hassony2
commented
4 years ago Hi @past9767 !
Note that the models are trained on small datasets which carry very specific biases (objects, points of view, ...), they will therefore not generalize to in-the-wild image !
However, if you still want to process a single image, the loader needs to be iterated upon for instance you can get the first batch data by running sample = next(iter(loader)).
Hope this helps !
Best,
Yana
Hello everyone,
i want to write an application that processes each frame of a video with the pretrained model of handobjectconsist (producing the MANO-mesh and Object Pose for each frame). I saw the code in the visualize.py file has a demonstration of the model, but it receives some sort of datastructure for the dataset which it gets by "dataset, input_res = get_dataset.get_dataset (...)", however i dont want to process a whole datastructure but just a single frame. I would like to know, what would be the easiest way to process a single frame when each RGB-frame is given as a single opencv mat type. I tried it like this (the getMat () funktion receives the frame from the video stream as a opencv mat):
resume = "releasemodels/fphab/hands_and_objects/checkpoint_200.pth" opts = reloadmodel.load_opts(resume) self.model, epoch = reloadmodel.reload_model(resume, opts) freeze.freeze_batchnorm_stats(self.model) self.model.cuda() self.model.eval()
mat = getMat() dataset = torch.utils.data.Dataset (np.array (mat)) loader = torch.utils.data.DataLoader(dataset,batchsize=1) , results, _ = self.model (loader)
However i get the error: Failed to call callback: 'DataLoader' object is not subscriptable Traceback (most recent call last):
Has anybody an idea how to fix this, Thanks in advance, Patrick