计算机视觉---手写体识别,SVM分类

这周三robomasters的大佬要纳新了,还说让带着博客去,赶紧来更新一期(为了大佬看着方便,我给每一行都加上了注释,大佬且慢端详),考虑了上次的KNN属于慵懒机器学习算法且准确率还那么低,这次换成SVM试一试。

我没有选用sk-learn的手写体数据集的原因是他的数据集每张图片是8×8的,而我下载的mnist的digits是一张包含5000张手写体图片的1000×2000的图片,也就是每张手写体图片是20×20的,心想也许这样的准确率会比较好。


计算机视觉---手写体识别,SVM分类_第1张图片
digits.png

这是这次的效果图,准确率也不是很好。


计算机视觉---手写体识别,SVM分类_第2张图片
效果图

这是该程序的准确率和初始化时间。


计算机视觉---手写体识别,SVM分类_第3张图片
运行截图

用MNIST自己的数据测试的准确率很高,可是测自己的手写体准确率很低,好多人都有这个问题,可能是写字的笔不一样吧。。。

看程序。

S.1 划分数据集

def initSvm():
    img = cv2.imread('digits.png')    #读取数据集
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    #转灰度图
    cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]    #划分数据集
    train = np.array(cells).reshape(-1,400).astype(np.float32)    #将图片转为行向量
    trainLabel = np.repeat(np.arange(10),500)    #建立索引
    return train, trainLabel

S.2 寻找数字位置,并预测数值

def findRoi(frame, thresValue):
    rois = []
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.dilate(gray,None,iterations=2)    #两次膨胀
    gray2 = cv2.erode(gray2,None,iterations=2)    #两次腐蚀
    edges = cv2.absdiff(gray,gray2)    #做差,建立sobel算子进行边缘检测
    x = cv2.Sobel(edges,cv2.CV_16S,1,0)    
    y = cv2.Sobel(edges,cv2.CV_16S,0,1)
    absX = cv2.convertScaleAbs(x)
    absY = cv2.convertScaleAbs(y)
    dst = cv2.addWeighted(absX,0.5,absY,0.5,0)
    ret, ddst = cv2.threshold(dst,thresValue,255,cv2.THRESH_BINARY)    #转为二值图
    im, contours, hierarchy = cv2.findContours(ddst,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)    #寻找边界
    for c in contours:
        x, y, w, h = cv2.boundingRect(c)
        if w > 10 and h > 20:
            rois.append((x,y,w,h))
    digits = []
    for r in rois:
        x, y, w, h = r
        digit= findDigit(edges[y:y+h,x:x+w], 50)    #预测数值
        #digits.append(cv2.resize(th,(20,20)))
        cv2.rectangle(frame, (x,y), (x+w,y+h), (153,153,0), 2)    #绘制矩形框
        cv2.putText(frame, str(digit), (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (127,0,255), 2)    #绘制预测数字
    return edges


def findDigit(roi, thresValue):
    ret, th = cv2.threshold(roi, thresValue, 255, cv2.THRESH_BINARY)
    th = cv2.resize(th,(20,20))    #图片转为20×20
    out = th.reshape(-1,400).astype(np.float32)    #图片转为行向量
    result = lsvc.predict(out)     #预测结果
    return result

S.3 训练模型,输出初始化时间及准确率

X, Y = initSvm()


X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)  

from sklearn.preprocessing import StandardScaler  
from sklearn.svm import LinearSVC  
  
ss = StandardScaler()    #数据标准化
  
X_train = ss.fit_transform(X_train)  
X_test = ss.transform(X_test)  
  
lsvc = LinearSVC()  
lsvc.fit(X_train, y_train)      #训练模型
y_predict = lsvc.predict(X_test)      #预测测试数据集

  
print 'The Accuracy of Linear SVC is: ', lsvc.score(X_test, y_test)     #获得评分
endtime = datetime.datetime.now()

print 'The time of SVM init is: ', (endtime - starttime).seconds, 's'    #计算初始化时间

