How To Turn A Numpy Array To A Numpy Object?

I have a NumPy array as follows: [[[ 0 0]] [[ 0 479]] [[639 479]] [[639 0]]] and I would like to convert it into something like so: [( 0 0) ( 0 479) (639 479)

Solution 1:

In [117]: arr = np.array([[[0,0]],[[0,479]],[[639,479]],[[639,0]]])                                  
In [118]: arr                                                                                        
Out[118]: 
array([[[  0,   0]],

       [[  0, 479]],

       [[639, 479]],

       [[639,   0]]])
In [119]: arr.shape                                                                                  
Out[119]: (4, 1, 2)

You apparently want a structured array, https://numpy.org/devdocs/user/basics.rec.html#

There's a handy tool for converting a numeric array to a structured one:

In[120]: importnumpy.lib.recfunctionsasrfIn[121]: rf.unstructured_to_structured(arr,names=['x','y'])                                         
Out[121]: 
array([[(  0,   0)],
       [(  0, 479)],
       [(639, 479)],
       [(639,   0)]], dtype=[('x', '<i8'), ('y', '<i8')])
In[122]: _.shapeOut[122]: (4, 1)

or using your desired dtype:

In [126]: rf.unstructured_to_structured(arr,dtype=np.dtype([('x', '<i2'), ('y', '<i2')]))            
Out[126]: 
array([[(  0,   0)],
       [(  0, 479)],
       [(639, 479)],
       [(639,   0)]], dtype=[('x', '<i2'), ('y', '<i2')])

or create a 'blank' array with the desired dtype and shape, and assign fields:

In [127]: res = np.zeros((4,1), dtype=np.dtype([('x', '<i2'), ('y', '<i2')]))                        
In [128]: res                                                                                        
Out[128]: 
array([[(0, 0)],
       [(0, 0)],
       [(0, 0)],
       [(0, 0)]], dtype=[('x', '<i2'), ('y', '<i2')])
In [129]: res['x'] = arr[:,:,0]                                                                      
In [130]: res['y'] = arr[:,:,1]                                                                      
In [131]: res                                                                                        
Out[131]: 
array([[(  0,   0)],
       [(  0, 479)],
       [(639, 479)],
       [(639,   0)]], dtype=[('x', '<i2'), ('y', '<i2')])

Or from a list of tuples (list of lists of tuples in your case):

In[132]: arr.tolist()                                                                               
Out[132]: [[[0, 0]], [[0, 479]], [[639, 479]], [[639, 0]]]

In[134]: [[tuple(i) for i in x]forxinarr.tolist()]                                              
Out[134]: [[(0, 0)], [(0, 479)], [(639, 479)], [(639, 0)]]

In[135]: np.array([[tuple(i) for i in x] for x in arr.tolist()], dtype=[('x', '<i2'), ('y', '<i2')])
     ...:                                                                                            
Out[135]: 
array([[(  0,   0)],
       [(  0, 479)],
       [(639, 479)],
       [(639,   0)]], dtype=[('x', '<i2'), ('y', '<i2')])