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Is it possible to train an AI to classify an image using only 1 image?

I started studying machine learning in python a few days ago and I was trying some examples online when I decided to try it for myself using a custom dataset.
However, I noticed that most datasets involve images that are taken from camera photos composing of hundreds, if not thousands of images with the same target image.
If I create a custom icon in Photoshop, do I need to take a picture of my monitor a thousand times to achieve this? Is it possible to train an AI using only a single PNG file?
My goal right now is to let the AI do object detection on another big image and it needs to find the custom icon inside the image, kind of like Finding Waldo. All of which are digital images straight from Photoshop though, so I don`t know if it is possible.
Right now, I am using a python-based Computer Vision library called ImageAI.

You can use a data preparation strategy called Data Augmentation.
There are mainly two types of Augmentation
Linear Transformation
Affine Transformation
Here is a good white paper
http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf

Objects Extraction from an Image using Python

I'm trying to implement a python program to remove the background and extract the object/objects in the foreground from any given static image (as shown in the attached images). This is similar to "iPhoneX Portrait Effect" or "Bokeh Effect". However, instead of blurring the background one needs to completely remove it.
In short, I want to extract objects from any given image and also create a mask. Below are the examples of both respectively:
Object Extraction:
Image Mask:
I have somewhere listened to Google's DeepLab, but I don't know how to start with it.
Can someone help me, please!
Any step by step tutorial will be very appreciated.
Thanks in advance!

This is a really hard task, there is a reason there are basically no good software to do this already, even in photoshop this is a struggle. I can advice you to start with open Cv and their implemented facial tracking which you may need to configure to work with animals if thats your goal
resources
facial detection:
https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_objdetect/py_face_detection/py_face_detection.html#face-detection
object detection:
https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html

Firstly you should collect data(image) for which object you want to detect or extract the object/objects in the foreground and also create a mask.
Then using tensorflow you can train an instance segmentation model Using your own dataset. (Ref : instance_segmentation)
After getting mask you can extract the foreground.

Finetuning a tensorflow object detection pretrained model

I'm working on a real-time object detector with tensorflow and opencv.
I've used different SSD and Faster-RCNN based frozen inference graphs and they almost never fail.
The video stream comes from a IR camera fixed to a wall that has a background that almost never changes. There are some misdetections at particular hours of the day (e.g. when the light changes in the afternoon) that occur in the background area or on small objects too close to the camera.
So to fix this little mistakes i wanted to finetune the model with images from the same background.
Being the background always the same, how do i approach the retraining of the model having 1000 misdetections pics that are all almost the same?

In case of variations in the background lighting, it might be possible to use Background Subtraction
https://docs.opencv.org/3.4.1/d1/dc5/tutorial_background_subtraction.html
,while dynmically updating it as shown here:
https://www.pyimagesearch.com/2015/06/01/home-surveillance-and-motion-detection-with-the-raspberry-pi-python-and-opencv/
Thank you.

Detect object in an image using openCV python on a raspberry pi

I have a small project that I am tinkering with. I have a small box and I have attached my camera on top of it. I want to get a notification if anything is added or removed from it.
My original logic was to constantly take images and compare it to see the difference but that process is not good even the same images on comparison gives out a difference. I do not know why?
Can anyone suggest me any other way to achieve this?

Well I can suggest a way of doing this. So basically what you can do is you can use some kind of Object Detection coupled with a Machine Learning Algo. So the way this might work is you first train your camera to recongnize the closed box. You can take like 10 pics of the closed box(just an example) and train your program to recognize that closed box. So the program will be able to detect when the box is closed. So when the box is not closed(i.e open or missing or something else) then you can code your program appropriately to fire off a signal or whatever it is you are trying to do. So the first obvious step is to write code for object detection. There are numerous ways of doing this alone like Haar Classification, Support Vector Machines. Once you have trained your program to look for the closed box you can then run this program to predict what's happening in every frame of the camera feed. Hope this answered your question! Cheers!

Tracking an object via OpenCV

I am trying to track an object and mask or Hide it (a CODE in this case) using OpenCV and Python. As example I am using this video , the code will showing on 1min19s :
https://www.dropbox.com/s/eu01hxoxmd3ns5f/capture-3.mp4?dl=0
What I need is to keep track on the CODE and mask or Hide it as the video without detecting other parts of the video as the code. Tracking by colour doesn't work here.
Any idea for a good approach on this problem?

Tracking is much more complex than searching the similar colour. OpenCV has implemented some tracking algorithms, what you need to do is to use the existing apis to solve the problem. Here is an introduction to OpenCV tracker.
If you do not stick to OpenCV, dlib is also a c++ library with python interface which also implements object tracking algorithms.