Hi,

I'm very impressed with the progress made on scikits since its
inception in 2009. I checked out the latest skimage from Git and tried
running the HoG code on a non-square image, but it returns an error.
Moreover, if I don't convert from RGB to gray, I get a shape mismatch
error.  In the latter case, a more user-friendly error message would
be more informative. Alternatively, you can either convert it to
grayscale or compute a gradient image for each channel and for each
pixel use the largest.

Here is how to reproduce the error:

In [1]: import skimage.color

In [2]: import skimage.data
** Message: pygobject_register_sinkfunc is deprecated (GstObject)

In [3]: import skimage.feature

In [4]: I=skimage.data.imread("motorbikes041.png")  # see attached

In [5]: I2=skimage.color.rgb2gray(I)

In [6]: print I2.shape, I2.dtype
(150, 200) float64

In [7]: skimage.feature.hog(I2)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)

/data/repo/scikits-image/skimage/<ipython console> in <module>()

/usr/local/lib/python2.6/dist-packages/scikits_image-0.7dev-py2.6-linux-i686.egg/skimage/feature/hog.pyc
in hog(image, orientations, pixels_per_cell, cells_per_block,
visualise, normalise)
    121
    122         orientation_histogram[:, :, i] = uniform_filter(temp_mag,
--> 123              size=(cy, cx))[cy / 2::cy, cx / 2::cx]
    124
    125     # now for each cell, compute the histogram


ValueError: shape mismatch: objects cannot be broadcast to a single shape

--------------

However, if I select a square subarray, it works fine.

In [8]: x=skimage.feature.hog(I2[:128,:128])

Thanks,

Damian