PIL, in conjunction with other libraries like NumPy and SciPy, can perform edge detection. First, import the necessary libraries and load the image using PIL’s Image module. Convert the image to grayscale for easier processing. Then, use the numpy.array() function to convert the image into an array of pixel values. Apply a Gaussian blur via scipy.ndimage.gaussian_filter() to reduce noise and detail. Next, calculate the intensity gradients of the image using numpy.gradient(). This will give two arrays representing changes in intensity along the x and y axes. Use these to compute the magnitude of the gradient at each point, which represents the edge strength. Finally, apply a threshold value to this result to detect edges: pixels with a gradient magnitude above the threshold are considered edges.
