Car Detection Python

Java Project Tutorial - Make Login and Register Form Step by Step Using NetBeans And MySQL Database - Duration: 3:43:32. Objectives - Understand how Computer Vision algorithm works - Develop real Computer Vision applications using Python - Learn how to develop OpenCV based applications - Obtain, Modify and Process Image for different purposes - Detect and match features to understand images - Make the computer detect objects and locate them in images - Learn how to use PCL to develop 3D data based applications. In addition I want to know how implement the calculation in C++. License Plate Detection (Plate localization) This is the first stage and at the end of this stage, we should be able to identify the license plate’s position on the car. # Importing NumPy,which is the fundamental package for scientific computing with Python # Reading Image. This article provides information and sample code to help you get started using the Custom Vision SDK with Python to build an object detection model. TLD stands for Tracking, learning and detection. "The car detection dataset has 720x1280 images, which we've pre-processed into 608x608 images. LINE DETECTION IN LOOP. This paper will introduce the processing of automatic vehicle detection and recognition. Here is the Python command with the passed. I think they used this one as the source:. 4 stem package. Watch Now This tutorial has a related video course created by the Real Python team. Nefian, Xavier Bouyssounousse, Terry Fong and George Bebis Abstract—Vehicle detection from aerial images is becoming an increasingly important research topic in surveillance, traffic monitoring and military applications. How to DETECT VEHICLES in a captured photo by normal digital camera? How detect the car speeds using opencv and python? a) Dose anyone have haar or HOG cascade classifier xml file for car. There are tons of interesting problems to solve! One. Vehicle Detection from Aerial Imagery Joshua Gleason, Ara V. The random module provides access to functions that support many operations. This project uses Python, OpenCv, Guassian smoothing, and Hough space to detect lane lines from dash cam video for self driving. 8" Color Display Bluetooth FM Transmitter for Car£¬USB C PD&QC3. Thus we could only count the white pixels in the area where the car would be, set a threshold, and return "TAKEN" if the number of white pixels ever exceeded the threshold. record and play, add_abbreviation). Python 3, OpenCV and Scikit-learn to do this project. Learn about the basics of computer vision and object detection in Python. Blob Detection With Python and OpenCV In my previous post, I described how to set up Python and OpenCV on your computer. Hello and welcome to another Python Plays GTA tutorial. Which algorithm do you use for object detection tasks? I have tried out quite a few of them in my quest to build the most precise model in the least amount of time. The main goal of the project is to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car. This is an example of how to detect vehicles in Python. Update, March 7, 2016: Part 3 is now available. This example shows how to use the foreground detector and blob analysis to detect and count cars in a video sequence. Object detection with deep learning and OpenCV. Figure 1 shows an example of the output of DetectNet when trained to detect vehicles in aerial imagery. Each vehicle is registered into a central database and linked to additional information such as the passport data. Self-Driving Cars with Python. Another important package is Pillow — a python imaging library. Working on python and open cv3. e object(car) detection in opencv python ,i am using opencv 3 and python 3. What is Object Detection? Object Detection is the process of finding real-world object instances like car, bike, TV, flowers, and humans in still images or Videos. 1 ( Python only ) because of which incorrect bounding boxes are returned. OpenCV-Python Tutorials Documentation, Release 1 vehicle who won 2005 DARPA Grand Challenge. When customers spend a long time searching and waiting for a parking spot, this negatively impacts customer satisfaction and can reduce the frequency of return visits. Object Detection. In this How-To, we are going to cover some advanced optimization techniques that can help you go even further with your XGBoost models, by using custom Python code. It detects facial features and ignores anything else, such as buildings, trees and bodies. Using analytics for insUrance fraUD Detection Digital transformation 5 2. So if you find any other things pleas comment below. SPI driver was implemented. We've got a 4-motor chassis kit, Raspberry PI and a motozero shield that can operate these 4 motors. SKU: P2018F056 Category: Python code Tags: Código de Python para Vehicle Make Detection por redes neuronales convolucionales, convolutional neural networks, Convolutional nöral ağlar, descriptor SIFT, konvolüsyonel nöral ağlar, konvolutionelle neuronale Netze, Python-Code für die Fahrzeugmarkenerkennung durch konvolutionelle neuronale. Python’s with statement was first introduced five years ago, in Python 2. This project is not part of Udacity SDCND but is based on other free courses and challanges provided by