# The crowd will tell us

## Pipeline

* _Gphoto2_ takes pictures (not vm) into a 'pictures/{1,2,3}' dir.
* These get splitted by *split_and_merge_output.py* to a 'segments' subdir. (To be able to detect smaller faces)
* Our modified _opencv-webcam-demo_ will analyse these images and output json
* The same *split_and_merge_output.py* process will detect the segment jsons and merge them back into the 'pictures/{1,2,3}' dir. It subsequently deletes the segmented photos

[TODO: remove duplicate faces on merge]

Now we have our dataset of photo's with JSONS.

To output:

* `parse_output.py -o ~/pictures/{1,2,3} --json --window-size 10 > ~/sdk-samples/output/talk1.json` Creates a json file suitable for webbrowser graphing
* `python parse_output.py -o ~/pictures/{1,2,3} --wav ~/pictures/{1,2,3}.wav --targetDir /home/crowd/sdk-samples/output/1 --window-size 10` Assumes a WAVE file that starts simultaneously with frame 1. and splits it into the windows
* Start server for graph.php & image.php `cd ~/sdk-samples && php -S 0.0.0.0:8000`
* Navigate to: `http://192.168.178.33:8000/graphs.php` to view images

Done :-)

## Commands

Start a few processes:

gphoto2 to capture images every 2 sec:

`gphoto2 --port usb: --capture-image-and-download -I 2 --filename=/home/crowd/output/frame%06n.jpg`

SET FPS IN parse_output.py

If using Sony A7s/r, Ubuntu 14.04 has a too old version of gphoto2, so use vm-host with gphoto. Mount pictures dir to vbox:

`sudo mount -t vboxsf pictures ~/pictures -o rw,uid=1000,gid=1000`

The modified 'webcam demo' to analyse and generate json:

`/home/crowd/build/opencv-webcam-demo/opencv-webcam-demo --data /home/crowd/affdex-sdk/data --faceMode 1 --numFaces 80 -o /home/crowd/output/segments/ --draw 0 --segments 1`

When using `--segments 1` also run:

`python split_and_merge_output.py --frameOutput ~/output --segmentDir ~/output/segments/`


Using split_and_merge_output, frames are split into segments so SMALL_FACES actually detects _really_ small faces. This requires enabling --segments on opencv-webcam-demo (so it detects/writes segment%06d files)
split_and_merge_output also remerges these segment%06.json files into a frame%06d.json file.


When done, generate output:

`parse_output.py -o ~/output1 --json --window-size 1 > ~/sdk-samples/output/talk1.json`
`parse_output.py -o ~/output2 --json --window-size 1 > ~/sdk-samples/output/talk2.json`
`parse_output.py -o ~/output3 --json --window-size 1 > ~/sdk-samples/output/talk3.json`

and audio, which expects wav to start simmmultaneously with frame00001:

`python parse_output.py -o ~/pictures/1 --wav ~/pictures/1.wav --targetDir /home/crowd/sdk-samples/output/1 --window-size 1`
`python parse_output.py -o ~/pictures/2 --wav ~/pictures/2.wav --targetDir /home/crowd/sdk-samples/output/2 --window-size 1`
`python parse_output.py -o ~/pictures/3 --wav ~/pictures/3.wav --targetDir /home/crowd/sdk-samples/output/3 --window-size 1`

Then start server:

`cd ~/sdk-samples && php -S 0.0.0.0:8000`

Navigate to: `http://192.168.178.33:8000/graphs.php` to view images