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Library designed to simplify camera projection tasks and calculations, particularly when working with image predictions and 3D point cloud data. This library provides functions to effectively incorporate point cloud data with image predictions.

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projkit

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Welcome to projkit, a Python library designed to simplify camera projection tasks and calculations, particularly when working with image predictions and 3D point cloud data. This library provides functions to effectively incorporate point cloud data with image predictions.

Installation

pip install projkit

Features

  • Camera Projection to Image Coordinates: Easily project point cloud data onto image coordinates using provided camera parameters.

    from projkit.camops import project_in_2d_with_K_R_t_dist_coeff
    from projkit.imutils import to_image, filter_image_and_world_points_with_img_dim
    
    ic, wc, z = project_in_2d_with_K_R_t_dist_coeff(K, R, t, d, wc)
    ic, wc, z = filter_image_and_world_points_with_img_dim(Nx, Ny, ic, wc)
    
    projection_on_image = to_image(Ny, Nx, ic, wc)
  • Intersection with Binary Mask: Determine intersections between projected data and a binary mask.

    from projkit.imutils import intersection
    binary_mask = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
    binary_mask[binary_mask > 0.50] = 255
    
    intersection_img, locations = intersection(binary_mask, ic, wc)
  • Identifying Data Holes in Mask: Identify locations in the mask that require interpolation due to missing point cloud data.

    import numpy as np
    from projkit.imutils import difference
    
    _missing_z_values_image = difference(Ny, Nx, ic, wc, binary_mask)
    x, y = np.where(_missing_z_values_image == 255)
    locations = list(zip(y, x))
  • Nearest Search Interpolation: Perform nearest search interpolation for dense regions in point cloud data.

    from projkit.imutils import nn_interpolation
    
    query = nn_interpolation(ic, wc)
    points = query.generate_points_for_nn_search(Ny, Nx, binary_mask)
    ic, wc, dist = query.query(points, dist_thresh=15)

    For larger datasets, utilize batch processing:

    from projkit.imutils import nn_interpolation
    from projkit.pyutils import batch_gen
    
    query = nn_interpolation(ic, wc)
    points = query.generate_points_for_nn_search(Ny, Nx, binary_mask)
    for i, batch in batch_gen(points, batch_size=500):
        ic, wc, dist = query.query(batch, dist_thresh=15)

Documentation

View the documentation for the project here.

About

Library designed to simplify camera projection tasks and calculations, particularly when working with image predictions and 3D point cloud data. This library provides functions to effectively incorporate point cloud data with image predictions.

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