patham9 / darknet-python

Python3 API Darknet

Python3 API for Darknet

Darknet is an OpenSource convoluted neural network library, see here

This package features a Python3 API for darknet together with debian packaging for easy distribution.

No config files, image label jpg directories (!) etc. Just a clean & simple Python3 API that works out of the box

Changelog

• version 0.2.3

  • Nasty memleak fixed, related to this : should use PyList/PyTuple_SetItem to steal the references.

• version 0.2.2

  • Added some options to the darknet_py test command

• version 0.2.1

  Fixed a memleak

  • The Predictor dtor was unable to free the cuda memory with free_network.
  • ..so added a call to cudaDeviceReset. Now there's no stray memory left on the GPU.

• version 0.2.0

Getting started

0. Install CUDA

This step is not obligatory. Just do it if you want to use GPUs.

It's recommended to use a GPU with "compute capability" >= 5, see the scores here.

Latest nvidia drivers and cuda can be found from nvidia

The recommended method is the "debian (local)" one. Before installing, you should remove an earlier installation of nvidia graphic drivers (say, use 'sudo apt-get remove --purge nvidia*')

That package from nvidia will install you a recent graphics card driver and cuda that are compatible with each other. This will overwrite the nvidia driver that comes with your standard package management and updates

It may happen that when you do an update in the future with apt, it will overwrite the package you have installed here. In that case, just do 'sudo apt-get remove --purge nvidia*' and install drivers directly from nvidia again.

(to disable ubuntu auto updates, search at launch "software & updates" => go to "updates" tab and disable updates to "never")

After installing the package, log out & in and then see your GPU's specs using the command 'nvidia-smi'.

1. Compile

You need at least:

sudo apt-get install build-essential cmake pkg-config swig python3 python3-dev python3-numpy python3-pil

To compile darknet with python bindings and to create a debian package for installing, just run

./easy_build.bash

It should detect the CUDA installation automagically if present

If you get this error:

cuda_runtime.h: No such file or directory

You must manually create a link to the correct cuda version, i.e.

sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda

2. Install

The debian package with

sudo dpkg -i build_dir/*.deb

3. Download parameters

With this command:

darknet_py_download

Or copy the directory ~/.darknet from another installation

4. Test

A quicktest:

cd example
python3 predict_2.py

If everything is OK, the program should report something like this:

Predicting took 0.03300929069519043 seconds

Test with any image using the script "darknet_py" like this:

darknet_py <yolo_flavor> somefile.jpg

where <yolo_flavor> can be v3, v2 or v3tiny

Python API

Images are passed to darknet core library using numpy arrays (through memory only, of course). Results are returned as python lists.

Python examples can be found in the example folder. It's as easy as this:

import numpy
import time
from PIL import Image
from darknet.api2.predictor import get_YOLOv2_Predictor, get_YOLOv3_Predictor, get_YOLOv3_Tiny_Predictor

filename = "dog.jpg"
image = Image.open(filename)
img = numpy.array(image)

# this will take few seconds .. but we need to create predictor only once
# predictor = get_YOLOv2_Predictor() # OK
predictor = get_YOLOv3_Predictor() # OK
# predictor = get_YOLOv3_Tiny_Predictor() # OK

t = time.time()
lis = predictor(img)
print("Predicting took", time.time()-t, "seconds") # takes around 0.2 secs on a decent (5.1 grade) GPU
for l in lis:
    print(l)

# each element of the list is a tuple with:
# (class_name, probability_in_%, left, right, top, bottom)
# in another words:
# (x0, y0) = (left, bottom)
# (x1, y1) = (right, top)
# in y, smallest value corresponds to bottom left corner in the image, so if you use with cv2 / numpy, remember to invert the y axis

GPU memory requirements

Approximately, as reported with nvidia-smi

Yolo v3          2400 MB
Tiny Yolo v3     230 MB
Yolo v2          1230 MB

A tip: if you want to save GPU memory, disable your desktop compositor.

Video Surveillance with Yolo

If you need video surveillance with object detection, that works out-of-the-box, try this program

Training

This python binding comes with a homebrewn tagger program, written in Python3 and Qt, called "DarkTurk (tm)" (as in mechanical turk). Install Python Qt with this command:

pip3 install --user --upgrade PySide2

Let's start by creating a scaffold directory structure:

darknet_py_gui --create_dir=true --directory=$HOME/darkturk/train1

See README.md in that newly created directory. It says:

names.txt      : one object class per line
net.cfg        : the neural network topology
train_images/  : place your training images here
valid_images/  : place your validation images here

So, proceed as instructed. "names.txt" has the object names (i.e. like in file "coco.names") and net.cfg describes the neural net (like in the file "yolov3.cfg"). Start py putting your jpg images into the "train_images/" folder. If you need image conversion to jpg, try the "darknet_py_convert" command in the "train_images/" folder.

DarkTurk(tm) will take automagically care of the rest of the necessary files.

So, now you're all set. Start DarkTurk with:

darknet_py_gui --directory=$HOME/darkturk/train1

You might want to press the "Help!" button for instructions

Once you have done all that tagging, close the program windows. You can resume tagging later if you want to with that same command.

Finally, it's time to train:

darknet_py_gui --train=true --directory=$HOME/darkturk/train1 

(you can also do the training with the canonical darknet commands as instructed in the darknet web pages)

The training can be stopped elegantly (its darknet!) from another terminal with the command:

killall darknet_py_gui

The trained network weights appear in the "backup/" subdirectory.

For a second training iteration with more images, create a new scaffold directory:

darknet_py_gui --create_dir=true --directory=$HOME/darkturk/train2

And copy the desired weight file from $HOME/darkturk/train1/backup/ into $HOME/darkturk/train2/net.weights

Then start the trainer again, this time with

darknet_py_gui --train=true --directory=$HOME/darkturk/train2

Continue like this ad nauseam.

Notes

The "yolov3.cfg" that comes with this package, has been modified for detection as suggested here

TODO

Need to migrate this to alexbe darknet asap. Any volunteers?

License

MIT

(for darknet itself, see the ext/darknet folder and darknet github)

Authors

Sampsa Riikonen

(for darknet, see darknet github)

Copyright

Sampsa Riikonen 2018

(for darknet, see darknet github)