I use python OpenCV to register images, and once I’ve found the homography matrix H, I use cv2.warpPerspective to compute final the transformation. However, it seems that cv2.warpPerspective is limited to short encoding for performance purposes, see here. I didn’t some test, and indeed the limit of image dimension is 32767 pixels so 2^15, which makes sense with the explanation
Tag: numpy
merge columns in numpy matrix
I have a NumPy matrix like this one (it could have several columns, this is just an example: I need to merge all columns in this matrix, replacing nan values with the corresponding non-nan value (if exists). Example output: Is there a way to achieve this with some built-in function in NumPy? EDIT: if there is more than one non-nan
How to efficiently do operation on pandas each group
So I have a data frame like this– What I am doing is grouping by id and doing rolling operation on the delay column like below– It is working just fine but I am curious whether .apply on grouped data frame is vectorized or not. Since my dataset is huge, is there a better-vectorized way to do this kind of
How to change pixel value based on a condition
The image is 1920 by 1080. How can I change the value of a pixel when a channel value is higher than the other? Here is what I did. Is there a more efficient way than iterating on each pixel? Answer Don’t use any loop for this, use ndarray capability and logical indexing. What you want to achieve is something
Python – compute average absolute difference of element and neighbors in NumPy array
I’m looking for a way to calculate the average absolute difference between neighboring elements in a NumPy array. Namely, given an array like The value for the middle square will be 2.5 (aka (4+3+2+1+1+2+3+4)/8). I know with SciPy’s correlate2d you can compute the average difference, but, as far as I know, not the average absolute difference (i.e. for the example
Construct graph connectivity matrices in COO format
I have faced the following subtask while working with graph data: I need to construct graph connectivity matrices in COO format for graphs with several fully-connected components from arrays of “border” indices. As an example, given array the resulting COO matrix should be That is, borders array contains ranges of nodes that should form fully-connected subgraphs (starting index included and
Is there built in function in numpy to iterate advanced in 3d array
I wanna make a function that takes an array as its first parameter takes an arbitrary sized and shaped arr array and overwrites all its values that are in the given [a,b] interval to be equal to c. The a, b, c numbers are given to the function as parameters.like input and output below Answer I think the way you’ve
Finding mean between two inputs from users
I’m trying to write a Python program using numpy, which prints the average/mean of all the even numbers bigger than 10 which are also between a specific lower and upper bound input by the user. So, if the user inputs 8 as the lower number and 16 as the upper number, then the output would be 14, but I can’t
NumPy + PyTorch Tensor assignment
lets assume we have a tensor representing an image of the shape (910, 270, 1) which assigned a number (some index) to each pixel with width=910 and height=270. We also have a numpy array of size (N, 3) which maps a 3-tuple to an index. I now want to create a new numpy array of shape (920, 270, 3) which
How do I reverse a cumulative count from a specific point based on a condition and then resume the count in a pandas data frame?
I am trying to count the number of days between dates (cumulatively), (grouped by a column denoted as id), however, I want to reset the counter whenever a condition is satisfied. I want to at the same time create a new column and add the values to that column for those particular rows. Additionally, I want to also count back