I will list some pretty self explanatory functions that are used or can be used through out this project, and I will be giving explanations to the functions that have a direct link to object detection. import time import random # Computer Vision import cv2 # Graphing import matplotlib.pyplot as plt import seaborn as sns # Math import numpy as np # File handling import pickle from os import listdir from os.path import isfile, join import os Functions # Neural Network from import imagenet_utils from import load_img, img_to_array from _v2 import preprocess_input from import to_categorical from sklearn.preprocessing import LabelBinarizer from import load_model import keras import tensorflow as tf # For measuring the inference time. OpenCV will be the library that will be used for object detection. These libraries will help load my preexisting Convolutional Neural Network and process the images that will be used to pass through the object detection model. Majority of the imports that I used are from tensorflow and keras. The main goal of this blog and project is to show a very basic form of object detection using a real world dataset/problem.

Maybe I will do another blog on the Neural Network that I created for this project.

I will provide my process step by step, but I will not cover the already built neural network that I use, which was created from scratch. There will be brief explanations on some of the methods that will be used, for I will not get into too much detail into certain methods because you can have many blogs on just one topic/method when it comes to object detection. This will result in an output of bounding boxes, class name, and probability score. This can happen with many different object detection methods. Object Detection is the process of using an image and or video feed as your input through your model, and that model detects any objects. This will result in and output of your class name and the probability score. Image Classification is the process of using an image as your input through your model, and that model detects similarities in the given image, to have an output of your desired class. Object Detection vs Image Classificationīefore we began, I will slightly assume that you already know the difference between object detection and image classification is, but this will serve as a quick recap. The purpose for a tool like this is to be able to detect objects in real time using a camera system. I have now decided to extend this to object detection. A few months ago I decided to create an Image Classification model using keras to detect threats such as firearms.

Limb detection opencv