ASL-ML/tempCodeRunnerFile.py at main · mohith2883/ASL-ML · GitHub

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from flask import Flask, render_template, Response, request, jsonify
import cv2
import pickle
import mediapipe as mp
import numpy as np

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)

labels_dict = {
    0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I',
    9: 'K', 10: 'L', 11: 'M', 12: 'N', 13: 'O', 14: 'P', 15: 'Q', 16: 'R',
    17: 'S', 18: 'T', 19: 'U', 20: 'V', 21: 'W', 22: 'X', 23: 'Y'
}
expected_features = 84

model_dict = pickle.load(open('model.p', 'rb'))
model = model_dict['model']

app = Flask(__name__)

current_prediction = "No Sign"

@app.route('/get_prediction')
def get_prediction():
    return jsonify({'prediction': current_prediction})

def predict_frame(frame):
    global current_prediction
    data_aux = []
    x_ = []
    y_ = []

    H, W, _ = frame.shape
    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    results = hands.process(frame_rgb)

    if results.multi_hand_landmarks:
        for hand_landmarks in results.multi_hand_landmarks:
            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:
            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_))

        current_features = len(data_aux)
        if current_features < expected_features:
            data_aux = np.pad(data_aux, (0, expected_features - current_features), 'constant')
        elif current_features > expected_features:
            data_aux = data_aux[:expected_features]

        x1 = int(min(x_) * W) - 10
        y1 = int(min(y_) * H) - 10
        x2 = int(max(x_) * W) - 10
        y2 = int(max(y_) * H) - 10

        data_aux = np.asarray(data_aux).reshape(1, -1)
        prediction = model.predict(data_aux)
        character = labels_dict[int(prediction[0])]
        current_prediction = character
        return character, x1, y1, x2, y2

    current_prediction = "No Sign"
    return "No Sign", 0, 0, 100, 100

def generate_frames():
    cap = cv2.VideoCapture(0)
    while True:
        success, frame = cap.read()
        if not success:
            break
        else:
            pred, x1, y1, x2, y2 = predict_frame(frame)
            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 0), 4)
            cv2.putText(frame, pred, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 1.3, (0, 0, 255), 3, cv2.LINE_AA)
            _, buffer = cv2.imencode('.jpg', frame)
            frame_bytes = buffer.tobytes()
            yield (b'--frame\r\n' b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n')
    cap.release()

@app.route('/')
def index():
    return render_template('index.html')

@app.route('/video_feed')
def video_feed():
    return Response(generate_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')

if __name__ == '__main__':
    app.run(debug=True)