svm_train.py 文件源码

def hog_compute(ims):
    samples=[]
    winSize = (64,64)
    blockSize = (16,16)
    blockStride = (8,8)
    cellSize = (8,8)
    nbins = 9
    derivAperture = 1
    winSigma = 4.
    histogramNormType = 0
    L2HysThreshold = 2.0000000000000001e-01
    gammaCorrection = 0
    nlevels = 64
    hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,
                            histogramNormType,L2HysThreshold,gammaCorrection,nlevels)
    #compute(img[, winStride[, padding[, locations]]]) -> descriptors
    winStride = (8,8)
    padding = (8,8)
    locations = ((10,20),(30,30),(50,50),(70,70),(90,90),(110,110),(130,130),(150,150),(170,170),(190,190))
    for im in ims:
        hist = hog.compute(im,winStride,padding,locations)
        samples.append(hist)
    return np.float32(samples)