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3-layers_of_databricks

avg-of-image-

Image Average Value

background-subtraction-cnt

BackgroundSubtractorCNT is a drop in replacement API for the background subtraction solutions supplied with OpenCV. It is much faster than any other background subtraction solutions in OpenCV-3.1.0 and above without NVidia CUDA, especially on low spec hardware. The fastest opencv background subtraction. Background subtraction is a basic operation for computer vision. If you have a fast system, then choosing one from the choices that come with OpenCV is fine. On the other hand, trying to use any of them on a low spec system will kill your FPS. Learn here why and how the fastest background subtraction is BackgroundSubtractorCNT.

background-subtraction-gmm

Spatiotemporal GMM for Background Subtraction with Superpixel Hierarchy. Mingliang Chen, Xing Wei, Qingxiong Yang, Qing Li, Gang Wang, and Ming-Hsuan Yang. Abstract—We propose a background subtraction algorithm using hierarchical superpixel segmentation, spanning trees and optical flow. First, we generate superpixel segmentation trees using a number of Gaussian Mixture Models (GMMs) by treating each GMM asonevertex to construct spanning trees.

background-subtraction-mog2

Background Subtraction is one of the major Image Processing tasks. It is used in various Image Processing applications like Image Segmentation, Object Detection, etc. OpenCV provides us 3 types of Background Subtraction algorithms:- BackgroundSubtractorMOG BackgroundSubtractorMOG2 BackgroundSubtractorGMG Normally, we can perform background Subtraction using matrix subtraction, i.e, just subtracting the static frame from the video. But this has a lot of drawbacks. It is a very less efficient algorithm for Background subtraction because it does not update itself. This problem is being handled by the Background Subtraction algorithms provided by OpenCV.

barcode-scanner

Scan barcodes on products, or Data Matrix and QR Codes containing URLs, contact info, etc. Note that this app can no longer be updated on Google Play, and there will be no further releases. Almost every question and negative review comment is addressed by one of the following. Please save everyone time by reading these first: Nobody is stealing your information. A Barcode Scanner With the Right Technology A barcode scanner uses either laser, LED, or 2D technology. Laser devices, the most common type, scan items from a few inches to several feet away depending on the exact model. This option features a laser beam that shoots at an internal mirror.

bilateral-filtering

A bilateral filter is a kind of filter that reduces the noise for the smoothening images. It’s a type of non-linear filter which replaces an image by the nearby average filter of the image. But the weight of pixels is not only depended only Euclidean distance of pixels but also on the radiometric differences. We are going to use this using the OpenCV method in python. A bilateral filter is used for smoothening images and reducing noise, while preserving edges. This article explains an approach using the averaging filter, while this article provides one using a median filter.

blur-detection

blur-video

Blurring a video, GIF, or image can help preserve the anonymity of those featured in it or add a certain mystery to your video. It can also help create a uniform look if you are editing multiple videos together for an advertisement and some of them are blurry.

blurry

a blurry image in the foreground of the photograph Recent Examples on the Web In fairness, the lines can be awfully blurry, but for the uninitiated, Da’Bayou is primarily focused on the urban fare of New Orleans itself, which tends to be more refined and cosmopolitan, drawing from a broader palette of influences.

brisque

score = brisque (A) calculates the no-reference image quality score for image A using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). BRISQUE relies on spatial Natural Scene Statistics (NSS) model of locally normalized luminance coefficients in the spatial domain, as well as the model for pairwise products of these coefficients.brisque compare A to a default model computed from images of natural scenes with similar distortions. A smaller score indicates better perceptual quality. Metric Description; brisque: Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). A BRISQUE model is trained on a database of images with known distortions, and BRISQUE is limited to evaluating the quality of images with the same type of distortion.

