aiphoto.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "from PIL import Image, ImageFilter, ImageChops\n",
    "\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "PATH_GOOD = \"/home/air/Documentos/Projects/AIPhoto/crops_good/\"\n",
    "PATH_BAD = \"/home/air/Documentos/Projects/AIPhoto/crops_bad/\"\n",
    "\n",
    "SIZE_INPUT = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model():\n",
    "    model = keras.Sequential()\n",
    "    \n",
    "    model.add(keras.layers.Conv2D(kernel_size=(3,3), input_shape=(128,128,3), \n",
    "                                  filters=3, activation=\"relu\", padding='same'))\n",
    "    \n",
    "    model.add(keras.layers.Conv2D(kernel_size=(2,2), filters=32, activation=\"relu\", \n",
    "                                  padding='same', kernel_regularizer=keras.regularizers.l2(0.01)))\n",
    "    \n",
    "    model.add(keras.layers.Conv2D(kernel_size=(3,3), filters=64, activation=\"relu\", \n",
    "                                 padding='same', kernel_regularizer=keras.regularizers.l2(0.02)))\n",
    "    \n",
    "    model.add(keras.layers.Conv2D(kernel_size=(1,1), filters=64, activation=\"relu\", \n",
    "                                 padding='same'))\n",
    "    \n",
    "    model.add(keras.layers.Conv2D(kernel_size=(1,1), filters=128, activation=\"relu\", \n",
    "                                 padding='same'))\n",
    "     \n",
    "    model.add(keras.layers.Conv2DTranspose(kernel_size=(2,2), filters=64, activation=\"relu\", \n",
    "                                 padding='same'))\n",
    "      \n",
    "    model.add(keras.layers.Conv2DTranspose(kernel_size=(3,3), filters=64, activation=\"relu\", \n",
    "                                 padding='same', \n",
    "                                           kernel_regularizer=keras.regularizers.l2(0.01)))\n",
    "    \n",
    "    model.add(keras.layers.Conv2DTranspose(kernel_size=(2,2), filters=32, activation=\"relu\", \n",
    "                                 padding='same'))\n",
    "    \n",
    "    \n",
    "    model.add(keras.layers.Conv2DTranspose(kernel_size=(3,3), filters=3, activation=\"relu\", \n",
    "                                 padding='same'))\n",
    "        \n",
    "    model.compile(keras.optimizers.Adamax(), \n",
    "                  loss='mae')\n",
    "  \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 128, 128, 3)       84        \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 128, 128, 32)      416       \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 128, 128, 64)      18496     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 128, 128, 64)      4160      \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 128, 128, 128)     8320      \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_1 (Conv2DTr (None, 128, 128, 64)      32832     \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_2 (Conv2DTr (None, 128, 128, 64)      36928     \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_3 (Conv2DTr (None, 128, 128, 32)      8224      \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTr (None, 128, 128, 3)       867       \n",
      "=================================================================\n",
      "Total params: 110,327\n",
      "Trainable params: 110,327\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataset():\n",
    "    array_good =[] \n",
    "    array_bad = [] \n",
    "    \n",
    "    good_photos = sorted(os.listdir(PATH_GOOD)) # Nombres de las fotos\n",
    "    bad_photos = sorted(os.listdir(PATH_BAD)) \n",
    "    \n",
    "    #Cargar en batches para que quepan en memoria (PC poco potente)\n",
    "    low = 150000\n",
    "    high = low + 30000\n",
    "    \n",
    "    good_photos = good_photos[low:high]\n",
    "    bad_photos = bad_photos[low:high]\n",
    "    \n",
    "    \n",
    "    total = 0\n",
    "    size = len(good_photos)\n",
    "    \n",
    "    for name in good_photos:\n",
    "        image_good = Image.open(PATH_GOOD+name)\n",
    "        image_bad = Image.open(PATH_BAD+name)\n",
    "        \n",
    "        array_good.append(np.array(image_good))\n",
    "        array_bad.append(np.array(image_bad))\n",
    "        \n",
    "        total +=1\n",
    "        print(\"Loaded {} of {}\".format(total, size))\n",
    "    \n",
    "    return (array_good, array_bad)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_image(path):\n",
    "    image = Image.open(path)\n",
    "    arr_boxes = []\n",
    "    size_x = image.width\n",
    "    size_y = image.height\n",
    "    \n",
    "    batches_width = size_x // SIZE_INPUT\n",
    "    batches_height = size_y // SIZE_INPUT\n",
    "    \n",
    "    print(\"{}x{}\".format(batches_height, batches_width))\n",
    "        \n",
    "    for i in range(128, size_y, SIZE_INPUT):\n",
    "        for j in range(128, size_x, SIZE_INPUT):\n",
    "            crop_good = image.crop((j-128, i-128, j, i))\n",
