def main():
start_time = time.time()
parser = argparse.ArgumentParser()
parser.add_argument("filename", help="Output png.")
parser.add_argument("-i", help="Number of input nodes.", type=int, default=16)
parser.add_argument("-l", help="Number of hidden layers.", type=int, default=12)
parser.add_argument("-x", help="Image width.", type=int, default=800)
parser.add_argument("-y", help="Image height.", type=int, default=600)
args = parser.parse_args()
# Initialize settings
NetSettings = namedtuple("NetSettings", "input hidden output")
ImgSettings = namedtuple("ImgSettings", "width height "
"mapR maxR "
"mapG maxG "
"mapB maxB")
netSettings = NetSettings(args.i, args.l, 3)
imgSettings = ImgSettings(args.x, args.y,
2, 155,
2, 155,
2, 155)
data = NeuralNet.initializeData(imgSettings.width, imgSettings.height)
# Output Image
outputImage = np.empty(imgSettings.width * imgSettings.height * 3, dtype=np.uint8)
input, layers, weights = NeuralNet.buildNeuralNet(netSettings.input, netSettings.hidden, netSettings.output)
result = NeuralNet.runNeuralNet(input, layers[-1], data)
# transform result in image data
for index in range(len(data)):
outputImage[index * 3] = int(result[index][imgSettings.mapR] * imgSettings.maxR)
outputImage[index * 3 + 1] = int(result[index][imgSettings.mapG] * imgSettings.maxG)
outputImage[index * 3 + 2] = int(result[index][imgSettings.mapB] * imgSettings.maxB)
with open(args.filename, 'w') as outfile:
pngWriter = png.Writer(imgSettings.width, imgSettings.height)
pngWriter.write(outfile, np.reshape(outputImage, (-1, imgSettings.width*3)))
print("--- %s seconds ---" % (time.time() - start_time))
