def predict(config, args):
plug = Wire(filename=os.path.join(args.data_dir, 'val.csv'),
train_mode=True)
loader = (plug
| CSVLoaderXYZ(
name='xyz', prefix=args.data_pre, **config.xy_splitter)
| ImageLoaderInference('image_loader', True, **config.loader))
unet = predictors[config.model_name](**config.model)
predictor = Predictor('unet_predictor', unet, need_setup=True)
predictor.setup(path=args.model_path, load_weights=True)
evaluator = Evaluator('evaluator', predictor.predictor, need_setup=False)
viewer = PolygonViewer('viewer', save=args.save, job_dir=config.job_dir)
polygonizer = Polygonizer('polygons')
generator, steps = loader.generator
while True:
x, y, z = generator.next()
batch = Wire(x=x, y=y)
prediction = batch | predictor | polygonizer
creator = ShapefileCreator('shapefile')
for i in range(len(x)):
shp_path = os.path.join(
'/Users/nikhilsaraf/Documents',
os.path.basename(z[i]).replace('image',
'pred').replace('.jpg', '.shp'))
creator.transform(polygons=prediction.polygons[i],
filename=shp_path,
transform=get_transform(
os.path.join(args.data_pre, z[i])))
def predict(config, args):
image, transform, crs = open_image(args.file_path)
adjusted_image = adjust_image(image)
tiles = tile_image(adjusted_image, 256, 256)
unet = UNetModel(**config.model)
predictor = Predictor('unet_predictor', unet, need_setup=True)
predictor.setup(path=args.model_path, load_weights=True)
predictions = Wire(x=tiles, batch_size=5) | predictor
remade_image = untile_image(predictions.predictions, adjusted_image.shape[0], adjusted_image.shape[1], 1)
polygons = Wire(predictions=remade_image) | Polygonizer('polygons')
(Wire(filename=args.file_path[:args.file_path.index('.')] + '.shp', transform=make_transform(transform)) + polygons
| ShapefileCreator('shapefile', crs=crs.to_dict()))