parser = argparse.ArgumentParser()
parser.add_argument('-x',
'--width',
type=int,
default=640,
help='Width of the generated images')
parser.add_argument('-y',
'--height',
type=int,
default=480,
help='Height of the generated images')
args = parser.parse_args()
generator = SheetMetalGenerator(args)
image, mask = generator.generate()
assert len(image.shape) == 3
assert image.shape[0] == args.height
assert image.shape[1] == args.width
assert image.shape[2] == 3
assert image.dtype == np.float64
assert len(mask.shape) == 2
assert mask.shape[0] == args.height
assert mask.shape[1] == args.width
assert mask.dtype == np.float64
utils.show_image([image, 'Sheet metal'], [mask, 'Specimen mask'])
#
# Parse command line arguments
#
parser = argparse.ArgumentParser ()
parser.add_argument ('-x', '--width', type=int, default=640, help='Width of the generated images')
parser.add_argument ('-y', '--height', type=int, default=480, help='Height of the generated images')
generator.background.BackgroundGenerator.add_to_args_definition (parser)
args = parser.parse_args ()
parts = [generator.background.BackgroundGenerator.create (args),
generator.fixture.FixtureGenerator (args)]
source = generator.generator.StackedGenerator (args, parts)
image, mask = source.generate ()
assert len (mask.shape) == 3
assert image.shape[0] == args.height
assert image.shape[1] == args.width
assert image.shape[2] == 3
assert image.dtype == np.float64
assert len (mask.shape) == 2
assert mask.shape[0] == args.height
assert mask.shape[1] == args.width
assert mask.dtype == np.float64
utils.show_image ([image, 'Fixture'], [mask, 'Mask'])
if args.performance:
start_time = time.process_time()
images, masks = next(data.generate)
model.predict(images)
elapsed_time = (time.process_time() - start_time) / (10 * args.performance)
print('Single run duration: {0:.4f} s'.format(elapsed_time))
start_time = time.process_time()
result = model.predict(images)[0]
print('Duration: {0:.4f}s'.format((time.process_time() - start_time) / 10))
print(result.shape)
mask = np.zeros(result.shape[0:2])
mask[result[:, :, 0] > 0.5] = 1.0
mask[result[:, :, 1] > 0.5] = 0.5
utils.show_image([images[0], 'Generated image'],
[mask, 'Predicted specimen features'])
#
# Tensorflow termination bug workaround
#
gc.collect()
#
parser = argparse.ArgumentParser()
parser.add_argument('-x',
'--width',
type=int,
default=640,
help='Width of the generated images')
parser.add_argument('-y',
'--height',
type=int,
default=480,
help='Height of the generated images')
parser.add_argument('-c',
'--color',
type=float,
default=0.3,
help='Color seed')
parser.add_argument('-s',
'--shine',
type=float,
default=0.1,
help='Shine seed')
args = parser.parse_args()
texture_creator = MetalTextureCreator(args.width, args.height, args.color,
args.shine)
image = texture_creator.create()
utils.show_image([image, 'Metal texture'])
args = parser.parse_args()
#
# Instantiate generator class from command line arguments
#
generator = BackgroundGenerator.create(args)
if args.profile:
pr = cProfile.Profile()
pr.enable()
#
# Generate image probe
#
image, mask = generator.generate()
assert mask is None
assert image.shape[0] == args.height
assert image.shape[1] == args.width
assert image.shape[2] == 3
assert image.dtype == np.float64
if args.profile:
pr.disable()
stats = pstats.Stats(pr, stream=sys.stdout).sort_stats('cumulative')
stats.print_stats()
utils.show_image([image, 'Generated background'])