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1. INSTALL VIDEO READER OPENCV FOR MAC INSTALL
2. INSTALL VIDEO READER OPENCV FOR MAC CODE
3. INSTALL VIDEO READER OPENCV FOR MAC MAC
4. INSTALL VIDEO READER OPENCV FOR MAC WINDOWS

That happens because OpenCV and matplotlib have different orders of primary colors. We expected a bright colored image but what we obtain is an image with some bluish tinge. What we get as an output is a bit different concerning color. png image gets transformed into a numpy array with a shape of 1300x1950 and has 3 channels. We will be using the colored 'mandrill' image for demonstration purpose. Read in the image using the imread function.Importing Images in OpenCV Using Jupyter notebooks: This is necessary to create a foundation before we move towards the advanced stuff. In this section we will perform simple operations on images using OpenCV like opening images, drawing simple shapes on images and interacting with images through callbacks. Images and OpenCVīefore we jump into the process of face detection, let us learn some basics about working with OpenCV. This means we can represent the above image in the form of a three-dimensional array. If we were to check the shape of the image above, we would get: Shape Let us look at the famous colored image of a mandrill which has been cited in many image processing examples.

As a result, 256 different shades of colors can be represented with 0 denoting black and 255 white. Colored imageĬolored images are represented as a combination of Red, Blue, and Green, and all the other colors can be achieved by mixing these primary colors in correct proportions.Ī colored image also consists of 8 bits per pixel. A grayscale image has only 1 channel where the channel represents dimension.

For example, the image below shows a grayscale image represented in the form of an array. This means it can have 256 different shades where 0 pixels will represent black color while 255 denotes white. Grayscale imageĪ grayscale image consists of 8 bits per pixel. Black is represented by the value 0 while 1 represents white. Binary ImageĪ binary image consists of 1 bit/pixel and so can have only two possible colors, i.e., black or white. Let us now have a look at the representation of the different kinds of images: 1. The following table shows the relationship more clearly. Naturally, more the number of bits/pixels, more possible colors in the images. The color of an image can be calculated as follows: Number of colors/ shades = 2^bpp where bpp represents bits per pixel. In order to be processed by a computer, an image needs to be converted into a binary form. You can think of pixels to be tiny blocks of information arranged in the form of a 2 D grid, and the depth of a pixel refers to the color information present in it. More the number of pixels in an image, the better is its resolution. Images as ArraysĪn image is nothing but a standard Numpy array containing pixels of data points. You can either use Jupyter notebooks or any Python IDE of your choice for writing the scripts.

INSTALL VIDEO READER OPENCV FOR MAC INSTALL

INSTALL VIDEO READER OPENCV FOR MAC WINDOWS

Please refer to the detailed documentation here for Windows and here for Mac.Ģ. It can be installed in either of the following ways:

INSTALL VIDEO READER OPENCV FOR MAC MAC

OpenCV-Python supports all the leading platforms like Mac OS, Linux, and Windows. This makes it a great choice to perform computationally intensive programs.

INSTALL VIDEO READER OPENCV FOR MAC CODE

OpenCV-Python is not only fast (since the background consists of code written in C/C++) but is also easy to code and deploy(due to the Python wrapper in foreground). You can think of it as a python wrapper around the C++ implementation of OpenCV. OpenCV-Python is the python API for OpenCV. OpenCV boasts of an active user base all over the world with its use increasing day by day due to the surge in computer vision applications. Although it is written in optimized C/C++, it has interfaces for Python and Java along with C++. OpenCV essentially stands for Open Source Computer Vision Library. The first release came a little later in the year 2000. OpenCV was started at Intel in the year 1999 by Gary Bradsky. Make sure that you have the following packages installed and running before installing OpenCV. Hands-on knowledge of Numpy and Matplotlib is essential before working on the concepts of OpenCV.

With the advent of technology, face detection has gained a lot of importance especially in fields like photography, security, and marketing. This technique is a specific use case of object detection technology that deals with detecting instances of semantic objects of a certain class (such as humans, buildings or cars) in digital images and videos. Face detection is a computer vision technology that helps to locate/visualize human faces in digital images.