191 lines
9.6 KiB
Python
191 lines
9.6 KiB
Python
'''
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OpenMVS python utilities.
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E.g., from MvsUtils import loadDMAP, loadMVSInterface
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'''
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import numpy as np
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def loadDMAP(dmap_path):
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with open(dmap_path, 'rb') as dmap:
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file_type = dmap.read(2).decode()
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content_type = np.frombuffer(dmap.read(1), dtype=np.dtype('B'))
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reserve = np.frombuffer(dmap.read(1), dtype=np.dtype('B'))
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has_depth = content_type > 0
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has_normal = content_type in [3, 7, 11, 15]
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has_conf = content_type in [5, 7, 13, 15]
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has_views = content_type in [9, 11, 13, 15]
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image_width, image_height = np.frombuffer(dmap.read(8), dtype=np.dtype('I'))
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depth_width, depth_height = np.frombuffer(dmap.read(8), dtype=np.dtype('I'))
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if (file_type != 'DR' or has_depth == False or depth_width <= 0 or depth_height <= 0 or image_width < depth_width or image_height < depth_height):
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print('error: opening file \'{}\' for reading depth-data'.format(dmap_path))
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return
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depth_min, depth_max = np.frombuffer(dmap.read(8), dtype=np.dtype('f'))
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file_name_size = np.frombuffer(dmap.read(2), dtype=np.dtype('H'))[0]
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file_name = dmap.read(file_name_size).decode()
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view_ids_size = np.frombuffer(dmap.read(4), dtype=np.dtype('I'))[0]
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reference_view_id, *neighbor_view_ids = np.frombuffer(dmap.read(4 * view_ids_size), dtype=np.dtype('I'))
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K = np.frombuffer(dmap.read(72), dtype=np.dtype('d')).reshape(3, 3)
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R = np.frombuffer(dmap.read(72), dtype=np.dtype('d')).reshape(3, 3)
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C = np.frombuffer(dmap.read(24), dtype=np.dtype('d'))
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data = {
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'has_normal': has_normal,
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'has_conf': has_conf,
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'has_views': has_views,
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'image_width': image_width,
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'image_height': image_height,
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'depth_width': depth_width,
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'depth_height': depth_height,
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'depth_min': depth_min,
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'depth_max': depth_max,
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'file_name': file_name,
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'reference_view_id': reference_view_id,
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'neighbor_view_ids': neighbor_view_ids,
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'K': K,
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'R': R,
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'C': C
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}
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map_size = depth_width * depth_height
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depth_map = np.frombuffer(dmap.read(4 * map_size), dtype=np.dtype('f')).reshape(depth_height, depth_width)
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data.update({'depth_map': depth_map})
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if has_normal:
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normal_map = np.frombuffer(dmap.read(4 * map_size * 3), dtype=np.dtype('f')).reshape(depth_height, depth_width, 3)
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data.update({'normal_map': normal_map})
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if has_conf:
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confidence_map = np.frombuffer(dmap.read(4 * map_size), dtype=np.dtype('f')).reshape(depth_height, depth_width)
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data.update({'confidence_map': confidence_map})
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if has_views:
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views_map = np.frombuffer(dmap.read(map_size * 4), dtype=np.dtype('B')).reshape(depth_height, depth_width, 4)
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data.update({'views_map': views_map})
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return data
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def loadMVSInterface(archive_path):
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with open(archive_path, 'rb') as mvs:
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archive_type = mvs.read(4).decode()
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version = np.frombuffer(mvs.read(4), dtype=np.dtype('I')).tolist()[0]
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reserve = np.frombuffer(mvs.read(4), dtype=np.dtype('I'))
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if archive_type != 'MVSI':
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print('error: opening file \'{}\''.format(archive_path))
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return
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data = {
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'project_stream': archive_type,
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'project_stream_version': version,
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'platforms': [],
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'images': [],
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'vertices': [],
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'vertices_normal': [],
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'vertices_color': []
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}
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platforms_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for platform_index in range(platforms_size):
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platform_name_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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platform_name = mvs.read(platform_name_size).decode()
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data['platforms'].append({'name': platform_name, 'cameras': []})
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cameras_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for camera_index in range(cameras_size):
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camera_name_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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camera_name = mvs.read(camera_name_size).decode()
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data['platforms'][platform_index]['cameras'].append({'name': camera_name})
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if version > 3:
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band_name_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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band_name = mvs.read(band_name_size).decode()
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data['platforms'][platform_index]['cameras'][camera_index].update({'band_name': band_name})
