OpenCV

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OpenCV is a very popular computer vision and image processing library.
There are bindings for C++, Java, JavaScript, and Python

Installation

  • Download Python 3

Free algorithms only

pip install opencv-contrib-python
  • Missing SURF and SIFT.
    • The patent for SIFT expired on March 6, 2020 so it might be added to opencv-contrib-python in a future update.
    • The patent for SURF should expire around 2029.

Non-free Algorithms

You need to compile OpenCV from source alongside OpenCV-contrib.
The easiest way is to use the automated script to compile opencv-contrib-python.
Email me if you want a prebuilt wheel for Python 3.6 or 3.7.

Compile opencv-contrib-python

You can build a wheel to install which includes NONFREE modules as follows:

sudo apt install -y libavcodec-dev libavformat-dev libswscale-dev
sudo apt install -y libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev
sudo apt install -y libpng-dev libjpeg-dev libopenexr-dev libtiff-dev libwebp-dev
sudo add-apt-repository "deb http://security.ubuntu.com/ubuntu xenial-security main"
sudo apt update
sudo apt install -y libjasper1 libjasper-dev
# sudo apt install -y tesseract-ocr* liblept5 leptonica-progs libleptonica-dev
  • Setup a build environment.
    • Note that whatever version of qt you use to build, you will also need in the environment you install the wheel in. Ideally, build it in the same environment you want to use it in.
conda create -n opencvbuild python=3.7
conda activate opencvbuild
conda install -c conda-forge pyqt
  • Make a wheel
git clone --recurse-submodules [email protected]:skvark/opencv-python.git
cd opencv-python
export ENABLE_CONTRIB=1
export CMAKE_ARGS="-DOPENCV_ENABLE_NONFREE=ON -DWITH_QT=5"
python setup.py bdist_wheel
Compilation Instructions

See https://www.pyimagesearch.com/2018/08/15/how-to-install-opencv-4-on-ubuntu for instructions.

cd ~
git clone [email protected]:opencv/opencv.git
git clone [email protected]:opencv/opencv_contrib.git
cd opencv
mkdir build
cd build
cmake -D CMAKE_BUILD_TYPE=RELEASE \
	-D CMAKE_INSTALL_PREFIX=/usr/local \
	-D INSTALL_PYTHON_EXAMPLES=ON \
	-D INSTALL_C_EXAMPLES=OFF \
	-D OPENCV_ENABLE_NONFREE=ON \
	-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
	-D PYTHON3_EXECUTABLE=~/anaconda3/envs/tf2/bin/python \
	-D PYTHON3_INCLUDE_DIRS=~/anaconda3/envs/tf2/python3.7m \
	-D PYTHON3_LIBRARIES=~/anaconda3/envs/tf2/lib/libpython3.7m.so \
	-D BUILD_EXAMPLES=ON ..
make -j16
sudo make install
sudo ldconfig

Usage

Getting Started

import cv2

# cv2.IMREAD_ANYCOLOR = 4
# cv2.IMREAD_ANYDEPTH = 2
# cv2.IMREAD_COLOR = 1
# cv2.IMREAD_GRAYSCALE = 0

# Use cv2.IMREAD_GRAYSCALE to read in grayscale
my_image = cv2.imread("my_image.png", cv2.IMREAD_GRAYSCALE)

# Perform some modification
# Do your machine learning here

# Write your output image
cv2.imwrite("my_modified_image.png", my_image)

Resizing an Image

Reference

import numpy as np
import cv2

img = cv2.imread('messi5.jpg')
new_img = cv2.resize(img, (500,200), interpolation = cv.INTER_CUBIC)

Video

Reading Video

video_capture = cv2.VideoCapture(path.join(videos_folder, video_filename))
if not video_capture.isOpened():
    print("Error opening video stream or file")
    sys.exit(0)

frame_num = 0
while video_capture.isOpened():
    ret, frame = video_capture.read()
    if ret:
        cv2.imshow('Frame', frame)

        frame_num = frame_num + 1
        if cv2.waitKey(25) & 0xFF == ord('q'):
            break
    else:
        break
Parameters
# Resolution
width = int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT))

total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
frame_pos = int(video_capture.get(cv2.CAP_PROP_POS_FRAMES))
fps = video_capture.get(cv2.CAP_PROP_FPS)

Writing Video

C++ Video Write
Note that OpenCV does not handle audio.

output_video = cv2.VideoWriter()
codec = cv2.VideoWriter_fourcc(*"avc1")
fps = 15
image_size = (100, 100)
output_video.open("video_output.mp4", codec, fps, image_size, True)
if not output_video.isOpened():
    print("Error opening output video")

# Write all of your frames
# while have_frames:
#   output_video.write(my_frame)

# Release the video
output_video.release()

Resources