Introduction to Computer Vision using OpenCV

@DylanSeychell
Introduction to
Computer Vision
using OpenCV
Dylan Seychell

I am Dylan Seychell
Academic and Software Engineer
AI, UX and Computer Vision
@DylanSeychell
Hello!
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@DylanSeychell
Presentation Overview
What is Computer Vision?
What is OpenCV?
Workshop:
Image Acquisition
Image Processing
Image Analysis/Understanding
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Computer Vision
Making computers get a high-level
understanding from images and videos.
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@DylanSeychell
Stages of Computer Vision
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Acquisition UnderstandingProcessing
Covered in this session

@DylanSeychell
OpenCV - enabling computer vision
Open Source Computer Vision library
Cross-platform
Free for use under open source BSD license
Can be easily used with Java, Python, C and C++
Supports Machine Learning libraries such as
TensorFlow and Caffe.
https://opencv.org
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This Session:
We’ll be using OpenCV with Python
New to Python? Check these slides
https://www.slideshare.net/dylsey/introduction-to-python-80851217
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CodeLab Part 1: Acquisition of Image Data
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Test the library:
In terminal/CMD type python
>>> import cv2
>>>
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Importing an image
Create a Python module and write the following code:
import cv2
img = cv2.imread('duomo.jpg',1)
cv2.imshow("Output Window", img)
cv2.waitKey()
This code imports an image and outputs it to a window and waits for any user
keyboard input to terminate.
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cv2.imread() function
This function is used to load an image and store it into a variable
This function accepts 2 parameters:
1. The filename of the image
2. Colour Approach:
a. 1: Colour, neglecting transparency
b. 0: Greyscale
c. -1: Colour together with the alpha channel
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Different output for different imread() arguments
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img = cv2.imread('duomo.jpg',1) img = cv2.imread('duomo.jpg',0)

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cv2.imshow() function
This function is used to display an image in a window.
This function accepts 2 parameters:
1. The name of the output window
2. The image to be displayed in the output window
NB 1: The window automatically fits the image size.
NB 2: Matplotlib can be used as an alternative
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cv2.waitKey() function
This is a keyboard binding function
cv2.waitKey()
A single argument value in milliseconds:
1. 0 or no argument: wait indefinitely for keyboard interrupt
2. Any other value: display the window for the duration of that value in ms
This function returns the ASCII value of the key pressed and if stored in a
variable, it can be used to perform subsequent logical operations.
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Using the webcam feed
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
# Our operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Display the resulting frame
cv2.imshow('frame',gray)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
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@DylanSeychell
cv2.VideoCapture() Object
The video capture object allows us to manipulate captured frames from a
camera.
cap = cv2.VideoCapture(0)
The argument is either the video filename or camera index, 0 for webcam.
Allows the handling of each frame.
After being used, the capture has to be released:
cap.release()
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Importing a video
cap = cv2.VideoCapture('vtest.avi')
while(cap.isOpened()): #returns true when there is another frame to process
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cv2.imshow('frame',gray)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
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CodeLab Part 2: Image Processing
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Create a new module and initialise it
import cv2
##Our Image Processing code goes here
cv2.waitKey()
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Image Type
Try printing these values:
print (type(img))
This will return <type 'numpy.ndarray'>
Therefore, we’d deal with a numpy array
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Image Shape
Try printing these values:
print (type(img))
print (img)
[[22 22 22 ..., 23 23 24]
[22 22 22 ..., 23 23 23]
[22 22 22 ..., 23 23 23]
...,
[13 13 13 ..., 5 6 6]
[13 13 13 ..., 11 11 10]
[13 13 13 ..., 12 12 10]]
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Greyscale

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Try the same thing with a coloured image
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Slicing Image Channels (Colours)
Load a coloured image and set unwanted channels to zero
img = cv2.imread("duomo.jpg", 1)
img[:,:,2] = 0 #red
img[:,:,1] = 0 #green
img[:,:,0] #blue
cv2.imshow("Output", img) #returns the blue channel
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Slicing by colour channel.
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img[:,:,2] = 0 #red
img[:,:,1] = 0 #green
img[:,:,0] #blue
img[:,:,2] #red
img[:,:,1] = 0 #green
img[:,:,0] = 0 #blue
img[:,:,2] = 0 #red
img[:,:,1] #green
img[:,:,0] = 0 #blue

@DylanSeychell
Blurring images in OpenCV
The blur function using average values
blur = cv2.blur(img,(5,5))
This method accepts 2 arguments:
1. The source image
2. A tuple with the size of the box filter
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Simple blurring using OpenCV.
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blur = cv2.blur(img,(10,10)) blur = cv2.blur(img,(5,5))

@DylanSeychell
Detecting Edges
Using Canny edge detection:
● Removes the noise using a Gaussian Filter
● Finds intensity gradient of the image
● Non-maximum suppression (remove unwanted pixels)
● Hysteresis Thresholding (difference between min and max values)
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Canny Edge Detection in OpenCV
edges = cv2.Canny(img,100,200)
This method accepts 3 arguments:
1. The source image
2. Min value
3. Max value
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Different minVal and maxVal values
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edges = cv2.Canny(img,50,60) edges = cv2.Canny(img,150,300)

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Choosing a region of interest
An inbuilt function to select a region of interest:
fromCenter = False
r = cv2.selectROI(img, fromCenter)
Arguments:
1. The source image
2. Flag to choose the origin of the bounding box
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Using the resultant RoI
Save the resultant RoI into another image
imCropT = img[int(r[1]):int(r[1]+r[3]), int(r[0]):int(r[0]+r[2])]
Cropping the image using Numpy array slicing in the form:
crop= img[yoffset:-yoffset, xoffset:-xoffset]
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Selecting a RoI and displaying it
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imCropT = img[int(r[1]):int(r[1]+r[3]),
int(r[0]):int(r[0]+r[2])]
cv2.imshow("Cropped", imCropT)

@DylanSeychell
Part 3: Analysis
This is a specialised field also known as Artificial
Vision. More resources related to this field will follow.
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Merging Computer Vision and AI.
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Image to TextObject Detection & Classification

Introduction to Computer Vision using OpenCV

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