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    Let us first see how we can install it in our computer:

                      Open Anaconda prompt and write the following command:

                      pip install opencv-python
                      Make sure you download the get-pip.py file and store it in the same directory as python is installed

                      before you install OpenCV.

                      APPLICATIONS OF OPENCV

                      OpenCV is widely used in various real-world applications that involve image and video processing.
                      It helps computers see, analyse and understand visual data.

                          It is used in face detection systems, where it identifies and recognises human faces in photos
                         or live video.  This technology  is  commonly  applied  in  attendance  systems,  mobile  phone

                         unlocking and surveillance.
                          In self-driving cars, OpenCV helps detect lanes, traffic signs, pedestrians and other vehicles.

                         It processes real-time camera input so that the vehicle can make safe driving decisions.

                          For QR code scanners, OpenCV detects and decodes QR codes from images or camera feeds.
                         This allows quick access to websites, payment systems and digital information.

                          In  medical imaging, OpenCV assists  in  analysing  X-rays, MRI scans  and  other  diagnostic
                         images. It supports doctors in identifying patterns, abnormalities and changes in tissues.

                          OpenCV is also widely used in security cameras, where it helps in motion detection, object
                         tracking and activity monitoring to enhance safety and surveillance systems.

                      HOW COMPUTERS SEE IMAGES
                      Computers  do  not  see  images  as pictures.  They interpret  them  as grids  of numbers. Every

                      image is made up of tiny units called pixels, arranged in rows and columns. Each pixel stores
                      numerical values that represent brightness or colour. The computer processes these numbers
                      mathematically to display, edit or analyse the image.

                      A grayscale image contains only shades of grey. In this type of image, each pixel stores a single
                      value between 0 and 255. A value of 0 represents black, 255 represents white and values in

                      between represent different shades of grey. This structure is similar to a two-dimensional NumPy
                      array, where each element corresponds to one pixel’s brightness.

                      A colour image contains more information. Each pixel stores three values representing Red (R),
                      Green (G) and Blue (B). These three colour channels combine in different intensities to produce a
                      wide range of colours. Because each pixel holds three numbers instead of one, a colour image can
                      be represented as a three-dimensional array. By analysing these numerical patterns, computers

                      can perform tasks such as image enhancement, object detection and recognition.

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