Project 1

Project_12 : Real-time Video Motion Tracking using Optical Flow and OpenCV with Tkinter GUI Aim: The aim of this project is to develop a real-time video motion tracking system using optical flow and OpenCV, with a user-friendly graphical interface to display the results. Here is the code  Importing Libraries …

Object tracking: Object tracking is the process of identifying and following an object as it moves in a video. The object can be anything that appears in the video, such as a person, a car, or a ball. Object tracking involves detecting the object in each frame of the video and then predicting its position in future frames…

Project title: Perform 2D Object Detection with TensorFlow's object detection API Objective: To perform the training on a dataset with one of the object detection models of Tensorflow API and evaluate the performance of the model. In order to perform the training on a custom dataset using object detection models of…

Object detection/Image segmentation using Mask_RCNN Architecture.Object detection: In computer vision, object detection involves both localizing one or more objects within an image and classifying each object in the image.For localizing the object we draw bounding boxes around each detected object in the image(frame).And…

Mini Project: Image Classifiaction using MobileNet architecture Learning Objectives : understand and implement an image classification model using MobileNet architecture. 1.MobileNets Architecture: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, Howard et al, 2017. MobileNets are a class…

#submitted  Project_01:Implement and deploy CNN model in real-time using python on Fashion MNIST dataset. I have uploaded project file in ipynb format. And also attaching google drive link of this project file. https://drive.google.com/drive/folders/1lmwwhWjJywQ6vZvWhvxfn2lXD43nDtkW?usp=sharing

Project_02:Implement Clustering and then predict the class of car from “Car dataset”. Attributes imformation: 1.symboling: -3, -2, -1, 0, 1, 2, 3 2.normalized-losses: continuous from 65 to 256 3.make: alfa-romero, audi, bmw, chevrolet, dodge, honda, isuzu, jaguar, mazda, mercedes-benz, mercury, mitsubishi,…

Project:Lane detection using Hough transform: Task: 1. Perform a lane detection operation for the given video by implementing Hough transform by extracting individual frames from the images. The video can be seen attached below. 2.Write a detailed report on the process involved and the nature of the output you obtain.…

Project_01:Data cleaning on Automobile 1985 dataset and perform descriptive analytics. Part_01:Data cleaning of Automobile 1985 dataset Domain Knowledge: This data set consists of three types of entities:  The specification of an auto in terms of various characteristics,  assigned insurance risk rating, This…

  Q.1 How does similarity is calculated if data is categorical in nature. Measuring similarity or distance between two data points is a core requirement for several data mining and knowledge discovery tasks that involve distance computation. Examples include clustering (kmeans), distance-based outlier detection,…

Q.1 What is a Neural Network? Neural Networks is a computational learning system that uses a network of functions to understand and translate a data input of one form into a desired output, usually in another form. The concept of the artificial neural network was inspired by human biology and the way neurons of…

Q.1 Apply knn to the “Surface defects in stainless steel plates” and identify the differences. Note:Coding file for this question is uploaded in .ipynb format Q.2 What are the pros and cons of knn Pros: It can be used for both classification and regression problems. The mathematics behind the algorithm…

Q.1 Pros and cons of SVM Pros: Even if input data are non-linear and non-separable, SVMs generate accurate classification results because of their robustness. It is useful to solve any complex problem with a suitable kernel function. It works really well with a clear margin of separation It is effective in high dimensional…

Q.1 Linear regression-Perform Gradient Descent in Python with any loss function Let y= mx+b is an eqaution of line,here x= dependent variable,x=independent variable,m= slope and b= intercept. we are going to use this eqaution to train our model and with given dataset and predict the value of x for any value of x. our Aim…

Q.1)Calculate all 4 business moments using pen and paper for the below data set? 1.First buisness moment - mean or expected value It is the measure of central tendency and can be calculated by formula as: `u=sumx.P(x)` `u=[(0times0.35)+(1times0.25)+(2times0.15)+(3times0.15)+(4times0.10)]` `u=1.4` 2.Second buisness moment-Variance…

Q.1 Why there is a difference in the formula of variance for population and sample Variance is calculated as the average sqaured deviation from the mean. variance of population calculates by using following formula       `sigma^2 = sum (X_i - mu)^2 div N`        where,    …

#Lucas Kanade Optical Flow method to perform sparse Optical Flow for pixels of interest. #Pixels or features of interest are derived from Shi-Tomasi corner detector algorithm. #Importing neccessary libraries import cv2 import numpy as np #defining featue detection function using Shi-Tomsi corner detection. def find_features(gray_image):…

import cv2 import numpy as np #Prewitt_filtered_image #Defining prewitt filter def prewitt_filter(image): vertical_filter = np.array([[-1,-1,-1],[0,0,0],[1,1,1]]) horizontal_filter = np.array([[-1,0,1],[-1,0,1],[-1,0,1]]) vertical_filtered = cv2.filter2D(image,-1,vertical_filter) horizontal_filtered =cv2.filter2D(image,-1,horizontal_filter)…

import cv2 # cv2 is the importing for opencv import numpy as np #Image rotation #Defining image rotation function def image_rotation(image,dimensions,angle,scale): (height,width) = image.shape[:2] rotation_matrix = cv2.getRotationMatrix2D((width/2,height/2),angle,scale) rotated_image = cv2.warpAffine(image,rotation_matrix,(width,height))…

Project_02:Perform Gradient descent for Artificial Neural Network in Python. Submitted ipynb format

Project_01 : To perform Logistic regression algorithm taught in first course with simple random sampling and stratified sampling and identify the differences in the result (Data will be given in the class) Completed & Submitted in ipynb format

Project 01 _English Dictionary App _Completed and submitted _Jypiter_notebook

Project 2 Library Book Management System _Completed & Submitted_Jyupiter Notebook