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Add demo, update doc
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‎docs/source/index.rst‎

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Discorpy is an open-source Python package implementing methods for calibrating and correcting distortion
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in lens-based imaging systems :cite:`Vo:2015` :cite:`Vo:2025`. Unlike existing approaches that require multiple calibration
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images or iterative optimization, Discorpy and its algorithms can independently characterize both radial and
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perspective distortion with high accuracy across a wide range of distortion strengths—using only a single calibration
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perspective distortion with high accuracy across a wide range of distortion strengths - using only a single calibration
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image and direct computation. This makes the software a practical tool for a wide range of imaging applications.
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To support different experimental conditions and enable fully automated workflows, Discorpy offers flexibility in

‎docs/source/technical_notes/fisheye_correction.rst‎

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---------------------------------------
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Other types of calibration images can also be used and are straightforward to process using the Discorpy API.
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The following shows the results of using a chessboard image. Both the `image <https://github.com/DiamondLightSource/discorpy/tree/master/data/fisheye>`__
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and the `code <https://github.com/DiamondLightSource/discorpy/tree/master/examples>`__
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The following shows the results of using a chessboard image and a dot-pattern image.
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Both the `images <https://github.com/DiamondLightSource/discorpy/tree/master/data/fisheye>`__
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and the `codes <https://github.com/DiamondLightSource/discorpy/tree/master/examples>`__
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are available on the Discorpy GitHub page. As can be seen, the calibration image clearly has perspective
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distortion, but this is no longer a problem with the latest developments.
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‎examples/fisheye_calibration_dot_pattern.py‎

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#==============================================================================
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# Demonstration on how to calibrate a fisheye camera using a dot-pattern image
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# =============================================================================
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import numpy as np
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import discorpy.losa.loadersaver as losa
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import discorpy.prep.preprocessing as prep
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import discorpy.proc.processing as proc
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import discorpy.util.utility as util
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import matplotlib.pyplot as plt
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file_path = r"..\data\fisheye\GoPro8_dot_pattern.jpg"
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test_file_path = r"..\data\fisheye\GoPro8_testing_image.jpg"
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output_base = r"F:\tmp\fisheye_correction_dot_pattern"
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print(" 1-> Load image ...")
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img0 = losa.load_image(file_path)
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(height, width) = img0.shape
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img_norm = prep.normalization_fft(img0, 10)
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num_factor = 5
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mat1 = prep.binarization(img_norm, ratio=0.3)
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(dot_size, dist_dot) = prep.calc_size_distance(mat1, ratio=0.3)
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print(" 2-> Calculate slope and distance between lines...")
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slope_hor = prep.calc_hor_slope(mat1, ratio=0.3)
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slope_ver = prep.calc_ver_slope(mat1, ratio=0.3)
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dist_hor = dist_ver = dist_dot
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print(f" Horizontal slope: {slope_hor} Distance: {dist_hor}")
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print(f" Vertical slope : {slope_ver} Distance: {dist_ver}")
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print(" 3-> Extract reference-points !!!!")
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list_points = prep.get_points_dot_pattern(mat1, binarize=False)
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list_points_hor_lines = list_points
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list_points_ver_lines = np.copy(list_points)
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hor_margin = (450, 100)
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ver_margin = (100, 100)
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list_points_hor_lines = prep.remove_points_using_parabola_mask(list_points_hor_lines,
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height, width,
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hor_curviness=0.4,
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ver_curviness=0.3,
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hor_margin=hor_margin,
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ver_margin=ver_margin)
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list_points_ver_lines = prep.remove_points_using_parabola_mask(list_points_ver_lines,
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height, width,
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hor_curviness=0.4,
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ver_curviness=0.3,
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hor_margin=hor_margin,
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ver_margin=ver_margin)
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# mask = prep.make_parabola_mask(height, width,hor_curviness=0.4, ver_curviness=0.3,
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# hor_margin=hor_margin, ver_margin=ver_margin)
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# plt.imshow(img_norm * mask, origin="lower")
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# plt.plot(list_points_hor_lines[:, 1], list_points_hor_lines[:, 0], ".", color="red")
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# plt.plot(list_points_ver_lines[:, 1], list_points_ver_lines[:, 0], ".", color="blue")
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# plt.show()
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print(" 4-> Group points into lines !!!!")
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list_hor_lines = prep.group_dots_hor_lines_based_polyfit(list_points_hor_lines,
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slope_hor, dist_hor,
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ratio=0.1, num_dot_miss=3,
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accepted_ratio=0.65, order=2)
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list_ver_lines = prep.group_dots_ver_lines_based_polyfit(list_points_ver_lines,
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slope_ver, dist_ver,
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ratio=0.1, num_dot_miss=3,
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accepted_ratio=0.65, order=2)
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list_hor_lines = prep.remove_residual_dots_hor(list_hor_lines, slope_hor, 2.0)
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list_ver_lines = prep.remove_residual_dots_ver(list_ver_lines, slope_ver, 2.0)
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plt.imshow(img_norm, origin="lower")
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for line in list_hor_lines:
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plt.plot(line[:, 1], line[:, 0], "-o", color="red")
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plt.show()
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plt.imshow(img_norm, origin="lower")
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for line in list_ver_lines:
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plt.plot(line[:, 1], line[:, 0], "-o", color="blue")
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plt.show()
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# Find center of distortion
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xcenter, ycenter = proc.find_center_based_vanishing_points_iteration(
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list_hor_lines, list_ver_lines, iteration=2)
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print(f"Center of distortion: X-center {xcenter}. Y-center {ycenter}")
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# Correct perspective distortion
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corr_hor_lines, corr_ver_lines = proc.correct_perspective_effect(
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list_hor_lines, list_ver_lines, xcenter, ycenter)
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# Calculate polynomial coefficients of the radial distortion
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list_bfact = proc.calc_coef_backward(corr_hor_lines, corr_ver_lines, xcenter,
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ycenter, num_factor)
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print(f"Polynomial coefficients of radial distortion: {list_bfact}")
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losa.save_metadata_json(output_base + "/distortion_parameters.json", xcenter,
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ycenter, list_bfact)
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# Load calibration image as color image
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img0 = losa.load_image(file_path, average=False)
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img_corr = util.unwarp_color_image_backward(img0, xcenter, ycenter, list_bfact,
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pad=400)
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### Using OpenCV-remap backend for fast computing.
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# img_corr = util.unwarp_image_backward_cv2(img0, xcenter, ycenter, list_bfact,
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# pad=400)
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losa.save_image(output_base + "/corrected_img.jpg", img_corr)
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# Load another image for correction.
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img0 = losa.load_image(test_file_path, average=False)
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img_corr = util.unwarp_color_image_backward(img0, xcenter, ycenter, list_bfact,
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pad=400)
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losa.save_image(output_base + "/corrected_test_img.jpg", img_corr)

examples/fisheye_calibration.py renamed to examples/fisheye_calibration_line_pattern.py

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