CODEWITHSUNDEEP
pythonarraysnumpyopencvindexing
Deve1opper
Deve1opper

Reputation: 37

Finding the difference between two values in a numpy array-with code

I have a numpy list which I initiate by (my_array=[] and has a shape of (0,)) then I append the wm and hm elements to it like so(r is a cascade with the format of-[[300 240 22 22]]): 

my_array=[]
for (x, y, w, h) in r:
    wm=int(x+ (w/2.))
    hm=int(y+ (h/2.))
    my_array.append([numpy.float32(wm), numpy.float32(hm)])
return numpy.array(my_array)

That code produces: wm element the hm element [[270.01 303.43] [310.17 306.37]] # second to last row [[269.82 303.38] [310.99 306.86]] # the last row the shape of the returned array is (2,2) and is dtype:float32

Now the problem is that when I tried to append the 303.43 it theoretically would be [-2][1] but it indexes 303.38. which is fine but I also need to index 303.43 as well.

What I found was that the first [] indexes either the wm[0] or hm[1] element, then the second [] indexes one of the two columns of values inside each element

-for example [0][-1] indexes the wm element[0] and last row [-1] I want to index the second last row as well and tried [0][-2] but it didn't work as intended(it indexed the 269.82).

So I tried [0][1][-2] but it didn't work due to IndexError: invalid index to scalar variable.

All I want to do is to find the difference between the last and second to last row for the 2 columns in the wm element(so in the example above it would be 269.82-270.1=-0.19 and 303.38-303.43=-0.05). All solutions presented in other questions dont work ([0][-1],[-1][0], you can try them yourself to find out) The indexing doesn't work. So is there a way around this problem? Please explain it fully because I am still kind of new to this! Thanks in advance!

Addition: Taking the last two blocks of data
Indexing the array (in the idle) fetches(I copied the last two blocks of the array):
[[293.51373 323.4329 ] [247.77493 316.02783]]

[[292.9887  322.23425]
 [247.24142 314.2921 ]]

On my program, it shows up as (the same array)
--wm element------------------hm element
[[293.51373 323.4329 ][247.77493 316.02783]] I consider this the second to last row
[[292.9887 322.23425][247.24142 314.2921 ]] and I thought this was the last row

This brought forth a lot of confusion for me, but I ignored the minor difference of the way they are displayed until now. Now, the question is how to index the 323.4329 and the 293.51373 numbers, it would be better if they can be indexed separately?

Upvotes: 0

Views: 1912

Answers (1)

hpaulj
hpaulj

Reputation: 231385

A sample r:

In [41]: r = np.array([[0,0,8,10],[1,1,6,8],[2,2,10,12]])                            
In [42]: r                                                                           
Out[42]: 
array([[ 0,  0,  8, 10],
       [ 1,  1,  6,  8],
       [ 2,  2, 10, 12]])

In [43]: my_array=[]                                                                 
In [45]: for (ex,ey,ew,eh) in r: 
    ...:     wm = int(ex+(ew/2)) 
    ...:     hm = int(ey+(eh/2)) 
    ...:     print(wm,hm) 
    ...:     my_array.append([wm,hm]) 
    ...:                                                                             
4 5
4 5
7 8

The resulting array:

In [46]: arr = np.array(my_array)                                                    
In [47]: arr                                                                         
Out[47]: 
array([[4, 5],
       [4, 5],
       [7, 8]])

Sample indexing:

In [48]: arr[:,0]                                                                    
Out[48]: array([4, 4, 7])   # the 3 wm values
In [49]: arr[-1,:]          # the last values produced by the last `r` row                                                         
Out[49]: array([7, 8])

Or a more symbolic array:

In [52]: arr = np.array([[f'wm{i}',f'hm{i}'] for i in range(3)])                     
In [53]: arr                                                                         
Out[53]: 
array([['wm0', 'hm0'],
       ['wm1', 'hm1'],
       ['wm2', 'hm2']], dtype='<U3')
In [54]: arr[:,0]                                                                    
Out[54]: array(['wm0', 'wm1', 'wm2'], dtype='<U3')
In [55]: arr[-1,:]                                                                   
Out[55]: array(['wm2', 'hm2'], dtype='<U3')

===

In [108]: arr = np.array([[313.5536, 330.60587], [368.23245, 332.70932]])            
In [109]: arr                                                                        
Out[109]: 
array([[313.5536 , 330.60587],      # 2nd to the last row
       [368.23245, 332.70932]])     # last row

Last row:

In [110]: arr[-1]                                                                         
Out[110]: array([368.23245, 332.70932])
In [111]: arr[-1,:]                                                                  
Out[111]: array([368.23245, 332.70932])

First column

In [112]: arr[:,0]                                                                   
Out[112]: array([313.5536 , 368.23245])

2nd to the last row:

In [113]: arr[-2,:]                                                                  
Out[113]: array([313.5536 , 330.60587])

Upvotes: 2

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