Tools to help compose images from daily self-portrait time lapses
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matthewearl Name intermediate files sequentially rather than using the input file…
… base name. This guarantees the order is the same as on input, but also allows the user to choose intermediate file format
Latest commit b1568b4 Jan 24, 2016

README.md

Photo-a-day Aligner

A tools to help with daily self-portrait projects:

  • pada.py which has a couple of sub-commands:
  • align: Take a set of photo-a-day images, and align them based on the detected face, and perform RGB scaling so that all the faces have the same average RGB value. Also outputs an image mask.png which is used by the next script. Duplicate images, images with no face, and images with more than one face are dropped at this stage.
  • framedrop: Produce a file list, based on the output files of the above script. The output will have approximately (100 / N) % of the input images (N is 10 by default). Output frames are selected to avoid temporal discontinuities in the face area.
  • make_vid.sh: A shell script which calls mencoder to encode the file list produced by the above into a .h264 MP4 file.

See below for usage details.

Recommended workflow

  1. Create a directory for your project.

  2. Copy examples/pada.conf into it. Change predictor_path to point to your dlib landmarks, [downloadable from here](http://sourceforge.net/projects/ dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2).

  3. Create a sub-directory input, and place your input frames into it. When lexicographically sorted the file names should be in the correct order.

  4. Run pada.py align to align and colour correct your input frames. At this point you can inspect the output in ./aligned. If the results are not satisfactory change settings and repeat this step.

  5. Run pada.py framedrop to select a sequence of good frames and output them to filtered.txt.

  6. Run make_vid.sh to convert the above file list into a video, output.mp4.

Usage

General pada.py options:

$ pada.py --help
usage: pada.py [-h] [--debug] [--config CONFIG] [--aligned-path ALIGNED_PATH]
               [--aligned-extension ALIGNED_EXTENSION]
               [--predictor-path PREDICTOR_PATH]
               [--filtered-files FILTERED_FILES]
               {print_config_paths,align,framedrop} ...

positional arguments:
  {print_config_paths,align,framedrop}
                        Sub-command help
    print_config_paths  print config paths and exit
    align               align a set of images
    framedrop           Drop frames from a set of images

optional arguments:
  -h, --help            show this help message and exit
  --debug               Print debug information
  --config CONFIG       Config file path
  --aligned-path ALIGNED_PATH
                        Path where aligned images will be stored
  --aligned-extension ALIGNED_EXTENSION
                        Extension (and filetype) to use for aligned images.
  --predictor-path PREDICTOR_PATH
                        DLib face predictor dat file
  --filtered-files FILTERED_FILES
                        File to write filtered files to

pada.py align options:

$ pada.py align --help
usage: pada.py align [-h] [--input-glob INPUT_GLOB] [--img-thresh IMG_THRESH]

optional arguments:
  -h, --help            show this help message and exit
  --input-glob INPUT_GLOB
                        Input files glob
  --img-thresh IMG_THRESH
                        Max duplicate frame delta

pada.py framedrop options:

$ pada.py framedrop --help
usage: pada.py framedrop [-h] [--erode-amount ERODE_AMOUNT]
                         [--frame-skip FRAME_SKIP]

optional arguments:
  -h, --help            show this help message and exit
  --erode-amount ERODE_AMOUNT
                        Amount to erode face mask by
  --frame-skip FRAME_SKIP
                        Ratio of input frames to output frames

Options can alternatively be specified in a pada.conf in the working directory, in the site config path, or global config path. To see the full list of config paths run pada.py print_config_paths

Requirements

pada.py requires numpy, dlib, scipy, cv2, and appdirs.

make_vid.sh requires mencoder and suitable codecs to be installed.