Sign-Language-Detection-Using-Python/create_dataset.py at main · bot041/Sign-Language-Detection-Using-Python · GitHub

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import os # file handling
import pickle # saving the processed dataset
import cv2    # loading images
import mediapipe as mp # extracting hand landmarks
import numpy as np

DATA_DIR = './data'

mp_hands = mp.solutions.hands
mp_drawing = mp.solutions.drawing_utils
mp_drawing_styles = mp.solutions.drawing_styles

hands = mp_hands.Hands(static_image_mode=True, min_detection_confidence=0.3)

data = [] # landmark features (x, y coordinates)
labels = [] # class numbers (0–9)

# Loop through each class folder
for dir_ in os.listdir(DATA_DIR):
    dir_path = os.path.join(DATA_DIR, dir_)
    if not os.path.isdir(dir_path):
        continue

    print(f"Processing class {dir_}...")

# Loop through each image inside that class
    for img_path in os.listdir(dir_path):
        data_aux = []
        x_ = []
        y_ = []

        img = cv2.imread(os.path.join(dir_path, img_path))
        if img is None:
            continue

        H, W, _ = img.shape
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # MediaPipe expects RGB format

# Run MediaPipe Hand Detection (21) landmarks sign
        results = hands.process(img_rgb)
        if results.multi_hand_landmarks:
            for hand_landmarks in results.multi_hand_landmarks:
                for i in range(len(hand_landmarks.landmark)):
                    x = hand_landmarks.landmark[i].x
                    y = hand_landmarks.landmark[i].y
                    x_.append(x)
                    y_.append(y)

                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_))

            data.append(data_aux)
            labels.append(int(dir_))

# save dataset
with open('data.pickle', 'wb') as f:
    pickle.dump({'data': data, 'labels': labels}, f)

print("✅ data.pickle created successfully!")


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

# DATA_DIR = './data'

# mp_hands = mp.solutions.hands
# mp_drawing = mp.solutions.drawing_utils
# mp_drawing_styles = mp.solutions.drawing_styles

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

# data = []
# labels = []

# for dir_ in os.listdir(DATA_DIR):
#     dir_path = os.path.join(DATA_DIR, dir_)
#     if not os.path.isdir(dir_path):
#         continue

#     print(f"Processing class {dir_}...")

#     for img_path in os.listdir(dir_path):
#         data_aux = []
#         x_ = []
#         y_ = []

#         img = cv2.imread(os.path.join(dir_path, img_path))
#         if img is None:
#             continue

#         H, W, _ = img.shape
#         img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

#         results = hands.process(img_rgb)
#         if results.multi_hand_landmarks:
#             for hand_landmarks in results.multi_hand_landmarks:
#                 for i in range(len(hand_landmarks.landmark)):
#                     x = hand_landmarks.landmark[i].x
#                     y = hand_landmarks.landmark[i].y
#                     x_.append(x)
#                     y_.append(y)

#                 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_))

#             data.append(data_aux)
#             labels.append(int(dir_))

# # save dataset
# with open('data.pickle', 'wb') as f:
#     pickle.dump({'data': data, 'labels': labels}, f)

# print("✅ data.pickle created successfully!")