numpy 배열 처리 파이썬

이미지의 모든 픽셀을 반복하는 대신 사용할 수 있습니다. numpy 배열 슬라이싱과 몇 가지 방법으로 속도를 높일 수 있습니다. 내부 루프를 제거하고 슬라이싱을 사용했습니다. .sum() 의 방법 numpy 배열 :

import PIL.Image as Img
import numpy as np
img = Img.open('rendering/testpic.jpg', 'r')
r, g, b = img.split()
channels = np.array([np.array(r), np.array(g), np.array(b)])
small_channels = []
for channel in channels:
    x_len = len(channel)//10
    y_len = len(channel[0])//10
    for chunk_x in range(x_len):
        for chunk_y in range(y_len):
            # slice all pixels within 10*10 box and sum them
            pix_sum = channel[chunk_x*10:10*(chunk_x+1),chunk_y*10:10*(chunk_y+1)].sum()
            channel[chunk_x, chunk_y] = pix_sum // 100
    small_channels.append(channel[:x_len,:y_len])
channels = np.array(small_channels)
grayscale = np.round((channels[0]*0.3+ channels[1]*0.6+ channels[2]*0.1)).astype('uint8')
pixels = np.stack([grayscale, grayscale, grayscale], axis = 2)
new_img = Img.fromarray(pixels)
new_img.show()

이 알고리즘은 테스트를 통해 3-4 배 더 빠릅니다. 이게 도움이 되길 바란다. 확실히 봐 numpy 배열-이미지에 특히 유용하며 많은 경우 계산이 더 빠릅니다.

이 경우 루프를 사용하지 않을 것입니다. cv2.resize() 일을 할 것입니다.

다음은 세 가지 접근법 사이의 시간 비교입니다.

import PIL.Image as Img
import numpy as np
from time import perf_counter
import cv2

def timer(method):
    def timed(*args, **kwargs):
        t1 = perf_counter()
        result = method(*args, **kwargs)
        t2 = perf_counter() 
        print(f'{method.__name__} time: {t2 - t1} seconds')
        return result
    return timed

@timer
def resize_1(image_path, shrink):
    img = Img.open(image_path, 'r')
    r, g, b = img.split()
    channels = np.array([np.array(r), np.array(g), np.array(b)])
    small_channels = []
    for channel in channels:
        x_len = len(channel)//shrink
        y_len = len(channel[0])//shrink
        for chunk_x in range(x_len):
            for chunk_y in range(y_len):
                pix_sum = 0
                for x_in_chunk in range(shrink):
                    for y_in_chunk in range(shrink):
                        pix_sum += channel[chunk_x*shrink+x_in_chunk,chunk_y*shrink+y_in_chunk]
                channel[chunk_x,chunk_y] = pix_sum // 100
        small_channels.append(channel[:x_len,:y_len])
    channels = np.array(small_channels)
    grayscale = np.round((channels[0]*0.3+ channels[1]*0.6+ channels[2]*0.1)).astype('uint8')
    pixels = np.stack([grayscale, grayscale, grayscale], axis = 2)
    return Img.fromarray(pixels)

@timer
def resize_2(image_path, shrink):
    img = Img.open(image_path, 'r')
    r, g, b = img.split()
    channels = np.array([np.array(r), np.array(g), np.array(b)])
    small_channels = []
    for channel in channels:
        x_len = len(channel)//shrink
        y_len = len(channel[0])//shrink
        for chunk_x in range(x_len):
            for chunk_y in range(y_len):
                # slice all pixels within 10*10 box and sum them
                pix_sum = channel[chunk_x*shrink:shrink*(chunk_x+1),
                          chunk_y*shrink:shrink*(chunk_y+1)].sum()
                channel[chunk_x, chunk_y] = pix_sum // 100
        small_channels.append(channel[:x_len,:y_len])
    channels = np.array(small_channels)
    grayscale = np.round((channels[0]*0.3+ channels[1]*0.6+ channels[2]*0.1)).astype('uint8')
    pixels = np.stack([grayscale, grayscale, grayscale], axis = 2)
    return Img.fromarray(pixels)

@timer
def resize_3(image_path, shrink):
    image = cv2.imread(image_path)
    size = image.shape[:-1]
    new_size = tuple(int(item / shrink) for item in size)[::-1]
    resized = cv2.resize(image, tuple(new_size))
    gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
    return gray

if __name__ == '__main__':
    img = 'sample_image.png'
    shrink_by = 10
    image1, image2, image3 = [item(img, shrink_by) for item in [resize_1, resize_2, resize_3]]
    image1.show()
    image2.show()
    cv2.imshow('resize_3', image3)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

밖:

resize_1 time: 1.980221013 seconds
resize_2 time: 0.3170622839999999 seconds
resize_3 time: 0.01659756599999973 seconds