def _mask_dataset(): # Load model and compile function with open('../cache/foreground_model.pkl', 'rb') as f: model, threshold = cPickle.load(f) x = T.matrix('x') y = model.get_output(x) output_func = theano.function(inputs=[x], outputs=(y >= threshold)) # Load data image_data = DataLoader('../data/cuhk_small.mat', verbose=True) # Pre-processing print "Pre-processing ..." images = image_data.get_all_images() images = [_input_preproc(image) for image in images] images = imageproc.images2mat(images).astype(theano.config.floatX) # Compute masks print "Computing masks ..." masks = output_func(images) # Save masks print "Saving data ..." mask_data = DataSaver() cur_index = 0 for gid in xrange(image_data.get_n_groups()): m, v = image_data.get_n_pedes_views(gid) mask_data.add_group(m, v) for pid in xrange(m): n_images = image_data.get_n_images(gid, pid) for vid, n in enumerate(n_images): view_masks = [0] * n for k in xrange(n): mask = masks[cur_index, :] mask = mask.reshape(160, 80, 1) orig_image = image_data.get_image(gid, pid, vid, k) orig_image = imageproc.imresize(orig_image, (160, 80, 3)) view_masks[k] = (mask * orig_image).astype(numpy.uint8) cur_index += 1 mask_data.set_images(gid, pid, vid, view_masks) mask_data.save('../data/cuhk_small_masked.mat')