我将处理您的问题如下：

1. 首先我们需要抓住我们感兴趣的点
2. 知道普通护照头像的大小（以像素为单位）

如何抓住兴趣点。

我们还有更多的方法：

1. 您可以使用windows油漆应用程序
2. 但是为了更加程序化，我们可以使用cv2. 我将向您展示如何使用 cv2 做到这一点。

另请注意，这不会产生高分辨率图像，您必须自己玩弄代码。

# imports 
import numpy as np
import cv2

width = height = 600 # normal passport photo size in pixels

# global variable that will update the points when we clicked on the image
pt1 = []
pt2 = np.float32([[0, 0], [height, 0], [0, width], [height, width]])
def mouseEvent(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        global pt1
        if len(pt1) == 4:
            pt1 = []
        else:
            pt1.append([x, y])

while 1:
    image = cv2.imread("img.jpg", cv2.IMREAD_UNCHANGED)
    cv2.imshow("Original Image", image)
    cv2.setMouseCallback("Original Image", mouseEvent)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    if len(pt1) == 4:
        break

然后我们使用两个cv2函数，它们是getPerspectiveTransform和warpPerspective。该getPerspectiveTransform()会接受两个点，这我们pt1和pt2那么我们要调用的warpPerspective()函数传递三个位置ARGS，图像，矩阵和图像形状：

# imports 
import numpy as np
import cv2

width = height = 600 # normal passport photo size in pixels

# global variable that will update the points when we clicked on the image
pt1 = []
pt2 = np.float32([[0, 0], [height, 0], [0, width], [height, width]])
def mouseEvent(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        global pt1
        if len(pt1) == 4:
            pt1 = []
        else:
            pt1.append([x, y])

while 1:
    image = cv2.imread("img.jpg", cv2.IMREAD_UNCHANGED)
    cv2.imshow("Original Image", image)
    cv2.setMouseCallback("Original Image", mouseEvent)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
    if len(pt1) == 4:
        break

我知道这不是一个好的解释，但你明白了。整个代码程序如下所示：

image = cv2.imread("img.jpg", 0)
matrix = cv2.getPerspectiveTransform(np.float32(pt1), pt2)
image = cv2.warpPerspective(image, matrix, image.shape)
cv2.imshow("Wrap Perspective", image)
cv2.waitKey(0)

1. 当您运行以下代码时，将显示一个图像。
2. 要使用此程序，您必须从 开始按顺序单击四个点A-D。例如，如果这是您的图片：

------------------
| (a)          (b)|
|                 |
|                 |
|                 |
|                 |
|                 |
| (c)          (d)|
-------------------

其中 a、b、c 和 d 是您对图像感兴趣的点crop。

演示

点击点1再2然后3，最后4得到上述结果

• “使用人工智能”是一个非答案（相当于“使用魔法”），级联分类器完全不适合选择这些角点，因为这些角点会发生一些旋转。众所周知，当事物旋转时，级联分类器会失败。

这是通过键入图像周围的黑线在 Python/OpenCV 中提取照片的一种方法。

输入：

 - Read the input
 - Pad the image with white so that the lines can be extended until intersection
 - Threshold on black to extract the lines
 - Apply morphology close to try to connect the lines somewhat
 - Get the contours and filter on area drawing the contours on a black background
 - Apply morphology close again to fill the line centers
 - Skeletonize to thin the lines
 - Get the Hough lines and draw them as white on a black background
 - Floodfill the center of the rectangle of lines to fill with mid-gray. Then convert that image to binary so that the gray becomes white and all else is black.
 - Get the coordinates of all non-black pixels and then from the coordinates get the rotated rectangle.
 - Use the angle and center of the rotated rectangle to unrotated both the padded image and this mask image via an Affine warp
 - (Alternately, get the four corners of the rotated rectangle from the mask and then project that to the padded input domain using the affine matrix)
- Get the coordinates of all non-black pixels in the unrotated mask and compute its rotated rectangle.
 - Get the bounding box of the (un-)rotated rectangle 
 - Use those bounds to crop the padded image
 - Save the results

import cv2
import numpy as np
import math
from skimage.morphology import skeletonize

# read image
img = cv2.imread('passport.jpg')
ht, wd = img.shape[:2]

# pad image with white by 20% on all sides
padpct = 20
xpad = int(wd*padpct/100)
ypad = int(ht*padpct/100)
imgpad = cv2.copyMakeBorder(img, ypad, ypad, xpad, xpad, borderType=cv2.BORDER_CONSTANT, value=(255,255,255))
ht2, wd2 = imgpad.shape[:2]

# threshold on black
low = (0,0,0)
high = (20,20,20)

# threshold
thresh = cv2.inRange(imgpad, low, high)

# apply morphology to connect the white lines
kernel = np.ones((5,5), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)

