如何使用open cv对图像应用透视变换？

Question

我正在尝试使用 open cv 对图像应用透视变换。我有卡片图像，其中我已将背景颜色转换为黑色和前景对象如下图所示的白色。现在我想对其应用透视变换，以便正确查看图像？。我的代码只显示完全黑色的东西。

图片： Binary image

代码：

import cv2,numpy as np
from operator import itemgetter
from glob import glob
import matplotlib.pyplot as plt
input_image2 = cv2.imread("/home/hamza/Desktop/card_in_polygon_format.jpeg")

orig_im_coor = np.float32([[90, 261], [235, 386], [417, 178], [268, 83]])
height , width = 450,350
new_image_coor =  np.float32([[0, 0], [width, 0], [0, height], [width, height]])

P = cv2.getPerspectiveTransform(orig_im_coor,new_image_coor)

perspective = cv2.warpPerspective(input_image2,P,(width,height))
cv2.imshow("Perspective transformation", perspective)
cv2.waitKey(0)
cv2.destroyAllWindows()

注意：每次我的代码都会得到黑白图像。如果它也能自己捕捉角落，那么不用手动取出它会很感激。

Answer 1

自动四边形拟合不是那么简单...

下面有一个很好的例子，不过是用C++实现的
我使用的方法更像下面的方法 - 更简单，但不太准确。

建议的解决方案使用以下阶段：

查找等值线（并获取最大的 - 以防超过一个等值线）。
使用 cv2.approxPolyDP.
将轮廓近似为多边形假设多边形是一个四边形。
按正确顺序对 4 个角进行排序。
注意：我排序角点的方法太复杂了，你可以用简单的逻辑来排序角点。

这是一个代码示例：

import cv2
import numpy as np


def find_corners(im):
    """ 
    Find "card" corners in a binary image.
    Return a list of points in the following format: [[640, 184], [1002, 409], [211, 625], [589, 940]] 
    The points order is top-left, top-right, bottom-left, bottom-right.
    """

    # Better approach: 

    # Find contours in img.
    cnts = cv2.findContours(im, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # [-2] indexing takes return value before last (due to OpenCV compatibility issues).

    # Find the contour with the maximum area (required if there is more than one contour).
    c = max(cnts, key=cv2.contourArea)

    # 
    epsilon = 0.1*cv2.arcLength(c, True)
    box = cv2.approxPolyDP(c, epsilon, True)

    # Draw box for testing
    tmp_im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
    cv2.drawContours(tmp_im, [box], 0, (0, 255, 0), 2)
    cv2.imshow("tmp_im", tmp_im)

    box = np.squeeze(box).astype(np.float32)  # Remove redundant dimensions


    # Sorting the points order is top-left, top-right, bottom-right, bottom-left.
    # Note: 
    # The method I am using is a bit of an "overkill".
    # I am not sure if the implementation is correct.
    # You may sort the corners using simple logic - find top left, bottom right, and match the other two points.
    ############################################################################
    # Find the center of the contour
    # https://docs.opencv.org/3.4/dd/d49/tutorial_py_contour_features.html
    M = cv2.moments(c)
    cx = M['m10']/M['m00']
    cy = M['m01']/M['m00']
    center_xy = np.array([cx, cy])

    cbox = box - center_xy  # Subtract the center from each corner

    # For a square the angles of the corners are:
    # -135   -45
    #
    #
    # 135     45
    ang = np.arctan2(cbox[:,1], cbox[:,0]) * 180 / np.pi  # Compute the angles from the center to each corner

    # Sort the corners of box counterclockwise (sort box elements according the order of ang).
    box = box[ang.argsort()]
    ############################################################################

    # Reorder points: top-left, top-right, bottom-left, bottom-right
    coor = np.float32([box[0], box[1], box[3], box[2]])

    return coor


input_image2 = cv2.imread("card_in_polygon_format.jpeg", cv2.IMREAD_GRAYSCALE)  # Read image as Grayscale
input_image2 = cv2.threshold(input_image2, 0, 255, cv2.THRESH_OTSU)[1]  # Convert to binary image (just in case...)

# orig_im_coor = np.float32([[640, 184], [1002, 409], [211, 625], [589, 940]])

# Find the corners of the card, and sort them
orig_im_coor = find_corners(input_image2)

height, width = 450, 350
new_image_coor =  np.float32([[0, 0], [width, 0], [0, height], [width, height]])

P = cv2.getPerspectiveTransform(orig_im_coor, new_image_coor)

perspective = cv2.warpPerspective(input_image2, P, (width, height))
cv2.imshow("Perspective transformation", perspective)
cv2.waitKey(0)
cv2.destroyAllWindows()

四边形拟合（不是最准确的）：

如何使用open cv对图像应用透视变换？

How to apply perspective transformation to the image using open cv?

opencv

image

image-processing

computer-vision

cv2