Udacity. It is a BSD-licence product thus free for both business and academic purposes. We need to detect edges for lane detection since the contrast between the lane and the surrounding road surface provides us with useful information on detecting the lane lines. “The best part of programming is the triumph of seeing the machine do something useful. Hi all, I am new to openCv and working to detect an Vehicle in a very crowded scene, i know that background subtraction and motion will not work because the camera is moving, is. Object detection / segmentation can help you identify the object in your image that matters, so you can guide the attention of your model during training. Sajjad Department of Computer Science and Engineering M. Vehicle Detection with Dlib 19. Face detection is a computer vision technology that helps to locate/visualize human faces in digital images. In my example, I wanted to recognize cars and be able to locate them in the video. If you come across any errors please leave a comment below. com site with modifications for speed detection. In this tutorial we will look at vehicle tracking using haar features. [email protected] Almost everything in Python is an object, with its properties and methods. Can anyone suggest good algorithm for detection of all colour cars and variable lighting condition thank you. The versions that are currently contained in the repository are Python 3. The system described in. For instance, important features like lines and curves can be extracted using edge detection. First load the data. We recently needed to develop a better way to detect erroneous listings in order to resolve them before they reach users. The book features the source code to 11 games. This control assumes. OpenCV color detection and filtering is an excellent place to start OpenCV Python development. Another important package is Pillow — a python imaging library. In this OpenCV with Python tutorial, we're going to discuss object detection with Haar Cascades. Vehicle Detection using opencv 2. Stolen cars - a list of stolen cars or unpaid fines is used to alert on a passing 'hot' cars. The random module provides access to functions that support many operations. How do I detect the speed of a car with opencv and. NavigationAssistant C++ implementation of optical flow estimation, lane detection and the detection of moving vehicle in. About This Video Learn complex topics such as artificial intelligence (AI) and machine learning through a … - Selection from Autonomous Cars: Deep Learning and Computer Vision in Python [Video]. This module is compatible with whatever method you choose. Send the foreground mask to cvBlob or OpenCVBlobsLib. Note that one image has only one. 2 in a Python 3. Applications range from simple ON/OFF switches to relay connections, PLC inputs and multi-sensor detection networks. Watch Now This tutorial has a related video course created by the Real Python team. Home/Python/ Vehicle Detection & Count Using OpenCV Python. Every day, public transportation passengers rely on trains, metros or trams to bring them safely and timely at their point of destination. Basic motion detection and tracking with Python and OpenCV. python detectnet on video clip -> car_ride. Hough lines transform: The Houg lines transform is an algorythm used to detect straight lines. "Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. 2007 under the guidance of dr. Step 2: Canny Edge Detection. It also features related projects, such as PyGaze Analyser and a webcam eye-tracker. Detection is also a first step prior to performing more sophisticated tasks such as tracking or categorization of vehicles by their type. (AP) — When Florida issued a call for python hunters, applications came in from near and very, very far — Iran, for instance, where one man sees a chance to fulfill his. It can calculate a rotation matrix and a translation vector between points to points. It works on all major credit cards. hi, i am doing a project to find vehicle speed using image processing. "- image: a python (PIL) representation of your image used for. Hi, I am doing a project using image processing car detection in a particular parking lot. Object detection is a technology that falls under the broader domain of Computer Vision. In this How-To, we are going to cover some advanced optimization techniques that can help you go even further with your XGBoost models, by using custom Python code. Face and Eye Detection With Raspberry Pi Zero and Opencv: In this instructable i'm going to show how you can detect face and eye using raspberry pi and opencv. For Vehicle Detection, you can use the following tutorial Simple Vehicle Tracking/Detection System | World of OpenCV, AI, Computer Vision and Robotics Examples and Tutorials For Speed Calculation, you can use basic math : Speed = Distance / time. The main goal of the project is to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car. In addition, you will find a blog on my favourite topics. The book features the source code to 11 games. 2014 ASEE Southeast Section Conference Raspberry Pi, Python, Digital Cameras, and Speed Detection: Lessons Learned Emily Minch Abstract - The purpose of this project was to develop a speed detection camera for a residential user. