OpenCV
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.
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:
- Install all dependencies listed in the build tutorial
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 libjasper-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
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() and frame_num < 30:
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))
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()