Processing Image:

           Processing any kind of function using coding is simple as explaining or instructing a normal human to do task, the difference is that the channel of communication is nothing but programing languages. In Image processing the minute detail are needing to make the image displayed correctly to the screen.

Today, we went over some old stuff such as how to utilize the git lab, setting up virtual environment for python, etc. The library we are using are OpenCV, Matplotlib, numpy. 

      The image processing starts when user uploads an image, which runs under OpenCV function ‘imread’ which reads the image, there are three ways you can read an image using ‘imread’ you can choose between Gray scale, Color or luma. Since, the image is bigger the size from the screen one need to fit the image according to the size of the dimensions of one’s computer screen. Make sure one gets the correct value to resize, or one can always use calculator if needed. Caution: if one gets the wrong values in the first step the whole program will be something like following image size(4:5) and result will also be not even close to accurate:

Once you get the correct dimensions of your computer screen the image size will be full screen and object detect will be 100% accurate. Something like following:

 After that we need to clear the image thought the process called ‘smoothing’, where we sperate the noise from the image, Now there is method of dilation which gives us the color information but we are not trying to sperate color information of the image. Therefore, we do not need ‘dilation’ of the image, means we do not need to specify the RGB patterns of image. Instead we process the image more by matching the sample image/object which is a standard image as a reference of the object we look in the image.  Yet the process we did so far is not detecting any object since we never programed the algorithm to detect a object in the image by which I mean that so far image understands that there some similarities in the both sample and user image. But does not know what the object is called. The above function helps us to understand the difference in image processing and Object recognizing.  This function will be used for demonstration of the whole image recognition project.