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class ( such as human faces, cars, fruits, etc. The programming language Python makes it relatively easy to step, or loop, through the elements of a list and compute some property for each of them. Project #4 in the Udacity self-driving car program involves using computer vision and machine learning techniques to identify road lanes and lane features from a vehicle’s dash-cam. You can also control multiple vehicles - great for families! - and assign more than one user to control a vehicle. Several C++ demos referenced above have simplified implementation in Python*, located in the python_demos directory. Automatically detect lane markings in images. 5 for Windows and Python 3. By Tomasz Kacmajor. Re: Vehicle Speed Camera using openCV, python & picamera Wed May 11, 2016 10:57 pm I haven't used this specific program but in some of my OpenCV experiments, an object was not detected as such until it was completely separate from the four edges of the frame. This project uses Python, OpenCv, Guassian smoothing, and Hough space to detect lane lines from dash cam video for self driving. We evaluate bird's eye view detection performance using the PASCAL criteria also used for 2D object detection. Persistence of Vision (POV) is a curious part of the human visual system. 0 Wireless Radio Adapter with 5 EQ Modes, Power Off, Hands-Free Calls, 3 USB Ports,4 Music Playing, Aux-in: MP3 Players & Accessories. Recognize objects of interest. The following are optional resources for longer-term study of the subject. 28 Jul 2018 Arun Ponnusamy. opencv vehicle speed detection free download. Randy wrote this guide to familiarize SAS users with Python and Python’s various scientific computing tools. ai, the lecture videos corresponding to the. It forwards the whole image only once through the network. 5 environment running on Anaconda 4. Python + OpenCV Neural Network + Haar-Cascade Classifiers Objective Modify a RC car to handle three tasks: self-driving on the track, stop sign and traffic light detection, and front collision avoidance. I look at what it takes to setup object detection and tracking using OpenCV and Python code. We've got a 4-motor chassis kit, Raspberry PI and a motozero shield that can operate these 4 motors. Using analytics for insUrance fraUD Detection Digital transformation 5 2. Eye Predictor for User Vehicle Safety Develop a computer vision model that can detect when an. Package authors use PyPI to distribute their software. Trivial to install and deploy, just copy the files. This year there was an enhancement in obstacle avoidance system using a laser scanner. Home/Python/ Vehicle Detection & Count Using OpenCV Python. If you have images of cars to train on, they probably contain a lot of background noise (other cars, people, snow, clouds, etc. * In this beginner’s BGE Python tutorial you’ll learn how to use Python scripting in Blender to make a car move, increase in speed, and stop. The rationale for such a model is that it can be used by insurance companies for faster processing of claims if users can upload pics and they can assess damage from them. It detects facial features and ignores anything else, such as buildings, trees and bodies. It allows for the recognition, localization, and detection of multiple objects within an image which provides us with a much better understanding of an image as a whole. Raspberry Pi Face Detection with OpenCV Car or Vehicle Detection is famous technology now. Filtering Hough lines. In this case, the region of interest is the bottom of the screen where I count cars as they exit the video (a. Python Games. Python examples (example source code) Organized by topic. This article marks the beginning of my efforts to create an object detection robot. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Python 3, OpenCV and Scikit-learn to do this project. Saliency detection in synthetic aperture radar (SAR) images is a difficult problem. In this article, object detection using the very powerful YOLO model will be described, particularly in the context of car detection for autonomous driving. Due to the realistic representations that occur inside of GTAV, we can use object detectors. ing that often boosts detection performance, such as extracting SIFT descriptors, region proposals, or slid-ing windows. Introduction. Though MAD and SD give different results they are intended to do the same work. Flowchart Python is essentially the Python programming language in visual form. In the previous blog, Introduction to Object detection, we learned the basics of object detection. Can anyone suggest good algorithm for detection of all colour cars and variable lighting condition thank you. Package authors use PyPI to distribute their software. Python; GUI Tk / Alarm 1: Animation 3: Back Fore ground 1: Beeper 1: Border 7: Button 32: Canvas 8: CheckBox. The full installation process for Docker or native Python is noted in the GitHub repository Readme. Automatic Vehicle Detection for Self Driving Cars. 7 (7 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. It detects facial features and ignores anything else, such as buildings, trees and bodies. Project #4 in the Udacity self-driving car program involves