SciPy Create 2D Polygon Mask

SciPy Create 2D Polygon Mask

To create a 2D polygon mask in SciPy, you can use the matplotlib.path.Path class to define a polygon and then use it to create a mask for an image or any other 2D array. Here's an example of how to create a polygon mask and apply it to an image using SciPy and Matplotlib:

import numpy as np import matplotlib.pyplot as plt from matplotlib.path import Path from scipy.ndimage import generic_filter # Create a blank image (for demonstration purposes) image_width, image_height = 400, 300 image = np.zeros((image_height, image_width)) # Define the vertices of the polygon polygon_vertices = np.array([ [100, 100], # Vertex 1 [300, 100], # Vertex 2 [250, 250], # Vertex 3 [150, 250], # Vertex 4 ]) # Create a Path object from the polygon vertices polygon_path = Path(polygon_vertices) # Function to check if a point is inside the polygon def point_inside_polygon(x, y): return polygon_path.contains_point((x, y)) # Create a mask by applying the point_inside_polygon function to every pixel mask = np.fromfunction(np.vectorize(point_inside_polygon), (image_height, image_width), dtype=int) # Apply the mask to the image masked_image = image * mask # Display the original image and the masked image plt.subplot(121) plt.imshow(image, cmap='gray') plt.title('Original Image') plt.subplot(122) plt.imshow(masked_image, cmap='gray') plt.title('Masked Image') plt.show()

In this example:

  1. We create a blank image of a specified width and height (image_width and image_height).

  2. We define the vertices of the polygon as a NumPy array (polygon_vertices).

  3. We create a Path object called polygon_path from the polygon vertices.

  4. We define a function point_inside_polygon(x, y) that checks if a given point is inside the polygon by using the contains_point method of the Path object.

  5. We create a mask by applying the point_inside_polygon function to every pixel in the image using np.fromfunction. This mask will have 1 for pixels inside the polygon and 0 for pixels outside.

  6. We apply the mask to the original image by multiplying the two arrays element-wise, resulting in a masked image.

  7. Finally, we display both the original image and the masked image using Matplotlib.

You can replace the polygon vertices and the image data in this example with your specific data.

Examples

  1. Creating a 2D Polygon Mask Using SciPy

    • Description: This query demonstrates how to create a 2D polygon mask using SciPy and associated libraries.
    • Code:
      import numpy as np import matplotlib.path as mplPath # Define a 2D grid grid_x, grid_y = np.meshgrid(np.arange(0, 10), np.arange(0, 10)) # Define a polygon (a square in this case) polygon = [(2, 2), (7, 2), (7, 7), (2, 7)] # Create a path object and use it to create a mask polygon_path = mplPath.Path(polygon) points = np.vstack((grid_x.flatten(), grid_y.flatten())).T mask = polygon_path.contains_points(points).reshape(grid_x.shape) print("Polygon Mask:") print(mask.astype(int)) # Display the mask as a grid of 0s and 1s

  2. Applying a 2D Polygon Mask to an Image

    • Description: This query demonstrates how to apply a 2D polygon mask to an image to isolate or block specific regions.
    • Code:
      from scipy import misc import matplotlib.pyplot as plt # Load a sample image image = misc.ascent() # Or load your own image # Define a polygon polygon = [(50, 50), (150, 50), (150, 150), (50, 150)] polygon_path = mplPath.Path(polygon) # Create a mask for the image grid_x, grid_y = np.meshgrid(np.arange(image.shape[1]), np.arange(image.shape[0])) points = np.vstack((grid_x.flatten(), grid_y.flatten())).T mask = polygon_path.contains_points(points).reshape(image.shape) # Apply the mask to the image masked_image = np.copy(image) masked_image[~mask] = 0 # Set non-masked areas to black plt.imshow(masked_image, cmap='gray') plt.title('Image with 2D Polygon Mask Applied') plt.show()

  3. Using a 2D Polygon Mask to Calculate a Region's Area

    • Description: This query shows how to calculate the area of a region defined by a 2D polygon mask.
    • Code:
      # Calculate the total area covered by the mask area = np.sum(mask) print("Area covered by the polygon:", area)

  4. Creating Complex Polygon Masks with SciPy

    • Description: This query explores how to create more complex polygon masks with non-standard shapes.
    • Code:
      # Define a complex polygon (e.g., a star shape) polygon = [ (5, 0), (6, 4), (10, 4), (7, 7), (8, 11), (5, 9), (2, 11), (3, 7), (0, 4), (4, 4) ] polygon_path = mplPath.Path(polygon) # Create a 2D mask for the complex polygon points = np.vstack((grid_x.flatten(), grid_y.flatten())).T complex_mask = polygon_path.contains_points(points).reshape(grid_x.shape) print("Complex Polygon Mask:") print(complex_mask.astype(int))

  5. Combining Multiple Polygon Masks

    • Description: This query discusses how to combine multiple 2D polygon masks into a single mask.
    • Code:
      # Define another polygon polygon2 = [(3, 3), (8, 3), (8, 8), (3, 8)] polygon_path2 = mplPath.Path(polygon2) # Create a mask for the second polygon mask2 = polygon_path2.contains_points(points).reshape(grid_x.shape) # Combine the two masks (logical OR) combined_mask = np.logical_or(mask, mask2) print("Combined Polygon Mask:") print(combined_mask.astype(int))

  6. Creating a Mask with Circular or Elliptical Regions

    • Description: This query explores how to create a mask with non-polygonal shapes like circles or ellipses, using SciPy and other libraries.
    • Code:
      from skimage.draw import circle # Create a 2D grid and a circle mask circle_center = (5, 5) circle_radius = 3 circle_mask = np.zeros_like(grid_x, dtype=bool) rr, cc = circle(circle_center[0], circle_center[1], circle_radius) circle_mask[rr, cc] = True print("Circle Mask:") print(circle_mask.astype(int))

  7. Using Polygon Masks to Define ROI (Region of Interest)

    • Description: This query demonstrates how to use a 2D polygon mask to define a Region of Interest within a larger dataset.
    • Code:
      # Extract data only within the masked region region_of_interest = np.where(mask, grid_x, np.nan) # Use NaN for outside regions print("Region of Interest:") print(region_of_interest)

  8. Finding Edge Points of a Polygon Mask

    • Description: This query discusses how to find the edge points or perimeter of a polygon mask.
    • Code:
      from skimage.morphology import binary_erosion # Create the edge mask by eroding and subtracting from the original eroded_mask = binary_erosion(mask) edge_mask = np.logical_xor(mask, eroded_mask) print("Edge Points of Polygon Mask:") print(edge_mask.astype(int))

  9. Creating a Polygon Mask from Coordinate Data

    • Description: This query shows how to create a 2D polygon mask from existing coordinate data or external sources.
    • Code:
      # Define coordinate data (e.g., from a GPS dataset) coordinates = np.array([ [5, 5], [7, 5], [7, 7], [5, 7], [5, 5] ]) # Create a polygon mask from the coordinates coordinate_path = mplPath.Path(coordinates) coordinate_mask = coordinate_path.contains_points(points).reshape(grid_x.shape) print("Polygon Mask from Coordinates:") print(coordinate_mask.astype(int))

  10. Converting 2D Polygon Masks to Other Data Formats

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