Automatically cropping an image with python/PIL

You can use numpy, convert the image to array, find all non-empty columns and rows and then create an image from these:

import Image
import numpy as np

image=Image.open('L_2d.png')
image.load()

image_data = np.asarray(image)
image_data_bw = image_data.max(axis=2)
non_empty_columns = np.where(image_data_bw.max(axis=0)>0)[0]
non_empty_rows = np.where(image_data_bw.max(axis=1)>0)[0]
cropBox = (min(non_empty_rows), max(non_empty_rows), min(non_empty_columns), max(non_empty_columns))

image_data_new = image_data[cropBox[0]:cropBox[1]+1, cropBox[2]:cropBox[3]+1 , :]

new_image = Image.fromarray(image_data_new)
new_image.save('L_2d_cropped.png')

The result looks like

If anything is unclear, just ask.

I tested most of the answers replied in this post, however, I was ended up my own answer. I used anaconda python3.

from PIL import Image, ImageChops

def trim(im):
    bg = Image.new(im.mode, im.size, im.getpixel((0,0)))
    diff = ImageChops.difference(im, bg)
    diff = ImageChops.add(diff, diff, 2.0, -100)
    #Bounding box given as a 4-tuple defining the left, upper, right, and lower pixel coordinates.
    #If the image is completely empty, this method returns None.
    bbox = diff.getbbox()
    if bbox:
        return im.crop(bbox)

if __name__ == "__main__":
    bg = Image.open("test.jpg") # The image to be cropped
    new_im = trim(bg)
    new_im.show()

Install Pillow

pip install Pillow

and use as

from PIL import Image
    
image=Image.open('L_2d.png')

imageBox = image.getbbox()
cropped = image.crop(imageBox)
cropped.save('L_2d_cropped.png')

When you search for boundaries by mask=imageComponents[3], you search only by blue channel.