def preprocess(self, img):
"""
Compute image operations to generate state representation
"""
# TODO: replace scikit image with opencv
# input shape: 300, 300, 3
img = transform.resize(img, self.config['resolution'], mode='reflect')
img = img.astype(np.float32)
if self.config['grayscale']:
img = rgb2gray(img) #3 dims 1 channel
img = np.moveaxis(img, 2, 0)
elif self.use_priors:
img = np.moveaxis(img, 2, 0)
img = torch.Tensor(img).unsqueeze(0).to(self.device)
if self.priors == 'manual':
img = multi_representation_transform(
img / 255., self.mode) # 3 dims, tensor, no cuda
elif self.priors == 'max_cover':
img = max_coverage_featureset_transform(
img / 255., self.k) # 3 dims, tensor, no cuda
else:
raise NotImplementedError
img = img.squeeze(0)
else:
img = np.moveaxis(img, 2, 0) # 4 dims
return img
def preprocess(self, img):
"""
Compute image operations to generate state representation
"""
# TODO: replace scikit image with opencv
img = transform.resize(img, self.config['resolution'], mode='reflect')
img = img.astype(np.float32)
if self.observation_space.shape[0] == 1:
img = rgb2gray(img)
img = np.moveaxis(img, 2, 0)
return img