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
