dsift.py 文件源码

def calculate_sift_grid(self, image, gridH, gridW):

        H, W = image.shape
        Npatches = gridH.size
        feaArr = np.zeros((Npatches, Nsamples * Nangles))

        # calculate gradient
        GH, GW = gen_dgauss(self.sigma)
        IH = signal.convolve2d(image, GH, mode='same')
        IW = signal.convolve2d(image, GW, mode='same')
        Imag = np.sqrt(IH ** 2 + IW ** 2)
        Itheta = np.arctan2(IH, IW)
        Iorient = np.zeros((Nangles, H, W))
        for i in range(Nangles):
            Iorient[i] = Imag * np.maximum(np.cos(Itheta - angles[i]) ** alpha, 0)
        for i in range(Npatches):
            currFeature = np.zeros((Nangles, Nsamples))
            for j in range(Nangles):
                currFeature[j] = np.dot(self.weights, \
                                        Iorient[j, gridH[i]:gridH[i] + self.pS, gridW[i]:gridW[i] + self.pS].flatten())
            feaArr[i] = currFeature.flatten()
        return feaArr