将输入图像路径传递给OpenCV。制作原始图像的副本，因为您在透视转换期间需要它。将原始图像的高度除以您想要调整到的高度。这将保持纵横比。最后，输出调整后的图像。
# Passing the image path
original_img = cv2.imread('sample.jpg')
copy = original_img.copy()
# The resized height in hundreds
ratio = original_img.shape[0] / 500.0
img_resize = imutils.resize(original_img, height=500)
# Displaying output
cv2.imshow('Resized image', img_resize)
# Waiting for the user to press any key
cv2.waitKey(0)
为了获得扭曲后的图像，您需要创建一个简单的模块来执行透视转换。 9、转换模块
该模块将对文档角的点进行排序。它还会将文档图像转换成不同的平面，并将相机角度更改为俯拍。
打开之前创建的那个transform.py文件，导入OpenCV库和NumPy库。
import numpy as np
import cv2
这个模块将含有两个函数。创建一个对文档角点的坐标进行排序的函数。第一个坐标将是左上角的坐标，第二个将是右上角的坐标，第三个将是右下角的坐标，第四个将是左下角的坐标。
def order_points(pts):
# initializing the list of coordinates to be ordered
rect = np.zeros((4, 2), dtype = "float32")
s = pts.sum(axis = 1)
# top-left point will have the smallest sum
rect[0] = pts[np.argmin(s)]
# bottom-right point will have the largest sum
rect[2] = pts[np.argmax(s)]
'''computing the difference between the points, the
top-right point will have the smallest difference,
whereas the bottom-left will have the largest difference'''
diff = np.diff(pts, axis = 1)
rect[1] = pts[np.argmin(diff)]
rect[3] = pts[np.argmax(diff)]
# returns ordered coordinates
return rect
创建将计算新图像的角坐标，并获得俯拍的第二个函数。然后，它将计算透视变换矩阵，并返回扭曲的图像。
def perspective_transform(image, pts):
# unpack the ordered coordinates individually
rect = order_points(pts)
(tl, tr, br, bl) = rect
'''compute the width of the new image, which will be the
maximum distance between bottom-right and bottom-left
x-coordinates or the top-right and top-left x-coordinates'''
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidth = max(int(widthA), int(widthB))
'''compute the height of the new image, which will be the
maximum distance between the top-left and bottom-left y-coordinates'''
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
maxHeight = max(int(heightA), int(heightB))
'''construct the set of destination points to obtain an overhead shot'''
dst = np.array([
[0, 0],
[maxWidth - 1, 0],
[maxWidth - 1, maxHeight - 1],
[0, maxHeight - 1]], dtype = "float32")
# compute the perspective transform matrix
transform_matrix = cv2.getPerspectiveTransform(rect, dst)
# Apply the transform matrix
warped = cv2.warpPerspective(image, transform_matrix, (maxWidth, maxHeight))
# return the warped image
return warped
