Mercurial > ift6266
view code_tutoriel/utils.py @ 194:c69c1d832a53
Fix import of scalar_series.
author | Arnaud Bergeron <abergeron@gmail.com> |
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date | Tue, 02 Mar 2010 17:28:14 -0500 |
parents | 4bc5eeec6394 |
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""" This file contains different utility functions that are not connected in anyway to the networks presented in the tutorials, but rather help in processing the outputs into a more understandable way. For example ``tile_raster_images`` helps in generating a easy to grasp image from a set of samples or weights. """ import numpy def scale_to_unit_interval(ndar,eps=1e-8): """ Scales all values in the ndarray ndar to be between 0 and 1 """ ndar = ndar.copy() ndar -= ndar.min() ndar *= 1.0 / (ndar.max()+eps) return ndar def tile_raster_images(X, img_shape, tile_shape,tile_spacing = (0,0), scale_rows_to_unit_interval = True, output_pixel_vals = True): """ Transform an array with one flattened image per row, into an array in which images are reshaped and layed out like tiles on a floor. This function is useful for visualizing datasets whose rows are images, and also columns of matrices for transforming those rows (such as the first layer of a neural net). :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can be 2-D ndarrays or None; :param X: a 2-D array in which every row is a flattened image. :type img_shape: tuple; (height, width) :param img_shape: the original shape of each image :type tile_shape: tuple; (rows, cols) :param tile_shape: the number of images to tile (rows, cols) :param output_pixel_vals: if output should be pixel values (i.e. int8 values) or floats :param scale_rows_to_unit_interval: if the values need to be scaled before being plotted to [0,1] or not :returns: array suitable for viewing as an image. (See:`PIL.Image.fromarray`.) :rtype: a 2-d array with same dtype as X. """ assert len(img_shape) == 2 assert len(tile_shape) == 2 assert len(tile_spacing) == 2 # The expression below can be re-written in a more C style as # follows : # # out_shape = [0,0] # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] - # tile_spacing[0] # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] - # tile_spacing[1] out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp in zip(img_shape, tile_shape, tile_spacing)] if isinstance(X, tuple): assert len(X) == 4 # Create an output numpy ndarray to store the image if output_pixel_vals: out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype='uint8') else: out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype) #colors default to 0, alpha defaults to 1 (opaque) if output_pixel_vals: channel_defaults = [0,0,0,255] else: channel_defaults = [0.,0.,0.,1.] for i in xrange(4): if X[i] is None: # if channel is None, fill it with zeros of the correct # dtype out_array[:,:,i] = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else out_array.dtype )+channel_defaults[i] else: # use a recurrent call to compute the channel and store it # in the output out_array[:,:,i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals) return out_array else: # if we are dealing with only one channel H, W = img_shape Hs, Ws = tile_spacing # generate a matrix to store the output out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype) for tile_row in xrange(tile_shape[0]): for tile_col in xrange(tile_shape[1]): if tile_row * tile_shape[1] + tile_col < X.shape[0]: if scale_rows_to_unit_interval: # if we should scale values to be between 0 and 1 # do this by calling the `scale_to_unit_interval` # function this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)) else: this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape) # add the slice to the corresponding position in the # output array out_array[ tile_row * (H+Hs):tile_row*(H+Hs)+H, tile_col * (W+Ws):tile_col*(W+Ws)+W ] \ = this_img * (255 if output_pixel_vals else 1) return out_array