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