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