OpenCV

From David's Wiki
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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.
  • 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.

Compile opencv-contrib-python
Compilation Instructions

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

Reference

# 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

face detection in 2 minutes

  1. 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).

source code

Algorithm
  1. Rotation calculation
  2. Projection calculation
  3. Bounding box calculation
  4. Disparity-to-depth matrix calculation

Resources