我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用mpl_toolkits.axes_grid1.ImageGrid()。
def plotFields(layer,fieldShape=None,channel=None,figOffset=1,cmap=None,padding=0.01): # Receptive Fields Summary try: W = layer.W except: W = layer wp = W.eval().transpose(); if len(np.shape(wp)) < 4: # Fully connected layer, has no shape fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape) else: # Convolutional layer already has shape features, channels, iy, ix = np.shape(wp) if channel is not None: fields = wp[:,channel,:,:] else: fields = np.reshape(wp,[features*channels,iy,ix]) perRow = int(math.floor(math.sqrt(fields.shape[0]))) perColumn = int(math.ceil(fields.shape[0]/float(perRow))) fig = mpl.figure(figOffset); mpl.clf() # Using image grid from mpl_toolkits.axes_grid1 import ImageGrid grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single') for i in range(0,np.shape(fields)[0]): im = grid[i].imshow(fields[i],cmap=cmap); grid.cbar_axes[0].colorbar(im) mpl.title('%s Receptive Fields' % layer.name) # old way # fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))]) # tiled = [] # for i in range(0,perColumn*perRow,perColumn): # tiled.append(np.hstack(fields2[i:i+perColumn])) # # tiled = np.vstack(tiled) # mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar(); mpl.figure(figOffset+1); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar()
def plotFields(layer,fieldShape=None,channel=None,maxFields=25,figName='ReceptiveFields',cmap=None,padding=0.01): # Receptive Fields Summary W = layer.W wp = W.eval().transpose(); if len(np.shape(wp)) < 4: # Fully connected layer, has no shape fields = np.reshape(wp,list(wp.shape[0:-1])+fieldShape) else: # Convolutional layer already has shape features, channels, iy, ix = np.shape(wp) if channel is not None: fields = wp[:,channel,:,:] else: fields = np.reshape(wp,[features*channels,iy,ix]) fieldsN = min(fields.shape[0],maxFields) perRow = int(math.floor(math.sqrt(fieldsN))) perColumn = int(math.ceil(fieldsN/float(perRow))) fig = mpl.figure(figName); mpl.clf() # Using image grid from mpl_toolkits.axes_grid1 import ImageGrid grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single') for i in range(0,fieldsN): im = grid[i].imshow(fields[i],cmap=cmap); grid.cbar_axes[0].colorbar(im) mpl.title('%s Receptive Fields' % layer.name) # old way # fields2 = np.vstack([fields,np.zeros([perRow*perColumn-fields.shape[0]] + list(fields.shape[1:]))]) # tiled = [] # for i in range(0,perColumn*perRow,perColumn): # tiled.append(np.hstack(fields2[i:i+perColumn])) # # tiled = np.vstack(tiled) # mpl.figure(figOffset); mpl.clf(); mpl.imshow(tiled,cmap=cmap); mpl.title('%s Receptive Fields' % layer.name); mpl.colorbar(); mpl.figure(figName+' Total'); mpl.clf(); mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap); mpl.title('%s Total Absolute Input Dependency' % layer.name); mpl.colorbar()
def plot_image_grid(images, num_rows, num_cols, save_path=None): """Plots images in a grid. Parameters ---------- images : numpy.ndarray Images to display, with shape ``(num_rows * num_cols, num_channels, height, width)``. num_rows : int Number of rows for the image grid. num_cols : int Number of columns for the image grid. save_path : str, optional Where to save the image grid. Defaults to ``None``, which causes the grid to be displayed on screen. """ figure = pyplot.figure() grid = ImageGrid(figure, 111, (num_rows, num_cols), axes_pad=0.1) for image, axis in zip(images, grid): axis.imshow(image.transpose(1, 2, 0), interpolation='nearest') axis.set_yticklabels(['' for _ in range(image.shape[1])]) axis.set_xticklabels(['' for _ in range(image.shape[2])]) axis.axis('off') if save_path is None: pyplot.show() else: pyplot.savefig(save_path, transparent=True, bbox_inches='tight')