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if version > 0:
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width, height = np.frombuffer(mvs.read(8), dtype=np.dtype('I')).tolist()
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data['platforms'][platform_index]['cameras'][camera_index].update({'width': width, 'height': height})
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K = np.asarray(np.frombuffer(mvs.read(72), dtype=np.dtype('d'))).reshape(3, 3).tolist()
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data['platforms'][platform_index]['cameras'][camera_index].update({'K': K, 'poses': []})
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identity_matrix = np.asarray(np.frombuffer(mvs.read(96), dtype=np.dtype('d'))).reshape(4, 3)
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poses_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for _ in range(poses_size):
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R = np.asarray(np.frombuffer(mvs.read(72), dtype=np.dtype('d'))).reshape(3, 3).tolist()
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C = np.asarray(np.frombuffer(mvs.read(24), dtype=np.dtype('d'))).tolist()
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data['platforms'][platform_index]['cameras'][camera_index]['poses'].append({'R': R, 'C': C})
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images_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for image_index in range(images_size):
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name_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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name = mvs.read(name_size).decode()
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data['images'].append({'name': name})
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if version > 4:
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mask_name_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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mask_name = mvs.read(mask_name_size).decode()
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data['images'][image_index].update({'mask_name': mask_name})
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platform_id, camera_id, pose_id = np.frombuffer(mvs.read(12), dtype=np.dtype('I')).tolist()
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data['images'][image_index].update({'platform_id': platform_id, 'camera_id': camera_id, 'pose_id': pose_id})
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if version > 2:
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id = np.frombuffer(mvs.read(4), dtype=np.dtype('I')).tolist()[0]
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data['images'][image_index].update({'id': id})
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if version > 6:
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min_depth, avg_depth, max_depth = np.frombuffer(mvs.read(12), dtype=np.dtype('f')).tolist()
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data['images'][image_index].update({'min_depth': min_depth, 'avg_depth': avg_depth, 'max_depth': max_depth, 'view_scores': []})
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view_score_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for _ in range(view_score_size):
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id, points = np.frombuffer(mvs.read(8), dtype=np.dtype('I')).tolist()
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scale, angle, area, score = np.frombuffer(mvs.read(16), dtype=np.dtype('f')).tolist()
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data['images'][image_index]['view_scores'].append({'id': id, 'points': points, 'scale': scale, 'angle': angle, 'area': area, 'score': score})
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vertices_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for vertex_index in range(vertices_size):
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X = np.frombuffer(mvs.read(12), dtype=np.dtype('f')).tolist()
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data['vertices'].append({'X': X, 'views': []})
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views_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for _ in range(views_size):
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image_id = np.frombuffer(mvs.read(4), dtype=np.dtype('I')).tolist()[0]
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confidence = np.frombuffer(mvs.read(4), dtype=np.dtype('f')).tolist()[0]
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data['vertices'][vertex_index]['views'].append({'image_id': image_id, 'confidence': confidence})
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vertices_normal_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for _ in range(vertices_normal_size):
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normal = np.frombuffer(mvs.read(12), dtype=np.dtype('f')).tolist()
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data['vertices_normal'].append(normal)
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vertices_color_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for _ in range(vertices_color_size):
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color = np.frombuffer(mvs.read(3), dtype=np.dtype('B')).tolist()
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data['vertices_color'].append(color)
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if version > 0:
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data.update({'lines': [], 'lines_normal': [], 'lines_color': []})
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lines_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for line_index in range(lines_size):
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pt1 = np.frombuffer(mvs.read(12), dtype=np.dtype('f')).tolist()
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pt2 = np.frombuffer(mvs.read(12), dtype=np.dtype('f')).tolist()
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data['lines'].append({'pt1': pt1, 'pt2': pt2, 'views': []})
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views_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for _ in range(views_size):
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image_id = np.frombuffer(mvs.read(4), dtype=np.dtype('I')).tolist()[0]
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confidence = np.frombuffer(mvs.read(4), dtype=np.dtype('f')).tolist()[0]
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data['lines'][line_index]['views'].append({'image_id': image_id, 'confidence': confidence})
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lines_normal_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for _ in range(lines_normal_size):
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normal = np.frombuffer(mvs.read(12), dtype=np.dtype('f')).tolist()
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data['lines_normal'].append(normal)
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lines_color_size = np.frombuffer(mvs.read(8), dtype=np.dtype('Q'))[0]
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for _ in range(lines_color_size):
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color = np.frombuffer(mvs.read(3), dtype=np.dtype('B')).tolist()
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data['lines_color'].append(color)
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if version > 1:
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transform = np.frombuffer(mvs.read(128), dtype=np.dtype('d')).reshape(4, 4).tolist()
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data.update({'transform': transform})
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if version > 5:
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rot = np.frombuffer(mvs.read(72), dtype=np.dtype('d')).reshape(3, 3).tolist()
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pt_min = np.frombuffer(mvs.read(24), dtype=np.dtype('d')).tolist()
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pt_max = np.frombuffer(mvs.read(24), dtype=np.dtype('d')).tolist()
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data.update({'obb': {'rot': rot, 'pt_min': pt_min, 'pt_max': pt_max}})
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return data
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