你传递的第三个参数是输出图像的大小，你让它等于校正后的内部矩形的大小，所以这就是你得到的。
我们要传递的是矩形的大小，这个矩形是用我们找到的单应性对长方体的四个外顶点进行校正后得到的。取长方体的左上角和右下角的原始坐标。使用cv2.perspectiveTrasform(array_of_outer_points, mat)找到它们的扭曲坐标，然后计算它们在x和y上的差：这些是我们需要插入cv2.warpPerspective的宽度和高度。
针对评论进行编辑
这是我在一个快速肮脏的可乐笔记本里找到的

import os
import numpy as np
import cv2
from google.colab import files
from io import BytesIO
from PIL import Image
import matplotlib.pyplot as plt

# Load the image from local disk
os.unlink('resistance.jpg')
uploaded=files.upload()

resistance.jpg（image/jpeg）- 546359字节，最后修改：不适用- 100%完成
将resistance.jpg保存为resistance.jpg

# Convert it to numpy array
in_img = np.array(Image.open(BytesIO(uploaded['resistance.jpg'])))

# Using Gimp, identify the outer corners of the box, and the
# "rounded" corners of the inner square 
outer_corners = np.float32(np.array(((144,261), (1194,138), (1249,1767), (51,1680))))
inner_corners = np.float32(np.array(((207,486),(1110,414),(1137,1398),(162,1383))))

# Map the inner corners to an axis-aligned square, offset 
# enough from the image left and top side so that the outer
# corners will fit in the image after warping. I arbitrarily
# choose the size to be 800x800, but if unit scaling were
# needed I would compute the area of the inner_corners
# quadrilateral, and make up a square of the same area.
out_inner_corners = np.float32(np.array(((400,400),(1200,400),(1200,1200),(400,1200))))

# Estimate the homography mapping inner_corners to out_inner_corners
H=cv2.getPerspectiveTransform(inner_corners, out_inner_corners)
print(H)

2019 - 06 - 22 00：00：00
[ 1.31087874e-01 1.07378738e+00 -1.19866159e+02]
[ 1.21497242e-04 9.80952996e-05 1.00000000e+00]]

# See where the outer corners would be mapped into. 
out_outer_corners = cv2.perspectiveTransform(outer_corners.reshape((-1,4,2)), H)
print(out_outer_corners)

[[[325.53592 171.86201]
[1274.9574 159.53275]
[1272.9131 1464.9971 ]
[ 314.19254 1443.8832 ]]

# All looks good, the outer corners are all inside an image of the same shape
# as the input one, so we can warp easily:
out_img = cv2.warpPerspective(in_img, H, dsize=(2048,1536))

# Display it
plt.imshow(out)

# Since we know where the outer corners are mapped, we can easily crop
corners = out_outer_corners.reshape((4,2))
out_top = int(np.round(np.min(corners[:, 1])))
out_bottom = int(np.round(np.max(corners[:, 1])))
out_left = int(np.round(np.min(corners[:, 0])))
out_right = int(np.round(np.max(corners[:, 0])))

out_img_cropped = out_img[out_top:out_bottom, out_left:out_right]
plt.imshow(out_img_cropped)