I got 3 NumPy data arrays r,g,b represented as a 2D float64 array (720×1024) Essentially per row, each channel is a bunch of floats: What I would like to to do is making it a channel that I can use in cv2.merge((r,g,b)) So that the float64 values per row get multiplied by 255 and something that cv2.merge() accepts. I think
Tag: arrays
How to change binary value to an array with individual states
I have a question. How can I import value like this: to an array like this: and vice versa? Answer np.binary_repr function turn integers into binary representation string, so you could do: Output: Reverse direction (as requested by @sai): Output: Explanation: I build list with desceding powers of 2 (in this case [8,4,2,1]) using list comprehension then multiply arr (i.e.
Setting the contents of one parameter of an object array equal to a string array in Python?
I’m making a grid-based game in which each tile and all of its associated information (collision, state, coordinates, etc.) are stored in a 2D list of Tiles. Tiles are the object I am using to store all of these parameters. I have a separate array of strings that stores what each tile is, for example “W” for wall or “F”
Minimize the maximum difference
I’m trying to solve this Problem from GFG with following approach: But i’m unable to pass the testcases: I’m unable to figure out fault in my logic or code. Could some suggest corrections or better approach? Answer I’m unable to figure out fault in my logic or code. As per your current logic, you decide to add or subtract k
Finding elements unique to each rows in a 2D array
Given a 2D Array (Python List), I need to find a new 1D such that it contains elements unique in each column. For example: should give me for example, [1,-1,0,3,4]. My trials so far: results is a 2D array of size 3 rows and n columns. last is the array which stores the end result, better said the unique values
How to do an outer product of 3 vectors to create a 3d matrix in numpy? (and same for nd)
If i want to do an outer product of 2 vectors to create a 2d matrix, each element a product of the two respective elements in the original vectors: I want the same for 3 (or for n) vectors. An equivalent non numpy answer: out: How to do this with numpy [no for loops]? Also, how to do this for
Return a numpy array, with numbers of elements specified in another array
Suppose I have two numpy arrays A, B, with A.shape = (2,4,3) and B.shape = (2,4): Now I would like to get a new array C with C.shape = (2+3+1,3) = (6,3), such that each integer in B specifies the number of the corresponding 3×1 array in A. In other words, A[i,j,:] should appear B[i,j] times in C. In our
How to decode binary content to original string content in python?
I have a string which I need to encode into binary. It is very important that I get a value string like ‘11010011100…’, because later I need to insert it into the lowest bit of pixels of a gray scale image. (Basically I am hiding a message inside an image.) I am on windows 10, using python 3.6.8. Following this
Conversion Between Base 64 String and Byte Array Varies in C# and Python
I have a C# byte array b_a that has 16 byte length. When I convert b_a to base 64 string aaa, it’s length returns 24 I want to convert aaa string to byte array in Python. But When I convert it, it’s length still returns 24. I want to get initial b_a with all. What is the point that I
What is the more efficient way to create a pairwise 2D array for a 1D numpy array?
Given 2 numPy arrays of length N, I would like to create a pairwise 2D array (N x N) based on a custom function. I would like to create an array C of size NxN. such that Example: I would like to create I know I can do this by nested loop: but looking for a neat and more efficient