Image segmentation in Python can be implemented using libraries like OpenCV, Scikit-image, or deep learning frameworks such as TensorFlow and PyTorch. For classical methods, use OpenCV's cv2.threshold for thresholding or cv2.watershed for advanced segmentation. Scikit-image provides additional options like skimage.segmentation for region-based segmentation.
Deep learning approaches involve using models like U-Net or DeepLab. You can train these models on labeled datasets, such as Cityscapes or PASCAL VOC, for pixel-wise classification. Pre-trained models are available in frameworks like TensorFlow Hub or PyTorch Hub for quicker implementation.
Preprocess the images (e.g., resizing, normalizing) before applying segmentation. Evaluate the results using metrics like Intersection over Union (IoU) to ensure accuracy. Visualization tools, like Matplotlib, can help interpret the segmentation outputs.