Real-time object detection and classification. Paper: version 1, version 2.
Read more about YOLO (in darknet) and download weight files here. In case the weight file cannot be found, I uploaded some of mine here, which include yolo-full
and yolo-tiny
of v1.0, tiny-yolo-v1.1
of v1.1 and yolo
, tiny-yolo-voc
of v2.
If above files are not working then you can downlaod YOLO weights by using the followin command.
$ wegt https://pjreddie.com/media/files/yolo.weights
Click on this image to see demo from yolov2:
Python, tensorflow, numpy, OpenCV, Cython
numpy : $ sudo pip install numpy
Cython: $ sudo apt-get install cython
Follow the given steps or you can refer this link
[compiler] sudo apt-get install build-essential
[required] sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
[optional] sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
sudo mkdir opencv
cd ~/opencv
sudo git clone https://github.com/opencv/opencv.git
sudo git clone https://github.com/opencv/opencv_contrib.git
cd ./opencv
sudo mkdir ./build
cd ./build
sudo cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=ON \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D WITH_TBB=ON \
-D WITH_V4L=ON \
-D WITH_QT=ON \
-D WITH_OPENGL=ON \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
-D BUILD_EXAMPLES=ON ..
nproc
make -j4
sudo make install
sudo sh -c 'echo "/usr/local/lib" >> /etc/ld.so.conf.d/opencv.conf'
sudo ldconfig
You can choose one of the following three ways to get started with darkflow.
-
Just build the Cython extensions in place. NOTE: If installing this way you will have to use
./flow
in the cloned darkflow directory instead offlow
as darkflow is not installed globally.python setup.py build_ext --inplace
-
Let pip install darkflow globally in dev mode (still globally accessible, but changes to the code immediately take effect)
pip install -e .
-
Install with pip globally
pip install .
# Have a look at its options
flow --h
First, let's take a closer look at one of a very useful option --load
# 1. Load tiny-yolo.weights
flow --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights
# 2. To completely initialize a model, leave the --load option
flow --model cfg/yolo-new.cfg
# 3. It is useful to reuse the first identical layers of tiny for `yolo-new`
flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights
# this will print out which layers are reused, which are initialized
All input images from default folder sample_img/
are flowed through the net and predictions are put in sample_img/out/
. We can always specify more parameters for such forward passes, such as detection threshold, batch size, images folder, etc.
# Forward all images in sample_img/ using tiny yolo and 100% GPU usage
flow --imgdir sample_img/ --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights --gpu 1.0
json output can be generated with descriptions of the pixel location of each bounding box and the pixel location. Each prediction is stored in the sample_img/out
folder by default. An example json array is shown below.
# Forward all images in sample_img/ using tiny yolo and JSON output.
flow --imgdir sample_img/ --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights --json
JSON output:
[{"label":"person", "confidence": 0.56, "topleft": {"x": 184, "y": 101}, "bottomright": {"x": 274, "y": 382}},
{"label": "dog", "confidence": 0.32, "topleft": {"x": 71, "y": 263}, "bottomright": {"x": 193, "y": 353}},
{"label": "horse", "confidence": 0.76, "topleft": {"x": 412, "y": 109}, "bottomright": {"x": 592,"y": 337}}]
- label: self explanatory
- confidence: somewhere between 0 and 1 (how confident yolo is about that detection)
- topleft: pixel coordinate of top left corner of box.
- bottomright: pixel coordinate of bottom right corner of box.
Training is simple as you only have to add option --train
. Training set and annotation will be parsed if this is the first time a new configuration is trained. To point to training set and annotations, use option --dataset
and --annotation
. A few examples:
# Initialize yolo-new from yolo-tiny, then train the net on 100% GPU:
flow --model cfg/yolo-new.cfg --load bin/tiny-yolo.weights --train --gpu 1.0
# Completely initialize yolo-new and train it with ADAM optimizer
flow --model cfg/yolo-new.cfg --train --trainer adam
During training, the script will occasionally save intermediate results into Tensorflow checkpoints, stored in ckpt/
. To resume to any checkpoint before performing training/testing, use --load [checkpoint_num]
option, if checkpoint_num < 0
, darkflow
will load the most recent save by parsing ckpt/checkpoint
.
