On Wed, Dec 4, 2013 at 2:38 PM, Josh Warner <silvertrumpet999@gmail.com>wrote:

Briefly, `img_as_float` assumes all inputs were properly scaled images of their reported dtype.

If `img_as_float` is handed an image with the datatype `np.float64`, it is caught and the input image is returned without modification... no scaling is applied or attempted.

Your input here appears to be a floating point array on the range [0.0, 777.0]. You will need to manually scale to the range [0, 1] - or, ideally, set the actual dtype (`np.int16` or `np.uint16`, in this case?) when you load your data. Then everything should work well.

You might find `rescale_intensity` helpful if you have data that should automatically be linearly rescaled to the dtype limits. For example: import numpy as np from skimage.exposure import rescale_intensity rescale_intensity(np.arange(1000, dtype=float)) Note however, that it's best practice to pass in an input range to rescale intensity (by default, it uses the min and max of the input data). If you're processing a series of images, you can't really tell signal from noise if you're always stretching to the min/max of each image. Best, -Tony

On Wednesday, December 4, 2013 2:16:10 PM UTC-6, Scott Classen wrote:

...

I have four 2D numpy arrays. 100x100, 200x200, 300x300, 400x400

I want to use pyramid_reduce on the 200x200, 300x300, and 400x400 arrays to downsample them to 100x100 arrays, then add them all together.

The arrays are in a list and I'm looping through the list (looping code left out for clarity) calculating binned images for each one:

g.binned = img_as_float(g.cropped)# no reducing here, just converting to scikit compatable float…I hope. g.binned = pyramid_reduce(img_as_float(g.cropped), downscale=2) g.binned = pyramid_reduce(img_as_float(g.cropped), downscale=3) g.binned = pyramid_reduce(img_as_float(g.cropped), downscale=4)

The g.cropped input arrays originated from numpy arrays with dtype=float64

I then print some useful information about my 4 arrays:

array data type: float64 shape: (100, 100) nanmax: 777.0 array data type: float64 shape: (100, 100) nanmax: 1.0 array data type: float64 shape: (100, 100) nanmax: 1.0 array data type: float64 shape: (100, 100) nanmax: 1.0

I'm curious why the g.binned array from g.binned = img_as_float(g.cropped)has not been scaled to 0,1? The others which have been through the pyramid_reduce function have apparently been scaled, but maybe not by the img_as_float routine, but by pyramid_reduce?

I then add the arrays

tmp_image = list[0].binned + list[1].binned + list[2].binned + list[3].binned

However, because the first array has not been properly scaled to 0,1 I get an error when I run the tmp_image through img_as_uint so I can write out my binary image file:

Traceback (most recent call last): File "./test.py", line 318, in <module> image_to_write = img_as_uint(tmp_image) File "/sw/lib/python2.7/site-packages/skimage/util/dtype.py", line 310, in img_as_uint return convert(image, np.uint16, force_copy) File "/sw/lib/python2.7/site-packages/skimage/util/dtype.py", line 191, in convert raise ValueError("Images of type float must be between -1 and 1.") ValueError: Images of type float must be between -1 and 1.

Any advice would be most appreciated.

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