Install Python3.8.2+PyTorch+Opencv+Sklearn

Environment

Let’s start to build a development environment.

1.1 Install python

Go to Internet to find Python, which is version 3.8.2. The version of Python is the base stone for every package’s version.

1.2 Install PyCharm

Go to Internet to find PyCharm.

Community edition is free and your computer’s operation system is windows, so download PyCharm-community-2021.3.exe.

During installing PyCharm, you meet a user interface like Figure1-1 and you can follow the configurable options or not. It doesn’t matter.

Figure1-1 Installation Options User Interface

1.3 Install requirement

(1)view Packages in your environment

# open shell and use the command shown below

pip list

(2)Install Packages by requirement.txt

• requirements.txt

certifi==2022.5.18.1 charset-normalizer==2.0.12 cycler==0.11.0 fonttools==4.33.3 future==0.18.2 idna==3.3 imageio==2.19.3 joblib==1.1.0 kiwisolver==1.4.2 matplotlib==3.5.2 networkx==2.8.2

numpy==1.22.4 opencv-contrib-python==4.1.2.30 packaging==21.3 pandas==1.4.2 Pillow==9.1.1 pyparsing==3.0.9 python-dateutil==2.8.2 pytz==2022.1 PyWavelets==1.3.0 requests==2.27.1

scikit-image==0.19.2 scikit-learn==1.1.0 scipy==1.8.1 six==1.16.0 threadpoolctl==3.1.0 tifffile==2022.5.4 torch==1.5.1+cpu torchvision==0.6.1+cpu urllib3==1.26.9

• command line

# Install by requirements.txt

pip install -r requirements.txt

(3)Install packages in manual mode

Firstly, you should know about mirror from:

更改pip镜像源的多种方法_惜洛-Jankin的博客-CSDN博客_pip更换镜像源

• Opencv

# Install numpy additionally

pip install opencv-contrib-python==4.1.2.30

• Pytorch

# CPU only

pip install torch==1.5.1+cpu torchvision==0.6.1+cpu -f https://download.pytorch.org/whl/torch_stable.html

• Sklearn

# Scikit-learn

pip install -U scikit-learn

# pandas matplotlib

pip install pandas matplotlib

# scikit-image

pip install scikit-image

The minimum version of Scikit-learn dependencies are listed below along with its purpose.

Dependency	Minimum Version	Purpose
numpy	1.17.3	build, install
scipy	1.3.2	build, install
joblib	1.0.0	install
threadpoolctl	2.0.0	install
cython	0.29.24	build
matplotlib	3.1.2	benchmark, docs, examples, tests
scikit-image	0.14.5	docs, examples, tests
pandas	1.0.5	benchmark, docs, examples, tests
seaborn	0.9.0	docs, examples
memory_profiler	0.57.0	benchmark, docs
pytest	5.0.1	tests
pytest-cov	2.9.0	tests
flake8	3.8.2	tests
black	22.3.0	tests
mypy	0.770	tests
pyamg	4.0.0	tests
sphinx	4.0.1	docs
sphinx-gallery	0.7.0	docs
numpydoc	1.2.0	docs, tests
Pillow	7.1.2	docs
sphinx-prompt	1.3.0	docs
sphinxext-opengraph	0.4.2	docs

1.4 Verify Installation

• Python

Run the command prompt and enter the command line shown below.

Python --version

• Pytorch

From the command line, type:

Python

then enter the following code:

import torch

x = torch.rand(5, 3)

print(x)

• Opencv

enter the following code:

import cv2

image = cv2.imread(“image.png”);

• Sklearn

enter the following code:

from sklearn.ensemble import HistGradientBoostingRegressor

import numpy as np

import matplotlib.pyplot as plt



# Simple regression function for X * cos(X)

rng = np.random.RandomState(42)

X_1d = np.linspace(0, 10, num=2000)

X = X_1d.reshape(-1, 1)

y = X_1d * np.cos(X_1d) + rng.normal(scale=X_1d / 3)



quantiles = [0.95, 0.5, 0.05]

parameters = dict(loss="quantile", max_bins=32, max_iter=50)

hist_quantiles = {

    f"quantile={quantile:.2f}": HistGradientBoostingRegressor(

        **parameters, quantile=quantile

    ).fit(X, y)

    for quantile in quantiles

}



fig, ax = plt.subplots()

ax.plot(X_1d, y, "o", alpha=0.5, markersize=1)

for quantile, hist in hist_quantiles.items():

    ax.plot(X_1d, hist.predict(X), label=quantile)

_ = ax.legend(loc="lower left")