S.4 打开摄像头,开始吧!

cap = cv2.VideoCapture(0)
width = 426*2
height = 480
videoFrame = cv2.VideoWriter('frame.avi',cv2.VideoWriter_fourcc('M','J','P','G'),25,(int(width),int(height)),True)
while True:
    ret, frame = cap.read()
    frame = frame[:,:426]
    edges = findRoi(frame, 50)
    newEdges = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
    newFrame = np.hstack((frame,newEdges))
    cv2.imshow('frame', newFrame)
    videoFrame.write(newFrame)    #保存视频
    key = cv2.waitKey(1) & 0xff
    if key == ord('q'):
        break

完整代码如下

#!/usr/bin/python3
# -*- coding: UTF-8 -*- 
import cv2
import numpy as np
from sklearn.cross_validation import train_test_split  
import datetime
starttime = datetime.datetime.now()


def initSvm():
    img = cv2.imread('digits.png')
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]
    train = np.array(cells).reshape(-1,400).astype(np.float32)
    trainLabel = np.repeat(np.arange(10),500)
    return train, trainLabel

def findRoi(frame, thresValue):
    rois = []
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    gray2 = cv2.dilate(gray,None,iterations=2)
    gray2 = cv2.erode(gray2,None,iterations=2)
    edges = cv2.absdiff(gray,gray2)
    x = cv2.Sobel(edges,cv2.CV_16S,1,0)
    y = cv2.Sobel(edges,cv2.CV_16S,0,1)
    absX = cv2.convertScaleAbs(x)
    absY = cv2.convertScaleAbs(y)
    dst = cv2.addWeighted(absX,0.5,absY,0.5,0)
    ret, ddst = cv2.threshold(dst,thresValue,255,cv2.THRESH_BINARY)
    im, contours, hierarchy = cv2.findContours(ddst,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    for c in contours:
        x, y, w, h = cv2.boundingRect(c)
        if w > 10 and h > 20:
            rois.append((x,y,w,h))
    digits = []
    for r in rois:
        x, y, w, h = r
        digit= findDigit(edges[y:y+h,x:x+w], 50)
        #digits.append(cv2.resize(th,(20,20)))
        cv2.rectangle(frame, (x,y), (x+w,y+h), (153,153,0), 2)
        cv2.putText(frame, str(digit), (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (127,0,255), 2)
    return edges

def findDigit(roi, thresValue):
    ret, th = cv2.threshold(roi, thresValue, 255, cv2.THRESH_BINARY)
    th = cv2.resize(th,(20,20))
    out = th.reshape(-1,400).astype(np.float32)
    result = lsvc.predict(out) 
    return result

X, Y = initSvm()


X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25, random_state = 0)  

from sklearn.preprocessing import StandardScaler  
from sklearn.svm import LinearSVC  
  
ss = StandardScaler()  
  
X_train = ss.fit_transform(X_train)  
X_test = ss.transform(X_test)  
  
lsvc = LinearSVC()  
lsvc.fit(X_train, y_train)  
y_predict = lsvc.predict(X_test)  

  
print 'The Accuracy of Linear SVC is: ', lsvc.score(X_test, y_test) 
endtime = datetime.datetime.now()

print 'The time of SVM init is: ', (endtime - starttime).seconds, 's'

cap = cv2.VideoCapture(0)
width = 426*2
height = 480
videoFrame = cv2.VideoWriter('frame.avi',cv2.VideoWriter_fourcc('M','J','P','G'),25,(int(width),int(height)),True)
while True:
    ret, frame = cap.read()
    frame = frame[:,:426]
    edges = findRoi(frame, 50)
    newEdges = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
    newFrame = np.hstack((frame,newEdges))
    cv2.imshow('frame', newFrame)
    videoFrame.write(newFrame)
    key = cv2.waitKey(1) & 0xff
    if key == ord('q'):
        break

参考原文:http://blog.csdn.net/uestc_c2_403/article/details/72848831

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