This is a small demo showing that the functionality of misas is not at all limited to the domain of medical imaging. However, the main use case for the authors of misas is currently medical imaging, therefore this is just a proof of concept. It should give you an idea on how to work with a new dataset. It can be extended if users need it. The example builds on the camvid example in the fastai1 documentation.

Please reach out via GitHub if you have problems using misas on your own dataset.

from fastai.vision.all import *
path = untar_data(URLs.CAMVID_TINY)
codes = np.loadtxt(path/'codes.txt', dtype=str)
codes
array(['Animal', 'Archway', 'Bicyclist', 'Bridge', 'Building', 'Car',
       'CartLuggagePram', 'Child', 'Column_Pole', 'Fence', 'LaneMkgsDriv',
       'LaneMkgsNonDriv', 'Misc_Text', 'MotorcycleScooter', 'OtherMoving',
       'ParkingBlock', 'Pedestrian', 'Road', 'RoadShoulder', 'Sidewalk',
       'SignSymbol', 'Sky', 'SUVPickupTruck', 'TrafficCone',
       'TrafficLight', 'Train', 'Tree', 'Truck_Bus', 'Tunnel',
       'VegetationMisc', 'Void', 'Wall'], dtype='<U17')
fnames = get_image_files(path/"images")
def label_func(fn): return path/"labels"/f"{fn.stem}_P{fn.suffix}"
cam_fn = fnames[0]
mask_fn = label_func(fnames[0])

cam_img = lambda: Image.open(cam_fn).convert("RGB")
mask = lambda: Image.open(mask_fn).convert("I")
dls = SegmentationDataLoaders.from_label_func(
    path, bs=8, fnames = fnames, label_func = label_func, codes = codes
)
dls.show_batch(max_n=6)
learn = unet_learner(dls, resnet34)
Downloading: "https://download.pytorch.org/models/resnet34-b627a593.pth" to /home/markus/.cache/torch/hub/checkpoints/resnet34-b627a593.pth
learn.fine_tune(6)
epoch train_loss valid_loss time
0 3.004354 2.548569 00:30
epoch train_loss valid_loss time
0 2.048129 1.562803 00:37
1 1.704228 1.354810 00:33
2 1.483399 1.031199 00:30
3 1.315127 0.921900 00:27
4 1.164001 0.827559 00:33
5 1.051317 0.808568 00:32
learn.show_results(max_n=6, figsize=(7,8))
interp = SegmentationInterpretation.from_learner(learn)
interp.plot_top_losses(k=3)
camvid = DataBlock(blocks=(ImageBlock, MaskBlock(codes)),
                   get_items = get_image_files,
                   get_y = label_func,
                   splitter=RandomSplitter(),
                   batch_tfms=aug_transforms(size=(120,160)))
dls = camvid.dataloaders(path/"images", path=path, bs=8)
dls.show_batch(max_n=6)
 
plt.imshow(cam_img().resize((128,128)))
<matplotlib.image.AxesImage at 0x7fc9817e9850>
from misas.core import *
from misas.core import default_cmap
learn.prepareSize = lambda item: item.resize((128,128))
class Fastai_model:
    def __init__(self, learner):
        self.trainedModel = learner
        self.resize128 = lambda x: x.resize ((128,128))
        self.trainedModel.remove_cbs(ProgressCallback)

        
    def prepareSize(self, item):
        return self.resize128(item)
    
    def predict(self, image):
        image = PILImage.create(np.array(image))
        output = self.trainedModel.predict(image)
        output = PILImage.create(output [0])
        output = Image.fromarray(np.array(output)) #mode="I"
        return output
Cam_vid = Fastai_model(learn)
plot_series(get_rotation_series(cam_img(), Cam_vid), vmax=31, vmin=0)
plot_series(get_zoom_series(cam_img(), Cam_vid), vmax=31, vmin=0, nrow=2)