文件列表:

compare_label_dbs.py (1265, 2018-09-22)
counts_over_days.png (174953, 2018-09-22)
data.py (7657, 2018-09-22)
day_count_stats.py (1197, 2018-09-22)
dump_bee_crops.py (1832, 2018-09-22)
freeze_graph.sh (255, 2018-09-22)
generate_graph_pbtxt.py (1181, 2018-09-22)
label.201802_sample.db (254976, 2018-09-22)
label_db.py (3032, 2018-09-22)
label_ui.py (5810, 2018-09-22)
materialise_label_db.py (1483, 2018-09-22)
merge_dbs.py (1017, 2018-09-22)
model.py (5127, 2018-09-22)
parse_predict_out.py (22, 2018-09-22)
plot.R (566, 2018-09-22)
predict.py (3709, 2018-09-22)
predict_from_frozen.py (3299, 2018-09-22)
rasp_pi (0, 2018-09-22)
rasp_pi\capture.service (274, 2018-09-22)
rasp_pi\capture_and_send.sh (755, 2018-09-22)
rasp_pi\capture_stills.py (903, 2018-09-22)
resize.py (568, 2018-09-22)
reverse_optimise.py (3339, 2018-09-22)
rgb_labels_predictions.png (370212, 2018-09-22)
rotate_ccw.sh (133, 2018-09-22)
run_sample_training_pipeline.sh (1410, 2018-09-22)
sample.py (1718, 2018-09-22)
sample_data (0, 2018-09-22)
sample_data\labels.db (9216, 2018-09-22)
sample_data\test (0, 2018-09-22)
sample_data\test\20180206_133348.jpg (699478, 2018-09-22)
sample_data\test\20180207_115534.jpg (713542, 2018-09-22)
sample_data\test\20180213_084656.jpg (684229, 2018-09-22)
sample_data\test\20180213_122022.jpg (710139, 2018-09-22)
sample_data\test_002_001.png (1351798, 2018-09-22)
sample_data\training (0, 2018-09-22)
... ...

# 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.  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.  ## usage ### 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 --to-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 ```  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 ( note: this still doesn't work; possibly because of something in these steps, or possibly something about the tf api support of the stick. see [this forum post](https://ncsforum.movidius.com/discussion/692/incorrect-inference-results-from-a-minimal-tensorflow-model#latest) for more info... ) ### 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

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