gistfile1.txt

def plot_prediction(result, sample, target, filename=None):
    sample.shape = 512, 512
    
    fig = pylab.figure()
    plot = fig.add_subplot(111)
    pylab.imshow(sample, cmap=pylab.cm.gray)
    
    denormalize = lambda x, rng: (x + 1.) * rng / 2

    def getcoords(normalized):
      min0, max0, min1, max1 = normalized.tolist()
      min0 = int(denormalize(min0, sample.shape[0]))
      max0 = int(denormalize(max0, sample.shape[0]))
      min1 = int(denormalize(min1, sample.shape[1]))
      max1 = int(denormalize(max1, sample.shape[1]))

      return min0, min1, max0 - min0, max1 - min1

    x, y, width, height = getcoords(target)
    rect = pylab.Rectangle((x, y), width, height, fill=True, alpha=.7,
                           facecolor='red')
    plot.add_patch(rect)

    x, y, width, height = getcoords(result)
    rect = pylab.Rectangle((x, y), width, height, fill=True, alpha=.7,
                           facecolor='blue')
    plot.add_patch(rect)

    if filename is None:
        pylab.show()
    else:
        pylab.savefig(filename)