Python_opencv库

1.车牌检测

'''

项目名称：opencv/cv2 车牌检测

简介：

1.训练级联表 ***.xml 【跳过...】

2.用如下代码加载级联表和目标图片识别车牌

注：推荐用anconda安装opencv库

'''

import cv2

detector = cv2.CascadeClassifier()  #实例化检测器  detector/检测器

ret = detector.load('plate_cascade.xml')     #加载级联表，注：Python加载文件需要load(加载)一下，C++不需要

# 检测级联表是否加载成功

if not ret:

    print('未找到级联表/load cascade err')

    quit()

# 读目标图片

img = cv2.imread('2.jpg')

#转灰度(黑白色)

gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY )  #三字节颜色转一字节颜色(彩色转黑白)  cvt/转换 covert

'''

颜色检测

颜色转换

色彩BGR、HVS两种格式

'''

# 目标锁定盒子

boxs = detector.detectMultiScale(gray,1.01,3)    #两个参数：金字塔层级系数(1~2之间，与车牌在整张图中的占比成反比；越小越检查出来的多)，非相关性抑制系数

print(len(boxs))    #找到的目标的个数

for box in boxs:

    x,y,w,h=box   # zy 位置   wh宽高

    g=img[y:y+h,x:x+w,:]

    name='%d_%d_%d_%d.jpg'%(x,y,w,h)

    print(name)   # 输出车牌截图名称【注：坐标命名规则】

    cv2.imwrite(name,g)   # 打印车牌的截图

2.图片人脸检测

# 人脸识别，正则分析

import cv2

import numpy as np

from PIL import Image

#pip install PIL

#pip install opencv-python

#pip install dlib

dector=cv2.CascadeClassifier()

ret=dector.load('haarcascade_frontalface_alt_tree.xml')

if not ret:

    print('未找到级联表文件:plate_cascade.xml')

    exit()

img=cv2.imread('e:/85n.jpg')

if img is None:

    print('文件不存在')

    exit()

#彩色转成灰度图像

gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# 正则化，亮度调成均匀的

gray=np.uint8(gray/gray.ptp()*255)

boxs=dector.detectMultiScale(gray,1.015,1)

platelist=[]

for box in boxs:

    x,y,w,h=box

    g=img[y:y+h,x:x+w,:]

    platelist.append(g)

    linew=h//100+1

    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),linew)

gimg=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)

image=Image.fromarray(gimg)

image.show()

image.close()

3.视频人脸检测

# 人脸识别，正则分析

import cv2

import numpy as np

from PIL import Image

#pip install PIL

#pip install opencv-python

#pip install dlib

dector=cv2.CascadeClassifier()

ret=dector.load('haarcascade_frontalface_alt_tree.xml')

if not ret:

    print('未找到级联表文件:plate_cascade.xml')

    exit()

img=cv2.imread('e:/85n.jpg')

if img is None:

    print('文件不存在')

    exit()

#彩色转成灰度图像

gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# 正则化，亮度调成均匀的

gray=np.uint8(gray/gray.ptp()*255)

boxs=dector.detectMultiScale(gray,1.015,1)

platelist=[]

for box in boxs:

    x,y,w,h=box

    g=img[y:y+h,x:x+w,:]

    platelist.append(g)

    linew=h//100+1

    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),linew)

gimg=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)

image=Image.fromarray(gimg)

image.show()

image.close()

4.数字识别

# -*- coding: utf-8 -*-

"""

Created on Thu May 17 19:30:13 2018

@author: AI04班级

"""

import cv2

import numpy as np

from PIL import Image  #Python Image Lib

import skimage.feature as feature2d

import sklearn.neighbors as nhb

from sklearn.externals import joblib  #对训练模型保存或读取

#cvhog=cv2.HOGDescriptor()

def imgPrepare(filename):

    img=cv2.imread(filename,0)

    img=np.uint8(img/img.ptp()*255)

    img=np.where(img>128,255,img)

    img=np.where(img<=128,0,img)

    img=np.bitwise_not(img)

    return img

def splitchar(img,axis=1):

    idxrowb=np.all(img<64,axis=axis)

    idxrowi=np.uint8(idxrowb).ravel()

    dy=idxrowi[1:]-idxrowi[:-1]

    #print(dy)

    rowb=np.argwhere(dy==255).ravel()

    rowe=np.argwhere(dy==1).ravel()

    #print(rowb,rowe)

    if axis==1:

        imglines=[img[b:e+1,:] for b,e in zip(rowb,rowe)]

    else:

        imglines=[img[:,b:e+1] for b,e in zip(rowb,rowe)]

    return imglines

def splitBox(img):

    idxrowb=np.all(img<64,axis=1)

    idxrowi=np.uint8(idxrowb).ravel()

    dy=idxrowi[1:]-idxrowi[:-1]

    #print(dy)

    rowb=np.argwhere(dy==255).ravel()

    rowe=np.argwhere(dy==1).ravel()

    b=0

    e=-1

    if len(rowe)>0:

        e=rowe[-1]+1

    if len(rowb)>0:

        b=rowb[0]

    return img[b:e,:]

def myResize(img,size=(48,48)):

    h,w=img.shape

    bw=max(h,w)

    bh=bw

    bimg=np.zeros((bh,bw),np.uint8)

    if bw==w:

        dh=(bh-h)//2

        bimg[dh:dh+h,:]=img[:,:]

    else:

        dw=(bw-w)//2

        bimg[:,dw:dw+w]=img[:,:]

    bimg=cv2.resize(bimg,size)

    return bimg

def getHog(img,cell=(16,16),block=(3,3)):

    vec=feature2d.hog(img,12,cell,block,'L2')

    return vec

#main

gimg=imgPrepare('e:/sx.jpg')

lines=splitchar(gimg,axis=1)

chars=[]

for line in lines:

    charlist=splitchar(line,axis=0)

    cchars=[  myResize(splitBox(c))  for c in charlist]

    chars.append(cchars)

chars=np.asarray(chars)

X=[]

Y=[]

y=0

for linech in chars:

    for ch in linech:

        chhog=getHog(ch)

        X.append(chhog)

        Y.append(y)

    y+=1

KNC=nhb.KNeighborsClassifier(algorithm='ball_tree',n_neighbors=3)

KNC.fit(X,Y)

joblib.dump(KNC,'knc.knn')

def predict(img):

     knc=nhb.KNeighborsClassifier(algorithm='ball_tree',n_neighbors=3)

     knc=joblib.load('knc.knn')

     lines=splitchar(img,axis=1)

     chars=[]

     for line in lines:

         charlist=splitchar(line,axis=0)

         cchars=[  myResize(splitBox(c))  for c in charlist]

         chars.append(cchars)

     chars=np.asarray(chars)

     Y=[]

     for linech in chars:

        x=[]

        for ch in linech:

            chhog=getHog(ch)

            x.append(chhog)

        y=knc.predict(x)

        print(y)

        Y.append(y)

     return Y

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