yolo_object_detection.py

# -*- coding: utf-8 -*-
"""yolo_object_detection.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1pJWCn-WfDdXSP6RqaJ5i7bksjAqfSt2Y

## Let's mount google drive first for a storage and GPU
"""

from google.colab import drive
drive.mount('/content/gdrive')

# make sure you use Tesla K80 GPU (12GB), by changing 'runtmie type' 
!nvidia-smi

"""## Clone Darknet GitHub repository"""

import os
os.environ['PATH'] += ':/usr/local/cuda/bin'
!rm -fr darknet
!git clone https://github.com/AlexeyAB/darknet/

!apt install gcc-5 g++-5 -y
!ln -s /usr/bin/gcc-5 /usr/local/cuda/bin/gcc 
!ln -s /usr/bin/g++-5 /usr/local/cuda/bin/g++

"""## Go to the darknet folder, enable GPU, then compile the entire thing"""

# Commented out IPython magic to ensure Python compatibility.
# %cd darknet
!sed -i 's/GPU=0/GPU=1/g' Makefile
!sed -i 's/OPENCV=0/OPENCV=1/g' Makefile
!make

"""## Get the YOLO Weights file"""

!wget https://pjreddie.com/media/files/yolov3.weights
!chmod a+x ./darknet

"""## To get the working Directory"""

!pwd

from google.colab import drive
drive.mount('/content/drive')

!apt install ffmpeg libopencv-dev libgtk-3-dev python-numpy python3-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libtiff5-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libv4l-dev libtbb-dev qtbase5-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils unzip

"""## It's time to upload the video"""

from google.colab import files

uploaded = files.upload()

!./darknet detector demo cfg/coco.data cfg/yolov3.cfg yolov3.weights -dont_show test.mp4 -i 0 -out_filename output1.avi -thresh 0.7

from google.colab import files
files.download('output1.avi')