19 2014 haarcascade_mcs_upperbody.xml
-rw-r--r-- 1 root root 1125633 9月 19 2014 haarcascade_profileface.xml
-rw-r--r-- 1 root root 324586 9月 19 2014 haarcascade_righteye_2splits.xml
-rw-r--r-- 1 root root 281945 9月 19 2014 haarcascade_smile.xml
-rw-r--r-- 1 root root 1046387 9月 19 2014 haarcascade_upperbody.xml
Haar特征值反映了图像的灰度变化情况。例如：脸部的一些特征能由矩形特征简单的描述，如：眼睛要比脸颊颜色要深，鼻梁两侧比鼻梁颜色要深，嘴巴比周围颜色要深等。
三、opencv api
要想使用opencv，就必须先知道其能干什么，怎么做。于是API的重要性便体现出来了。就本例而言，使用到的函数很少，也就普通的读取图片，灰度转换，显示图像，简单的编辑图像罢了。
读取图片：
import cv2
image = cv2.imread(imagepath)灰度转换
灰度转换的作用就是：转换成灰度的图片的计算强度得以降低。
import cv2
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)画图
opencv 的强大之处的一个体现就是其可以对图片进行任意编辑，处理。
下面的这个函数最后一个参数指定的就是画笔的大小。
import cv2
cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)显示图像
编辑完的图像要么直接的被显示出来，要么就保存到物理的存储介质。
import cv2
cv2.imshow("Image Title",image)获取人脸识别训练数据
看似复杂，其实就是对于人脸特征的一些描述，这样opencv在读取完数据后很据训练中的样品数据，就可以感知读取到的图片上的特征，进而对图片进行人脸识别。
import cv2
face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')我们可以在刚才的/usr/share/opencv/haarcascades/目录下复制，也可以到opencv在GitHub上共享出来的具有普适的训练好的数据。
训练数据参考地址：
https://github.com/opencv/opencv/tree/master/data/haarcascades
四、探测人脸
说白了，就是根据训练的数据来对新图片进行识别的过程。
import cv2
# 探测图片中的人脸
faces = face_cascade.detectMultiScale(
gray,
scaleFactor = 1.15,
minNeighbors = 5,
minSize = (5,5),
# flags = cv2.cv.CV_HAAR_SCALE_IMAGE
)我们可以随意的指定里面参数的值，来达到不同精度下的识别。返回值就是opencv对图片的探测结果的体现。
处理人脸探测的结果
结束了刚才的人脸探测，我们就可以拿到返回值来做进一步的处理了。但这也不是说会多么的复杂，无非添加点特征值罢了。
import cv2
print ("发现{0}个人脸!".format(len(faces)))
for(x,y,w,h) in faces:
cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)五、实例
# coding:utf-8
import sys
reload(sys)
sys.setdefaultencoding('utf8')
"""
opencv实现人脸识别
参考：
1、https://github.com/opencv/opencv/tree/master/data/haarcascades
2、http://www.cnblogs.com/hanson1/p/7105265.html
"""
import cv2
# 待检测的图片路径
imagepath="nba.jpg"
image = cv2.imread(imagepath)
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
'''
# 获取人脸识别训练数据
对于人脸特征的一些描述，opencv在读取完数据后很据训练中的样品数据，
就可以感知读取到的图片上的特征，进而对图片进行人脸识别。
xml数据下载，
参考：https://github.com/opencv/opencv/tree/master/data/haarcascades
'''
face_cascade = cv2.CascadeClassifier(r'./haarcascade_frontalface_default.xml')
# 探测人脸
# 根据训练的数据来对新图片进行识别的过程。
faces = face_cascade.detectMultiScale(
gray,
scaleFactor = 1.15,
minNeighbors = 5,
minSize = (5,5),
#flags = cv2.HAAR_SCALE_IMAGE
)
# 我们可以随意的指定里面参数的值，来达到不同精度下的识别。返回值就是opencv对图片的探测结果的体现。
# 处理人脸探测的结果
print ("发现{0}个人脸!".format(len(faces)))
for(x,y,w,h) in faces:
cv2.rectangle(image,(x,y),(x+w,y+w),(0,255,0),2)
# cv2.circle(image,((x+x+w)/2,(y+y+h)/2),w/2,(0,255,0),2)
cv2.imshow("image",image)
cv2.waitKey(0)
cv2.destroyAllWindows()nba.jpg
运行：
/home/mymotif/PycharmProjects/py2/venv/bin/python /home/mymotif/PycharmProjects/py2/OpencvDemo/FaceRecognition3.py
发现5个人脸!
