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Computer Vision project with code | Harry potter cloak | Explained in 10 min | OpenCV python project

Computer vision, a fascinating field at the intersection of computer science and image processing, opens up a realm of possibilities for creating enchanting projects. In this blog post, we’ll delve into the world of Harry Potter and recreate the magic of his invisibility cloak using Python and OpenCV.

 

Flow of Article:

  1. Basics about Python and OpenCV
  2. Understanding how it works?
  3. Coding in phases

 

Video Explanation

 

Understanding the Basics of Harry Potter’s Cloak Project

Before we embark on the journey of crafting the invisibility cloak, let’s grasp the fundamental concepts involved in computer vision. Computer vision involves processing and analyzing visual data to make informed decisions—an essential field with applications ranging from facial recognition to augmented reality.

 

Python and OpenCV:

For our magical project, we’ll harness the power of Python, a versatile programming language, and OpenCV (Open Source Computer Vision Library), a popular open-source computer vision and machine learning software library. OpenCV simplifies the implementation of various computer vision tasks, making it an ideal choice for our enchanting endeavor.

 

The Code Breakdown:

Now, let’s dissect the Python code that brings Harry Potter’s invisibility cloak to life.

 

Initialization and Trackbars:

# Initializing camera and creating a window for trackbars
cap = cv2.VideoCapture(0)
bars = cv2.namedWindow(“bars”)

# Creating trackbars for tuning HSV values
cv2.createTrackbar(“upper_hue”, “bars”, 110, 180, hello)
cv2.createTrackbar(“upper_saturation”, “bars”, 255, 255, hello)
cv2.createTrackbar(“upper_value”, “bars”, 255, 255, hello)
cv2.createTrackbar(“lower_hue”, “bars”, 68, 180, hello)
cv2.createTrackbar(“lower_saturation”, “bars”, 55, 255, hello)
cv2.createTrackbar(“lower_value”, “bars”, 54, 255, hello)

The code initializes the camera and sets up trackbars for adjusting upper and lower HSV (Hue, Saturation, Value) values, crucial for masking the cloak.

 

Capturing Initial Frame:

while(True):
    cv2.waitKey(1000)
    ret, init_frame = cap.read()
    if(ret):
        break

The final Loop:

In this loop, the code continuously captures frames, processes them based on the HSV values obtained from trackbars, and combines them to create the illusion of an invisibility cloak. The resulting frame is displayed in real-time, allowing users to witness the magic.

while(True):
ret, frame = cap.read()
inspect = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

# Extracting HSV values from trackbars
upper_hue = cv2.getTrackbarPos(“upper_hue”, “bars”)
upper_saturation = cv2.getTrackbarPos(“upper_saturation”, “bars”)
# … (similarly for other values)

# Creating a mask and performing image processing
kernel = numpy.ones((3, 3), numpy.uint8)
upper_hsv = numpy.array([upper_hue, upper_saturation, upper_value])
lower_hsv = numpy.array([lower_hue, lower_saturation, lower_value])

mask = cv2.inRange(inspect, lower_hsv, upper_hsv)
# … (mask processing steps)

# Combining frames to achieve the desired effect
final = cv2.bitwise_or(frame_inv, blanket_area)

# Displaying the magical result
cv2.imshow(“Harry’s Cloak”, final)

# Breaking the loop if ‘q’ is pressed
if(cv2.waitKey(3) == ord(‘q’)):
break;

Code Repository for this project can be found here: Repo

 

In this captivating project merging image processing and computer vision, several key concepts are harnessed to recreate Harry Potter’s iconic invisibility cloak. Leveraging the versatility of Python, a programming language known for its simplicity and readability, and the powerful OpenCV library, the project engages fundamental principles of computer vision. The cornerstone lies in manipulating the HSV (Hue, Saturation, Value) color space through trackbars, allowing real-time adjustments to create a dynamic cloak-masking effect. The concept of image masking, achieved through thresholding and filtering using OpenCV functions, is pivotal in isolating the cloak from the background. Additionally, the utilization of bitwise operations for combining frames contributes to the illusion of invisibility. The project not only showcases the seamless integration of Python and OpenCV but also exemplifies the potential for creativity in computer vision projects, making it an engaging exploration for enthusiasts and learners alike.

 

Conclusion:

In conclusion, this project serves as a captivating introduction to computer vision using Python and OpenCV. By understanding the basics and exploring the code, you can embark on your own magical journey of creating innovative computer vision projects. The possibilities are endless, and with the right knowledge and creativity, you can bring your own enchanting ideas to life. Happy coding!