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import cv2 |
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import numpy as np |
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import argparse |
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def get_center_point(num_cams,cameras): |
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A = np.zeros((3 * num_cams, 3 + num_cams)) |
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b = np.zeros((3 * num_cams, 1)) |
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camera_centers=np.zeros((3,num_cams)) |
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for i in range(num_cams): |
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if 'cam_%d' % i in cameras: |
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P0 = cameras['cam_%d' % i][:3, :] |
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else: |
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P0 = cameras[i] |
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K = cv2.decomposeProjectionMatrix(P0)[0] |
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R = cv2.decomposeProjectionMatrix(P0)[1] |
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c = cv2.decomposeProjectionMatrix(P0)[2] |
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c = c / c[3] |
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camera_centers[:,i]=c[:3].flatten() |
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v=R[2,:] |
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A[3 * i:(3 * i + 3), :3] = np.eye(3) |
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A[3 * i:(3 * i + 3), 3 + i] = -v |
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b[3 * i:(3 * i + 3)] = c[:3] |
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return camera_centers |
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def normalize_cameras(original_cameras_filename,output_cameras_filename,num_of_cameras, scene_bounding_sphere=3.0): |
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cameras = np.load(original_cameras_filename) |
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if num_of_cameras==-1: |
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all_files=cameras.files |
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maximal_ind=0 |
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for field in all_files: |
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maximal_ind=np.maximum(maximal_ind,int(field.split('_')[-1])) |
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num_of_cameras=maximal_ind+1 |
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camera_centers = get_center_point(num_of_cameras, cameras) |
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center = np.array([0, 0, 0]) |
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max_radius = np.linalg.norm((center[:, np.newaxis] - camera_centers), axis=0).max() * 1.1 |
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normalization = np.eye(4).astype(np.float32) |
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normalization[0, 0] = max_radius / scene_bounding_sphere |
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normalization[1, 1] = max_radius / scene_bounding_sphere |
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normalization[2, 2] = max_radius / scene_bounding_sphere |
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cameras_new = {} |
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for i in range(num_of_cameras): |
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cameras_new['scale_mat_%d' % i] = normalization |
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cameras_new['world_mat_%d' % i] = cameras['cam_%d' % i].copy() |
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np.savez(output_cameras_filename, **cameras_new) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser(description='Normalizing cameras') |
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parser.add_argument('--input_cameras_file', type=str, default="cameras.npz", |
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help='the input cameras file') |
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parser.add_argument('--output_cameras_file', type=str, default="cameras_normalize.npz", |
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help='the output cameras file') |
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parser.add_argument('--number_of_cams',type=int, default=-1, |
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help='Number of cameras, if -1 use all') |
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args = parser.parse_args() |
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normalize_cameras(args.input_cameras_file, args.output_cameras_file, args.number_of_cams) |
