CODEWITHSUNDEEP
pythonimageperformancenumpyimage-processing
Mika Vatanen
Mika Vatanen

Reputation: 4027

How to map pixels (R, G, B) in a collection of images to a distinct pixel-color-value indices?

Lets say one has 600 annotated semantic segmentation mask images, which contain 10 different colors, each representing one entity. These images are in a numpy array of shape (600, 3, 72, 96), where n = 600, 3 = RGB channels, 72 = height, 96 = width.

How to map each RGB-pixel in the numpy array to a color-index-value? For example, a color list would be [(128, 128, 0), (240, 128, 0), ...n], and all (240, 128, 0) pixels in the numpy array would be converted to index value in unique mapping (= 1).

How to do this efficiently and with less code? Here's one solution I came up with, but it's quite slow.

# Input imgs.shape = (N, 3, H, W), where (N = count, W = width, H = height)
def unique_map_pixels(imgs):
  original_shape = imgs.shape

  # imgs.shape = (N, H, W, 3)
  imgs = imgs.transpose(0, 2, 3, 1)

  # tupleview.shape = (N, H, W, 1); contains tuples [(R, G, B), (R, G, B)]
  tupleview = imgs.reshape(-1, 3).view(imgs.dtype.descr * imgs.shape[3])

  # get unique pixel values in images, [(R, G, B), ...]
  uniques = list(np.unique(tupleview))

  # map uniques into hashed list ({"RXBXG": 0, "RXBXG": 1}, ...)
  uniqmap = {}
  idx = 0
  for x in uniques:
    uniqmap["%sX%sX%s" % (x[0], x[1], x[2])] = idx
    idx = idx + 1
    if idx >= np.iinfo(np.uint16).max:
      raise Exception("Can handle only %s distinct colors" % np.iinfo(np.uint16).max)

  # imgs1d.shape = (N), contains RGB tuples
  imgs1d = tupleview.reshape(np.prod(tupleview.shape))

  # imgsmapped.shape = (N), contains uniques-index values
  imgsmapped = np.empty((len(imgs1d))).astype(np.uint16)

  # map each pixel into unique-pixel-ID
  idx = 0
  for x in imgs1d:
    str = ("%sX%sX%s" % (x[0], x[1] ,x[2]))
    imgsmapped[idx] = uniqmap[str]
    idx = idx + 1

  imgsmapped.shape = (original_shape[0], original_shape[2], original_shape[3]) # (N, H, W)
  return (imgsmapped, uniques)

Testing it:

import numpy as np
n = 30
pixelvalues = (np.random.rand(10)*255).astype(np.uint8)
images = np.random.choice(pixelvalues, (n, 3, 72, 96))

(mapped, pixelmap) = unique_map_pixels(images)
assert len(pixelmap) == mapped.max()+1
assert mapped.shape == (len(images), images.shape[2], images.shape[3])
assert pixelmap[mapped[int(n*0.5)][60][81]][0] == images[int(n*0.5)][0][60][81]
print("Done: %s" % list(mapped.shape))

Upvotes: 4

Views: 3166

Answers (3)

Mendrika
Mendrika

Reputation: 31

This is what I do:

def rgb2mask(img): 
    if img.shape[0] == 3:
       img = img.rollaxis(img, 0, 3) 

    W = np.power(256, [[0],[1],[2]])

    img_id = img.dot(W).squeeze(-1) 
    values = np.unique(img_id)

    mask = np.zeros(img_id.shape)
    cmap = {}

    for i, c in enumerate(values):
        idx = img_id==c
        mask[idx] = i 
        cmap[tuple(img[idx][0])] = i
    return mask, cmap

If you want to map values according to an already existing dictionary, check out my answer on this thread: Convert RGB image to index image

Upvotes: 0

Rahul kumar
Rahul kumar

Reputation: 96

I have an image of 3 channels. I have pixel values of 3 channels that if a pixel has these 3 values in its 3 channels then it belongs to class 'A'. Basically I want to generate an array of channels equal to number of classes with each class separate in a particular channel. This can be done

seg_channel = np.zeros((image.shape[0], image.shape[1], num_classes))
pixel_class_dict={'1': [128, 64, 128]. '2': [230, 50, 140]} #num_classes=2
for channel in range(num_classes):
    pixel_value= pixel_class_dict[str(channel)]
    for i in range(image.shape[0]):
        for j in range(image.shape[1]):
            if list(image[i][j])==pixel_value:
                classes_channel[i,j,channel]=1

There is another way also to do this efficiently

import numpy as np
import cv2
for class_id in self.pixel_class_dict:
      class_color = np.array(self.pixel_class_dict:[class_id])
      seg_channel[:, :, class_id] = cv2.inRange(mask, class_color, class_color).astype('bool').astype('float32')

Upvotes: 0

Divakar
Divakar

Reputation: 221674

Here's a compact vectorized approach without those error checks -

def unique_map_pixels_vectorized(imgs):
    N,H,W = len(imgs), imgs.shape[2], imgs.shape[3]
    img2D = imgs.transpose(0, 2, 3, 1).reshape(-1,3)
    ID = np.ravel_multi_index(img2D.T,img2D.max(0)+1)
    _, firstidx, tags = np.unique(ID,return_index=True,return_inverse=True)
    return tags.reshape(N,H,W), img2D[firstidx]

Runtime test and verification -

In [24]: # Setup inputs (3x smaller than original ones)
    ...: N,H,W = 200,24,32
    ...: imgs = np.random.randint(0,10,(N,3,H,W))
    ...: 

In [25]: %timeit unique_map_pixels(imgs)
1 loop, best of 3: 2.21 s per loop

In [26]: %timeit unique_map_pixels_vectorized(imgs)
10 loops, best of 3: 37 ms per loop ## 60x speedup!

In [27]: map1,unq1 = unique_map_pixels(imgs)
    ...: map2,unq2 = unique_map_pixels_vectorized(imgs)
    ...: 

In [28]: np.allclose(map1,map2)
Out[28]: True

In [29]: np.allclose(np.array(map(list,unq1)),unq2)
Out[29]: True

Upvotes: 5

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