Dear Yuanyuan, First of all, it is not a good idea to initialize the array with values using `np.empty`. I'd recommend to use either `np.random.rand` or `np.random.randint`. As for main point of your question, I believe you might need http://scikit-image.org/docs/dev/api/skimage.html#img-as-float (see also http://scikit-image.org/docs/dev/user_guide/data_types.html ). So, you can either create an array of floats [0:1) via `np.random.rand`, or create an array of uints via `np.random.randint`, and call `img_as_float`. Then `equalize_adapthist` should work flawlessly. Regards, Egor 2016-12-27 1:27 GMT+03:00 wine lover <winecoding@gmail.com>:

Dear All,

I was trying to use the above code segment for performing Contrast Limited Adaptive Histogram Equalization (CLAHE). def clahe_equalized(imgs): imgs_equalized = np.empty(imgs.shape) for i in range(imgs.shape[0]):

print('imgs[i,0] ',imgs[i,0].dtype) print('imgs[i,0] ',imgs[i,0].max()) print('imgs[i,0] ',imgs[i,0].min()) imgs_equalized[i,0] = exposure.equalize_adapthist( imgs[i,0],clip_limit=0.03) return imgs_equalized

The dtype is float64, maximum value is 255.0 and minimum value is 0.0

Running the program generates the following error message ( I only keep the related ones)

imgs_equalized[i,0] = exposure.equalize_adapthist( imgs[i,0],clip_limit=0.03) raise ValueError("Images of type float must be between -1 and 1.") ValueError: Images of type float must be between -1 and 1.

In accordance with the above error message and image characteristics, what are the best way to handle this scenario.

I have been thinking of two approaches

1. add imgs[i,0] = imgs[i,0]/255. which scale it to 0 and 1 2. convert imgs[i,0] from float64 to unit8

but imgs[i,0] = imgs[i,0].astype(np.unit8) gives the error message such as imgs[i,0]=imgs[i,0].astype(np.unit8)

AttributeError: 'module' object has no attribute 'unit8'

Would you like to give any advice on this problem? Thank you very much!

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