我的OpenCV重映射（）有什么问题？

Question

I took the code in the answer https://stackoverflow.com/a/10374811/4828720 from Image transformation in OpenCV and tried to adapt it to an image of mine.

My source image:

In it, I identified the pixel coordinates of the centers of the checkered bricks, illustrated here:

My target resolution is 784. I calculated the destination coordinates of the pixels. My resulting code is this:

import cv2
from scipy.interpolate import griddata
import numpy as np

source = np.array([
    [315, 15],
    [962, 18],
    [526, 213],
    [754, 215],
    [516, 434],
    [761, 433],
    [225, 701],
    [1036, 694],
], dtype=int)

destination = np.array([
     [14, 14],
     [770, 14],
     [238, 238],
     [546, 238],
     [238, 546],
     [546, 546],
     [14, 770],
     [770, 770]
], dtype=int)

source_image = cv2.imread('frames.png')

grid_x, grid_y = np.mgrid[0:783:784j, 0:783:784j]
grid_z = griddata(destination, source, (grid_x, grid_y), method='cubic')
map_x = np.append([], [ar[:,1] for ar in grid_z]).reshape(784,784)
map_y = np.append([], [ar[:,0] for ar in grid_z]).reshape(784,784)
map_x_32 = map_x.astype('float32')
map_y_32 = map_y.astype('float32')
warped_image = cv2.remap(source_image, map_x_32, map_y_32, cv2.INTER_CUBIC)
cv2.imwrite("/tmp/warped2.png", warped_image)

If I run this, none of the source points end up at their intended destination, but I get a warped mess instead. I added the destination points on top here:

Where am I going wrong? I noticed that my grid and map arrays are not as nicely distributed as the ones in the example. Do I have too few points? Do I need them in a regular grid? I tried only using the four points in the outer corners with no luck either.

Answer 1

If you only have 8 points for warping an no real distortion in your image, I'd suggest to use perspective transformation as described here .

The link you are quoting tries to eliminate additional distortions which lead to non-straight lines, but all lines in your image are straight.

Code would look like:

import cv2
import numpy as np
import matplotlib.pyplot as plt

img = cv2.imread('image.png')
rows,cols,ch = img.shape

pts1 = np.float32([
    [315, 15],
    [962, 18],
    [225, 701],
    [1036, 694],
], dtype=int)

pts2 = np.float32([
     [14, 14],
     [770, 14],
     [14, 770],
     [770, 770]
], dtype=int)

M = cv2.getPerspectiveTransform(pts1,pts2)

dst = cv2.warpPerspective(img,M,(784,784))

plt.subplot(121),plt.imshow(img),plt.title('Input')
plt.subplot(122),plt.imshow(dst),plt.title('Output')
plt.show()

Answer 2

The whole problem was that I, again, got confused by numpy's row/column indexing instead of x/y. Someone in the #opencv IRC channel pointed it out. My source and destination arrays had to have their columns switched:

source = np.array([
    [15, 315],
    [18, 962],
    [213, 526],
    [215, 754],
    [434, 516],
    [433, 761],
    [701, 225],
    [694, 1036],
], dtype=int)

destination = np.array([
     [14, 14],
     [14, 770],
     [238, 238],
     [238, 546],
     [546, 238],
     [546, 546],
     [770, 14],
     [770, 770]
], dtype=int)

Then it worked as intended (ignore the ugly warping, this was a simplified list of coordinates to find the bug):