用skimage (Python)批量处理Tiff文件

Question

我希望；在Python中打开、处理和保存多个触发器。

我有以下代码要打开、处理和保存1(1) TIFF，但是多个文件有问题：

import skimage.io
import skimage.viewer
import skimage                 
import skimage.io    
         
# Read 1 image.TIF:
image = skimage.io.imread(fname=path)
image[2,1]= 1.0

# Process the file (make binary)
gray_image = skimage.color.rgb2gray(image)

# Blur the image to denoise (larger sigma = more noise removed)
blurred_image = skimage.filters.gaussian(gray_image, sigma=5)

# Adding threshold, t:
t = 0.8
binary_mask = blurred_image < t

# Save the file to another location:
skimage.io.imsave(fname=path, arr = binary_mask)

任何帮助都是非常感谢的！

Answer 1

以下是一种可能有用的多处理方法：

import skimage
from concurrent.futures import ProcessPoolExecutor
from glob import glob
import os.path

source_dir = '<your source directory>'
target_dir = '<your target directory>'
filetype = '*.tif'

def process(path):
    image = skimage.io.imread(fname=path)
    image[2,1] = 1.0
    gray_image = skimage.color.rgb2gray(image)
    blurred_image = skimage.filters.gaussian(gray_image, sigma=5)
    outpath = os.path.join(target_dir, os.path.basename(path))
    arr = blurred_image < 0.8
    skimage.io.imsave(fname=outpath, arr=arr)

def main():
    with ProcessPoolExecutor() as executor:
        filelist = glob(os.path.join(source_dir, filetype))
        executor.map(process, filelist)

if __name__ == '__main__':
    main()

使用glob标识与*.tif模式匹配的所有文件，然后使用ProcessPoolExecutor的映射函数在自己的进程中处理每个文件。由于处理主要是CPU密集型的，因此多处理可能是最适合这种情况的。

Answer 2

有必要并行化吗？您正在执行的处理过程并不是很大。如果不需要并行处理，只需在图像上运行for循环即可。

import skimage.io
import skimage.viewer
import skimage                 
import skimage.io
import os
import glob

# set up an in and out directory
in_dir = 'directory\with\images'
out_ir = 'directory\for\procecessed\images'

# make a list of all of the raw image files
os.chdir(in_dir)
filelist = glob.glob('*.png') # change to whatever file pattern you need here


for file_iter in filelist:
        
    os.chdir(in_dir)
    image = skimage.io.imread(fname=file_iter)
    image[2,1]= 1.0

    # Process the file (make binary)
    gray_image = skimage.color.rgb2gray(image)

    # Blur the image to denoise (larger sigma = more noise removed)
    blurred_image = skimage.filters.gaussian(gray_image, sigma=5)

    # Adding threshold, t:
    t = 0.8
    binary_mask = blurred_image < t

    # Save the file to another location:
    out_filename = file_iter[:-4] + 'processed.png' # make new filename based on old filename
    os.chdir(out_dir)
    skimage.io.imsave(fname=out_filename, arr = binary_mask)