计算机视觉---手写体识别，K-最临近分类

参考了前辈的文章，对MNIST库的手写体进行训练，可能是中外手写习惯的不同，准确率不是很高，建议自己搜集数据库，可以在周围同学处搜集素材，本文对MNIST中的digits.png划分为5000个样本进行训练。

S.1 对digits.png进行划分，得到训练的数据集

def initKnn():
    knn = cv2.ml.KNearest_create()    #建立knn模型
    img = cv2.imread('digits.png')    #获取数据集
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    #转灰度图
    cells = [np.hsplit(row,100) for row in np.vsplit(gray,50)]    #划分图片
    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)    #创建索引
    return knn, train, trainLabel

S.2 对图片进行形态学处理，并边缘检测，框选数字所在区域，并显示响应预测数值

def findRoi(frame, thresValue):
    rois = []
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)    
    gray2 = cv2.dilate(gray,None,iterations=2)    #膨胀两次
    gray2 = cv2.erode(gray2,None,iterations=2)    #腐蚀两次
    edges = cv2.absdiff(gray,gray2)    #做差，创建sobel算子边缘检测
    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)    #二值化
    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, th = findDigit(knn, 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)    #绘制矩形框
        cv2.putText(frame, str(digit), (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (127,0,255), 2)
    return edges

S.3 对检测到的ROI区域进行预测，输出预测值

def findDigit(knn, 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)
    ret, result, neighbours, dist = knn.findNearest(out, k=5)
    return int(result[0][0]), th

完整代码如下

#!/usr/bin/python3
# -*- coding: UTF-8 -*- 
import cv2
import numpy as np


def initKnn():
    knn = cv2.ml.KNearest_create()
    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 knn, 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, th = findDigit(knn, 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(knn, 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)
    ret, result, neighbours, dist = knn.findNearest(out, k=5)
    return int(result[0][0]), th

knn, train, trainLabel = initKnn()
knn.train(train,cv2.ml.ROW_SAMPLE,trainLabel)
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(' '):
        break

参考原文：http://blog.csdn.net/littlethunder/article/details/51615237