Using numpy.histogram on an array of images

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I'm trying to calculate image histograms of an numpy array of images. The array of images is of shape (n_images, width, height, colour_channels) and I want to return an array of shape (n_images, count_in_each_bin (i.e. 255)). This is done via two intermediary steps of averaging each colour channel for each image and then flattening each 2D image to a 1D one.

I think have successfully done this with the code below, however I have cheated a bit with the for loop at the end. My question is this - is there a way of getting rid of the last for loop and using an optimised numpy function instead?

def histogram_helper(flattened_image: np.array) -> np.array:
    counts, _ = np.histogram(flattened_image, bins=[n for n in range(0, 256)])
    return counts

# Using 10 RGB images of width and height 300
images = np.zeros((10, 300, 300, 3))

# Take the mean of the three colour channels
channel_avg = np.mean(images, axis=3)

# Flatten each image in the array of images, resulting in a 1D representation of each image.
flat_images = channel_avg.reshape(*channel_avg.shape[:-2], -1)

# Now calculate the counts in each of the colour bins for each image in the array.
# This will provide us with a count of how many times each colour appears in an image.
result = np.empty((0, len(self.histogram_bins) - 1), dtype=np.int32)
for image in flat_images:
    colour_counts = self.histogram_helper(image)
    colour_counts = colour_counts.reshape(1, -1)
    result = np.concatenate([result, colour_counts])
numpy python
1 Answers

You don't necessarily need to call np.histogram or np.bincount for this, since pixel values are in the range 0 to N. That means that you can treat them as indices and simply use a counter.

Here's how I would transform the initial images, which I imaging are of dtype np.uint8:

images = np.random.randint(0, 255, size=(10, 5, 5, 3))  # 10 5x5 images, 3 channels
reshaped = np.round(images.reshape(images.shape[0], -1, images.shape[-1]).mean(-1)).astype(images.dtype)

Now you can simply count the histograms using unbuffered addition with np.add.at:

result = np.zeros((images.shape[0], 256), int)
index = np.arange(len(images))[:, None]
np.add.at(result, (index, reshaped), 1)

The last operation is in-place and therefore returns None, but the answer will be in result nevertheless.

user_29887300
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