    "            arr_boxes.append(np.array(crop_good))\n",
    "\n",
    "    return (arr_boxes, batches_width, batches_height)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" \n",
    "    Hace una imagen a partir de una lista de ndarrays.\n",
    "    width y height son el numero de bloques en los que se dividio la imagen original\n",
    "\"\"\" \n",
    "def make_image(array, width, height):\n",
    "    newIm = Image.new('RGB', (128*width, 128*height), color=(0,0,0))\n",
    "    \n",
    "    for i in range(height):\n",
    "        for j in range(width):\n",
    "            im = Image.fromarray(array[i*width+j].astype('uint8'))\n",
    "            newIm.paste(im, (j*128,i*128))\n",
    "    \n",
    "    return newIm\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(train_data, valid_data, model):\n",
    "    model.fit(train_data, valid_data, epochs=4000, validation_split=0.3, batch_size=32, \n",
    "              metrics=['acc', 'mse'],\n",
    "             callbacks=[keras.callbacks.EarlyStopping(patience=5),\n",
    "                       keras.callbacks.TensorBoard(log_dir='tf_logs', histogram_freq=0, batch_size=32, write_graph=True, write_images=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model, array_boxes):\n",
    "    return model.predict(array_boxes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def numpy_to_list(tensor_list):\n",
    "    lista = []\n",
    "    for tensor in tensor_list:\n",
    "        lista.append(tensor)\n",
    "    return lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Loaded 1039 of 30000\n",
      "Loaded 1040 of 30000\n",
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      "Loaded 1044 of 30000\n",
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      "Loaded 1150 of 30000\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 1412 of 30000\n",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-4bfb886ab441>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-4-18ad1be9d07d>\u001b[0m in \u001b[0;36mload_dataset\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgood_photos\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mimage_good\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPATH_GOOD\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mimage_bad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPATH_BAD\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode)\u001b[0m\n\u001b[1;32m   2263\u001b[0m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2265\u001b[0;31m     \u001b[0mprefix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2267\u001b[0m     \u001b[0mpreinit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "data = load_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 21000 samples, validate on 9000 samples\n",
      "Epoch 1/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5501 - val_loss: 5.9434\n",
      "Epoch 2/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5991 - val_loss: 5.9404\n",
      "Epoch 3/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5555 - val_loss: 6.2471\n",
      "Epoch 4/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5725 - val_loss: 5.9387\n",
      "Epoch 5/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5461 - val_loss: 5.9425\n",
      "Epoch 6/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5668 - val_loss: 5.8328\n",
      "Epoch 7/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5307 - val_loss: 6.0831\n",
      "Epoch 8/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5455 - val_loss: 5.9065\n",
      "Epoch 9/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5392 - val_loss: 5.9414\n",
      "Epoch 10/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5494 - val_loss: 5.9713\n",
      "Epoch 11/4000\n",
      "21000/21000 [==============================] - 169s 8ms/step - loss: 5.5757 - val_loss: 6.0708\n"
     ]
    }
   ],
   "source": [
    "train_model(np.array(data[1]), np.array(data[0]), model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_weights('v3-percent.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('v3-percent.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7x12\n"
     ]
    }
   ],
   "source": [
    "#Ejemplo de imagen\n",
    "#Cambiar la ruta para proba otra.\n",
    "ase = load_image('/home/air/Descargas/WhatsApp Image 2018-07-09 at 09.36.12.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = make_image(ase[0],ase[1],ase[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "im.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "im2_pred = predict(model, np.array(ase[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalizar si algo se pasa de 255 o de 0\n",
    "im2_pred = np.clip(im2_pred, 0,255)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mostrar imagen\n",
    "are = numpy_to_list(im2_pred)\n",
    "make_image(are , ase[1], ase[2]).show()"
   ]
  }
 ],
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