# Resume the most recent checkpoint for training
flow --train --model cfg/yolo-new.cfg --load -1
# Test with checkpoint at step 1500
flow --model cfg/yolo-new.cfg --load 1500
# Fine tuning yolo-tiny from the original one
flow --train --model cfg/tiny-yolo.cfg --load bin/tiny-yolo.weights
Example of training on Pascal VOC 2007:
# Download the Pascal VOC dataset:
curl -O https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar
# An example of the Pascal VOC annotation format:
vim VOCdevkit/VOC2007/Annotations/000001.xml
# Train the net on the Pascal dataset:
flow --model cfg/yolo-new.cfg --train --dataset "~/VOCdevkit/VOC2007/JPEGImages" --annotation "~/VOCdevkit/VOC2007/Annotations"
The steps below assume we want to use tiny YOLO and our dataset has 3 classes
-
Create a copy of the configuration file
tiny-yolo-voc.cfg
and rename it according to your preferencetiny-yolo-voc-3c.cfg
(It is crucial that you leave the originaltiny-yolo-voc.cfg
file unchanged, see below for explanation). -
In
tiny-yolo-voc-3c.cfg
, change classes in the [region] layer (the last layer) to the number of classes you are going to train for. In our case, classes are set to 3.... [region] anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 bias_match=1 classes=3 coords=4 num=5 softmax=1 ...
-
In
tiny-yolo-voc-3c.cfg
, change filters in the [convolutional] layer (the second to last layer) to num * (classes + 5). In our case, num is 5 and classes are 3 so 5 * (3 + 5) = 40 therefore filters are set to 40.... [convolutional] size=1 stride=1 pad=1 filters=40 activation=linear [region] anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52 ...
-
Change
labels.txt
to include the label(s) you want to train on (number of labels should be the same as the number of classes you set intiny-yolo-voc-3c.cfg
file). In our case,labels.txt
will contain 3 labels.label1 label2 label3
-
Reference the
tiny-yolo-voc-3c.cfg
model when you train.flow --model cfg/tiny-yolo-voc-3c.cfg --load bin/tiny-yolo-voc.weights --train --annotation train/Annotations --dataset train/Images
-
Why should I leave the original
tiny-yolo-voc.cfg
file unchanged?When darkflow sees you are loading
tiny-yolo-voc.weights
it will look fortiny-yolo-voc.cfg
in your cfg/ folder and compare that configuration file to the new one you have set with--model cfg/tiny-yolo-voc-3c.cfg
. In this case, every layer will have the same exact number of weights except for the last two, so it will load the weights into all layers up to the last two because they now contain different number of weights.
For a demo that entirely runs on the CPU:
flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi
For a demo that runs 100% on the GPU:
flow --model cfg/yolo-new.cfg --load bin/yolo-new.weights --demo videofile.avi --gpu 1.0
To use your webcam/camera, simply replace videofile.avi
with keyword camera
.
To save a video with predicted bounding box, add --saveVideo
option.
Please note that return_predict(img)
must take an numpy.ndarray
. Your image must be loaded beforehand and passed to return_predict(img)
. Passing the file path won't work.
Result from return_predict(img)
will be a list of dictionaries representing each detected object's values in the same format as the JSON output listed above.
from darkflow.net.build import TFNet
import cv2
options = {"model": "cfg/yolo.cfg", "load": "bin/yolo.weights", "threshold": 0.1}
tfnet = TFNet(options)
imgcv = cv2.imread("./sample_img/sample_dog.jpg")
result = tfnet.return_predict(imgcv)
print(result)
## Saving the lastest checkpoint to protobuf file
flow --model cfg/yolo-new.cfg --load -1 --savepb
## Saving graph and weights to protobuf file
flow --model cfg/yolo.cfg --load bin/yolo.weights --savepb
When saving the .pb
file, a .meta
file will also be generated alongside it. This .meta
file is a JSON dump of everything in the meta
dictionary that contains information nessecary for post-processing such as anchors
and labels
. This way, everything you need to make predictions from the graph and do post processing is contained in those two files - no need to have the .cfg
or any labels file tagging along.
The created .pb
file can be used to migrate the graph to mobile devices (JAVA / C++ / Objective-C++). The name of input tensor and output tensor are respectively 'input'
and 'output'
. For further usage of this protobuf file, please refer to the official documentation of Tensorflow
on C++ API here. To run it on, say, iOS application, simply add the file to Bundle Resources and update the path to this file inside source code.
Also, darkflow supports loading from a .pb
and .meta
file for generating predictions (instead of loading from a .cfg
and checkpoint or .weights
).
## Forward images in sample_img for predictions based on protobuf file
flow --pbLoad built_graph/yolo.pb --metaLoad built_graph/yolo.meta --imgdir sample_img/
If you'd like to load a .pb
and .meta
file when using return_predict()
you can set the "pbLoad"
and "metaLoad"
options in place of the "model"
and "load"
options you would normally set.
That's all.
Credits for this code go to Trieu. I've merely created a wrapper to get people started.