如果你想要“任何帮助”，并愿意使用Imagemagick 7，那么有一个简单的解决方案使用其积极的修剪。
输入：

magick -fuzz 20% img.png +repage -bordercolor black -border 2 -background black -define trim:percent-background=0% -trim +repage img_trim.png

下面是一个Python/OpenCV的解决方案。它首先对图像进行阈值处理，使背景为黑色，其余部分为白色。它测试阈值图像的每条边，计算平均值，并寻找平均值最低的边。如果平均值==255，它就停在那条边上。如果不是，它就修剪掉那条边并重复。一旦所有边的平均值都是255。它完全停止并使用每一侧上的增量来计算原始输入的裁剪。
输入：

• 注意：我不得不调整您发布的图片的裁剪，以确保所有边的背景都是纯黑色的。如果您提供单独的图片会有帮助。如果边仍然是轻微的灰色，那么我会增加上限。*

import cv2
import numpy as np

# read image
img = cv2.imread('star_arrow.png')
h, w = img.shape[:2]

# threshold so border is black and rest is white. Note this is has pure black for the background, so threshold at black and invert. Adjust lower and upper if the background is not pure black.
lower = (0,0,0)
upper = (0,0,0)
mask = cv2.inRange(img, lower, upper)
mask = 255 - mask

# define top and left starting coordinates and starting width and height
top = 0
left = 0
bottom = h
right = w

# compute the mean of each side of the image and its stop test
mean_top = np.mean( mask[top:top+1, left:right] )
mean_left = np.mean( mask[top:bottom, left:left+1] )
mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
mean_right = np.mean( mask[top:bottom, right-1:right] )

mean_minimum = min(mean_top, mean_left, mean_bottom, mean_right)

top_test = "stop" if (mean_top == 255) else "go"
left_test = "stop" if (mean_left == 255) else "go"
bottom_test = "stop" if (mean_bottom == 255) else "go"
right_test = "stop" if (mean_right == 255) else "go"

# iterate to compute new side coordinates if mean of given side is not 255 (all white) and it is the current darkest side
while top_test == "go" or left_test == "go" or right_test == "go" or bottom_test == "go":

    # top processing
    if top_test == "go":
        if mean_top != 255:
            if mean_top == mean_minimum:
                top += 1
                mean_top = np.mean( mask[top:top+1, left:right] )
                mean_left = np.mean( mask[top:bottom, left:left+1] )
                mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
                mean_right = np.mean( mask[top:bottom, right-1:right] )
                mean_minimum = min(mean_top, mean_left, mean_right, mean_bottom)
                #print("top",mean_top)
                continue
        else:
            top_test = "stop"   

    # left processing
    if left_test == "go":
        if mean_left != 255:
            if mean_left == mean_minimum:
                left += 1
                mean_top = np.mean( mask[top:top+1, left:right] )
                mean_left = np.mean( mask[top:bottom, left:left+1] )
                mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
                mean_right = np.mean( mask[top:bottom, right-1:right] )
                mean_minimum = min(mean_top, mean_left, mean_right, mean_bottom)
                #print("left",mean_left)
                continue
        else:
            left_test = "stop"  

    # bottom processing
    if bottom_test == "go":
        if mean_bottom != 255:
            if mean_bottom == mean_minimum:
                bottom -= 1
                mean_top = np.mean( mask[top:top+1, left:right] )
                mean_left = np.mean( mask[top:bottom, left:left+1] )
                mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
                mean_right = np.mean( mask[top:bottom, right-1:right] )
                mean_minimum = min(mean_top, mean_left, mean_right, mean_bottom)
                #print("bottom",mean_bottom)
                continue
        else:
            bottom_test = "stop"    

    # right processing
    if right_test == "go":
        if mean_right != 255:
            if mean_right == mean_minimum:
                right -= 1
                mean_top = np.mean( mask[top:top+1, left:right] )
                mean_left = np.mean( mask[top:bottom, left:left+1] )
                mean_bottom = np.mean( mask[bottom-1:bottom, left:right] )
                mean_right = np.mean( mask[top:bottom, right-1:right] )
                mean_minimum = min(mean_top, mean_left, mean_right, mean_bottom)
                #print("right",mean_right)
                continue
        else:
            right_test = "stop" 

# crop input
result = img[top:bottom, left:right]

# print crop values 
print("top: ",top)
print("bottom: ",bottom)
print("left: ",left)
print("right: ",right)
print("height:",result.shape[0])
print("width:",result.shape[1])

# save cropped image
#cv2.imwrite('border_image1_cropped.png',result)
cv2.imwrite('img_cropped.png',result)
cv2.imwrite('img_mask.png',mask)

# show the images
cv2.imshow("mask", mask)
cv2.imshow("cropped", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

阈值图像：

裁剪的输入：