CODEWITHSUNDEEP
pythonarraysnumpy
question_mark_77
question_mark_77

Reputation: 133

How to change 2D array elements in Numpy?

I am trying to iterate the source array in order to change a few elements in the destination array, but I can’t get the correct indexes, I have incomprehensible offsets. In general, my task is to change the element if the conditions require it 

import numpy as np

x = np.linspace(0,1000, 1000/50)
y = np.linspace(0,1000, 1000/50)
X,Y = np.meshgrid(x,y)

source =  np.column_stack([X.ravel(), Y.ravel()]).astype(int)
destination = source.copy()

for x, i in np.ndenumerate(source):
    # if smth:
    destination[i] = np.array([x[0] + 10, x[1]])

I think I should not use the source array, but only iterate over the destination array(сannot be done using standard methods), tell me the correct solution, thanks in advance.

Current output:
[421 789]
[473 789]
[526 789]
[578 789]
[631 789]
[684 789]


Required output:
[431 789]
[483 789]
[536 789]
[588 789]
[641 789]
[694 789]

I’ll explain more simply, I have a grid, it has points, I need to shift the points, say 88, 89, 90, 10 pixels to the right, for this I need to have an array of source and destination (where these points are offset), enumerate most likely it doesn’t suit me, but the usual editing of an array like for x in destination: when editing x gives the desired result, but this does not apply to ndarray

enter image description here

for x, i in enumerate(destination):
     inside = cv2.pointPolygonTest(cnt, (destination[x,0], 
     destination[x,1]), False)
   if inside > 0:
    cv2.circle(img, (destination[x,0], destination[x,1]), 10, 
  (255,0,0), 2)
    destination[x] = np.array([destination[x,0] + 10, destination[x,1]])

 # Contour(cnt)
 [[550  42]
 [600  42]
 [690 273]
 [640 273]]

As you understand, I need to shift everything that is circled in blue by 10 pixels

Upvotes: 1

Views: 1446

Answers (2)

seralouk
seralouk

Reputation: 33137

What you are asking is not clear at all. How to do you want to modify the destination?

Also, are you sure you need np.ndenumerate and not enumerate ?

Here is a way to modify it based on the values that are in sources. At the position (index) x, the destination will become equal to the x element of the first column of source+ 10 and equal to the x element of the second column of source.

for index, value in enumerate(source):
    destination[index] = [source[index,0]+10, source[index,1]] # x+10, y same

Upvotes: 1

CypherX
CypherX

Reputation: 7353

Solution

From data creation to target-region modification, we can divide it into a three step process. The modification can be achieved using numpy indexing along with conditional region selection by means of numpy.where and numpy.logical_and (if necessary).

1. Make Data

import numpy as np

x = np.linspace(0,1000, int(1000/50))
y = np.linspace(0,1000, int(1000/50))
X,Y = np.meshgrid(x,y)

source =  np.column_stack([X.ravel(), Y.ravel()]).astype(int)
destination = source.copy()

2. Use Conditional Statement to Find Target Region

target_index = np.where(np.logical_and(destination[:,1]==789, destination[:,0]>=421))
destination[target_index]

Output: 

array([[ 421,  789],
       [ 473,  789],
       [ 526,  789],
       [ 578,  789],
       [ 631,  789],
       [ 684,  789],
       [ 736,  789],
       [ 789,  789],
       [ 842,  789],
       [ 894,  789],
       [ 947,  789],
       [1000,  789]])

3. Make changes to the target region

scope = destination[target_index] 
scope[:,0] = scope[:,0] + 10
destination[target_index] = scope
destination[target_index]

Output: 

array([[ 431,  789],
       [ 483,  789],
       [ 536,  789],
       [ 588,  789],
       [ 641,  789],
       [ 694,  789],
       [ 746,  789],
       [ 799,  789],
       [ 852,  789],
       [ 904,  789],
       [ 957,  789],
       [1010,  789]])

Upvotes: 1

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