GUI_For_Detection.ipynb

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      "(1, 1, 200, 7)\n",
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      "(1, 1, 200, 7)\n",
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      "(1, 1, 200, 7)\n",
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      "(1, 1, 200, 7)\n",
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      "(1, 1, 200, 7)\n",
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      "(1, 1, 200, 7)\n",
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      "(1, 1, 200, 7)\n",
      "1/1 [==============================] - 0s 62ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in Tkinter callback\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Super\\anaconda3\\lib\\tkinter\\__init__.py\", line 1892, in __call__\n",
      "    return self.func(*args)\n",
      "  File \"C:\\Users\\Super\\AppData\\Local\\Temp\\ipykernel_3104\\979652970.py\", line 88, in helloCallBack\n",
      "    l5.config(text=\"Good Brightness\") if isbright(img) else l5.config(text=\" Low Brightness\")\n",
      "  File \"C:\\Users\\Super\\AppData\\Local\\Temp\\ipykernel_3104\\979652970.py\", line 25, in isbright\n",
      "    image = cv2.resize(image, (dim, dim))\n",
      "cv2.error: OpenCV(4.6.0) D:\\a\\opencv-python\\opencv-python\\opencv\\modules\\imgproc\\src\\resize.cpp:4052: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from tkinter import *\n",
    "import tkinter as tk\n",
    "from PIL import ImageTk, Image\n",
    "import cv2\n",
    "from tensorflow.keras.applications.mobilenet_v2 import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import img_to_array\n",
    "from tensorflow.keras.models import load_model\n",
    "from imutils.video import VideoStream\n",
    "import tensorflow_hub as hub\n",
    "import numpy as np\n",
    "import imutils\n",
    "import time\n",
    "import cv2\n",
    "import tkinter\n",
    "import os\n",
    "from tensorflow.keras.models import load_model\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import datetime as dt\n",
    "\n",
    "\n",
    "def isbright(image, dim=10, thresh=0.5):\n",
    "    # Resize image to 10x10\n",
    "    image = cv2.resize(image, (dim, dim))\n",
    "    # Convert color space to LAB format and extract L channel\n",
    "    L, A, B = cv2.split(cv2.cvtColor(image, cv2.COLOR_BGR2LAB))\n",
    "    # Normalize L channel by dividing all pixel values with maximum pixel value\n",
    "    L = L/np.max(L)\n",
    "    # Return True if mean is greater than thresh else False\n",
    "    return np.mean(L) > thresh\n",
    "\n",
    "\n",
    "    \n",
    " \n",
    "\n",
    "def stopcam():\n",
    "    cap.release()\n",
    "    l3.config(text = \"Camera Closed\")\n",
    "\n",
    "\n",
    "def detect_and_predict_mask(frame, faceNet, maskNet):\n",
    "        (h, w) = frame.shape[:2]\n",
    "        blob = cv2.dnn.blobFromImage(frame, 1.0, (224, 224),(104.0, 177.0, 123.0))\n",
    "        faceNet.setInput(blob)\n",
    "        detections = faceNet.forward()\n",
    "        print(detections.shape)\n",
    "        faces = []\n",
    "        global score\n",
    "        global score_lite\n",
    "        locs = []\n",
    "        preds = []\n",
    "      \n",
    "        for i in range(0, detections.shape[2]):\n",
    "            confidence = detections[0, 0, i, 2]\n",
    "            if confidence > 0.5:\n",
    "                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])\n",
    "                (startX, startY, endX, endY) = box.astype(\"int\")\n",
    "                (startX, startY) = (max(0, startX), max(0, startY))\n",
    "                (endX, endY) = (min(w - 1, endX), min(h - 1, endY))\n",
    "                face = frame[startY:endY, startX:endX]\n",
    "                face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)\n",
    "                face = cv2.resize(face, (224, 224))\n",
    "                face = img_to_array(face)\n",
    "                face = preprocess_input(face)\n",
    "                faces.append(face)\n",
    "                locs.append((startX, startY, endX, endY))\n",
    "        if len(faces) > 0:\n",
    "                \n",
    "                faces = np.array(faces, dtype=\"float32\")\n",
    "                preds = maskNet.predict(faces, batch_size=32)\n",
    "                score_lite=preds\n",
    "                score = np.argmax(preds)\n",
    "              \n",
    "        return (locs, preds)\n",
    "def helloCallBack():\n",
    "        global cap\n",
    "        cap=cv2.VideoCapture(0)\n",
    "        prototxtPath = r\"face_detector\\deploy.prototxt\"\n",
