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Road Lane Detection Computer Vision Python Flask Web app

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Road Lane Detection Computer Vision Python Flask Web app. Lane Detection on Road Images which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast, curves etc. Used Image warping and sliding window approach to find and plot the lane lines.

The Steps Involved are:

  1. Computing the camera calibration matrix and distortion coefficients given a set of chessboard images. (9x6)
  2. Apply a distortion correction to raw images.
  3. Use color transforms, gradients, etc., to create a thresholded binary image.
  4. Apply a perspective transform to rectify binary image ("birds-eye view") to get a warped image.
  5. Detect lane pixels and fit to find the lane boundary.
  6. Warp the detected lane boundaries back onto the original image.

Python Packages Used:

  1. Flask==1.1.0
  2. gunicorn==19.6.0
  3. pandas==0.22.0
  4. numpy==1.11.2
  5. scipy==0.18.1
  6. scikit-learn>=0.18
  7. opencv-python==


Camera Calibration:

The first step in the pipeline is to undistort the camera. Some images of a 9x6 chessboard are given and are distorted. Our task is to find the Chessboard corners an plot them. For this, after loading the images we calibrate the camera. Open CV functions like findChessboardCorners(), drawChessboardCorners() and calibrateCamera() help us do this.

Undistortion of Input Image:

The images uploaded are initially undistorted using cv2.undistort() which takes in an image and returns the undistorted one.

Color transforms, gradients or other methods to create a thresholded binary image:

Detecting edges around trees or cars is okay because these lines can be mostly filtered out by applying a mask to the image and essentially cropping out the area outside of the lane lines. It's most important that we reliably detect different colors of lane lines under varying degrees of daylight and shadow. So, that our self driving car does not become blind in extreme daylight hours or under the shadow of a tree.

I performed gradient threshold and color threshold individually and then created a binary combination of these two images to map out where either the color or gradient thresholds were met called the combined_binary in the code.

Perspective Transform:

Perspective Transform is the Bird's eye view for Lane images. We want to look at the lanes from the top and have a clear picture about their curves. Implementing Perspective Transform was the most interesting one for me. I used values of src and dst as shown below:

src = np.float32([[590,450],[687,450],[1100,720],[200,720]])

dst = np.float32([[300,0],[900,0],[900,720],[300,720]])

Also, made a function warper(img, src, dst) which takes in the Binary Warped Image and return the perspective transform using cv2.getPerspectiveTransform(src, dst) and cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST).

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  5. Please note that any digital products presented on the website do not contain malicious code, viruses or advertising. You buy the original files from the developers. We do not sell any products downloaded from other sites.
  6. You can download the product after the purchase by a direct link on this page.


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Online movie ticket booking Project in python django

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Movie Ticket Booking System is to manage the details of Shows, Booking, Payment,Movie, Customer. It manages all the information about Shows, Seats, Customer, Shows. The project is totally built at administrative end and thus only the administrator is guaranteed the access. The purpose of the project is to build an application program to reduce the manual work for managing the Shows, Booking,
Seats, Payment. It tracks all the details about the Payment,Movie, Customer.

Online movie ticket booking Project in python django Features :-

User Features :

  1. Login
  2. Register.
  3. Search Movies.
  4. Upcomming Movies.
  5. Current Shows.
  6. Book Movies.
  7. Pay.

Admin Features :

  1. Add Movies .
  2. Edit Movies.
  3. Delete Movies
  4. Booking Hiostory.
  5. User details.
  6. All Crud Operations.

Technology Used :

  1. We have developed this project using the below technology
  2. HTML : Page layout has been designed in HTML
  3. CSS : CSS has been used for all the desigining part
  4. JavaScript : All the validation task and animations has been developed by JavaScript
  5. Python : All the business logic has been implemented in Python
  6. SQLite : SQLite database has been used as database for the project
  7. Django : Project has been developed over the Django Framework

Supported Operating System

  • We can configure this project on following operating system.
  • Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
  • Python 3.7, PIP, Django.
  • Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.

Installation Steps :-

  • Install Python 3.7 Or Higher
  • Install Django version 2.2.0
  • pip install Pillow
  • Finally run cmd - python runserver

User Login :-

  1. User Id-
  2. Password- ram12345

Admin Login :-

  1. Admmin
  2. Password- admin@12345
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Artificial Intelligence Project Chess Game Python Flask with Source Code

This is a simple chess engine/interface created using flask.  It uses chessboard.js and chess.js for the logic of the frontend chessboard, and python chess for the
logic of the backend chessboard. All calculation is done on the backend using python. In order to run this application on your own machine, please install flask and python chess.


