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https://github.com/matpalm/bnn

bee detection tensorflow conv net for a rasp pi on side of a hive
https://github.com/matpalm/bnn

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bee detection tensorflow conv net for a rasp pi on side of a hive

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README 

        
# BNN v2



unet style image translation from image of hive entrance to bitmap of location of center of bees.



trained in a semi supervised way on a desktop gpu and deployed to run in real time on the hive using

either a [raspberry pi](https://www.raspberrypi.org/) using a [neural compute stick](https://developer.movidius.com/)

or a [je vois embedded smart camera](http://jevois.org/)



see [this blog post](http://matpalm.com/blog/counting_bees/) for more info..



here's an example of predicting bee position on some held out data. the majority of examples trained had ~10 bees per image.



![rgb_labels_predictions.png](rgb_labels_predictions.png)



the ability to locate each bee means you can summarise with a count. note the spike around 4pm when the bees at this time of year come back to base.



![counts_over_days.png](counts_over_days.png)



## usage



see `run_sample_training_pipeline.sh` for an executable end to end smoke test walkthrough of these steps (using sample data)



### dependencies



code depends in `requirements.txt`



```

sudo apt-get install python3-tk python3-pil python3-pil.imagetk

sudo pip3 install -r requirements.txt

```



### gathering data



the `rasp_pi` sub directory includes one method of collecting images on a raspberry pi.



### labelling



start by using the `label_ui.py` tool to manually label some images and create a sqlite `label.db`



the following command starts the labelling tool for some already labelled (by me!) sample data provided with in this repro.



```

./label_ui.py \

--image-dir sample_data/training/ \

--label-db sample_data/labels.db \

--width 768 --height 1024

```



hints



* left click to label the center of a bee

* right click to remove the closest label

* press up to toggle labels on / off. this can help in tricky cases.

* use left / right to move between images. it's often helpful when labelling to quickly switch back/forth between images to help distinguish background

* use whatever system your OS provides to zoom in; e.g. in ubuntu super+up / down



you can merge entries from `a.db` into `b.db` with `merge_db.py`



```

./merge_dbs.py --from-db a.db --into-db b.db

```



### training



before training we materialise a `label.db` (which is a database of x,y coords)

into black and white bitmaps using `./materialise_label_db.py`



```

./materialise_label_db.py \

--label-db sample_data/labels.db \

--directory sample_data/labels/ \

--width 768 --height 1024

```



we can visualise the training data with `data.py`. this will generate a number of `test*png` files with

the input data on the left (with data augmentation) and the output labels on the right.



```

./data.py \

--image-dir sample_data/training/ \

--label-dir sample_data/labels/ \

--width 768 --height 1024

```



![sample_data/test_002_001.png](sample_data/test_002_001.png)



train with `train.py`.



`run` denotes the subdirectory for ckpts and tensorboard logs; e.g. `--run r12` checkpoints

under `ckpts/r12/` and logs under `tb/r12`.



use `--help` to get complete list of options including model config, defining validation data and stopping conditions.



e.g. to train for a short time on `sample_data` run the following... (for a more realistic result we'd want

to train for many more steps on much more data)



```

./train.py \

--run r12 \

--steps 300 \

--train-steps 50 \

--train-image-dir sample_data/training/ \

--test-image-dir sample_data/test/ \

--label-dir sample_data/labels/ \

--width 768 --height 1024

```



progress can be visualised with tensorboard (serves at localhost:6006)



```

tensorboard --log-dir tb

```



### inference



predictions can be run with `predict.py`.

to specifiy what type of output set one of the following...



* `--output-label-db` to create a label db; this can be merged with a human labelled db, using `./merge_dbs.py` for semi supervised learning

* `--export-pngs centroids` to export output bitmaps equivalent as those made by `./materialise_label_db.py`

* `--export-pngs predictions` to export explicit model output (i.e. before connected components post processing)



NOTE: given the above step that only runs a short period on a small dataset we DON'T expect this to give

a great result; these instructions are more included to prove the plumbing works...



```

./predict.py \

--run r12 \

--image-dir sample_data/unlabelled \

--output-label-db sample_predictions.db \

--export-pngs predictions

```



output predictions can be compared to labelled data to calculate precision recall.

(we deem a detection correct if it is within a thresholded distance from a label)



```

./compare_label_dbs.py --true-db ground_truth.db --predicted-db predictions.db

precision 0.936  recall 0.797  f1 0.861

```



### running on compute stick



after loads of hacking i did eventually get this running on the compute stick.

some info is included in [this issue](https://github.com/matpalm/bnn/issues/8)

with additional background info in [this forum post](https://ncsforum.movidius.com/discussion/692/incorrect-inference-results-from-a-minimal-tensorflow-model#latest)



[update] given how long ago this was, i'm hoping things are easier these days :D



### some available datasets



* [Jonathan Byrne's](https://github.com/squeakus) [to-bee-or-not-to-bee](https://www.kaggle.com/jonathanbyrne/to-bee-or-not-to-bee) kaggle dataset