A Quick Overview of Computer Vision

Computer vision is a branch of machine learning that allows computers to interpret and understand visual information. For instance, if you want to differentiate between dogs and cats, you would need a collection of images representing each class. The more diverse and high-quality your images, the better your model will perform.

But how does a machine distinguish between a cat and a dog? By analyzing the pixels and patterns in the images. Through this pixel analysis, computer vision systems can identify objects, recognize movements, and estimate depth.

Machine learning algorithms play a crucial role here, extracting various features and patterns using techniques like edge detection, feature extraction, and pattern recognition.

Now that we have a foundational understanding of computer vision, let's implement this knowledge in a straightforward project: coding a QR code detector.

Step 1: Import Required Libraries

For this project, we will utilize NumPy and OpenCV. If OpenCV is not installed on your system, you can easily add it using pip in your terminal or Jupyter notebook:

pip install opencv-python

Now, let's import the necessary modules:

import numpy as np

import cv2

Step 2: Initialize Webcam and Detector

Next, we need to set up our webcam to capture video input and create a detector object. We will use the QRCodeDetector class from OpenCV for this purpose.

# Initialize webcam

cap = cv2.VideoCapture(0)

# Create QR Code Detector

detector = cv2.QRCodeDetector()

To ensure everything is functioning properly, let’s implement a way to release the webcam and destroy all OpenCV windows when we exit the program.

cap.release()

cv2.destroyAllWindows()

If you encounter any issues, such as the webcam not opening, make sure the application has permission to access your camera.

Step 3: Implement a Continuous Loop

The next step involves creating a loop to continuously capture video frames. As videos consist of a series of images (or frames), we need to start our webcam and keep recording.

while True:

success, img = cap.read() # Capture each frame

Now, we need to extract data from the captured image to locate the QR code. This involves detecting and decoding the QR code within the frame:

value, points, qrcode = detector.detectAndDecode(img)

Step 4: Handle Detected QR Codes

If the detection is successful, we can retrieve the coordinates of the QR code’s corners and draw a rectangle around it. Let’s add the necessary logic:

if value != "":

x1, y1 = points[0][0]

x2, y2 = points[0][2]

# Draw a rectangle around the detected QR code

cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 5)

cv2.putText(img, str(value), (30, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

Finally, display the processed image and allow the user to exit the loop:

cv2.imshow('QR Code Detector', img)

if cv2.waitKey(1) & 0xFF == 27:

break

Step 5: Test Your QR Code Detector

Run the complete code and see your QR code detector in action!

cap.release()

cv2.destroyAllWindows()

Congratulations! You have successfully built a QR code detector using Python and OpenCV. This tutorial has provided you with a foundational understanding of computer vision principles and demonstrated how to apply them in a fun and practical project.

For further exploration, consider creating personalized QR codes or integrating this functionality into a web app using Streamlit.

Now it’s your turn to experiment and innovate! Good luck!

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