using computer vision and machine learning techniques to identify road lanes and lane features from a vehicle’s dash-cam. Everything was tailored to one specific object, but it should be trivial to add more categories and retrain the model for them. For face_recognition type in — pip install pypi For cv2 type in — pip install opencv-python and for numpy type in — pip install numpy. hi, i am doing a project to find vehicle speed using image processing. This repository contains works on a computer vision software pipeline built on top of Python to identify Lanes and vehicles in a video. Because of such IT systems the level of human interaction has been reduced to a greater. DeepDetect is an Open-Source Deep Learning platform made by Jolibrain's scientists for the Enterprise. I started looking for an alternative solution and stumbled upon the tutorial on car detection that simplecv provides, BUT it worked if you hardcoded the color. Due to the realistic representations that occur inside of GTAV, we can use object detectors. LibROSA - A python module for audio and music analysis. In addition I want to know how implement the calculation in C++. Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images. In this tutorial we will look at vehicle tracking using haar features. Blob Detection With Python and OpenCV In my previous post, I described how to set up Python and OpenCV on your computer. Boosted Gabor Features Applied to Vehicle Detection Hong Cheng, Nanning Zheng, Chong Sun Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, China. Face detection is a computer vision technology that helps to locate/visualize human faces in digital images. 28 Jul 2018 Arun Ponnusamy. 5 environment running on Anaconda 4. 24-7 people and vehicle detection for public transportation. YOLO: Real-Time Object Detection. Automate the Boring Stuff with Python frames all of programming as these small triumphs; it makes the boring fun. Start motors B and C (drive forward with a curve away from the line). Just go to google. Objectives - Understand how Computer Vision algorithm works - Develop real Computer Vision applications using Python - Learn how to develop OpenCV based applications - Obtain, Modify and Process Image for different purposes - Detect and match features to understand images - Make the computer detect objects and locate them in images - Learn how to use PCL to develop 3D data based applications. It was the only vehicle to complete the first DIYRobocar Race. Face Detection, Face Recognition Welcome to the ultimate online course on Python for Computer Vision! What you'll learn Use OpenCV to work with image files. First, the paper introduces VEDAI (Vehicle Detection in Aerial Imagery), a new database designed to address the task of small vehicle detection in aerial images within a realistic industrial framework. Hello and welcome to another self-driving cars tutorial, in this tutorial we're going to use the TensorFlow Object Detection API to determine whether or not other vehicles on the road are too close. 5 Would appreciate it if you could provide a sample code for vehicle detection in python. Learn how to package your Python code for PyPI. In addition I want to know how implement the calculation in C++. Later its. Learning Python! *This tutorial is up to date with the latest version of Blender. Thus, a detector for an autonomous car must remain reasonably accurate while operating on strictly the input image. You can detect an object from an image into another image using what is called Haar Cascade. , from Stanford and deeplearning. I have a code for it but when i run the code the output is not displayed. PyPI helps you find and install software developed and shared by the Python community. How to Create a Very Simple Program in Python. This control assumes. First, it takes an image, then by skin color segmentation, it detects human skin color, then it detect human face. There are also some Python implementations of Michele Basseville's Statistical Change Point Detection techniques available in tutorial format on this Github repo. I haven't considered this package yet - I will add it to the list of candidates. System Design The system consists of three subsystems: input unit (camera, ultrasonic sensor), processing unit (computer) and RC car control unit. YOLO Object Detection with OpenCV and Python. “The best part of programming is the triumph of seeing the machine do something useful. Persistence of Vision (POV) is a curious part of the human visual system. Join Barton Poulson for an in-depth discussion in this video, Anomaly detection in Python, part of Data Science Foundations: Data Mining. It’s handy when you have two related operations which you’d like to execute as a pair, with a block of code in between. The vehicle senses its location and orientation using the integrated. PyPI helps you find and install software developed and shared by the Python community. time() to keep track of time. In the article, Pandas in Python: A Guide for Beginners, published this week on Course Report, Joe writes about the popular Python library, which he describes as follows: "Pandas is a catch-all Python library; a resource for doing data analysis and manipulation; any kind of data processing, analyzing, filtering, and aggregating. Perform image manipulation with OpenCV, including smoothing, blurring, thresholding, and morphological operations. 