Machine learning using python. Taking a look at how we use various libraries such as cv2, numpy and matplotlib. Beginner friendly code. Lots of comments and straight to the point.

1. Computer Vision
Because Artificial Intelligence is the future

2. What it is
• Applying machine learning in order for a machine to understand a certain image
and its characteristics
• In this example, we want the machine to learn characteristics of a certain bee
(template.jpg) and pick it from a picture of a number of bees.
• Template.jpg is the template we are using to train our model
• We are picking our bee from Bee1.jpg which has a number of bees in it:

3. Libraries used
• import cv2 #our computer library of choice which is open cv
• import numpy as np #library for mathemitical computation
• from matplotlib import pyplot as plt
• %matplotlib inline #to have our images shown within jupyter using the above line

4. Reading the image and the template
• image = cv2.imread('Bee1.jpg') #the image from which we are picking the bee
• img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
• img_copy = img.copy() #making a numpy copy of our original image so we don’t
distort it when we are working on it
• template = cv2.imread('template_bee.jpg', 0) # this is the template showing us the
image of the bee we are looking for
• w, h= template.shape[::-1] #image dimensions and python starts counting from 0 and
so the -1

5. Matching our template with our image
• #Apply template matching using the function matchTemplate
• res = cv2.matchTemplate(img_copy,template,cv2.TM_CCOEFF_NORMED)
• min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
• #define the threshold; allowance for error
• threshold = 0.8
• loc = np.where(res >= threshold)

6. Identifying our bee
• #draw a bounding rectangle
• for pt in zip(*loc[::-1]): #for a certain point in our image
• cv2.rectangle(img_copy,pt,(pt[0]+w,pt[1]+h), (0,255,255),2) #draw our
rectangle
• plt.imshow(img_copy, cmap="gray") #this is plotting our image

7. Result
Our template: