Crash Course: Computer Vision With OpenCV And Python

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Installation
  1. Install Mini Conda on your Windows/Mac/Linux machine. Download  ‘Miniconda3 64 bit’ for your platform here and install it:https://docs.conda.io/en/latest/miniconda.html
  2.  Start a new Miniconda command prompt (Anaconda prompt) via Windows start menu or Linux bash terminal: anaconda
  3.  Let’s install a new Python environment: conda create -n py36 python=3.6

4.  Now activate the created environment:

conda activate py36

5. Now you can install  OpenCV Python libraries:

conda install -c anaconda opencv pip install opencv-python pip install numpy First OpenCV example

Using some text editor (e.g. Visual Studio Code), create some text file ‘hello.py’ and type in:

import cv2 img = cv2.imread("example.jpg") cv2.imshow("image", img) cv2.waitKey(0)

The example will use some JPG file as input and display it on the screen. Run the example with:

python hello.py Screenshot from 2021-02-05 17-55-10 Filtering images by color

We will transform an RGB image to HSV color space and then filter all green pixels in the HSV color space.

import cv2 import numpy as np # ----read and show image----- img = cv2.imread("example2.jpg") img = cv2.resize(img, (400, 300)) cv2.imshow("image", img) # ----filter by color----- hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) lower_green = np.array([20,40,20]) upper_green = np.array([100,255,200]) mask = cv2.inRange(hsv, lower_green, upper_green) cv2.imshow("mask", mask) seg = cv2.bitwise_and(img,img, mask= mask) cv2.imshow("segmentation", seg) cv2.waitKey(0)

Screenshot from 2021-02-05 18-00-57

Filling holes

We use the dilate and erode operations to fill in holes:

import cv2 import numpy as np #----read and show image--- img = cv2.imread("example2.jpg") img = cv2.resize(img, (400, 300)) cv2.imshow("image", img) #---- filter by color---- hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) lower_green = np.array([20,40,20]) upper_green = np.array([100,255,200]) mask = cv2.inRange(hsv, lower_green, upper_green) cv2.imshow("mask", mask) seg = cv2.bitwise_and(img,img, mask= mask) cv2.imshow("segmentation", seg) #---- fill-in holes------ kernel = np.ones((5,5), np.uint8) seg = cv2.dilate(seg, kernel, iterations=2) seg = cv2.erode(seg, kernel, iterations=2) cv2.imshow("segmentation2", seg) cv2.waitKey(0)

Screenshot from 2021-02-05 21-46-01

Using camera as input

Plug-in a USB camera into your computer and run the following code. It will capture one image from the camera and show it on the screen in a loop.

TODO: Add the color filtering code from above example.

import cv2 cam = cv2.VideoCapture(0) while True: ret, img = cam.read() cv2.imshow("cam",  img) cv2.waitKey(1) Using video as input import cv2 cam = cv2.VideoCapture('somevideo.mp4') while True: ret, img = cam.read() cv2.imshow("cam",  img) cv2.waitKey(1) Other useful packages

pip install pyrealsense2 pip install apriltag …

More examples

Tutorials: https://docs.opencv.org/master/d6/d00/tutorial_py_root.html

Code:  https://github.com/opencv/opencv

Documentation

Python:  https://docs.python.org/3/

OpenCV:  https://docs.opencv.org/master/index.html

Miniconda:  https://docs.conda.io/en/latest/miniconda.html

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