  1. Play against Artificial Intelligence bot with multi level .
  2. See game moves in a pretty formatted table. (Standard Algebraic Notation).
  3. Reset the game whenever you want.
  4. Undo and redo your moves.

Installation Step : 

  1. You have to install the required packages, you can do it:
  2. Install flask by running:
        pip install flask
    Install python chess by running:
        pip install python-chess[uci,gaviota]
  3. Run command - python

Download Source Code

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Weather Forecast Project In Python Django With Source Code

Python weathercast involves predicting things like cloud cover, rain or snow, wind speed, and temperature before they happen. ... We forecast the weather by looking at current conditions, motion of air and clouds, historical patterns, pressure changes, and computer models.

Features Weather Forecast Project In Python Django:

  1. Time to time update weather
  2. Tamprature Update
  3. Last 7 days data Predict
  4. change weather in every hours as according to weather changes.
  5. provide accurate data information about weather.
  6. user can search weather anytime and anywhere.
  7. any places data can be search and provide information as according to weather.
  8. help user to travel.
  9. help User to future plans for holidays.


Weather forecasts are made by collecting as much data as possible about the current state of the atmosphere (particularly the temperature, humidity and wind) and using understanding of atmospheric processes (through meteorology) to determine how the atmosphere evolves in the future.

However, the chaotic nature of the atmosphere and incomplete understanding of the processes mean that forecasts become less accurate as the range of the forecast increases.

Traditional observations made at the surface of atmospheric pressure, temperature, wind speed, wind direction, humidity, precipitation are collected routinely from trained observers, automatic weather stations or buoys.

During the data assimilation process, information gained from the observations is used in conjunction with a numerical model's most recent forecast for the time that observations were made to produce the meteorological analysis.

Numerical weather prediction models are computer simulations of the atmosphere.

They take the analysis as the starting point and evolve the state of the atmosphere forward in time using understanding of physics and fluid dynamics.

The complicated equations which govern how the state of a fluid changes with time require supercomputers to solve them.

The output from the model provides the basis of the weather forecast.

Technology Overview :

  1. Python version 3.8.1
  2. Django 3.1

## Front-end Part

  1. * HTML
  2. * CSS
  3. * Bootstrap
  4. * JavaScript

## Back-end

  1. * Django
  2. * SQLite 3

Download Link

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Image to Cartoon Python OpenCV Machine Learning

Image to Cartoon Python OpenCV Machine Learning Free Source Code . This Project web app project you can directly select image then you can convert any image to cartoon . its very interesting project . This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project.

Image to Cartoon Python OpenCV Machine Learning Free Source Code

Required modules in this Projects

  1. CV2: Imported to use OpenCV for image processing
  2. easygui: Imported to open a file box. It allows us to select any file from our system.
  3. Numpy: Images are stored and processed as numbers. These are taken as arrays. We use NumPy to deal with arrays.
  4. Imageio: Used to read the file which is chosen by file box using a path.
  5. Matplotlib: This library is used for visualization and plotting. Thus, it is imported to form the plot of images.
  6. OS: For OS interaction. Here, to read the path and save images to that path.
  7. Flask: Flask is a micro web framework written in Python.

Steps to develop Image to cartoon 

  1. Importing the required modules
  2. Building a File Box to choose a particular file
  3. How is an image stored?
  4. Transforming an image to grayscale
  5. Smoothening a grayscale image
  6. Retrieving the edges of an image
  7. Preparing a Mask Image
  8. Giving a Cartoon Effect
  9. Plotting all the transitions together
  10. Functionally of save or download  button

Application tested on:

  1. python 3.7
  2. tensorflow 2.1.0
  3. tf_slim 1.1.0
  4. ffmpeg 3.4.8
  5. Cuda version 10.1
  6. OS: Windows 10

Algorithmia For Video Convert 

We used the Serveless AI Layer product of Algorithmia for inference on videos. To learn more on how to deploy your model in Algorithmia, check here -

Download Source Code


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Online Book Store Project in Python Django

This Online Book Store  Project in Django created based on python, Django, and SQLITE3 Database. The Online Book Store System is a simple project similar like shopping cart or ecommerce  but is only for book shopping. Categories wise books available its very good project for Final Year student academic Purpose. This project built with django framework and it's allows users to search and purchase book online based on category, author and subject.

This Online Book Store  Project in Django  Framework, Also includes a Download Source Code for free, just find the downloadable source code below and click download now.