opencv2.4自带的训练数据集不能识别歪头的人脸：
上图只能识别到3个人脸、高圆圆的脸不能被识别。
六、识别眼睛
代码：
import cv2
face_cascade = cv2.CascadeClassifier("./haarcascade_frontalface_default.xml")
eye_cascade = cv2.CascadeClassifier('./haarcascade_eye.xml')
img = cv2.imread("gyy.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex, ey, ew, eh) in eyes:
cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (0, 255, 0), 2)
cv2.imwrite('./mygyy.jpg',img,)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()haarcascade_eye.xml也是到/usr/share/opencv/haarcascades/haarcascade_eye.xml拷贝
图片gyy.jpg
识别后的输出图像：
七、使用dlib库：
安装：
pip install dlib -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com
pip install scikit-image -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com获得训练模型数据：
wget http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2
wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2解压后得到：dlib_face_recognition_resnet_model_v1.dat shape_predictor_68_face_landmarks.dat
图片放在当前目录的faces字目录下:（这里还是选择刚才的两个高圆圆的图片）
代码：
# -*- coding: utf-8 -*-
import sys
import dlib
import cv2
import os
import glob
current_path = os.getcwd() # 获取当前路径
predictor_path = current_path + "/shape_predictor_68_face_landmarks.dat"
face_rec_model_path = current_path + "/dlib_face_recognition_resnet_model_v1.dat"
faces_folder_path = current_path + "/faces/"
detector = dlib.get_frontal_face_detector()
shape_predictor = dlib.shape_predictor(predictor_path)
face_rec_model = dlib.face_recognition_model_v1(face_rec_model_path)
for img_path in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
print("Processing file: {}".format(img_path))
# opencv 读取图片，并显示
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
# opencv的bgr格式图片转换成rgb格式
b, g, r = cv2.split(img)
img2 = cv2.merge([r, g, b])
dets = detector(img, 1)
print("Number of faces detected: {}".format(len(dets)))
for index, face in enumerate(dets):
print('face {}; left {}; top {}; right {}; bottom {}'.format(index, face.left(), face.top(), face.right(),
face.bottom()))
shape = shape_predictor(img2, face)
for i, pt in enumerate(shape.parts()):
# print('Part {}: {}'.format(i, pt))
pt_pos = (pt.x, pt.y)
cv2.circle(img, pt_pos, 2, (255, 0, 0), 1)
# print(type(pt))
# print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))
cv2.namedWindow(img_path + str(index), cv2.WINDOW_AUTOSIZE)
cv2.imshow(img_path + str(index), img)
face_descriptor = face_rec_model.compute_face_descriptor(img2, shape)
print(face_descriptor)
k = cv2.waitKey(0)
cv2.destroyAllWindows()
运行：
/home/mymotif/PycharmProjects/py3/venv/bin/python /home/mymotif/PycharmProjects/py3/OpencvDemo/my_face_recogniton.py
Processing file: /home/mymotif/PycharmProjects/py3/OpencvDemo/faces/gyy1.jpg
Number of faces detected: 1
face 0; left 882; top 263; right 1067; bottom 449
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Processing file: /home/mymotif/PycharmProjects/py3/OpencvDemo/faces/test4.jpg
Number of faces detected: 3
face 0; left 395; top 229; right 469; bottom 304
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face 1; left 308; top 191; right 370; bottom 253
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face 2; left 218; top 205; right 280; bottom 267
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歪头的还是没有被识别。
dlib另一更清晰的列子（把识别后图片存到res_imgs子目录）
# _*_ coding: utf-8 _*_
import cv2
