图像处理中的随机采样

Question

有没有人能帮上忙呢？我想通过使用随机采样来减小图像大小，但无法确定如何设置输入补丁的限制。

# New smaller image
img_small = np.zeros((img.shape[0] // factor, img.shape[1] // factor),
                     dtype=np.int64)   

# Loop over the rows of the smaller image
for i in range(img_small.shape[0]):
    # Loop over the columns of the smaller image
    for j in range(img_small.shape[1]):
        # The input patch should consist of rows from factor * i to
        # factor * (i + 1) - 1, and columns from factor * j to
        # factor * (j + 1) - 1

        # input_patch = img[  # Extract the input patch

        # Can use np.random.choice(input_patch.flatten(), ...) to choose random
        # pixels from input_patch 

        # img_small[i, j] =  # Set the output pixel
            img_small[i, j] = 

Answer 1

限制在注释中给出，只需将它们应用于数组即可。使用示例图像

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并使用您的代码(添加图像加载并将其转换为灰度-如果需要颜色，您将需要添加颜色处理)：

from PIL import Image
import numpy as np
from matplotlib.pyplot import imshow

# load the image and convert to greyscale
image = Image.open('imglrg0.jpg').convert('LA')
# convert image to numpy array
img_lrg = np.asarray(image)
#imshow(img_lrg)

factor = 8

# New smaller image
img_small = np.zeros((img_lrg.shape[0] // factor, img_lrg.shape[1] // factor),
                     dtype=np.int64)   

# Loop over the rows of the smaller image
for i in range(img_small.shape[0]):
    # Loop over the columns of the smaller image
    for j in range(img_small.shape[1]):
        # The input patch should consist of rows from factor * i to
        # factor * (i + 1) - 1, and columns from factor * j to
        # factor * (j + 1) - 1

        # input_patch = img[  # Extract the input patch
        input_patch = img_lrg[i * factor:(i+1) * factor - 1, j * factor:(j+1) * factor - 1]

        # Can use np.random.choice(input_patch.flatten(), ...) to choose random
        # pixels from input_patch

        # img_small[i, j] =  # Set the output pixel
        img_small[i, j] = np.random.choice(input_patch.flatten())

imshow(np.asarray(img_small))

这将导致(对于factor=8。这不是最好的结果，但很容易辨认。也许可以尝试一下采样，以提高性能。我只是简单地使用matplotlib来快速显示结果，所以它是非彩色的。)：

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就像在采样上增加一样:像这样选择三个点的平均值img_small[i, j] = np.average(np.random.choice(input_patch.flatten(), 3))会带来实质性的改进：

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