{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e992bb3",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 29\u001b[0m\n\u001b[0;32m     26\u001b[0m cap \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mVideoCapture(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m---> 29\u001b[0m     ret, frame \u001b[38;5;241m=\u001b[39m \u001b[43mcap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Read a frame from the webcam\u001b[39;00m\n\u001b[0;32m     31\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m ret:\n\u001b[0;32m     32\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "from keras.preprocessing.image import img_to_array\n",
    "\n",
    "# Load your pre-trained model\n",
    "model = keras.models.load_model('D:\\Trained model\\sign_language_model.h5')  \n",
    "\n",
    "# Define a mapping from class indices to text labels\n",
    "class_to_text_mapping = {\n",
    "    0: 'A',\n",
    "    1: 'B',\n",
    "    # Add more mapping as needed\n",
    "}\n",
    "\n",
    "# Create a function to preprocess frames from the camera\n",
    "def preprocess_frame(frame):\n",
    "    # Resize and normalize the frame\n",
    "    frame = cv2.resize(frame, (200, 200))\n",
    "    frame = frame.astype(\"float\") / 255.0\n",
    "    frame = img_to_array(frame)\n",
    "    frame = np.expand_dims(frame, axis=0)\n",
    "    return frame\n",
    "\n",
    "# Start capturing video from your webcam (you may need to change the index depending on your setup)\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "    ret, frame = cap.read()  # Read a frame from the webcam\n",
    "\n",
    "    if not ret:\n",
    "        continue\n",
    "\n",
    "    # Preprocess the frame\n",
    "    processed_frame = preprocess_frame(frame)\n",
    "\n",
    "    # Make a prediction\n",
    "    predictions = model.predict(processed_frame)\n",
    "    predicted_class = np.argmax(predictions)\n",
    "\n",
    "    # Get the corresponding text label\n",
    "    predicted_text = class_to_text_mapping[predicted_class]\n",
    "\n",
    "    # Display the frame and predicted text on the screen\n",
    "    cv2.putText(frame, predicted_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)\n",
    "    cv2.imshow('Sign Language Detection', frame)\n",
    "\n",
    "    # Break the loop if the 'q' key is pressed\n",
    "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "        break\n",
    "\n",
    "# Release the camera and close the OpenCV windows\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()\n"
   ]
  }
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