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Features Of OpenCV
The library is open-source which means that the source code is publicly available.
We can customize the code to meet the specific business requirements.
We can even write more code to add extra functionality.
It is available for free to use in commercial products
Since the OpenCV library is originally written in C/C++, it is fast and efficient.
Although Python is slower as compared to C++, Python is easily extended with C/C++ which allows us to write computationally intensive code in C++.
It is easy to create Python wrappers that we can use as Python modules.
So the execution of the program will be as fast as C++ since the original code in the background is in C++.
Easy to Integrate
The OpenCV Python library makes use of numpy, which is a highly optimized library to perform numerical operations on multidimensional arrays.
So all the OpenCV array-like structures are converted into numpy arrays and this makes it easier to integrate with other libraries that use numpy like SciPy and Matplotlib.
OpenCV contains implementations of more than 2500 algorithms. It also has interfaces for multiple languages like Python, java, etc that makes it easy to integrate.
Ease For Coding
Python has a rich set of libraries that provides a powerful environment for scientific calculations.
The libraries like SciPy, numpy, scikit-learn, matplotlib made complex tasks easier to do in Python
Building prototypes application is fast in Python.
We can integrate our application with web frameworks like Django which is a high-level web framework that encourages rapid development
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Features Of OpenCV
There are different types of features of OpenCV which is listed below:
Reading and writing images
Capturing and saving images
Filtering, transforming, and general processing of images
Performing feature detection
OpenCV In Pattern Recognition
Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms.
Pattern recognition is the process of recognizing patterns by using machine learning algorithm. Pattern recognition can be defined as the classification of data based on knowledge already gained or on statistical information extracted from patterns and/or their representation. One of the important aspects of the pattern recognition is its application potential.
Examples: Speech recognition, speaker identification, multimedia document recognition (MDR), automatic medical diagnosis.
Ensemble Learning Assignment Help
A group of predictors is called an ensemble; thus, this technique is called Ensemble Learning, and an Ensemble Learning algorithm is called an Ensemble method. Ensemble methods, including bagging, boosting, and stacking. We will also explore Random Forests.
Bagging and Pasting: One way to get a diverse set of classifiers is to use very different training algorithms, as just discussed. Another approach is to use the same training algorithm for every predictor and train them on different random subsets of the training set. When sampling is performed with replacement, this method is called bagging (short for boot-strap aggregating). When sampling is performed without replacement, it is called pasting.
Bagging and Pasting in Scikit-Learn: Scikit-Learn offers a simple API for both bagging and pasting with the BaggingClassifier class (or BaggingRegressor for regression). The following code trains an ensemble of 500 Decision Tree classifiers: each is trained on 100 training instances randomly sampled from the training set with replacement (this is an example of bagging, but if you want to use pasting instead, just set bootstrap=False). The n_jobs parameter tells Scikit-Learn the number of CPU cores to use for training and predictions (–1 tells Scikit-Learn to use all available cores):