M.Ozan Unal

Yolo on Google Colab

Run YOLO V3 on Colab for images/videos

Hello there, Today, we will be discussing how we can use the Darknet project on Google Colab platform. For those who are not familiar with these terms:

YOLO in action!

  • The Darknet project is an open-source project written in C, which is a framework to develop deep neural networks.
  • Yolo V3 is an object detection algorithm. It is one of the state of the art solution when accuracy/processing power needed metric is considered.
  • Google Cola is a cloud-based data science workspace similar to the jupyter notebook. Each Collabrotary session is equipped with a virtual machine running 13 GB of ram and either a CPU, GPU, or TPU processor. In most case, all the required packages are already installed on these machines and you can quite easily start development using Google Collaboratory. It saves us from installing process and it provides us easy to accessible GPU’s which is also free under some constraints.

Have a look Ted Talk by Joseph Redmon the developer of the darknet project. The talk is about Darknet and YOLO projects which titled as “How computers learn to recognize objects instantly” . Darknet project aims to create a new neural network framework which is completely focused on simplicity and performance. The thing which I like about is its clarity and performance. All the code is written in C, to define a deep learning network you should only create a config file which defines the layers. By this way, it does not lose its performance capabilities also it provides us easy to use interface for development with this library.

Since I love both YOLO project and Google Colab, I decided to create a tutorial to use them together. I create a GitHub repository and a Collaboratory notebook for this purpose

Please check

Install

Go to the directory, clear and install everthing

  • Clone the project
  • Change make file configurations and make OPENCV and GPU enable
  • Install opencv library

import cv2, os
import matplotlib.pyplot as plt
%matplotlib inline
  


!ls
!cd /content
!rm -fr darknet
!git clone https://github.com/AlexeyAB/darknet/
% cd darknet
!sed -i 's/OPENCV=0/OPENCV=1/g' Makefile
!sed -i 's/GPU=0/GPU=1/g' Makefile
!sed -i 's/CUDNN=0/CUDNN=1/g' Makefile
!apt update
!apt-get install libopencv-dev

Compile and Configure

  • Compile YOLO
  • Download YOLO weights
!make &> compile.log

!wget https://pjreddie.com/media/files/yolov3.weights

--2019-08-11 23:58:44--  https://pjreddie.com/media/files/yolov3.weights
Resolving pjreddie.com (pjreddie.com)... 128.208.4.108
Connecting to pjreddie.com (pjreddie.com)|128.208.4.108|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 248007048 (237M) [application/octet-stream]
Saving to: ‘yolov3.weights’

yolov3.weights      100%[===================>] 236.52M  62.1MB/s    in 4.1s    

2019-08-11 23:58:48 (57.7 MB/s) - ‘yolov3.weights’ saved [248007048/248007048]

Test An Image

def predictImage(imageDir):
  os.system("cd /content/darknet && ./darknet detect cfg/yolov3.cfg yolov3.weights {}".format(imageDir))
  image = cv2.imread("/content/darknet/predictions.jpg")
  height, width = image.shape[:2]
  resized_image = cv2.resize(image,(3*width, 3*height), interpolation = cv2.INTER_CUBIC)

  fig = plt.gcf()
  fig.set_size_inches(18, 10)
  plt.axis("off")
  #plt.rcParams['figure.figsize'] = [10, 5]
  plt.imshow(cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB))
  plt.show()

!wget https://github.com/mozanunal/yoloOnGoogleColab/raw/master/test/test.jpg
!ls

--2019-08-11 23:58:49--  https://github.com/mozanunal/yoloOnGoogleColab/raw/master/test/test.jpg
Resolving github.com (github.com)... 192.30.253.113
Connecting to github.com (github.com)|192.30.253.113|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/mozanunal/yoloOnGoogleColab/master/test/test.jpg [following]
--2019-08-11 23:58:50--  https://raw.githubusercontent.com/mozanunal/yoloOnGoogleColab/master/test/test.jpg
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 68535 (67K) [image/jpeg]
Saving to: ‘test.jpg’

test.jpg            100%[===================>]  66.93K  --.-KB/s    in 0.05s   

2019-08-11 23:58:50 (1.32 MB/s) - ‘test.jpg’ saved [68535/68535]

3rdparty      CMakeLists.txt	      image_yolov3.sh	     results
appveyor.yml  compile.log	      include		     scripts
backup	      darknet		      json_mjpeg_streams.sh  src
build	      DarknetConfig.cmake.in  LICENSE		     test.jpg
build.ps1     darknet.py	      Makefile		     video_v2.sh
build.sh      darknet_video.py	      net_cam_v3.sh	     video_yolov3.sh
cfg	      data		      obj		     yolov3.weights
cmake	      image_yolov2.sh	      README.md

predictImage("test.jpg")

png

Test with Video

def predictVideo(videoDir):
  os.system(""" cd /content/darknet && ./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights \
  -dont_show {} -i 0 -out_filename res.avi
  """.format(videoDir))

!wget https://github.com/mozanunal/yoloOnGoogleColab/raw/master/test/test.avi
!ls

--2019-08-11 23:59:01--  https://github.com/mozanunal/yoloOnGoogleColab/raw/master/test/test.avi
Resolving github.com (github.com)... 192.30.253.113
Connecting to github.com (github.com)|192.30.253.113|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: https://raw.githubusercontent.com/mozanunal/yoloOnGoogleColab/master/test/test.avi [following]
--2019-08-11 23:59:02--  https://raw.githubusercontent.com/mozanunal/yoloOnGoogleColab/master/test/test.avi
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 15897530 (15M) [application/octet-stream]
Saving to: ‘test.avi’

test.avi            100%[===================>]  15.16M  50.0MB/s    in 0.3s    

2019-08-11 23:59:02 (50.0 MB/s) - ‘test.avi’ saved [15897530/15897530]

3rdparty	compile.log		json_mjpeg_streams.sh  src
appveyor.yml	darknet			LICENSE		       test.avi
backup		DarknetConfig.cmake.in	Makefile	       test.jpg
build		darknet.py		net_cam_v3.sh	       video_v2.sh
build.ps1	darknet_video.py	obj		       video_yolov3.sh
build.sh	data			predictions.jpg        yolov3.weights
cfg		image_yolov2.sh		README.md
cmake		image_yolov3.sh		results
CMakeLists.txt	include			scripts

predictVideo("test.avi")

!du -h res.avi

93M	res.avi

from google.colab import files

files.download('/content/darknet/res.avi')

See you later!