Question

所以我必须从页面上识别手写大写字符，所以我在考虑使用SVM分类器。现在，我得到的所有字符都是40x40尺寸，并且只有黑白像素。

我在OpenCv官方site上找到了有关如何进行培训的简短教程，但是在svm.train函数中遇到了此错误。

我试图像本教程中那样在20x20上调整图像的大小，但是我认为我被所有参数所阻塞：这是错误error

import numpy as np
import cv2 as cv
import sys
import os

bin_n = 16 # Number of bins
affine_flags = cv.WARP_INVERSE_MAP|cv.INTER_LINEAR
hogdata = []
def readImg(path):
    # Load an color image in grayscale
    img = cv.imread(path,0)
    deskew(img, path)

    hog(img)

def deskew(img, path):
    SZ = 40
    m = cv.moments(img)
    if abs(m['mu02']) < 1e-2:
        return img.copy()
    skew = m['mu11']/m['mu02']
    M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]])
    img = cv.warpAffine(img,M,(SZ, SZ),flags=affine_flags)
    cv.imwrite("out/img.png",img)
    hog(img)

def hog(img):
    gx = cv.Sobel(img, cv.CV_32F, 1, 0)
    gy = cv.Sobel(img, cv.CV_32F, 0, 1)
    mag, ang = cv.cartToPolar(gx, gy)
    bins = np.int32(bin_n*ang/(2*np.pi))    # quantizing binvalues in (0...16)
    bin_cells = bins[:10,:10], bins[10:,:10], bins[:10,10:], bins[10:,10:]
    mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
    hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
    hist = np.hstack(hists)     # hist is a 64 bit vector
    hogdata.append(hist)


if __name__ == "__main__":


    for i in range(1,964):
        readImg("out/.char_"+str(i)+".png")


    trainData = np.float32(hogdata).reshape(-1,64)
    responses = np.repeat(np.arange(10),250)[:,np.newaxis]


    svm = cv.ml.SVM_create()
    svm.setKernel(cv.ml.SVM_LINEAR)
    svm.setType(cv.ml.SVM_C_SVC)
    svm.setC(2.67)
    svm.setGamma(5.383)
    svm.train(trainData, cv.ml.ROW_SAMPLE, responses)
    svm.save('svm_data.dat')

我希望能够将火车数据用于识别。

我遇到了openCV错误：尝试使用SVM训练一些数据以进行字符识别时，断言失败

0 个答案: