fhog.py 文件源码

def getFeatureMaps(image, k, mapp):
    kernel = np.array([[-1.,  0., 1.]], np.float32)

    height = image.shape[0]
    width = image.shape[1]
    assert(image.ndim==3 and image.shape[2])
    numChannels = 3 #(1 if image.ndim==2 else image.shape[2])

    sizeX = width // k
    sizeY = height // k
    px = 3 * NUM_SECTOR
    p = px
    stringSize = sizeX * p

    mapp['sizeX'] = sizeX
    mapp['sizeY'] = sizeY
    mapp['numFeatures'] = p
    mapp['map'] = np.zeros((mapp['sizeX']*mapp['sizeY']*mapp['numFeatures']), np.float32)

    dx = cv2.filter2D(np.float32(image), -1, kernel)   # np.float32(...) is necessary
    dy = cv2.filter2D(np.float32(image), -1, kernel.T)

    arg_vector = np.arange(NUM_SECTOR+1).astype(np.float32) * np.pi / NUM_SECTOR
    boundary_x = np.cos(arg_vector) 
    boundary_y = np.sin(arg_vector)

    # 200x speedup
    r, alfa = func1(dx, dy, boundary_x, boundary_y, height, width, numChannels) #with @jit
    # ~0.001s

    nearest = np.ones((k), np.int)
    nearest[0:k//2] = -1

    w = np.zeros((k, 2), np.float32)
    a_x = np.concatenate((k/2 - np.arange(k/2) - 0.5, np.arange(k/2,k) - k/2 + 0.5)).astype(np.float32)
    b_x = np.concatenate((k/2 + np.arange(k/2) + 0.5, -np.arange(k/2,k) + k/2 - 0.5 + k)).astype(np.float32)
    w[:, 0] = 1.0 / a_x * ((a_x*b_x) / (a_x+b_x))
    w[:, 1] = 1.0 / b_x * ((a_x*b_x) / (a_x+b_x))

    '''
    # original implementation
    mapp['map'] = func2(dx, dy, boundary_x, boundary_y, r, alfa, nearest, w, k, height, width, sizeX, sizeY, p, stringSize) #func2 without @jit  #
    '''
    # 500x speedup
    mapp['map'] = func2(dx, dy, boundary_x, boundary_y, r, alfa, nearest, w, k, height, width, sizeX, sizeY, p, stringSize) #with @jit
    # ~0.001s

    return mapp