Hi Juan,

Thanks for pointing the typo. I corrected it, and looks like imgs[i,0]=imgs[i,0].astype(np.unit8) does not solve the problem.

Here is the screenshot of result

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

Thanks,

Yuanyuan

On Tue, Dec 27, 2016 at 12:10 AM, Juan Nunez-Iglesias <jni.soma@gmail.com> wrote:

Typo: unit8 -> uint8

On 27 Dec 2016, 9:27 AM +1100, wine lover <winecoding@gmail.com>, wrote:

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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scikit-image@python.org
https://mail.python.org/mailman/listinfo/scikit-image

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!  

_______________________________________________
scikit-image mailing list
scikit-image@python.org
https://mail.python.org/mailman/listinfo/scikit-image