Fast way to convert 3d numpy array (RGB image) to a boolean array

Question

How can I speed up this function? It takes 1.3 seconds on a 512x512 image.

def bool_map(image):
  '''
  Returns an np.array containing booleans,
  where True means a pixel has red value > 200.
  '''
  bool_map = np.zeros(image.shape, dtype=np.bool_)
  for row in range(image.shape[0]):
    for col in range(image.shape[0]):
      if image[row, col, 0] > 200:
        bool_map[row, col] = True
  return bool_map

Answer 1

Take advantage of numpy's vector operations and write image[:,:,0] > 200 : this should be much faster.

>>> i = np.random.randint(0, 256, (512, 512, 3))
>>> b = i[:,:,0] > 200
>>> b
array([[False, False,  True, ..., False,  True, False],
       [False, False,  True, ..., False, False, False],
       [False,  True, False, ..., False, False, False],
       ..., 
       [False, False,  True, ..., False, False, False],
       [False,  True, False, ..., False, False, False],
       [ True, False,  True, ..., False, False, False]], dtype=bool)
>>> %timeit b = i[:,:,0] > 200
1000 loops, best of 3: 202 µs per loop

Answer 2

Python positively crawls compared to C, especially when working with numeric data. To make this faster, leverage NumPy's bulk array operations.

Your code can be replaced with the equivalent:

return image[:,:,0] > 200