Creating a NuGet Package

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Face detection with Python

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Here is how you can implement Face detection with Python and  OpenCV  in less than 25 lines of code: Face detection with Python and OpenCV Install OpenCV. Now download the code from  repo Now Let's break down the code # Get user supplied values imagePath   =   sys . argv [ 1 ] cascPath   =   sys . argv [ 2 ] The above lines takes image and cascade as input. The default cascade will help in detecting image with OpenCV. # Create the haar cascade faceCascade   =   cv2 . CascadeClassifier ( cascPath ) Now we create a cascade, this loads the face cascade into memory for its use. Cascade is just an XML which contains data to detect faces. # Read the image image   =   cv2 . imread ( imagePath ) gray   =   cv2 . cvtColor ( image ,   cv2 . COLOR_BGR2GRAY ) Here we read the image and convert it into Grayscale. A lot of operations in OpenCV are done in GrayScale. # Detect faces in th...
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Face detection with OpenCV

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Face detection with OpenCV OpenCV is the most popular library for computer vision. Originally written in C/C++, it now provides bindings for Python. OpenCV uses machine learning algorithms to search for faces within a picture. For something as complicated as a face, there isn’t one simple test that will tell you if it found a face or not. Instead, there are thousands of small patterns/features that must be matched. The algorithms break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to solve. These tasks are also called  classifiers . For something like a face, you might have 6,000 or more classifiers, all of which must match for a face to be detected (within error limits, of course). But therein lies the problem: For face detection, the algorithm starts at the top left of a picture and moves down across small blocks of data, looking at each block, constantly asking, “ Is this a face? … Is this a face? … Is this a face ?...
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Top 5 mistakes to avoid when writing Apache Spark applications

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Top 5 Mistakes to Avoid When Writing Apache Spark Applications Spark is one of the big data engines that are trending in recent times. One of the main reasons is that is because of its ability to process real-time streaming data. Its advantages over traditional MapReduce are: It is faster than MapReduce Well equipped with Machine Learning abilities. Supports multiple programming languages. However, in spite of having all these advantages over Hadoop, we often get stuck in certain situations which arise due to inefficient codes are written for applications. The situations and their solutions are discussed below: Always try to use reducebykey instead of groupbykey Reduce should be lesser than TreeReduce Always try to lower the side of maps as much as possible Try not to shuffle more Try to keep away from Skews as well as partitions too Do not let the jobs to slow down: When the application is shuffled, it takes more time around 4 long hours to run. This make...
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