# get contours
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]

# filter on area
mask = np.zeros((ht2,wd2), dtype=np.uint8)
for cntr in contours:
    area = cv2.contourArea(cntr)
    if area > 20:
        cv2.drawContours(mask, [cntr], 0, 255, 1)

# apply morphology to connect the white lines and divide by 255 to make image in range 0 to 1
kernel = np.ones((5,5), np.uint8)
bmask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)/255

# apply thinning (skeletonizing)
skeleton = skeletonize(bmask)
skeleton = (255*skeleton).clip(0,255).astype(np.uint8)

# get hough lines
line_img = np.zeros_like(imgpad, dtype=np.uint8)
lines= cv2.HoughLines(skeleton, 1, math.pi/180.0, 90, np.array([]), 0, 0)
a,b,c = lines.shape
for i in range(a):
    rho = lines[i][0][0]
    theta = lines[i][0][1]
    a = math.cos(theta)
    b = math.sin(theta)
    x0, y0 = a*rho, b*rho
    pt1 = ( int(x0+1000*(-b)), int(y0+1000*(a)) )
    pt2 = ( int(x0-1000*(-b)), int(y0-1000*(a)) )
    cv2.line(line_img, pt1, pt2, (255, 255, 255), 1)

# floodfill with mid-gray (128)
xcent = int(wd2/2)
ycent = int(ht2/2)
ffmask = np.zeros((ht2+2, wd2+2), np.uint8)
mask2 = line_img.copy()
mask2 = cv2.floodFill(mask2, ffmask, (xcent,ycent), (128,128,128))[1]

# convert mask2 to binary
mask2[mask2 != 128] = 0
mask2[mask2 == 128] = 255
mask2 = mask2[:,:,0]

# get coordinates of all non-zero pixels
# NOTE: must transpose since numpy coords are y,x and opencv uses x,y
coords = np.column_stack(np.where(mask2.transpose() > 0))

# get rotated rectangle from coords
rotrect = cv2.minAreaRect(coords)
(center), (width,height), angle = rotrect
# from https://www.pyimagesearch.com/2017/02/20/text-skew-correction-opencv-python/
# the `cv2.minAreaRect` function returns values in the
# range [-90, 0); as the rectangle rotates clockwise the
# returned angle trends to 0 -- in this special case we
# need to add 90 degrees to the angle
if angle < -45:
    angle = -(90 + angle)
 
# otherwise, just take the inverse of the angle to make
# it positive
else:
    angle = -angle

# compute correction rotation
rotation = -angle - 90

# compute rotation affine matrix
M = cv2.getRotationMatrix2D(center, rotation, scale=1.0)
    
# unrotate imgpad and mask2 using affine warp
rot_img = cv2.warpAffine(imgpad, M, (wd2, ht2), flags=cv2.INTER_CUBIC, borderValue=(0,0,0))
rot_mask2= cv2.warpAffine(mask2, M, (wd2, ht2), flags=cv2.INTER_CUBIC, borderValue=(0,0,0))

# get coordinates of all non-zero pixels
# NOTE: must transpose since numpy coords are y,x and opencv uses x,y
coords2 = np.column_stack(np.where(rot_mask2.transpose() > 0))

# get bounding box
x,y,w,h = cv2.boundingRect(coords2)
print(x,y,w,h)

# crop rot_img
result = rot_img[y:y+h, x:x+w]

# save resulting images
cv2.imwrite('passport_pad.jpg',imgpad)
cv2.imwrite('passport_thresh.jpg',thresh)
cv2.imwrite('passport_morph.jpg',morph)
cv2.imwrite('passport_mask.jpg',mask)
cv2.imwrite('passport_skeleton.jpg',skeleton)
cv2.imwrite('passport_line_img.jpg',line_img)
cv2.imwrite('passport_mask2.jpg',mask2)
cv2.imwrite('passport_rot_img.jpg',rot_img)
cv2.imwrite('passport_rot_mask2.jpg',rot_mask2)
cv2.imwrite('passport_result.jpg',result)

# show thresh and result    
cv2.imshow("imgpad", imgpad)
cv2.imshow("thresh", thresh)
cv2.imshow("morph", morph)
cv2.imshow("mask", mask)
cv2.imshow("skeleton", skeleton)
cv2.imshow("line_img", line_img)
cv2.imshow("mask2", mask2)
cv2.imshow("rot_img", rot_img)
cv2.imshow("rot_mask2", rot_mask2)
cv2.imshow("result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()

填充图像：

阈值图像：

形态学清洗图像：

面具 1 图像：

骨架图像：

(Hough) 线图：

洪水填充线图像 - Mask2：

未旋转的填充图像：

未旋转的 Mask2 图像：

裁剪图像：

• 使其适用于所有图像非常困难。在您的所有图像中，黑线不够突出或不够暗。您在一张图像时也有多余的黑线。图像也会扭曲，因此黑线不是直线，因此霍夫线不会检测到一条线，而是每边检测到多条线。