Computer vision
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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.
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![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 dont
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](https://image.slidesharecdn.com/computervision-181105065652/85/Computer-vision-4-320.jpg)
![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](https://image.slidesharecdn.com/computervision-181105065652/85/Computer-vision-6-320.jpg)
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- 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 dont 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