brisque-no--reference-

Metric Description; brisque: Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). A BRISQUE model. Image Quality Assessment (IQA) algorithms take an arbitrary image as input and output a quality score as output. There are three types of IQAs: Full-Reference IQA: Here you have a ‘clean’ reference (non-distorted) image to measure the quality of your distorted image. This measure may be used in assessing the quality of an image compression algorithm where we have access to both the original image and its compressed version. Reduced-Reference IQA: Here you don’t have a reference image, but an image having some selective information about it (e.g. watermarked image) to compare and measure the quality of distorted image. Objective Blind or No-Reference IQA: The only input the algorithm gets is the image whose quality you want to measure. This is thus called, No-Reference or Objective-Blind. No-Reference IQA In this post, we will discuss one of the No-Reference IQA Metrics, called Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Before we go deeper into the theory, let’s first understand two basic terms: Distorted Image: As the name suggests, a distorted image is a version of the original image that is distorted by blur, noise, watermarking, color transformations, geometric transformations and so on and so forth. Natural Image: An image directly captured by a camera with no post processing is a natural image in our context. Here is an example of natural image and a distorted image. Image Quality Assessment : BRISQUE Kushashwa Ravi Shrimali JUNE 20, 2018 32 COMMENTS How-To Machine Learning Tutorial Photography is the favorite hobby of millions of people around the world. After all, how difficult can it be! In the words of Diane Arbus, a famous American photographer — “Taking pictures is like tiptoeing into the kitchen late at night and stealing Oreo cookies.” Taking a photo is easy, but taking a high-quality photo is hard. It requires good composition and lighting. The right lens and superior equipment can make a big difference. But above all, a high-quality photo requires good taste and judgment. You need an eye of an expert. But is there a mathematical quality measure that captures this human judgment? The answer is both yes and no! There are some measures of quality that are easy for an algorithm to capture. For example, we can look at the information captured by the pixels and flag an image as noisy or blurry. On the other hand, some measures of quality are almost impossible for an algorithm to capture. For example, an algorithm would have a tough time assessing the quality of a picture that requires cultural context. In this post, we will learn about an algorithm for predicting image quality score. Note: This tutorial has been tested on Ubuntu 18.04, 16.04, with Python 3.6.5, Python 2.7 and OpenCV 3.4.1 and 4.0.0-pre versions. What is Image Quality Assessment (IQA)? Image Quality Assessment (IQA) algorithms take an arbitrary image as input and output a quality score as output. There are three types of IQAs: Full-Reference IQA: Here you have a ‘clean’ reference (non-distorted) image to measure the quality of your distorted image. This measure may be used in assessing the quality of an image compression algorithm where we have access to both the original image and its compressed version. Reduced-Reference IQA: Here you don’t have a reference image, but an image having some selective information about it (e.g. watermarked image) to compare and measure the quality of distorted image. Objective Blind or No-Reference IQA: The only input the algorithm gets is the image whose quality you want to measure. This is thus called, No-Reference or Objective-Blind. No-Reference IQA In this post, we will discuss one of the No-Reference IQA Metrics, called Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Before we go deeper into the theory, let’s first understand two basic terms: Distorted Image: As the name suggests, a distorted image is a version of the original image that is distorted by blur, noise, watermarking, color transformations, geometric transformations and so on and so forth. types of distortions Fig. 1 Distortions used in TID 2008 Database Natural Image: An image directly captured by a camera with no post processing is a natural image in our context. Here is an example of natural image and a distorted image. collage of natural and distorted Fig. 2 Natural Image (left) and Noisy Image (distorted, right) As you can imagine, it is not always clear-cut whether an image is distorted or it’s natural. For example, when a video is smartly rendered with motion blur, the algorithm may get confused about its quality because of the intentional blur. So one has to use this quality measure in the right context. is trained on a database of images with known distortions, and BRISQUE is limited to evaluating the quality of images with the same type of distortion.

camshift-object-detection

www.geeksforgeeks.org/track-objects-with... Camshift or we can say Continuously Adaptive Meanshift is an enhanced version of the meanshift algorithm which provides more accuracy and robustness to the model. With the help of Camshift algorithm, the size of the window keeps updating when the tracking window tries to converge. The tracking is done by using the color information of the object.

canny-edge-detector

The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986. Canny also produced a computational theory of edge detection explaining why the technique works. The Canny edge detector applied to a color photograph of a steam engine. Canny edge detection is a image processing method used to detect edges in an image while suppressing noise. The main steps are as follows: Step 1 - Grayscale Conversion Step 2 - Gaussian Blur

color-detection

Color Detector allows you to find out just that! Simply point the mouse cursor anywhere on the screen, and the application will tell you for the pixel where the mouse cursor is pointing: the RGB color values, the HTML color hex code, and the name of the color in plain English. This is an image color picker that can help us find the color from an image, support HTML hex code, RGB color code and CMYK color code. Free online color tool, no install needed, easy and convenient operation, just take a photo and upload it, then click on the picture, you will get color code, share this with your friends, maybe they will like it too.Color Detector allows you to find out just that! Simply point the mouse cursor anywhere on the screen, and the application will tell you for the pixel where the mouse cursor is pointing: the RGB color values, the HTML color hex code, and the name of the color in plain English.. Color Detector is a tiny freeware utility developed by Cosmin Software. It was programmed for calibrating the hexadecimal value of a certain color. You can pick the color that you need by moving your mouse pointer anywhere on your screen, and then pressing the "F5" key to capture it.

color-detection-in-video

color-edge-detection

A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line. Edge detection is one of the most commonly used operations in image processing and pattern recognition, the reason for this is that edges form the outline of an object. The Process of Canny edge detection algorithm can be broken down to 5 different steps: 1 Apply Gaussian filter to smooth the image in order to remove the noise. 2 Find the intensity gradients of the image. 3 Apply non-maximum suppression to get rid of spurious response to edge detection. 4 Apply double threshold to determine potential edges. The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in 1986.

comcast-telecom-consumer-complaints

project work based on Comcast telecom consumer complaints

covid-app-workmaster

App making

cyclegan

CycleGAN is a model that aims to solve the image-to-image translation problem. The goal of the image-to-image translation problem is to learn the mapping between an input image and an output image using a training set of aligned image pairs. However, obtaining paired examples isn't always feasible.

datascience-project-1

Project 1- Datascience

datascience-project-2

Project 2- Datascience

dbt_muneeswari

dbt_muneeswari_exercise1

dense-optical-flow

deoldify

DeOldify is a state of the art way to colorize black & white images. You can try it right now by visiting the free Google Colab notebook for photos or video. The notebooks are open source, and available to all. To see the evolution of DeOldify, check out the Github project and archive.

detect-corner

Detect the corners in the image

detection-of-moving-object

Dectect and track the moving objects with camera.

edge--sharpen-

Sharpening the Edges

edge-detection

Detecting Edges in the video