def plot_image_grid(images, num_rows, num_cols, save_path=None): """Plots images in a grid. Parameters ---------- images : numpy.ndarray Images to display, with shape ``(num_rows * num_cols, num_channels, height, width)``. num_rows : int Number of rows for the image grid. num_cols : int Number of columns for the image grid. save_path : str, optional Where to save the image grid. Defaults to ``None``, which causes the grid to be displayed on screen. """ figure = pyplot.figure() grid = ImageGrid(figure, 111, (num_rows, num_cols), axes_pad=0.1) for image, axis in zip(images, grid): axis.imshow(image.transpose(1, 2, 0), interpolation='nearest') axis.set_yticklabels(['' for _ in range(image.shape[1])]) axis.set_xticklabels(['' for _ in range(image.shape[2])]) axis.axis('off') if save_path is None: pyplot.show() else: pyplot.savefig(save_path, transparent=True, bbox_inches='tight',dpi=212) pyplot.close()
def save_imshow_grid(images, logs_dir, filename, shape): """ Plot images in a grid of a given shape. """ fig = plt.figure(1) grid = ImageGrid(fig, 111, nrows_ncols=shape, axes_pad=0.05) size = shape[0] * shape[1] for i in trange(size, desc="Saving images"): grid[i].axis('off') grid[i].imshow(images[i]) plt.savefig(os.path.join(logs_dir, filename))
def _get_grids(fig, rows, cols, axes_pad=0): grids = [] for row in range(rows): grid_id = int("%d%d%d" % (rows, 1, row + 1)) grid = ImageGrid(fig, grid_id, nrows_ncols=(1, cols), axes_pad=(0.05, axes_pad), share_all=True, cbar_location="right", cbar_mode="single", cbar_size="10%", cbar_pad="5%") grids.append(grid) return grids
def save_imshow_grid(images, logs_dir, filename, shape): """ Plot images in a grid of a given shape. """ pickle.dump(images, open(os.path.join(logs_dir, "image.pk"), "wb")) fig = plt.figure(1) grid = ImageGrid(fig, 111, nrows_ncols=shape, axes_pad=0.05) size = shape[0] * shape[1] for i in trange(size, desc="Saving images"): grid[i].axis('off') grid[i].imshow(images[i]) Image.fromarray(images[i]).save(os.path.join(logs_dir,str(i)),"jpeg") plt.savefig(os.path.join(logs_dir, filename))
def main(argv=None): input.init_dataset_constants() num_images = GRID[0] * GRID[1] FLAGS.batch_size = num_images with tf.Graph().as_default(): g_template = model.generator_template() z = tf.placeholder(tf.float32, shape=[FLAGS.batch_size, FLAGS.z_size]) #np.random.seed(1337) # generate same random numbers each time noise = np.random.normal(size=(FLAGS.batch_size, FLAGS.z_size)) with pt.defaults_scope(phase=pt.Phase.test): gen_images_op, _ = pt.construct_all(g_template, input=z) sess = tf.Session() init_variables(sess) gen_images, = sess.run([gen_images_op], feed_dict={z: noise}) gen_images = (gen_images + 1) / 2 sess.close() fig = plt.figure(1) grid = ImageGrid(fig, 111, nrows_ncols=GRID, axes_pad=0.1) for i in xrange(num_images): im = gen_images[i] axis = grid[i] axis.axis('off') axis.imshow(im) plt.show() fig.savefig('montage.png', dpi=100, bbox_inches='tight')
def generate_images_line_save(self, line_segment, query_id, image_original_space=None): """ ID of query point from which query line was generated is added to the filename of the saved line query. :param line_segment: :param query_id: :return: """ try: if image_original_space is not None: x = self.generative_model.decode(image_original_space.T) else: x = self.generative_model.decode(to_vector(self.dataset.data["features"][ query_id]).T) # comes from dataset.data["features"], so is already in original space in which ALI operates. save_path = os.path.join(self.save_path_queries, "pointquery_%d_%d.png" % (self.n_queries + 1, query_id)) if x.shape[1] == 1: plt.imsave(save_path, x[0, 0, :, :], cmap=cm.Greys) else: plt.imsave(save_path, x[0, :, :, :].transpose(1, 2, 0), cmap=cm.Greys_r) decoded_images = self.generative_model.decode(self.dataset.scaling_transformation.inverse_transform( line_segment)) # Transform to original space, in which ALI operates. figure = plt.figure() grid = ImageGrid(figure, 111, (1, decoded_images.shape[0]), axes_pad=0.1) for image, axis in zip(decoded_images, grid): if image.shape[0] == 1: axis.imshow(image[0, :, :].squeeze(), cmap=cm.Greys, interpolation='nearest') else: axis.imshow(image.transpose(1, 2, 0).squeeze(), cmap=cm.Greys_r, interpolation='nearest') axis.set_yticklabels(['' for _ in range(image.shape[1])]) axis.set_xticklabels(['' for _ in range(image.shape[2])]) axis.axis('off') save_path = os.path.join(self.save_path_queries, "linequery_%d_%d.pdf" % (self.n_queries + 1, query_id)) plt.savefig(save_path, transparent=True, bbox_inches='tight') except Exception as e: print "EXCEPTION:", traceback.format_exc() raise e
