mlp/src/process.py

Overview:

This Python code defines a function named segment that performs semantic segmentation on an input image frame using a pre-trained DeepLabV3 model. Semantic segmentation is a computer vision task that involves labeling each pixel of an image with a class representing what is being depicted.

segment (function):

Parameters:

• frame: a numpy array representing the image frame to be segmented. This array is typically of shape (H, W, 3) where H and W are the height and width of the image, respectively.

Returns: a 2D numpy array representing the segmented image. Each pixel in this array corresponds to a class label identified by the semantic segmentation model.

Behavior:

• Model loading: the function loads a pre-trained DeepLabV3 model with a ResNet-101 backbone. This model has been trained on a standard dataset (likely COCO or a similar large-scale dataset) for the task of semantic segmentation.
• Image processing: the input frame, originally a numpy array, is converted to a PIL image. This image is then converted to a tensor and normalized using the standard mean and standard deviation values for the dataset on which the model was trained.
• Segmentation: the function adds an extra batch dimension to the tensor and then feeds it to the pre-trained model. The segmentation is performed in a no-gradient context (torch.no_grad()), meaning that gradients are not computed, which saves memory and computational resources during inference.
• Output processing: the output from the model is a tensor where each pixel’s value corresponds to the predicted class for that pixel. The function converts this tensor to a 2D numpy array, where each element represents the class label for the corresponding pixel in the original image.