Admin Features of Online Book Store  Project in Django

  1. Dashboard – For the admin dashboard, you will be able to all the basic access in the whole system. Such as summary of products, orders, and the categories.
  2. Manage Books– The admin has access to the books management information system. He can add, update and delete the books.
  3. Manage Categories – The page where the admin can add, edit and delete categories information.
  4. Manage Orders – As the main functions of the admin, the admin can accept or reject the order from the customers on a case to case basis and the list of customer orders are listed.
  5. Manage User– The admin can manage the user’s account. Admin can add, update and Block user in the system.
  6. Login and Logout – By default one of the security features of this system is the secure login and logout system.

Customer Features of Online Book Store  Project in Django

  1. Login Page – Customer enter their website credentials on this page to gain access in order to log in.
  2. Register Page– The page where new customer created their login credentials for the website.
  3. Home Page– When customer visit the website, this is the system’s default page. This page shows the books for sale in the store, or by entering a keyword in the search box above the books.
  4. Book View Page – The page on which the product’s specific information is shown, as well as the page on which the customer adds the product to his or her cart.
  5. Cart List Page– The page that lists the items that customer have chosen. This is the page where the customer can complete the order checkout process.
  6. My Order Page – The page that lists the customer’s orders.
  7. bcash and Credit Card Payments – This Online Book Store  Project in Django in Django has a payment method that uses Paypal and Credit Card Payments.

Installation Steps : Online Book Store  Project in Django

  1. Download and extract File
  2. goto Project directory and open cmd
  3. install Requirement package - python -m pip install –-user -r requirements.txt
  4. run project - python runserver 

Download Link

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Insurance Claim Prediction Machine Learning Project with Source Code

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Latest Machine Learning Project with Source Code

insurance claim prediction machine learning. Insurance companies are extremely interested in the prediction of the future. Accurate prediction gives a chance to reduce financial loss for the company. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Furthermore, because of the payment errors, processing the claims again accounts for a significant portion of administrative costs.

Dataset :

This dataset contains 7 features as shown below:

age: age of the policyholder
sex: gender of policyholder (female=0, male=1)
BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 25
steps: average walking steps per day of the policyholder
children: number of children/dependents of the policyholder
smoker: smoking state of policyholder (non-smoke=0;smoker=1)
region: the residential area of the policyholder in the US (northeast=0, northwest=1, southeast=2, southwest=3)
charges: individual medical costs billed by health insurance.

Installation Steps :-

  1. Install Python 3.7.0
  2. Install all dependencies cmd -python -m pip install –-user -r requirements.txt
  3. Finally run cmd - python

Download Link 

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Medical Insurance Cost Prediction Project in Python Flask

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Latest Machine Learning Project with Source Code

Medical Insurance Cost Prediction using Random Forest Regressor.

To predict things have been never so easy. I used to wonder how Insurance amount is charged normally. So, in the mean time I came across this dataset and thought of working on it! Using this I wanted to know how few features determine our insurance amount!


  1. Exploring the dataset
  2. Converting Categorical values to Numerical
  3. Plotting Heatmap to see dependency of Dependent valeu on Independent features
  4. Data Visualization (Plots of feature vs feature)
  5. Plotting Skew and Kurtosis
  6. Data Preparation
  7. Prediction using Linear Regression
  8. Prediction using SVR
  9. Prediction using Ridge Regressor
  10. Prediction using Random Forest Regressor
  11. Performing Hyper tuning for above mentioned models
  12. Plotting Graph for all Models to compare performance
  13. Preparing model for deployment
  14. Deployed model using Flask


Model gave 86% accuracy for Medical Insurance Amount Prediction using Random Forest Regressor


The dataset used can be downloaded here (Kaggle) - Click to Download

Installation Steps :-

  1. Install Python 3.7.0
  2. Install all dependencies cmd -python -m pip install –-user -r requirements.txt
  3. Finally run cmd - python


Download Link 

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Breast Cancer Prediction Machine Learning Project Source Code

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Latest Machine Learning Project with Source Code

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Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. There are some devices that detect the breast cancer but many times they lead to false positives, which results is patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called benign which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. We take a dataset of the previous breast cancer patients and train the model to predict whether the cancer is benign or malignant. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman.


Logistic Regression model is developed based on 10 features that classify whether the breast cancer is benign or malignant. For classifying the patient, users are requested to submit their data on this following form as per the value range.

Languages  Used

  • Python: language
  • NumPy: library for numerical calculations
  • Pandas: library for data manipulation and analysis
  • SkLearn: library which features various classification, regression and clustering algorithms
  • Flask: microframework for building web applications using Python.

Installation Steps :-

  • Install Python 3.7.0
  • Install all dependencies cmd -python -m pip install –-user -r requirements.txt
  • Finally run cmd - python