> For the complete documentation index, see [llms.txt](https://ykkim.gitbook.io/dlip/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ykkim.gitbook.io/dlip/image-processing/tutorial/tutorial-installation-for-py-opencv.md).

# Tutorial: Installation for Py OpenCV

## Installation for Visual Studio Code <a href="#installation" id="installation"></a>

> (updated 2022.4)

This installation guide is for programming Python OpenCV. Make sure you install the correct software version as instructed.

> For DLIP Lectures:

* Python >3.9
* Anaconda for Python >3.9
* OpenCV 4.x

### 1. Install Anaconda

**Anaconda** : Python and libraries package installer.

Follow: [How to install Anaconda](https://ykkim.gitbook.io/dlip/installation-guide/anaconda#conda-installation)

###

### 2. Install Python via Anaconda

> Python 3.9 (2022-1)

Python is already installed by installing Anaconda. But, we will make a virtual environment for a specific Python version.

* Open Anaconda Prompt(admin mode)

  <figure><img src="https://user-images.githubusercontent.com/23421059/169198062-246162fb-1e21-4d63-9377-a50bf75ef060.png" alt=""><figcaption></figcaption></figure>
* First, update conda and pip

```
conda update -n base -c defaults conda
pip install --upgrade pip
```

![](https://user-images.githubusercontent.com/23421059/169187097-2e482777-fb8b-45c0-b7f6-408073d8b15b.png)

* Then, Create virtual environment for Python 3.9.
* Name the $ENV as `py39`. If you are in base, enter `conda activate py39`

```
conda create -n py39 python=3.9.12
```

![image](https://user-images.githubusercontent.com/23421059/169187275-6699f8ee-a4fc-449e-97d5-c087439d0098.png)

* After installation, activate the newly created environment

```
conda activate py39
```

![image](https://user-images.githubusercontent.com/23421059/169187341-0aaa7552-fac3-43fe-9702-66321c67fc06.png)

### 3. Install Libraries

#### Install Numpy, OpenCV, Matplot, Jupyter

```python
conda activate py39
conda install -c anaconda seaborn jupyter
conda install -c anaconda numpy
conda install -c conda-forge opencv
```

### 4. Install Visual Studio Code

Follow: [How to Install VS Code](/dlip/installation-guide/ide/vscode.md#installation)

### 5. Setup Configuration in Visual Studio Code

Follow: [Python in VS Code](https://ykkim.gitbook.io/dlip/installation-guide/ide/vscode/python-vscode)

Follow: [Jupyter Notebook in VS Code](/dlip/installation-guide/ide/vscode/notebook-with-vscode.md)

***

## EXERCISE

First, download the test image file: [Click here](https://github.com/ykkimhgu/DLIP-src/blob/main/tutorial-install/testImage.JPG)

> The image file must be in the same folder as the source file

Create a new source file as `TU_OpenCVtest.py`

### Exercise 1

Run python code and submit the final output image

```python
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt

# a simple numpy test
a = np.array([1,2,3])
print(a*a)

# Load image
img = cv.imread('testImage.jpg')

# Display Image
cv.namedWindow('source', cv.WINDOW_AUTOSIZE) 
cv.imshow('source',img)
cv.waitKey(0)
```

### Exercise 2

Run python code and submit the final output image

```python
import cv2 as cv

# Open the video camera no.0
cap = cv.VideoCapture(0)

# If not success, exit the program
if not cap.isOpened():
    print('Cannot open camera')

cv.namedWindow('MyVideo', cv.WINDOW_AUTOSIZE)

while True:
    # Read a new frame from video
    ret, frame = cap.read()

    # If not success, break loop
    if not ret:
        print('Cannot read frame')
        break

    cv.imshow('MyVideo', frame)

    if cv.waitKey(30) & 0xFF == 27:
        print('Press ESC to stop')
        break

cv.destroyAllWindows()
cap.release()

```