Hi all,

The getAffineTransform and invertAffineTransform output the transformation matrix with dtype='float64'.
Is there anyway to make it output more accuracy? say dtype='float128'?
I MAY need more accuracy in my applications.

In my application, I choose three points with gps locations and xy-points in the image to compute the matrix and I compute the inverse_matrix too.
Then, I use the same three points and the inverse_matrix, input the xy values and compute them back to the gps locations.
The largest error between the computed gps location and measured gps location is about

3.04283202e-06 which is not really bad. (about 30cm )

```
M2 = np.array([51, 788, 1.0])
M2 = M2.reshape(3, 1)
result = np.matmul(inv_M, M2)
# p = inv_M * p'
diff = result - np.array([23.90368083, 121.53650361]).reshape(2,1)
print diff
[[-2.86084081e-08]
[ 3.04283202e-06]]
```

But for other test points, the errors are too much.
For example, let's see the bottom point (gps 23.90377194, 121.53645972).
The error is 0.00021149 in longitude which is too much (**about 21 meters**).

```
M2 = np.array([910, 958, 1.0])
M2 = M2.reshape(3, 1)
result = np.matmul(inv_M, M2) # p = inv_M * p'
diff = result - np.array([23.90377194, 121.53645972]).reshape(2,1) //compare to the measured gps location
print diff
[[0.00015057]
[0.00021149]]
```

Here is my ipynb
link text

original image // you can use mouse to get the xy values of feature points.
link text

feature points with gps locations // note that this is a resized diagram, the xy value is meanless.
link text
the gps locations were provided by vendor and they claimed the errors should be <= 30cm...

To check if the gps locations is trustable or not, I tried to plot those gps locations to ROS rviz and check their relative locations to the labeled image. Finally, I think the gps locations is trustable,

here is the png for checking gps locations link text

Any idea?