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Data Processing Pipeline

Option 1 is for converting raw video inputs directly into training data. Option 2 splits this process into separate steps.

Option 1: Videos --> Labels

To do step 3 in separate commands, see option 2.

  1. Collect videos.

  2. Put videos into folder. This is what the myVideos folder structure should look like.

    .
├── myVideos                    
│   ├── vids          
│   │   ├── GH0101.mp4      
│   │   ├── GH0102.mp4                
│   │   
│   └── ...     
│

  3. Run script from trash_bot directory. This will split into frames, run colmap, and create colmap visualization videos.

    python data_cleaning/script_colmap.py --dir myVideos/ --write saveVizVideosHere --split 1

  4. This is what the myVideos folder will look like after videos are split. Colmap will then automatically run on the frames folder.

    .
├── myVideos                    
│   ├── vids          
│   │   ├── GH0101.mp4      
│   │   ├── GH0102.mp4      
│   │   ├── ...
│   │          
│   ├── frames          
│   │   ├── GH0101_frames 
│   │   │   ├── theframe001.png   
│   │   │   ├── theframe002.png     
│   │   │   ├── ...
│   │   ├── GH0102_frames                
│   │   
│   └── ...     
│

  5. Clean data!!

    • Look through viz videos and delete bad images from the original images folder.

Option 2: Videos --> Frames --> Extract Labels --> Visualize

  1. Collect videos.

  2. Split videos (or single video --f 0 )into frames. Saves to --save directory.

     cd trash_bot
 python data_cleaning/split_video.py --dir data/vids --f 1 --save video_frames

  3. Run COLMAP to get ground truth labels. See below for details.

     DATASET_PATH=~/may6th/frames/GH061507_frames/
 
 colmap automatic_reconstructor \
--workspace_path $DATASET_PATH \
--image_path $DATASET_PATH/images

  4. Convert COLMAP bin outputs to txt files.

    colmap model_converter --input_path ~/may6th/frames/GH061507_frames/sparse/0/ --output_path ~/may6th/frames/GH061507_frames/sparse/0/ --output_type TXT

  5. Write relative translations and rotations to labels.json.

    python data_cleaning/write_translations.py --dir ~/may6th/frames/GH061507_frames/

  6. Visualize translations and clean ground truth labels.

    python data_cleaning/labels_tester.py --dir ~/may6th/frames/GH061507_frames/ --write ~/may_videos/colmap_results/

Running Colmap:

For getting ground truth labels.

  1. Create a folder of runs.

    .
├── frames                    
│   ├── GH0001          # Run 1 folder
│   │   ├── images      # images folder          
│   │   
│   ├── GH0002          # Run 2 folder
│   │   ├── images      # images folder          
│   │   
│   └── ...     
│

  2. Run script. This runs COLMAP, writes labels.json file, outputs video for COLMAP visualization, and removes dense/stereo folder. Provide output folder in --write for colmap visualization videos.

     python scripts/script_colmap.py --dir ~/may6th/frames/ \
       --write ~/may6th/colmap_done_videos/ --split 0

  3. Your directory should look like this.

    .
├── frames                    
│   ├── GH0001              # Run 1 folder
│   │   ├── images          # images folder  
│   │   ├── labels.json     # labels file    
│   │   ├── sparse          # COLMAP output        
│   │   ├── ...          
│   │   
│   ├── GH0002              # Run 2 folder
│   │   ├── images          # images folder    
│   │   ├── labels.json     # labels file    
│   │   ├── sparse          # COLMAP output        
│   │   ├── ...          
│   │   
│   └── ...     
│

To use masking with COLMAP:

For masking out the gripper for better COLMAP results.

  1. Create mask. Provide directory with all runs that need masking.

     python data_cleaning/mask.py --dir ~/may6th/finished/usemask/

    This saves a folder called mask_images inside each run folder. Double check the mask is in the correct spot.

  2. Run COLMAP. See COLMAP section for more details.

     python scripts/script_colmap.py --dir ~/may6th/finished/usemask/ --write ~/may6th/colmap_done_videos/ --use_mask 1