def predict(image_path, checkpoint_path, save_path):
model = Unet()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.load_state_dict(torch.load(checkpoint_path, map_location=device))
image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
size = image.shape
image = Compose([
ToTensor(),
Resize((512, 512)),
])(image)
image = image.unsqueeze(0)
mask = model(image)[0]
mask[mask < 0.5] = 0
mask[mask > 0.5] = 255
mask = Resize(size)(mask)
mask = mask.detach().numpy()
cv2.imwrite('result.png', mask[0])
pass
from mirror import mirror
from mirror.visualisations import *
from PIL import Image
from torchvision.models import resnet101, resnet18, vgg16
from torchvision.transforms import ToTensor, Resize, Compose
# create a model
model = vgg16(pretrained=True)
cat = Image.open("./cat.jpg")
# resize the image and make it a tensor
input = Compose([Resize((224, 224)), ToTensor()])(cat)
# add 1 dim for batch
input = input.unsqueeze(0)
# call mirror with the input and the model
mirror(input, model, visualisations=[DeepDream])