import numpy as np
import dlib
def rect_to_bb(rect):
"""
:param rect: dlib 脸部区域检测输出
:return: 返回一个矩形坐标
"""
x = rect.left()
y = rect.top()
w = rect.right() - x
h = rect.bottom() - y
return x, y, w, h
def shape_to_np(shape, dtype="int"):
"""
:param shape: dlib脸部特征检测的输出
:param dtype:
:return:
"""
coords = np.zeros((68, 2), dtype=dtype)
for i in range(0, 68):
coords[i] = (shape.part(i).x, shape.part(i).y)
return coords
def resize(image, width=1200):
"""
:param image: 要检测的图片
:param width:
:return:
"""
r = width * 1.0 / image.shape[1]
dim = (width, int(image.shape[0] * r))
resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
return resized
def detect(image_file):
"""
:param image_file: image_file_path
:return:
"""
count = 0
image = cv2.imread('./faces/'+image_file)
# image = resize(image, width=1200)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
detector = dlib.get_frontal_face_detector()
rects = detector(gray, 1)
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
for (i, rect) in enumerate(rects):
count += 1
shape = predictor(gray, rect)
shape = shape_to_np(shape)
(x, y, w, h) = rect_to_bb(rect)
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
for (x, y) in shape:
cv2.circle(image, (x, y), 1, (0, 0, 255), -1)
cv2.imwrite('./res_imgs/detect_res_'+image_file, image)
return './res_imgs/detect_res_'+image_file, count
if __name__ == '__main__':
detect('test4.jpg')八、使用face_recognition
安装
pip install face_recognition -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com下载源码：
git clone https://github.com/ageitgey/face_recognition把要识别的图片gyy.jpg复制到下面目录下
~/face_recognition/examples将find_faces_in_picuture.py中的图片文件名，原先是biden的，改成高圆圆的图片名，修改后保存为find_faces_in_picuture1.py：
from PIL import Image
import face_recognition
# Load the jpg file into a numpy array
image = face_recognition.load_image_file("gyy.jpg")
# Find all the faces in the image using the default HOG-based model.
# This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
# See also: find_faces_in_picture_cnn.py
face_locations = face_recognition.face_locations(image)
print("I found {} face(s) in this photograph.".format(len(face_locations)))
for face_location in face_locations:
# Print the location of each face in this image
top, right, bottom, left = face_location
print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
# You can access the actual face itself like this:
face_image = image[top:bottom, left:right]
pil_image = Image.fromarray(face_image)
pil_image.show()
运行结果：
/home/mymotif/PycharmProjects/my3/venv/bin/python /home/mymotif/PycharmProjects/my3/face_recognition-examples/find_faces_in_picture1.py
I found 1 face(s) in this photograph.
A face is located at pixel location Top: 263, Left: 882, Bottom: 449, Right: 1067
进程完成，退出码 0
ps：
把find_faces_in_picuture1.py和图片gyy.jpg移到其它目录执行会出错：
Traceback (most recent call last):
File "/home/mymotif/PycharmProjects/my3/OpencvDemo/find_faces_in_picture1.py", line 5, in
image = face_recognition.load_image_file("gyy.jpg")
AttributeError: module 'face_recognition' has no attribute 'load_image_file'
进程完成，退出码 1
为什么必须要在~/face_recognition/examples下做，还没找到出错原因和解决方法，求指点。（经测试大部分目录只有find_faces_in_picuture1.py和gyy.jpg两个文件可以运行，有些会产生上述错误，换个新目录试试）
face_recognition：可以做到两张人脸对比参考：
应用一个基于Python的开源人脸识别库，face_recognition
python一行代码实现人脸识别