Hi Yuanyuan,
This warning is raised when the value range of a float image is less than
1/256, because in those cases, most image viewers will show the image as having
a single shade of grey. For example, if you have an image of physical
measurements in the range [0, 1e-5], and you save that as float, in many
viewers it will appear as all black.
It is not something to worry about if you just want to save the data for later.
We implemented it to help you figure out what is happening when the image
doesn’t “look right” in a viewer. =)
Juan.
On 8 Dec. 2016, 10:51 AM +1100, wine lover <winecod...@gmail.com>, wrote:
> Hi Juan, Hi Imanol,
>
> Thank you so much for your replies, which are very helpful.
>
> During saving the image, I got some warning messages such as follows. What
> does it indicate?
> /development/lib/python3.4/site-packages/scikit_image-0.12.3-py3.4-linux-x86_64.egg/skimage/io/_io.py:132:
> UserWarning: /data/train/image_sampled.tif is a low contrast image
> warn('%s is a low contrast image' % fname)
>
> Thanks,
> Yuanyuan
>
> > On Wed, Dec 7, 2016 at 4:54 AM, Imanol Luengo
> > <imanol.lue...@nottingham.ac.uk> wrote:
> > > Hello,
> > > I would say there are two differences between 'Saving the data' and
> > > 'Displaying the data'. An image is discretized to `uint8` or `uint16`
> > > prior to being saved as standard formates (`.png` or `.jpg`). You could
> > > do something like
> > > ```
> > > import numpy as np
> > > from skimage import io, util
> > >
> > > A = np.random.rand(100,100)
> > > io.imsave('tmp.png', A)
> > > B = util.img_as_float(io.imread('tmp.png')
> > >
> > > assert np.allclose(A, B) # ERROR
> > > ```
> > > But you will find some discretization errors, which makes `B != A`.
> > > Having said that, if you want to preserve the data in `B`, I think the
> > > best option is to export the data using another format, e.g. numpy arrays:
> > > ```
> > > import numpy as np
> > >
> > > A = np.random.rand(100,100)
> > > np.save('tmp.npy', A)
> > > B = np.load('tmp.npy')
> > >
> > > assert np.allclose(A, B) # True
> > > ```
> > > Or alternatively, if you really want to save the data in a visualizable
> > > format, exporting the image as `.tif` format, which preserves data
> > > information, should also work:
> > > ```
> > > A = np.random.rand(100,100)
> > > io.imsave('tmp.tif', A)
> > > B = io.imread('tmp.tif')
> > >
> > > assert np.allclose(A, B) # True
> > > ```
> > > However, I would personally store my data in non-visualizable formats
> > > such as `.npy, .h5` (the later if you work with tons of data) as they
> > > usually offer another advantages (e.g. Datasets in HDF5).
> > > Hope it helps,
> > > Imanol
> > >
> > > On 07/12/16 04:41, wine lover wrote:
> > > > Dear All,
> > > >
> > > > In a program, I generate an numpy array, with shape (128,128), which is
> > > > supposed to represent an image.
> > > > For instance, I have an array temp_mask, which is of type float32 and
> > > > shape (128,128), the maximum value is 1.0 and the minimum value is 0.0.
> > > > I saved it using io.imsave(‘mask_image’,temp_mask) However, after I
> > > > re-opened this image using img_mask = io.imread(‘mask_image’). The read
> > > > image turns out to have type unit16, the max value becomes 65535 and
> > > > the min value is 0 . It seems to me that io.imsave automatically
> > > > transform the float32 array into an unit16 array.
> > > > Is it possible to save the image while keeping the original type? If
> > > > not, what’s the correct way to save an image represented as an array,
> > > > with type float32 and the range of value [0.0,1.0]?
> > > >
> > > > Thank you very much!
> > > >
> > > >
> > > >
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