Creating Custom Object Classes In Python In 20 Minutes! For Beginners and Intermediate

Welcome to Lesson 11 where you will be Creating Object Classes using Python in 20 Minutes! Coding Tutorial for Beginners course!
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[@rishabhinc2936]

Hey bro ,i need help.i have this project.... fight detection . THE END RESULT SHOULD BE LIKE human should be boxed and the box should turn red or a alarm sound be player when they fight.

[@SalteeKiller]

Creating a fight detection system using computer vision involves several steps. Here’s a basic outline of how you might approach it using OpenCV:
1. Collecting and Preparing Data:
• Gather a dataset that includes both images/videos of fights and non-fights.
2. Object Detection:
• Use a pre-trained object detection model (like YOLO, SSD, or others available in OpenCV) to detect humans in the video frames.
3. Motion and Action Recognition:
• Analyze the behavior in the frames to detect aggressive actions that indicate a fight (rapid movements, aggressive gestures, etc.).
4. Alarm Triggering or Visual Indicators:
• When a fight is detected, trigger an alarm sound or change the color of the bounding box around the humans to red.
Here’s an example of how you might begin implementing this using OpenCV:
import cv2 as cv
# Load pre-trained models (change the paths to your model files)
human_cascade = cv.CascadeClassifier('path_to_human_cascade.xml') # Haar cascade for human detection
# Load your preferred pre-trained object detection model here (like YOLO, SSD, etc.)
# Load video
cap = cv.VideoCapture('path_to_video.mp4') # Replace with your video file path
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Convert frame to grayscale (if using Haar cascade)
gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
# Detect humans using Haar cascade
humans = human_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
# Process detected humans
for (x, y, w, h) in humans:
cv.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2) # Draw red bounding box around humans
# Add logic to trigger an alarm or perform action recognition here if needed
# Show frame
cv.imshow('Fight Detection', frame)
# Press 'q' to exit
if cv.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv.destroyAllWindows()
This code sets up a basic video capture using OpenCV and applies a Haar cascade for human detection. When humans are detected, it draws a red bounding box around them. You would need to integrate more advanced algorithms for action recognition and alarm triggering based on the behavioral analysis of the detected humans. Additionally, replace the placeholder paths with your actual model files and video file path.
Remember, creating an accurate fight detection system involves a deeper understanding of computer vision, machine learning, and potentially real-time action recognition techniques. This code provides a starting point but may need more refinement based on the specific requirements of your project.

[@rishabhinc2936]

@@SalteeKiller thanx for your time sir .really appreciate it.i will comment back after a month