preprocessing/SensorData.py

import os, struct
import numpy as np
import zlib
import imageio
import cv2
import png
from concurrent.futures import ThreadPoolExecutor, as_completed
from PIL import Image
import io
import multiprocessing
from tqdm import tqdm

COMPRESSION_TYPE_COLOR = {-1:'unknown', 0:'raw', 1:'png', 2:'jpeg'}
COMPRESSION_TYPE_DEPTH = {-1:'unknown', 0:'raw_ushort', 1:'zlib_ushort', 2:'occi_ushort'}

class RGBDFrame():

  def load(self, file_handle):
    self.camera_to_world = np.asarray(struct.unpack('f'*16, file_handle.read(16*4)), dtype=np.float32).reshape(4, 4)
    self.timestamp_color = struct.unpack('Q', file_handle.read(8))[0]
    self.timestamp_depth = struct.unpack('Q', file_handle.read(8))[0]
    self.color_size_bytes = struct.unpack('Q', file_handle.read(8))[0]
    self.depth_size_bytes = struct.unpack('Q', file_handle.read(8))[0]
    self.color_data = file_handle.read(self.color_size_bytes)
    self.depth_data = file_handle.read(self.depth_size_bytes)


  def decompress_depth(self, compression_type):
    if compression_type == 'zlib_ushort':
       return self.decompress_depth_zlib()
    else:
       raise


  def decompress_depth_zlib(self):
    return zlib.decompress(self.depth_data)


  def decompress_color(self, compression_type):
    if compression_type == 'jpeg':
       return self.decompress_color_jpeg()
    else:
       raise


  def decompress_color_jpeg(self):
    return imageio.imread(self.color_data)


class SensorData:

  def __init__(self, filename):
    self.version = 4
    self.load(filename)


  def load(self, filename):
    with open(filename, 'rb') as f:
      version = struct.unpack('I', f.read(4))[0]
      assert self.version == version
      strlen = struct.unpack('Q', f.read(8))[0]
      self.sensor_name = f.read(strlen).decode('utf-8')
      self.intrinsic_color = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4)
      self.extrinsic_color = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4)
      self.intrinsic_depth = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4)
      self.extrinsic_depth = np.asarray(struct.unpack('f'*16, f.read(16*4)), dtype=np.float32).reshape(4, 4)
      self.color_compression_type = COMPRESSION_TYPE_COLOR[struct.unpack('i', f.read(4))[0]]
      self.depth_compression_type = COMPRESSION_TYPE_DEPTH[struct.unpack('i', f.read(4))[0]]
      self.color_width = struct.unpack('I', f.read(4))[0]
      self.color_height =  struct.unpack('I', f.read(4))[0]
      self.depth_width = struct.unpack('I', f.read(4))[0]
      self.depth_height =  struct.unpack('I', f.read(4))[0]
      self.depth_shift =  struct.unpack('f', f.read(4))[0]
      num_frames =  struct.unpack('Q', f.read(8))[0]
      self.frames = []
      for i in range(num_frames):
        frame = RGBDFrame()
        frame.load(f)
        self.frames.append(frame)


  def export_depth_images(self, output_path, image_size=None, frame_skip=1):
    if not os.path.exists(output_path):
      os.makedirs(output_path)
    print('exporting', len(self.frames)//frame_skip, ' depth frames to', output_path)
    for f in range(0, len(self.frames), frame_skip):
      depth_data = self.frames[f].decompress_depth(self.depth_compression_type)
      depth = np.fromstring(depth_data, dtype=np.uint16).reshape(self.depth_height, self.depth_width)
      if image_size is not None:
        depth = cv2.resize(depth, (image_size[1], image_size[0]), interpolation=cv2.INTER_NEAREST)
      #imageio.imwrite(os.path.join(output_path, str(f) + '.png'), depth)
      with open(os.path.join(output_path, str(f) + '.png'), 'wb') as f: # write 16-bit
        writer = png.Writer(width=depth.shape[1], height=depth.shape[0], bitdepth=16)
        depth = depth.reshape(-1, depth.shape[1]).tolist()
        writer.write(f, depth)

  def export_color_images(self, output_path, image_size=None, frame_skip=1):
    if not os.path.exists(output_path):
      os.makedirs(output_path)
    print('exporting', len(self.frames)//frame_skip, 'color frames to', output_path)
    for f in range(0, len(self.frames), frame_skip):
      color = self.frames[f].decompress_color(self.color_compression_type)
      if image_size is not None:
        color = cv2.resize(color, (image_size[1], image_size[0]), interpolation=cv2.INTER_NEAREST)
      imageio.imwrite(os.path.join(output_path, str(f) + '.jpg'), color)