    "        weightsPath = r\"face_detector\\res10_300x300_ssd_iter_140000.caffemodel\"\n",
    "        faceNet = cv2.dnn.readNet(prototxtPath, weightsPath)\n",
    "        maskNet = tf.keras.models.load_model((\"mask_MobileNet_detector.model\"), \n",
    "                                     custom_objects={'KerasLayer':hub.KerasLayer})\n",
    "        \n",
    "        \n",
    "        while True:\n",
    "            img=cap.read()[1]\n",
    "            l5.config(text=\"Good Brightness\") if isbright(img) else l5.config(text=\" Low Brightness\")\n",
    "            obj=img\n",
    "            (locs, preds) = detect_and_predict_mask(obj, faceNet, maskNet)\n",
    "            for (box, pred) in zip(locs, preds):\n",
    "                (startX, startY, endX, endY) = box\n",
    "                pred=score \n",
    "                if score==0:\n",
    "                    label=(\"cloth\")\n",
    "                    label = \"{}: {:.2f}%\".format(label,100 * np.max(score_lite))\n",
    "                    color = (0, 225, 0)\n",
    "                    l3.config(text = \"cloth mask\",bg='green')\n",
    "                elif score == 1:\n",
    "                    label=(\"n95\")\n",
    "                    label = \"{}: {:.2f}%\".format(label,100 * np.max(score_lite))\n",
    "                    color = (0, 225, 0)\n",
    "                    l3.config(text = \"n95 mask\",bg='green')\n",
    "                elif score == 2:\n",
    "                    label=(\"n95v\")\n",
    "                    label = \"{}: {:.2f}%\".format(label,100 * np.max(score_lite))\n",
    "                    color = (0, 225, 0)\n",
    "                    l3.config(text = \"n95v Mask\",bg='green')\n",
    "                elif  score==3:\n",
    "                    label=(\"No Mask\")\n",
    "                    label = \"{}: {:.2f}%\".format(label,100 * np.max(score_lite))\n",
    "                    color = (0, 0, 255)\n",
    "                    l3.config(text = \"No mask\",bg='red')\n",
    "                else:\n",
    "                    label=(\"srg\")\n",
    "                    l3.config(text = \"Sargical Mask\",bg='green')\n",
    "                    label = \"{}: {:.2f}%\".format(label,100 * np.max(score_lite))\n",
    "                    color = (0, 225, 0)\n",
    "                   \n",
    "            \n",
    "            cv2.putText(obj, label, (startX, startY - 10),\n",
    "            cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)\n",
    "            cv2.rectangle(obj, (startX, startY), (endX, endY), color, 2)\n",
    "            obj=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "            obj=ImageTk.PhotoImage(Image.fromarray(obj))\n",
    "            l1['image']=obj\n",
    "            win.update()\n",
    "        return cap\n",
    "        \n",
    "\n",
    "            \n",
    "            \n",
    "    \n",
    "#Create an instance of tkinter frame\n",
    "win= Tk()\n",
    "global my_text\n",
    "win.configure(bg='#F1EB9C')\n",
    "win.title('Mask Detection System')\n",
    "p1 = PhotoImage(file = 'bt1.png')\n",
    "#win.iconphoto(False, p1)\n",
    "#Set the geometry of frame\n",
    "win.geometry(\"1250x750\")\n",
    "btn1 = Button(win,bg='#540b0e', text = 'open Your Camera !',foreground='white',height= 3, width=20,border=5, command=helloCallBack)\n",
    "btn1.grid(row=1,column=0)\n",
    "date = dt.datetime.now()\n",
    "label = Label(win, bg=\"#ffbf69\",border=5,foreground='#3a0ca3', text=f\"{date:%A, %B %d, %Y}\", font=\"Calibri, 10\",height= 3, width=20)\n",
    "label.grid(row=0,column=0)\n",
    "btn1 = Button(win,bg='#a7c957',foreground='white' ,border=5,text = 'Stop Your Camera !',height= 3, width=20, command=stopcam)\n",
    "btn1.grid(row=1,column=2)\n",
    "l2=Label(win,text='MASK STATUS',font=\" 6\", bg='#CCFF00',height= 3, width=20,)\n",
    "l2.grid(row=0,column=3) \n",
    "l3=Label(win,bg='#9b5de5',font=\" 6\",height= 3, width=20)\n",
    "l3.grid(row=1,column=3)\n",
    "l4=Label(win,text='Brightness STATUS',font=\" 6\", bg='#8338ec',height= 3, width=20)\n",
    "l4.grid(row=0,column=4) \n",
    "l5=Label(win,bg='#a9def9',font=\" 6\",height= 3, width=20)\n",
    "l5.grid(row=1,column=4)\n",
    "f1 = Frame(win,)  \n",
    "f1.grid(row=4,column=0)  \n",
    "l1 = Label(f1,)  \n",
    "l1.grid(row=4,column=0) \n",
    "\n",
    "\n",
    "win.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e906adf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04c8dc5e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8155296",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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