models.py 文件源码

def jacobian(self, params, x, y):
        s = params[0]
        r = params[1:4]
        t = params[4:6]
        w = params[6:]

        mean3DShape = x[0]
        blendshapes = x[1]

        R = cv2.Rodrigues(r)[0]
        P = R[:2]
        shape3D = mean3DShape + np.sum(w[:, np.newaxis, np.newaxis] * blendshapes, axis=0)

        nPoints = mean3DShape.shape[1]

        #nSamples * 2 poniewaz kazdy punkt ma dwa wymiary (x i y)
        jacobian = np.zeros((nPoints * 2, self.nParams))

        jacobian[:, 0] = np.dot(P, shape3D).flatten()

        stepSize = 10e-4
        step = np.zeros(self.nParams)
        step[1] = stepSize;
        jacobian[:, 1] = ((self.fun(x, params + step) - self.fun(x, params)) / stepSize).flatten()
        step = np.zeros(self.nParams)
        step[2] = stepSize;
        jacobian[:, 2] = ((self.fun(x, params + step) - self.fun(x, params)) / stepSize).flatten()
        step = np.zeros(self.nParams)
        step[3] = stepSize;
        jacobian[:, 3] = ((self.fun(x, params + step) - self.fun(x, params)) / stepSize).flatten()

        jacobian[:nPoints, 4] = 1
        jacobian[nPoints:, 5] = 1

        startIdx = self.nParams - self.nBlendshapes
        for i in range(self.nBlendshapes):
            jacobian[:, i + startIdx] = s * np.dot(P, blendshapes[i]).flatten()

        return jacobian

    #nie uzywane