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In the previous post, Pytorch Tutorial for beginners, we discussed PyTorch, it’s strengths and why you should learn it. We also had a brief look at Tensors – the core data structure used in PyTorch. In this article, we will jump into some hands-on examples of using pre-trained networks present in TorchVision module – pre trained models for Image Classification. Torchvision package consists of popu

In our previous posts, we discussed how to perform Body and Hand pose estimation using the OpenPose library. Recently, as part of our consulting business, we got a chance to try the state-of-the-art pose-estimation system ( wrnchAI ) built by wrnch and compare it’s performance with OpenPose. We evaluated the Human Body Pose Estimation systems and report our findings by comparing wrnchAI vis-a-vis

In this tutorial, we will discuss the various Face Detection methods in OpenCV, Dlib, and Deep Learning and compare the methods quantitatively. We will share code in C++ and Python for the following Face Detectors: Haar Cascade Face Detector in OpenCV Deep Learning based Face Detector in OpenCV. HoG Face Detector in Dlib Deep Learning based Dlib Face Detection We will not go into the theory of any

The output of an object detector is an array of bounding boxes around objects detected in the image or video frame, but we do not get any clue about the shape of the object inside the bounding box. Wouldn’t it be cool if we could find a binary mask containing the object instead of just the bounding box? In this post, we will learn how to do just that. We will show how to use a Convolutional Neural

In this post, we will understand what is Yolov3 and learn how to use YOLOv3 — a state-of-the-art object detector — with OpenCV. YOLOv3 is the latest variant of a popular object detection algorithm YOLO – You Only Look Once. The published model recognizes 80 different objects in images and videos, but most importantly, it is super fast and nearly as accurate as Single Shot MultiBox (SSD). Starting

In this tutorial, Deep Learning based Human Pose Estimation using OpenCV. We will explain in detail how to use a pre-trained Caffe model that won the COCO keypoints challenge in 2016 in your own application. We will briefly go over the architecture to get an idea of what is going on under the hood. 1. Pose Estimation (a.k.a Keypoint Detection) Pose Estimation is a general problem in Computer Visio

In this post, we will learn how to perform feature-based image alignment using OpenCV. We will share code in both C++ and Python. We will demonstrate the steps by way of an example in which we will align a photo of a form taken using a mobile phone to a template of the form. The technique we will use is often called “feature based” image alignment because in this technique a sparse set of features

In this tutorial, we will learn about popular colorspaces used in Computer Vision and use it for color based segmentation. We will also share demo code in C++ and Python. In 1975, the Hungarian Patent HU170062 introduced a puzzle with just one right solution out of 43,252,003,274,489,856,000 (43 quintillion) possibilities. This invention now known as the Rubik’s Cube took the world by storm sellin

In OpenCV the class VideoCapture handles reading videos and grabbing frames from connected cameras. There is a lot of information you can find about the video file you are playing by using the get(PROPERTY_NAME) method in VideoCapture. One of the common properties you may want to know is to find frame rate or frames per second. You can download all code and example images used in this post here. H

In this tutorial we will learn how to estimate the pose of a human head in a photo using OpenCV and Dlib. In many applications, we need to know how the head is tilted with respect to a camera. In a virtual reality application, for example, one can use the pose of the head to render the right view of the scene. In a driver assistance system, a camera looking at a driver’s face in a vehicle can use

Figure 1 : Seamless Cloning Example : An airplane cloned into the picture of an evening sky. One of the exciting new features introduced in OpenCV 3 is called Seamless Cloning. With this new feature you can copy an object from one image, and paste it into another image making a composition that looks seamless and natural.  The above image was created using a scene of a sky and that of an airplane.

In this tutorial we will learn how to morph one face into another using OpenCV. I have chosen to use the photos of the top three American Presidential candidates, but this is not a political post and I have no political agenda. And yes, that is the prettiest picture of Donald Trump I could find! In my previous posts I have covered Facial Landmark Detection and Delaunay Triangulation. It is a good

In this tutorial I will explore a few ways to speed up Dlib’s Facial Landmark Detector. Dlib’s Facial Landmark Detector Dlib has a very good implementation of a very fast facial landmark detector. I had reviewed it in my post titled Facial Landmark Detection. Subsequently, I wrote a series of posts that utilize Dlib’s facial landmark detector. Facial Landmark DetectionDelaunay TriangulationFace Mo

In this tutorial we will learn how to create an average face using OpenCV ( C++ / Python ). Most people would agree that the woman in Figure 1 is pretty. Can you guess her ethnicity ? Why is her skin flawless ? Well, she is not real. She is not completely virtual either. Her face is the average face of all female employees who worked at my company, Sight Commerce Inc., around 2011. It is difficult

Home > Uncategorized > applyColorMap for pseudocoloring in OpenCV ( C++ / Python ) In this tutorial we will first show a simple way to pseudocolor / false color a grayscale image using OpenCV’s predefined colormaps. We will also show a way to define a custom colormap if you would rather use your own. As always I am sharing C++ and Python code that you can download here. This post is dedicated to N

A master wordsmith can tell a heart breaking story in just a few words. For sale: baby shoes, never worn. A great artist can do so much with so little! The same holds true for great programmers and engineers. They always seem to eek out that extra ounce of performance from their machines. This is what often differentiates a great product from a mediocre one and an exceptional programmer from a run

# Standard imports import cv2 import numpy as np; # Read image im = cv2.imread("blob.jpg", cv2.IMREAD_GRAYSCALE) # Set up the detector with default parameters. detector = cv2.SimpleBlobDetector() # Detect blobs. keypoints = detector.detect(im) # Draw detected blobs as red circles. # cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob im_with_ke

Are you curious to explore the world of Artificial Intelligence and Computer Vision but don’t exactly know where to begin? Your search ends right here. You will learn and get exposed to a wide range of exciting topics like Image & Video Manipulation, Image Enhancement, Filtering, Edge Detection, Object Detection and Tracking, Face Detection and the OpenCV Deep Learning Module.