124 lines
4.6 KiB
Python
124 lines
4.6 KiB
Python
import numpy as np
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from scipy.optimize import minimize
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def solve_new_camera_params_central(three_d_points, focal_length, imshape, new_2d_points):
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"""
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Solve for new camera parameters by minimizing the error between the original 2D projection points and the new 2D projection points.
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Args:
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three_d_points (torch.Tensor): N*3 3D points
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focal_length (float): Focal length of the original camera
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imshape (tuple): Image size, e.g., [512, 896]
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original_2d_points (torch.Tensor): N*2 original 2D projection points
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new_2d_points (torch.Tensor): N*2 new 2D projection points
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Returns:
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m, n, p, q: Parameters in the new camera intrinsic matrix
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"""
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# Objective function: minimize the error between the original projection points and the new projection points
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def objective(params):
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m, s, p, q = params
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# Construct the new camera intrinsic matrix
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K_new = np.array([
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[focal_length * m , 0, imshape[1] / 2 + p],
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[0, focal_length * m * s, imshape[0] / 2 + q],
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[0, 0, 1]
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])
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# Compute the new 2D projection points
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new_projections = []
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for point in three_d_points:
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X, Y, Z = point
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u = (K_new[0, 0] * X / Z) + K_new[0, 2]
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v = (K_new[1, 1] * Y / Z) + K_new[1, 2]
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new_projections.append([u, v])
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new_projections = np.array(new_projections)
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# Calculate the error between the original 2D projection points and the new projection points
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# Special handling for the 0th projection point
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error0 = np.sum((new_2d_points[:1] - new_projections[:1]) ** 2)
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error = np.sum((new_2d_points[1:] - new_projections[1:]) ** 2)
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return error0 * 8 + error
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# Initialize parameters m, beta, p, q
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initial_params = [1.0, 1.0, 0.0, 0.0] # Initial values
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# Use least squares to solve for p, q
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result = minimize(objective, initial_params, bounds=[(0.7, 1.4), (0.8, 1.15), (-imshape[1], imshape[1]), (-imshape[0], imshape[0])])
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# Output the solution result
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m, s, p, q = result.x
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print(f"debug: solved camera params m={m}, s={s}, p={p}, q={q}")
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K_final = np.array([
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[focal_length * m, 0, imshape[1] / 2 + p],
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[0, focal_length * m * s, imshape[0] / 2 + q],
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[0, 0, 1]
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])
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return K_final, m, s
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def solve_new_camera_params_down(three_d_points, focal_length, imshape, new_2d_points):
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"""
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Solve for new camera parameters by minimizing the error between the original 2D projection points and the new 2D projection points.
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Args:
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three_d_points (torch.Tensor): N*3 3D points
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focal_length (float): Focal length of the original camera
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imshape (tuple): Image size, e.g., [512, 896]
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original_2d_points (torch.Tensor): N*2 original 2D projection points
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new_2d_points (torch.Tensor): N*2 new 2D projection points
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Returns:
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m, n, p, q: Parameters in the new camera intrinsic matrix
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"""
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# Objective function: minimize the error between the original projection points and the new projection points
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def objective(params):
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m, s, p, q = params
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# Construct the new camera intrinsic matrix
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K_new = np.array([
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[focal_length * m , 0, imshape[1] / 2 + p],
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[0, focal_length * m * s, imshape[0] / 2 + q],
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[0, 0, 1]
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])
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# Compute the new 2D projection points
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new_projections = []
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for point in three_d_points:
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X, Y, Z = point
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u = (K_new[0, 0] * X / Z) + K_new[0, 2]
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v = (K_new[1, 1] * Y / Z) + K_new[1, 2]
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new_projections.append([u, v])
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new_projections = np.array(new_projections)
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# Calculate the error between the original 2D projection points and the new projection points
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# Special handling for the 0th projection point
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error0 = np.sum((new_2d_points[:1] - new_projections[:1]) ** 2)
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error = np.sum((new_2d_points[1:] - new_projections[1:]) ** 2)
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return error0 + error * 4
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# Initialize parameters m, beta, p, q
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initial_params = [1.0, 1.0, 0.0, 0.0] # Initial values
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# Use least squares to solve for p, q
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result = minimize(objective, initial_params, bounds=[(0.7, 1.4), (0.8, 1.15), (-imshape[1], imshape[1]), (-imshape[0], imshape[0])])
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# Output the solution result
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m, s, p, q = result.x
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print(f"debug: solved camera params m={m}, s={s}, p={p}, q={q}")
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K_final = np.array([
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[focal_length * m, 0, imshape[1] / 2 + p],
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[0, focal_length * m * s, imshape[0] / 2 + q],
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[0, 0, 1]
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])
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return K_final, m, s
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