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EGFE (Tkinter).ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "acf43a2d-909c-4268-9eba-742d82f4d983",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import threading\n",
+    "import cv2\n",
+    "import numpy as np\n",
+    "import tkinter as tk\n",
+    "from tkinter import Label\n",
+    "from PIL import Image, ImageTk\n",
+    "import pyttsx3\n",
+    "from keras.models import load_model\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "19b13281-85bf-4381-9d5f-f503b1658a8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = load_model('emotion_model-099.keras')\n",
+    "engine = pyttsx3.init()\n",
+    "\n",
+    "frame_count = 0\n",
+    "predict_interval = 5\n",
+    "current_emotion_label = \"Neutral\"\n",
+    "last_spoken = None\n",
+    "\n",
+    "emoji_path = {\n",
+    "    0: 'D:/New download/Emoji/Angry.png',\n",
+    "    1: 'D:/New download/Emoji/Disgusted.png',\n",
+    "    2: 'D:/New download/Emoji/Fear.png',\n",
+    "    3: 'D:/New download/Emoji/Happy.png',\n",
+    "    4: 'D:/New download/Emoji/Neutral.png',\n",
+    "    5: 'D:/New download/Emoji/Sad.png',\n",
+    "    6: 'D:/New download/Emoji/Surprised.png'\n",
+    "}\n",
+    "emotion_labels = ['Angry', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', 'Surprise']\n",
+    "\n",
+    "face_cascade = cv2.CascadeClassifier(r'D:\\New download\\haarcascade_frontalface_default.xml')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "30c4b41c-27df-4819-8914-fa3a7a459f43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def preprocess_face(face_img):\n",
+    "    gray = cv2.cvtColor(face_img, cv2.COLOR_BGR2GRAY)\n",
+    "    resized = cv2.resize(gray, (100, 100))\n",
+    "    resized = resized.astype('float32') / 255.0\n",
+    "    resized = np.reshape(resized, (1, 100, 100, 1))\n",
+    "    return resized\n",
+    "\n",
+    "def overlay_emoji_on_frame(frame, emoji_img, x, y, w, h):\n",
+    "    emoji_resized = cv2.resize(emoji_img, (w, h))\n",
+    "    if emoji_resized.shape[2] == 4:\n",
+    "        emoji_rgb = emoji_resized[:, :, :3]  \n",
+    "        alpha_mask = emoji_resized[:, :, 3]  \n",
+    "        alpha_mask = alpha_mask / 255.0\n",
+    "        for c in range(0, 3):  \n",
+    "            frame[y:y + h, x:x + w, c] = (alpha_mask * emoji_rgb[:, :, c] + (1 - alpha_mask) * frame[y:y + h, x:x + w, c])\n",
+    "    else:\n",
+    "        frame[y:y + h, x:x + w] = cv2.addWeighted(frame[y:y + h, x:x + w], 0.5, emoji_resized, 0.5, 0)\n",
+    "    return frame\n",
+    "\n",
+    "def predict_emotion_async(face):\n",
+    "    global current_emotion_label\n",
+    "    processed_face = preprocess_face(face)\n",
+    "    emotion_prediction = model.predict(processed_face)\n",
+    "    max_index = int(np.argmax(emotion_prediction))\n",
+    "    current_emotion_label = emotion_labels[max_index]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "7c4211fd-e1a4-4600-aa05-687f8c3202e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 810ms/step\n"
+     ]
+    }
+   ],
+   "source": [
+    "def update_frame():\n",
+    "    global frame_count, last_spoken, current_emotion_label\n",
+    "\n",
+    "    ret, frame = cap.read()\n",
+    "    if not ret:\n",
+    "        print(\"Error: Failed to capture video feed.\")\n",
+    "        return\n",
+    "    \n",
+    "    frame = cv2.resize(frame, (320, 240))\n",
+    "    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
+    "    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)\n",
+    "\n",
+    "    for (x, y, w, h) in faces:\n",
+    "        face = frame[y:y + h, x:x + w]\n",
+    "\n",
+    "        if frame_count % predict_interval == 0:\n",
+    "            threading.Thread(target=predict_emotion_async, args=(face,)).start()\n",
+    "\n",
+    "        cv2.putText(frame, current_emotion_label, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\n",
+    "        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)\n",
+    "\n",
+    "        emoji_img_path = emoji_path.get(emotion_labels.index(current_emotion_label), None)\n",
+    "        if emoji_img_path and os.path.exists(emoji_img_path):\n",
+    "            emoji_img = cv2.imread(emoji_img_path, -1)\n",
+    "            frame = overlay_emoji_on_frame(frame, emoji_img, x, y, w, h)\n",
+    "\n",
+    "        if current_emotion_label != last_spoken:\n",
+    "            last_spoken = current_emotion_label\n",
+    "            engine.say(current_emotion_label)\n",
+    "            engine.runAndWait()\n",
+    "\n",
+    "    frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
+    "    img = Image.fromarray(frame)\n",
+    "    imgtk = ImageTk.PhotoImage(image=img)\n",
+    "\n",
+    "    lblVideo.imgtk = imgtk\n",
+    "    lblVideo.configure(image=imgtk)\n",
+    "\n",
+    "    frame_count += 1\n",
+    "    lblVideo.after(10, update_frame)\n",
+    "\n",
+    "root = tk.Tk()\n",
+    "root.title(\"Emoji Generator from Facial Expression\")\n",
+    "root.config(bg='khaki')\n",
+    "root.geometry('400x350')\n",
+    "\n",
+    "heading = Label(root,text=\"Emoji Generator from\\nFacial Expression\",font=('Times New Roman', 24, 'bold'),justify=\"center\",bg=\"khaki\",fg=\"saddle brown\")\n",
+    "heading.pack(pady=10) \n",
+    "\n",
+    "lblVideo = Label(root)\n",
+    "lblVideo.pack()\n",
+    "\n",
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "update_frame()\n",
+    "\n",
+    "root.mainloop()\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cebe5ae5-1d4f-45b7-8b08-f4739a4d030b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}