# slic
labels = segmentation.slic(im,
compactness=args.compactness,
n_segments=args.num_superpixels)
labels = labels.reshape(im.shape[0] * im.shape[1])
u_labels = np.unique(labels)
l_inds = []
for i in range(len(u_labels)):
l_inds.append(np.where(labels == u_labels[i])[0])
# train
from model import MyNet
model = MyNet(data.size(1), args.nChannel, args.nConv)
if use_cuda:
model.cuda()
model.train()
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)
label_colours = np.random.randint(255, size=(100, 3))
for batch_idx in range(args.maxIter):
# forwarding
optimizer.zero_grad()
output = model(data)[0]
output = output.permute(1, 2, 0).contiguous().view(-1, args.nChannel)
ignore, target = torch.max(output, 1)
im_target = target.data.cpu().numpy()
nLabels = len(np.unique(im_target))
if args.visualize:
im_target_rgb = np.array([label_colours[c % 100] for c in im_target])
im_target_rgb = im_target_rgb.reshape(im.shape).astype(np.uint8)
'Dot': 'dot',
'Miss_print': 'miss_print',
'Over_ink': 'over_ink',
'Set_off': 'set_off',
'Under_ink': 'under_ink',
'Wiping': 'wiping',
}
class_names = ['dot', 'miss_print', 'over_ink', 'set_off', 'under_ink', 'wiping']
# instantiate the CNN
use_cuda = torch.cuda.is_available()
layer_sizes = [512, 256, 128]
model = MyNet(output_size=6, layer_sizes=layer_sizes)
if use_cuda:
model = model.cuda()
model.load_state_dict(torch.load('model/model_resnet101_512_256_128_back.pt', map_location=torch.device('cpu')))
# ### Get the directory path from the argument
if __name__ == "__main__":
dir_path = sys.argv[1].strip()
#in put the directory path
#dir_path = input('Enter the images directory path: ').strip()
#print(dir_path)
#delete the output folder if exists
for files in os.listdir(dir_path):
if files == 'output':
