main.py

import cv2
import numpy as np

# Load the cascade classifier for hand detection
cascade_classifier = cv2.CascadeClassifier('haarcascade_hand.xml')


# Function to count fingers
def count_fingers(contour):
    hull = cv2.convexHull(contour, returnPoints=False)
    if len(hull) > 3:
        defects = cv2.convexityDefects(contour, hull)
        if defects is not None:
            finger_count = 0
            for i in range(defects.shape[0]):
                s, e, f, d = defects[i, 0]
                start = tuple(contour[s][0])
                end = tuple(contour[e][0])
                far = tuple(contour[f][0])
                a = np.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)
                b = np.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2)
                c = np.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2)
                angle = np.arccos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c))
                if angle <= np.pi / 2:
                    finger_count += 1
            return finger_count
    return 0


# Capture video from webcam
cap = cv2.VideoCapture(0)

while True:
    # Read a frame from the video capture
    ret, frame = cap.read()
    if not ret:
        break

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect hands
    hands = cascade_classifier.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Draw bounding boxes around the detected hands and count fingers
    for (x, y, w, h) in hands:
        # Draw rectangle around the hand
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

        # Extract region of interest (ROI) for hand
        roi_gray = gray[y:y + h, x:x + w]
        roi_color = frame[y:y + h, x:x + w]

        # Threshold the ROI
        ret, thresh = cv2.threshold(roi_gray, 127, 255, 0)

        # Find contours in the thresholded image
        contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        # Count fingers using contour analysis
        for contour in contours:
            if cv2.contourArea(contour) > 1000:
                fingers = count_fingers(contour)

                # Draw finger count on the frame
                cv2.putText(frame, f'Fingers: {fingers}', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

    # Display the frame
    cv2.imshow('Hand Detection', frame)

    # Check for key press and exit if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the video capture object and close all windows
cap.release()
cv2.destroyAllWindows()