Return a normalization layer Parameters: norm_type (str) -- the name of the normalization layer: batch | instance | none For Ba

3x3 convolution with padding

args: pic_path : the picture you want to predict model : the model you want to predict note: step one : generate s

Make the ground-truth for each landmark. img: the original color image labels: the json labels with the Gaussian centers {'x': x, 'y': y}

predict road edge :param: x, [image tensor, predicted segmentation tensor], [N, C+1, H, W]

Load the mapping that associates pascal classes with label colors Returns: np.ndarray with dimensions (2, 3)

Load the mapping that associates pascal classes with label colors Returns: np.ndarray with dimensions (2, 3)

This is a typical image normalization function where the minimum and maximum of the image is needed source: https://en.wikipedia.org/wiki/Norm

This is a typical image normalization function where the minimum and maximum of the image is needed source: https://en.wikipedia.org/wiki/Norm

args: pic_path : the picture you want to predict model : the model you want to predict note: step one : generate s

Make a square gaussian kernel. size: is the dimensions of the output gaussian sigma: is full-width-half-maximum, which can be thought of

Make the image-ground-truth pair

Reduces a series of points to a simplified version that loses detail, but maintains the general shape of the series.

Loads image to label pairs for tool pose estimation db_elements: the names of the video files db_root_dir: dataset directory with subf