Emojinator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:72: UserWarning: h5py is running against HDF5 1.10.2 when it was built against 1.10.3, this may cause problems\n",
      "  '{0}.{1}.{2}'.format(*version.hdf5_built_version_tuple)\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "from keras.models import load_model\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0816 15:46:35.110440  4468 deprecation_wrapper.py:119] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "W0816 15:46:36.343177  4468 deprecation_wrapper.py:119] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "W0816 15:46:36.774208  4468 deprecation_wrapper.py:119] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3976: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n",
      "W0816 15:46:36.896070  4468 deprecation_wrapper.py:119] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:131: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0816 15:46:36.898064  4468 deprecation_wrapper.py:119] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "W0816 15:46:36.928554  4468 deprecation.py:506] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "W0816 15:46:36.930590  4468 nn_ops.py:4224] Large dropout rate: 0.6 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "W0816 15:46:37.136259  4468 deprecation_wrapper.py:119] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "W0816 15:46:37.700337  4468 deprecation_wrapper.py:119] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "W0816 15:46:38.769756  4468 deprecation.py:323] From C:\\Users\\LASYA\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    }
   ],
   "source": [
    "model = load_model('emojinator.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    emojis = get_emojis()\n",
    "    cap = cv2.VideoCapture(0)\n",
    "    x, y, w, h = 300, 50, 350, 350\n",
    "\n",
    "    while (cap.isOpened()):\n",
    "        ret, img = cap.read()\n",
    "        img = cv2.flip(img, 1)\n",
    "        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "        mask2 = cv2.inRange(hsv, np.array([2, 50, 60]), np.array([25, 150, 255]))\n",
    "        res = cv2.bitwise_and(img, img, mask=mask2)\n",
    "        gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)\n",
    "        median = cv2.GaussianBlur(gray, (5, 5), 0)\n",
    "\n",
    "        kernel_square = np.ones((5, 5), np.uint8)\n",
    "        dilation = cv2.dilate(median, kernel_square, iterations=2)\n",
    "        opening = cv2.morphologyEx(dilation, cv2.MORPH_CLOSE, kernel_square)\n",
    "        ret, thresh = cv2.threshold(opening, 30, 255, cv2.THRESH_BINARY)\n",
    "\n",
    "        thresh = thresh[y:y + h, x:x + w]\n",
    "        contours = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[1]\n",
    "        if len(contours) > 0:\n",
    "            contour = max(contours, key=cv2.contourArea)\n",
    "            if cv2.contourArea(contour) > 2500:\n",
    "                x, y, w1, h1 = cv2.boundingRect(contour)\n",
    "                newImage = thresh[y:y + h1, x:x + w1]\n",
    "                newImage = cv2.resize(newImage, (50, 50))\n",
    "                pred_probab, pred_class = keras_predict(model, newImage)\n",
    "                print(pred_class, pred_probab)\n",
    "                img = overlay(img, emojis[pred_class], 400, 250, 90, 90)\n",
    "\n",
    "        x, y, w, h = 300, 50, 350, 350\n",
    "        cv2.imshow(\"Frame\", img)\n",
    "        cv2.imshow(\"Contours\", thresh)\n",
    "        k = cv2.waitKey(10)\n",
    "        if k == 27:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keras_predict(model, image):\n",
    "    processed = keras_process_image(image)\n",
    "    pred_probab = model.predict(processed)[0]\n",
    "    pred_class = list(pred_probab).index(max(pred_probab))\n",
    "    return max(pred_probab), pred_class\n",
    "\n",
    "\n",
    "def keras_process_image(img):\n",
    "    image_x = 50\n",
    "    image_y = 50\n",
    "    img = cv2.resize(img, (image_x, image_y))\n",
    "    img = np.array(img, dtype=np.float32)\n",
    "    img = np.reshape(img, (-1, image_x, image_y, 1))\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_emojis():\n",
    "    emojis_folder = 'hand_emo/'\n",
    "    emojis = []\n",
    "    for emoji in range(len(os.listdir(emojis_folder))):\n",
    "        print(emoji)\n",
    "        emojis.append(cv2.imread(emojis_folder+str(emoji)+'.png', -1))\n",
    "    return emojis\n",
    "\n",
    "def overlay(image, emoji, x,y,w,h):\n",
    "    emoji = cv2.resize(emoji, (w, h))\n",
    "    try:\n",
    "        image[y:y+h, x:x+w] = blend_transparent(image[y:y+h, x:x+w], emoji)\n",
    "    except:\n",
    "        pass\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def blend_transparent(face_img, overlay_t_img):\n",
    "    # Split out the transparency mask from the colour info\n",
    "    overlay_img = overlay_t_img[:,:,:3] # Grab the BRG planes\n",
    "    overlay_mask = overlay_t_img[:,:,3:]  # And the alpha plane\n",
    "\n",
    "    # Again calculate the inverse mask\n",
    "    background_mask = 255 - overlay_mask\n",
    "\n",
    "    # Turn the masks into three channel, so we can use them as weights\n",
    "    overlay_mask = cv2.cvtColor(overlay_mask, cv2.COLOR_GRAY2BGR)\n",
    "    background_mask = cv2.cvtColor(background_mask, cv2.COLOR_GRAY2BGR)\n",
    "\n",
    "    # Create a masked out face image, and masked out overlay\n",
    "    # We convert the images to floating point in range 0.0 - 1.0\n",
    "    face_part = (face_img * (1 / 255.0)) * (background_mask * (1 / 255.0))\n",
    "    overlay_part = (overlay_img * (1 / 255.0)) * (overlay_mask * (1 / 255.0))\n",
    "\n",
    "    # And finally just add them together, and rescale it back to an 8bit integer image\n",
    "    return np.uint8(cv2.addWeighted(face_part, 255.0, overlay_part, 255.0, 0.0))"
   ]
  },
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   "execution_count": 7,
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-bde3a1c33150>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mkeras_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-3-ee3ff6dbcf41>\u001b[0m in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mwhile\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mcap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misOpened\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m         \u001b[0mret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcap\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0mhsv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mCOLOR_BGR2HSV\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "keras_predict(model, np.zeros((50, 50, 1), dtype=np.uint8))\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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