OpenCV: Difference between revisions
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You need to compile [https://github.com/opencv/opencv OpenCV] from source alongside [https://github.com/opencv/opencv_contrib OpenCV-contrib].<br> | You need to compile [https://github.com/opencv/opencv OpenCV] from source alongside [https://github.com/opencv/opencv_contrib OpenCV-contrib].<br> | ||
The easiest way is to use the automated script to compile opencv-contrib-python.<br> | The easiest way is to use [https://github.com/opencv/opencv-python#manual-builds the automated script] to compile opencv-contrib-python.<br> | ||
{{hidden | Compile opencv-contrib-python | | {{hidden | Compile opencv-contrib-python | | ||
You can build a wheel to install which includes NONFREE modules as follows: | You can build a wheel to install which includes NONFREE modules as follows: | ||
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This is a summary of the [https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html#ga617b1685d4059c6040827800e72ad2b6 <code>cv2.stereoRectify</code>] function which produces a rectification rotation and projection matrix for calibrated cameras (i.e. cameras with known intrinsics and extrinsics). | This is a summary of the [https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html#ga617b1685d4059c6040827800e72ad2b6 <code>cv2.stereoRectify</code>] function which produces a rectification rotation and projection matrix for calibrated cameras (i.e. cameras with known intrinsics and extrinsics). | ||
[https://github.com/opencv/opencv/blob/4.x/modules/calib3d/src/calibration.cpp#LL2561C6-L2561C21 source code] | |||
;Algorithm | ;Algorithm | ||
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# Projection calculation | # Projection calculation | ||
# Bounding box calculation | # Bounding box calculation | ||
# Disparity-to-depth matrix calculation | |||
==Resources== | ==Resources== | ||
*[https://docs.opencv.org/master/d6/d00/tutorial_py_root.html OpenCV Python Tutorial] | *[https://docs.opencv.org/master/d6/d00/tutorial_py_root.html OpenCV Python Tutorial] |
Latest revision as of 19:16, 20 June 2023
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.
- You can use
opencv-python
if you do not need extra contrib modules.
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.
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
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
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
# cv2.IMREAD_ANYDEPTH
# cv2.IMREAD_COLOR
# cv2.IMREAD_GRAYSCALE
# cv2.IMREAD_UNCHANGED # G or BGR or BGRA
# 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
# Resize to resolution
new_img = cv2.resize(img, (500,200), interpolation=v2.INTER_CUBIC)
# Resize by factor
cv2.resize(img, (0, 0), fx=1/4, fy=1/4, interpolation=cv2.INTER_AREA)
- Interpolation options
- INTER_NEAREST
- INTER_LINEAR
- INTER_CUBIC
- INTER_AREA
- INTER_LANCZOS4
For downscaling, use `INTER_AREA` to avoid aliasing. However, `INTER_NEAREST` will give the the optimal speed.
For upscaling, use `INTER_CUBIC` for best results or `INTER_LINEAR` for best performance.
Face Detection
- Download haarcascade_frontalface_default.xml
face_cascade = cv2.CascadeClassifier(
'haarcascade_frontalface_default.xml')
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(image_gray, 1.1, 4)
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()
Algorithms
Stereo Rectify
This is a summary of the cv2.stereoRectify
function which produces a rectification rotation and projection matrix for calibrated cameras (i.e. cameras with known intrinsics and extrinsics).
- Algorithm
- Rotation calculation
- Projection calculation
- Bounding box calculation
- Disparity-to-depth matrix calculation