Mercurial > ift6266
comparison data_generation/transformations/image_tiling.py @ 167:1f5937e9e530
More moves - transformations into data_generation, added "deep" folder
| author | Dumitru Erhan <dumitru.erhan@gmail.com> |
|---|---|
| date | Fri, 26 Feb 2010 14:15:38 -0500 |
| parents | transformations/image_tiling.py@8ce089f30463 |
| children |
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| 166:17ae5a1a4dd1 | 167:1f5937e9e530 |
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| 1 """ | |
| 2 Illustrate filters (or data) in a grid of small image-shaped tiles. | |
| 3 | |
| 4 Note: taken from the pylearn codebase on Feb 4, 2010 (fsavard) | |
| 5 """ | |
| 6 | |
| 7 import numpy | |
| 8 from PIL import Image | |
| 9 | |
| 10 def scale_to_unit_interval(ndar,eps=1e-8): | |
| 11 ndar = ndar.copy() | |
| 12 ndar -= ndar.min() | |
| 13 ndar *= 1.0 / (ndar.max()+eps) | |
| 14 return ndar | |
| 15 | |
| 16 def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0,0), | |
| 17 scale_rows_to_unit_interval=True, | |
| 18 output_pixel_vals=True | |
| 19 ): | |
| 20 """ | |
| 21 Transform an array with one flattened image per row, into an array in which images are | |
| 22 reshaped and layed out like tiles on a floor. | |
| 23 | |
| 24 This function is useful for visualizing datasets whose rows are images, and also columns of | |
| 25 matrices for transforming those rows (such as the first layer of a neural net). | |
| 26 | |
| 27 :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can be 2-D ndarrays or None | |
| 28 :param X: a 2-D array in which every row is a flattened image. | |
| 29 :type img_shape: tuple; (height, width) | |
| 30 :param img_shape: the original shape of each image | |
| 31 :type tile_shape: tuple; (rows, cols) | |
| 32 :param tile_shape: the number of images to tile (rows, cols) | |
| 33 | |
| 34 :returns: array suitable for viewing as an image. (See:`PIL.Image.fromarray`.) | |
| 35 :rtype: a 2-d array with same dtype as X. | |
| 36 | |
| 37 """ | |
| 38 assert len(img_shape) == 2 | |
| 39 assert len(tile_shape) == 2 | |
| 40 assert len(tile_spacing) == 2 | |
| 41 | |
| 42 out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp | |
| 43 in zip(img_shape, tile_shape, tile_spacing)] | |
| 44 | |
| 45 if isinstance(X, tuple): | |
| 46 assert len(X) == 4 | |
| 47 if output_pixel_vals: | |
| 48 out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype='uint8') | |
| 49 else: | |
| 50 out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype) | |
| 51 | |
| 52 #colors default to 0, alpha defaults to 1 (opaque) | |
| 53 if output_pixel_vals: | |
| 54 channel_defaults = [0,0,0,255] | |
| 55 else: | |
| 56 channel_defaults = [0.,0.,0.,1.] | |
| 57 | |
| 58 for i in xrange(4): | |
| 59 if X[i] is None: | |
| 60 out_array[:,:,i] = numpy.zeros(out_shape, | |
| 61 dtype='uint8' if output_pixel_vals else out_array.dtype | |
| 62 )+channel_defaults[i] | |
| 63 else: | |
| 64 out_array[:,:,i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals) | |
| 65 return out_array | |
| 66 | |
| 67 else: | |
| 68 H, W = img_shape | |
| 69 Hs, Ws = tile_spacing | |
| 70 | |
| 71 out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype) | |
| 72 for tile_row in xrange(tile_shape[0]): | |
| 73 for tile_col in xrange(tile_shape[1]): | |
| 74 if tile_row * tile_shape[1] + tile_col < X.shape[0]: | |
| 75 if scale_rows_to_unit_interval: | |
| 76 this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)) | |
| 77 else: | |
| 78 this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape) | |
| 79 out_array[ | |
| 80 tile_row * (H+Hs):tile_row*(H+Hs)+H, | |
| 81 tile_col * (W+Ws):tile_col*(W+Ws)+W | |
| 82 ] \ | |
| 83 = this_img * (255 if output_pixel_vals else 1) | |
| 84 return out_array | |
| 85 | |
| 86 |