data_processing.py

import os
import pickle
import mediapipe as mp
import cv2

# Initialize MediaPipe Hands module
mp_hands = mp.solutions.hands
hands = mp_hands.Hands(static_image_mode=True, min_detection_confidence=0.3)

DATA_DIR = './data'
data = []
labels = []

# Iterate through each directory and image in the data folder
for dir_ in os.listdir(DATA_DIR):
    for img_path in os.listdir(os.path.join(DATA_DIR, dir_)):
        data_aux = []
        x_ = []
        y_ = []

        # Read the image
        img = cv2.imread(os.path.join(DATA_DIR, dir_, img_path))
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        # Process the image with MediaPipe Hands
        results = hands.process(img_rgb)

        # Check if any hand landmarks are detected
        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                # Iterate over each landmark
                for i in range(len(hand_landmarks.landmark)):
                    x = hand_landmarks.landmark[i].x
                    y = hand_landmarks.landmark[i].y

                    # Append the x and y values to the corresponding lists
                    x_.append(x)
                    y_.append(y)

                # Normalize the x and y coordinates relative to the minimum values
                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_))
                    data_aux.append(y - min(y_))

            # Add the processed data and label to the lists
            data.append(data_aux)
            labels.append(dir_)

# Save the processed data and labels into a pickle file
with open('data.pickle', 'wb') as f:
    pickle.dump({'data': data, 'labels': labels}, f)