  def save_mat_to_file(self, matrix, filename):
    with open(filename, 'w') as f:
      for line in matrix:
        np.savetxt(f, line[np.newaxis], fmt='%f')


  def export_poses(self, output_path, frame_skip=1):
    if not os.path.exists(output_path):
      os.makedirs(output_path)
    print('exporting', len(self.frames)//frame_skip, 'camera poses to', output_path)
    for f in range(0, len(self.frames), frame_skip):
      self.save_mat_to_file(self.frames[f].camera_to_world, os.path.join(output_path, str(f) + '.txt'))


  def export_intrinsics(self, output_path):
    if not os.path.exists(output_path):
      os.makedirs(output_path)
    print('exporting camera intrinsics to', output_path)
    self.save_mat_to_file(self.intrinsic_color, os.path.join(output_path, 'intrinsic_color.txt'))
    self.save_mat_to_file(self.extrinsic_color, os.path.join(output_path, 'extrinsic_color.txt'))
    self.save_mat_to_file(self.intrinsic_depth, os.path.join(output_path, 'intrinsic_depth.txt'))
    self.save_mat_to_file(self.extrinsic_depth, os.path.join(output_path, 'extrinsic_depth.txt'))

class OptimizedSensorData(SensorData):
    def __init__(self, filename):
        super().__init__(filename)
        self._num_workers = max(1, multiprocessing.cpu_count() - 1)  # 默认值
        
    @property
    def num_workers(self):
        return self._num_workers
        
    @num_workers.setter
    def num_workers(self, value):
        self._num_workers = max(1, value)  # 确保至少有1个线程

    def _process_depth_frame(self, args):
        f, output_path, image_size = args
        depth_data = self.frames[f].decompress_depth(self.depth_compression_type)
        depth = np.fromstring(depth_data, dtype=np.uint16).reshape(self.depth_height, self.depth_width)
        
        if image_size is not None:
            depth = cv2.resize(depth, (image_size[1], image_size[0]), interpolation=cv2.INTER_NEAREST)
            
        output_file = os.path.join(output_path, f"{f}.png")
        with open(output_file, 'wb') as fp:
            writer = png.Writer(width=depth.shape[1], height=depth.shape[0], bitdepth=16)
            depth = depth.reshape(-1, depth.shape[1]).tolist()
            writer.write(fp, depth)
        return f

    def _process_color_frame(self, args):
        f, output_path, image_size = args
        color = self.frames[f].decompress_color(self.color_compression_type)
        
        # Convert to PIL Image for faster processing
        if isinstance(color, np.ndarray):
            color = Image.fromarray(color)
            
        if image_size is not None:
            color = color.resize((image_size[1], image_size[0]), Image.NEAREST)
            
        output_file = os.path.join(output_path, f"{f}.jpg")
        color.save(output_file, 'JPEG', quality=95, optimize=True)
        return f

    def export_depth_images_parallel(self, output_path, image_size=None, frame_skip=1):
        if not os.path.exists(output_path):
            os.makedirs(output_path)
            
        frames_to_process = range(0, len(self.frames), frame_skip)
        args_list = [(f, output_path, image_size) for f in frames_to_process]
        
        print(f'Exporting {len(frames_to_process)} depth frames to {output_path} using {self.num_workers} workers')
        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
            futures = [executor.submit(self._process_depth_frame, args) for args in args_list]
            
            for _ in tqdm(as_completed(futures), total=len(futures), desc="Processing depth frames"):
                pass

    def export_color_images_parallel(self, output_path, image_size=None, frame_skip=1):
        if not os.path.exists(output_path):
            os.makedirs(output_path)
            
        frames_to_process = range(0, len(self.frames), frame_skip)
        args_list = [(f, output_path, image_size) for f in frames_to_process]
        
        print(f'Exporting {len(frames_to_process)} color frames to {output_path} using {self.num_workers} workers')
        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
            futures = [executor.submit(self._process_color_frame, args) for args in args_list]
            
            for _ in tqdm(as_completed(futures), total=len(futures), desc="Processing color frames"):
                pass

    def export_poses_parallel(self, output_path, frame_skip=1):
        if not os.path.exists(output_path):
            os.makedirs(output_path)
            
        print(f'Exporting {len(self.frames)//frame_skip} camera poses to {output_path}')
        
        def save_pose(f):
            self.save_mat_to_file(self.frames[f].camera_to_world, 
                                os.path.join(output_path, f"{f}.txt"))
            return f
            
        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
            futures = [executor.submit(save_pose, f) 
                      for f in range(0, len(self.frames), frame_skip)]
            
            for _ in tqdm(as_completed(futures), total=len(futures), desc="Processing poses"):
                pass