def predict_img(img_path):
"""Inference a single image."""
# switch to CUDA device if possible
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Use GPU: {}'.format(str(device) != 'cpu'))
# load model
print('Loading model...')
model = ResnetUnetHybrid.load_pretrained(device=device)
model.eval()
# load image
img = cv2.imread(img_path)[..., ::-1]
img = image_utils.scale_image(img)
img = image_utils.center_crop(img)
inp = image_utils.img_transform(img)
inp = inp[None, :, :, :].to(device)
# inference
print('Running the image through the network...')
output = model(inp)
# transform and plot the results
output = output.cpu()[0].data.numpy()
image_utils.show_img_and_pred(img, output)
def predict_img(img_path):
"""Inference a single image."""
# load image
img = cv2.imread(img_path)[..., ::-1]
pI = PredictInterface()
output = pI.predict(img)
image_utils.show_img_and_pred(img, output)