Hi Yuanyuan,

In your example the image data range is not being rescaled as it already has dtype float. `img_as_float` will rescale from [0:255] to [0:1] only if the dtype of input ndarray is of integer family (and, in your case, uint8).

Take a look:

In [3]: nd_int = np.random.randint(0, 255, (3, 3))

In [4]: nd_int

Out[4]: 

array([[ 85,  15,  60],

       [225, 252,  32],

       [162, 173,  34]])

In [5]: nd_int = nd_int.astype(np.uint8)

In [6]: skimage.img_as_float(nd_int)

Out[6]: 

array([[ 0.33333333,  0.05882353,  0.23529412],

       [ 0.88235294,  0.98823529,  0.1254902 ],

       [ 0.63529412,  0.67843137,  0.13333333]])

Please, notice that if your data lies in a range [0:255], but the ndarray dtype is not uint8 (e.g. uint16, int8, etc), you will get different results.

Regards,

Egor

2016-12-27 19:12 GMT+03:00 wine lover <winecoding@gmail.com>:

Hi Egor,

Thank you for the suggestion. This is how I modify the code 

imgs_equalized = np.random.rand(imgs.shape[0],imgs.shape[1],imgs.shape[2],imgs.shape[3])

    for i in range(imgs.shape[0]):

         print('imgs[i,0] ',imgs[i,0].shape)

         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[i,0]=img_as_float(imgs[i,0])

         print('afte applying astype')

         print('imgs[i,0] ',imgs[i,0].shape)

         print('imgs[i,0] ',imgs[i,0].dtype)

         print('imgs[i,0] ',imgs[i,0].max())

         print('imgs[i,0] ',imgs[i,0].min())

the output is 

imgs[i,0]  (584, 565)

imgs[i,0]  float64

imgs[i,0]  255.0

imgs[i,0]  0.0

afte applying astype

imgs[i,0]  (584, 565)

imgs[i,0]  float64

imgs[i,0]  255.0

imgs[i,0]  0.0

Looks like it does not convert the image type as I expected, in specific, the maximum value.

Thanks,

Yuanyuan

On Tue, Dec 27, 2016 at 1:39 AM, Egor Panfilov <egor.v.panfilov@gmail.com> wrote:

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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