Oh, right, sorry, now I see what you're doing. Arrays are homogeneous, meaning every value has the same type. If you write: imgs[i, 0] = imgs[i, 0].astype(np.uint8) you are not changing the type of imgs, so you explicitly cast to uint8 and then the assignment (=) implicitly casts it back to float64. Oops! =) Please follow the advice of Egor and find the img_as_ubyte and img_as_float methods, and use those to convert images of different types. Juan. On 28 Dec 2016, 2:48 AM +1100, wine lover , wrote:

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

- add imgs[i,0] = imgs[i,0]/255.   which scale it to 0 and 1 -  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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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

- add imgs[i,0] = imgs[i,0]/255.   which scale it to 0 and 1 -  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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