def atan2(y_x): y, x = y_x[0], y_x[1]+K.epsilon() atan = K.tf.atan(y/x) angle = K.tf.where(K.tf.greater(x,0.0), atan, K.tf.zeros_like(x)) angle = K.tf.where(K.tf.logical_and(K.tf.less(x,0.0), K.tf.greater_equal(y,0.0)), atan+np.pi, angle) angle = K.tf.where(K.tf.logical_and(K.tf.less(x,0.0), K.tf.less(y,0.0)), atan-np.pi, angle) retu angle
# traditional orientation estimation
def orientation(image, stride=8, window=17): with K.tf.name_scope('orientation'): assert image.get_shape().as_list()[3] == 1, 'Images must be grayscale' strides = [1, stride, stride, 1] E = np.ones([window, window, 1, 1]) sobelx = np.reshape(np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=float), [3, 3, 1, 1]) sobely = np.reshape(np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]], dtype=float), [3, 3, 1, 1]) gaussian = np.reshape(gaussian2d((5, 5), 1), [5, 5, 1, 1]) with K.tf.name_scope('sobel_gradient'): Ix = K.tf.nn.conv2d(image, sobelx, strides=[1,1,1,1], padding='SAME', name='sobel_x') Iy = K.tf.nn.conv2d(image, sobely, strides=[1,1,1,1], padding='SAME', name='sobel_y') with K.tf.name_scope('eltwise_1'): Ix2 = K.tf.multiply(Ix, Ix, name='IxIx') Iy2 = K.tf.multiply(Iy, Iy, name='IyIy') Ixy = K.tf.multiply(Ix, Iy, name='IxIy') with K.tf.name_scope('range_sum'): Gxx = K.tf.nn.conv2d(Ix2, E, strides=strides, padding='SAME', name='Gxx_sum') Gyy = K.tf.nn.conv2d(Iy2, E, strides=strides, padding='SAME', name='Gyy_sum') Gxy = K.tf.nn.conv2d(Ixy, E, strides=strides, padding='SAME', name='Gxy_sum') with K.tf.name_scope('eltwise_2'): Gxx_Gyy = K.tf.subtract(Gxx, Gyy, name='Gxx_Gyy') theta = atan2([2*Gxy, Gxx_Gyy]) + np.pi # two-dimensional low-pass filter: Gaussian filter here with K.tf.name_scope('gaussian_filter'): phi_x = K.tf.nn.conv2d(K.tf.cos(theta), gaussian, strides=[1,1,1,1], padding='SAME', name='gaussian_x') phi_y = K.tf.nn.conv2d(K.tf.sin(theta), gaussian, strides=[1,1,1,1], padding='SAME', name='gaussian_y') theta = atan2([phi_y, phi_x])/2 retu theta
