Mercurial > ift6266
view data_generation/pipeline/visualizer.py @ 358:31641a84e0ae
Initial commit for the experimental setup of the denoising convolutional network
author | humel |
---|---|
date | Thu, 22 Apr 2010 00:49:42 -0400 |
parents | 5e0e5f1860ec |
children |
line wrap: on
line source
#!/usr/bin/python import numpy import Image from image_tiling import tile_raster_images import pylab import time class Visualizer(): def __init__(self, num_columns=10, image_size=(32,32), to_dir=None, on_screen=False): self.list = [] self.image_size = image_size self.num_columns = num_columns self.on_screen = on_screen self.to_dir = to_dir self.cur_grid_image = None self.cur_index = 0 def visualize_stop_and_flush(self): self.make_grid_image() if self.on_screen: self.visualize() if self.to_dir: self.dump_to_disk() self.stop_and_wait() self.flush() self.cur_index += 1 def make_grid_image(self): num_rows = len(self.list) / self.num_columns if len(self.list) % self.num_columns != 0: num_rows += 1 grid_shape = (num_rows, self.num_columns) self.cur_grid_image = tile_raster_images(numpy.array(self.list), self.image_size, grid_shape, tile_spacing=(5,5), output_pixel_vals=False) def visualize(self): pylab.imshow(self.cur_grid_image) pylab.draw() def dump_to_disk(self): gi = Image.fromarray((self.cur_grid_image * 255).astype('uint8'), "L") gi.save(self.to_dir + "/grid_" + str(self.cur_index) + ".png") def stop_and_wait(self): # can't raw_input under gimp, so sleep) print "New image generated, sleeping 5 secs" time.sleep(5) def flush(self): self.list = [] def get_parameters_names(self): return [] def regenerate_parameters(self): return [] def after_transform_callback(self, image): self.transform_image(image) def end_transform_callback(self, final_image): self.visualize_stop_and_flush() def transform_image(self, image): sz = self.image_size self.list.append(image.copy().reshape((sz[0] * sz[1])))