サンプル: 平均化フィルタ

目的

平均化フィルタを使用して特定の画像をぼかします。 フィルタサイズは19 19であり、その係数は一様に です。

プログラム

from PIL import Image
import nlcpy as vp
from matplotlib import pyplot as plt

def create_image(nx ,ny, nb):
    rng = vp.random.default_rng(seed=0)
    im = vp.empty((ny, nx, 3), dtype='u4')
    dx = nx // nb
    dy = ny // nb
    for w in range(0, nx, dx):
        for h in range(0, ny, dy):
            pixel = rng.integers(0, 255, 3)
            im[h:h+dy, w:w+dx] = pixel
    return im

def zero_padding(im, kw, kh):
    ex = kw // 2
    ey = kh // 2
    sx = im.shape[1] + ex * 2
    sy = im.shape[0] + ey * 2
    im_new = vp.zeros((sy, sx, im.shape[2]), dtype=im.dtype)
    im_new[ey:-ey, ex:-ex] = im
    return im_new

def save_image(im_in, im_out, path=''):
    fig = plt.figure()
    ax1 = fig.add_subplot(121)
    ax2 = fig.add_subplot(122)

    ax1.set_title('Base Image', fontsize=18)
    ax2.set_title('Filtered Image', fontsize=18)

    ax1.set_xticks([])
    ax1.set_yticks([])
    ax2.set_xticks([])
    ax2.set_yticks([])

    # Here, im_in and im_out must be transferred from Vector Engine
    # to Vector Host by get()
    # because ndarray of NLCPy does not support dtype='u1'.
    # Please note that both im_in.get() and im_out.get() return a
    # NumPy ndarrays.
    im_in_np = im_in.get().astype('u1')
    im_out_np = im_out.get().astype('u1')
    im_in = Image.fromarray(im_in_np)
    im_out = Image.fromarray(im_out_np)

    ax1.imshow(im_in)
    ax2.imshow(im_out)

    plt.savefig(path)

def create_kernel(kw, kh):
    kernel = vp.empty((kh, kw), dtype='f8')
    kernel.fill(1. / kernel.size)
    return kernel

def convolve(im, kernel):
    ex = kernel.shape[1] // 2
    ey = kernel.shape[0] // 2
    im_filtered = vp.zeros_like(im)[ey:-ey, ex:-ex]
    im_filtered = im_filtered.astype(dtype='f8', copy=True)
    ix = im.shape[1] - kernel.shape[1] + 1
    iy = im.shape[0] - kernel.shape[0] + 1
    for w in range(kernel.shape[1]):
        for h in range(kernel.shape[0]):
            im_filtered[...] += im[h:iy+h, w:ix+w] * kernel[h, w]
    return im_filtered

def averaging_filter(
        nx, # The number of pixels in x-direction for creating image.
        ny, # The number of pixels in y-direction for creating image.
        nb, # The number of blocks in each axis for creating image.
        kw, # The kernel width.
        kh, # The kernel height.
):
    print("creating image...", end="", flush=True)
    im_in = create_image(nx ,ny, nb)
    print("done", flush=True)

    print("smoothing image...", end="", flush=True)
    im_padded = zero_padding(im_in, kw, kh)
    kernel = create_kernel(kw, kh)
    im_out = convolve(im_padded, kernel)
    print("done", flush=True)

    print("saving image...", end="", flush=True)
    save_image(im_in, im_out, './averaging_filter.png')
    print("done", flush=True)

if __name__ == "__main__":
    averaging_filter(1000, 1000, 20, 19, 19)

結果