08/08/2019; 6 minutes to read +4; In this article. Working on python and open cv3. 1 responses on "104. Vehicle shadow, camera illumination, illuminated footages and footages where head light reflections are being present are some of the factors. The application uses TensorFlow and other public API libraries to detect multiple objects in an uploaded image. It won’t be perfect, but it will be able to run on a Pi and still deliver good results. For this project, I created a vehicle detection and tracking pipeline with OpenCV, SKLearn, histogram of oriented gradients (HOG), and support vector machines (SVM). "The car detection dataset has 720x1280 images, which we've pre-processed into 608x608 images. were used on Bearcat II to detect and avoid unexpected obstacles, which did not provide us with accurate data. 5 Box Plots and Outlier Detection using Python" nicktumi 24th July 2018 at 9:44 pm Log in to Reply. There are several ways to perform vehicle detection, tracking and counting. ®You can make almost any RC car self driving using the donkey library, but we recommend you build the Donkey2 which is a tested hardware and software setup. In this example we are going to determine if a particular car is parked in a certain parking spot. I'm very comfortable with python, so I would prefer the solution be implemented in it (most of my code is python for other parts of my work). Load and predict with deep neural network module. Python is an object oriented programming language. of computer science and engineering, indian institue of technology, kharagpur. Vehicle Detection Solution at a Shopping Center Parking Ramp. It detects facial features and ignores anything else, such as buildings, trees and bodies. Number Plate Detection in OpenCV - Python. I think they used this one as the source:. Watch Now This tutorial has a related video course created by the Real Python team. Python is a superb language for teaching programming, both at the introductory level and in more advanced courses. Now OpenCV has a very rich python api. 2 in a Python 3. Home/Python/ Vehicle Detection & Count Using OpenCV Python. There are many ways object detection can be used as well in many fields of practice. edu Jim Reesman Stanford University [email protected] Why outliers treatment is important? Because, it can drastically bias/change the fit estimates and predictions. The project repo. Canny Edge Detection is a popular edge detection algorithm. Send the foreground mask to cvBlob or OpenCVBlobsLib. Matlab post. This Project is based on the fourth task of the Udacity Self-Driving Car Nanodegree program. Anomaly detection is the problem of identifying data points that don't conform to expected (normal) behaviour. To detect car across an image, we define windows of. This tutorial is a follow-up to Face Recognition in Python, so make sure you've gone through that first post. Computer Vision is an AI based, that is, Artificial Intelligence based technology that allows computers to understand and label images. Wait for the Color Sensor to detect the color black, then start tasks 1 and 2. This article provides information and sample code to help you get started using the Custom Vision SDK with Python to build an object detection model. 2008 Maemo Useful things for the Maemo platform, the operating system for the even more useful Internet Tablets. You can detect car number plates with Python and OpenCV2. 2014 ASEE Southeast Section Conference Raspberry Pi, Python, Digital Cameras, and Speed Detection: Lessons Learned Emily Minch Abstract - The purpose of this project was to develop a speed detection camera for a residential user. Later its. Let me illustrate this using the cars dataset. Object detection with Python (Tensorflow) Deep Learning Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. We have a haar cascade file trained on cars. #!/usr/bin/python # The contents of this file are in the public domain. Automate the Boring Stuff with Python frames all of programming as these small triumphs; it makes the boring fun. You can detect car number plates with Python and OpenCV2. In this post, I would like to describe the usage of the random module in Python. We provide an in depth analysis of proposal methods regarding recall, repeatability, and impact on DPM and R-CNN detector performance. 6 for Linux, all 64 bit. Face Detection using Python and OpenCV with webcam OpenCV Python program for Vehicle detection in a Video frame Python Program to detect the edges of an image using OpenCV | Sobel edge detection method. In previous tutorials, we outlined the basics behind physical computing and the Raspberry Pi by activating LEDs and scripts using a simple one button circuit. Hello and welcome to another Python Plays GTA tutorial. ing that often boosts detection performance, such as extracting SIFT descriptors, region proposals, or slid-ing windows. Learn how to package your Python code for PyPI. Today we will be showing you how you can make your very own object detection system in just 10 lines of python code. Now OpenCV has a very rich python api. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. A Abstract Recently, Viola and Jones [1] have proposed a detector using Adaboost to select and combine weak classifiers from a very large pool of weak classifiers, and it has been proven to be very successful for detecting faces. Trivial to install and deploy, just copy the files. In this How-To, we are going to cover some advanced optimization techniques that can help you go even further with your XGBoost models, by using custom Python code. Then, I'll establish end time t2. The Basics Of Persistence Of Vision Projects. It allows for the recognition, localization, and detection of multiple objects within an image which provides us with a much better understanding of an image as a whole. Fire Detection with Computer Vision. py put the following code, here we import the libraries: #Import the neccesary libraries import numpy as np import argparse import cv2. TLD stands for Tracking, learning and detection. Being able to detect lane lines is a critical task for any self-driving autonomous vehicle. ravi kumar satzoda mrs. Another important package is Pillow — a python imaging library. Bridgewater State University COMP 502 Project, May 2015 Self driving RC car: OpenCV neural network - Steering Haar-cascade classifiers - Stop sign and traffic light detection Ultrasonic sensor - Front collision avoidance Raspberry Pi - Data streaming (video and sensor) Arduino - RC car control. Print both input and swapped numbers Print both input and swapped numbers 6 With example program illustrate how the Dictionary can be used to count the occurrence of words in a file. Car driving using hand detection in Python. NavigationAssistant C++ implementation of optical flow estimation, lane detection and the detection of moving vehicle in. The vehicle senses its location and orientation using the integrated. Virtualization. This repository contains works on a computer vision software pipeline built on top of Python to identify Lanes and vehicles in a video. Download Open Datasets on 1000s of Projects + Share Projects on One Platform. Start motors B and C (drive forward with a curve toward the line). Please share your code, if you already have similar python implementation Here is a sample youtube video using this. Thanks Andrei Cheremskoy for pointing this out. In contrast with problems like classification, the output of object detection is variable in length, since the number of objects detected may change from image to image. cv2: This is the OpenCV module for Python used for face detection and face recognition. How do I detect the speed of a car with opencv and. In this course you will learn how to code in python, while working with tensorflow and analyze credit card fraud with practical examples. Far objects are thus filtered based on their bounding box height in the image plane. You can also control multiple vehicles - great for families! - and assign more than one user to control a vehicle. timeout: Specifies the connection timeout in seconds. Face detection is a computer vision technology that helps to locate/visualize human faces in digital images. The Python code was tested with the help of available database of video and image, the output was verified. • Object Detection 4 Chapter 1. TLD stands for Tracking, learning and detection. Not only does the program need to detect motion, it needs to time the moving car as it passes across the camera's field of view, and it needs to know how far the car traveled. Below we show the output from our visualization on the HOG features for the false car detection. They are cost effective, with many user-adjustable features built into the sensor’s software – power that you can unlock with our SenixVIEW PC software. This problem appeared as an assignment in the coursera course Convolution Networks which is a part of the Deep Learning Specialization (taught by Prof. Explore Popular Topics Like Government, Sports, Medicine, Fintech, Food, More. However, what if you want to loop through the cars and find a specific one? And what if you had not 3 cars, but 300? The solution is an array! An array can hold many values under a single name, and you can access the values by referring to an index number. This is my first instructable on opencv. There are also some Python implementations of Michele Basseville's Statistical Change Point Detection techniques available in tutorial format on this Github repo. This project is not part of Udacity SDCND but is based on other free courses and challanges provided by Udacity. If you need a different version you can compile it following the steps described at. A car detection model implemented in Tensorflow. To detect car across an image, we define windows of. Let me illustrate this using the cars dataset. Ths post is a chapter from Randy Betancourt’s Python for SAS Users quick start guide. Wait for the Color Sensor to detect the color black, then start tasks 1 and 2. [email protected] This Project is based on the fourth task of the Udacity Self-Driving Car Nanodegree program. WEST PALM BEACH, Fla. In day today life as the population is increasing rapidly, the demand for vehicles is also increasing. Zero dependencies. Lee ECE Department Carnegie Mellon University U. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: