inference_classifier.py

import pickle
import cv2
import mediapipe as mp
import numpy as np

# Load the trained model from the pickle file
model_dict = pickle.load(open('./model.p', 'rb'))
model = model_dict['model']

# Initialize webcam capture
cap = cv2.VideoCapture(0)

# Set up mediapipe for hand tracking
mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles

# Initialize hand detector
hands = mp_hands.Hands(static_image_mode=True, min_detection_confidence=0.3)

# Labels for prediction (26 alphabets A-Z)
labels_dict = {i: chr(65 + i) for i in range(26)}  # {'0': 'A', '1': 'B', ..., '25': 'Z'}

while True:
    data_aux = []  # List to store features
    x_ = []  # Temporary list for x coordinates
    y_ = []  # Temporary list for y coordinates

    # Read the frame from the webcam
    ret, frame = cap.read()
    if not ret:
        print("Failed to grab frame.")
        break

    # Get frame dimensions
    H, W, _ = frame.shape

    # Convert frame to RGB for hand detection
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

    # Process the frame with mediapipe to detect hands
    results = hands.process(frame_rgb)

    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            # Draw hand landmarks on the frame
            mp_drawing.draw_landmarks(
                frame,
                hand_landmarks,
                mp_hands.HAND_CONNECTIONS,
                mp_drawing_styles.get_default_hand_landmarks_style(),
                mp_drawing_styles.get_default_hand_connections_style())

        for hand_landmarks in results.multi_hand_landmarks:
            # Extract x and y coordinates from hand landmarks
            for i in range(len(hand_landmarks.landmark)):
                x = hand_landmarks.landmark[i].x
                y = hand_landmarks.landmark[i].y

                # Collect x and y values in separate lists for normalization
                x_.append(x)
                y_.append(y)

            # Normalize and append features to data_aux
            for i in range(len(hand_landmarks.landmark)):
                x = hand_landmarks.landmark[i].x
                y = hand_landmarks.landmark[i].y
                data_aux.append(x - min(x_))  # Normalize x
                data_aux.append(y - min(y_))  # Normalize y

        # Define bounding box for hand detection (optional)
        x1 = int(min(x_) * W) - 10
        y1 = int(min(y_) * H) - 10
        x2 = int(max(x_) * W) + 10
        y2 = int(max(y_) * H) + 10

        # Predict the character from the model
        prediction = model.predict([np.asarray(data_aux)])

        # If the model returns the character directly, no need to convert it to an integer
        predicted_character = prediction[0]  # Directly use the predicted character

        # Draw bounding box and predicted text on the frame
        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 0), 4)
        cv2.putText(frame, predicted_character, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 0, 0), 3, cv2.LINE_AA)

    else:
        # If no hands are detected, show "Undetected" text
        cv2.putText(frame, "Undetected", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 0, 255), 3, cv2.LINE_AA)

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

    # Exit the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release resources and close windows
cap.release()
cv2.destroyAllWindows()