image_similarity.ipynb

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   "source": [
    "import tensorflow_datasets as tfds\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras import layers\n",
    "\n",
    "import functools\n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors # similarity\n",
    "\n",
    "import ipywidgets as w # interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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    "id": "pV2uoprA64tw"
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   "outputs": [],
   "source": [
    "ds = tfds.load(\"imagenette/160px\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
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    "id": "Hai3hlWv7CEk"
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   "source": [
    "def extract_image(example):\n",
    "    image = example['image']\n",
    "    return image\n",
    "\n",
    "def preprocess_image(image, height, width):\n",
    "    image = tf.image.resize(image, (height, width))\n",
    "    image = tf.cast(image, tf.float32) / 255.0\n",
    "    return image\n",
    "\n",
    "def get_image_batches(batch_size = 128, height = 256, width = 256):\n",
    "    partial_preprocess_image = functools.partial(preprocess_image, height=height, width=width)\n",
    "    train_ds = ds['train']\n",
    "    train_ds = ( train_ds.map(extract_image)\n",
    "                .map(partial_preprocess_image)\n",
    "                .cache()\n",
    "                .shuffle(buffer_size=1000)\n",
    "                .batch(batch_size)\n",
    "                .prefetch(tf.data.AUTOTUNE)\n",
    "                )\n",
    "    return train_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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    "id": "WtvNnDmm7D6G"
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   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "IMG_WIDTH = IMG_HEIGHT = 32\n",
    "train_ds = get_image_batches(batch_size = BATCH_SIZE, height = IMG_HEIGHT, width = IMG_WIDTH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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    "executionInfo": {
     "elapsed": 14404,
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     "timestamp": 1683565823137,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9600, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "images = np.array([img for batch in train_ds.take(300) for img in batch])\n",
    "print(images.shape)"
   ]
  },
  {
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   "execution_count": 17,
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      "Model: \"model_9\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
      "                                                                 \n",
      " conv2d (Conv2D)             (None, 32, 32, 16)        448       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 32, 32, 32)        4640      \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 32, 32, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 16, 16, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 16, 16, 128)       73856     \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 16, 16, 64)        73792     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 8, 8, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 8, 8, 32)          18464     \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 8, 8, 16)          4624      \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 8, 8, 8)           1160      \n",
      "                                                                 \n",
      " flatten_5 (Flatten)         (None, 512)               0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 195,480\n",
      "Trainable params: 195,480\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encoder = keras.models.load_model(\"saved_models/encoder.keras\")\n",
    "encoder.summary()"
   ]
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    "executionInfo": {
     "elapsed": 31419,
     "status": "ok",
     "timestamp": 1683565856077,
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   "outputs": [
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     "text": [
      "300/300 [==============================] - 31s 101ms/step\n"
     ]
    },
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       "NearestNeighbors(metric='cosine')"
      ]
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   "source": [
    "features = encoder.predict(images, batch_size = BATCH_SIZE)\n",
    "\n",
    "knn = NearestNeighbors(n_neighbors=5, metric=\"cosine\")\n",
    "knn.fit(features)"
   ]
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       "interactive(children=(IntSlider(value=0, continuous_update=False, description='start_image_idx', max=9599), In…"
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       "<function __main__.show_similar_images(start_image_idx, n_neighbors=10)>"
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    "def show_similar_images(start_image_idx, n_neighbors=10):\n",
    "    input_image = images[start_image_idx]\n",
    "    input_image = np.expand_dims(input_image, 0)\n",
    "    fig, axes = plt.subplots(1, n_neighbors+1, figsize=(n_neighbors * 2, 5))\n",
    "\n",
    "    feature = encoder.predict(input_image)\n",
    "    distances, nbors = knn.kneighbors(feature, n_neighbors=n_neighbors)\n",
    "    distances, nbors = distances[0], nbors[0]\n",
    "    nbor_images = [images[i] for i in nbors]\n",
    "\n",
    "    for i in range(len(nbor_images)+1):\n",
    "        ax = axes[i]\n",
    "        ax.set_axis_off()\n",
    "        if i == 0:\n",
    "            ax.imshow(input_image.squeeze(0))\n",
    "            ax.set_title(\"Input\")\n",
    "        else:\n",
    "            ax.imshow(nbor_images[i-1])\n",
    "            ax.set_title(f\"Sim: {1 - distances[i-1]:.2f}\")\n",
    "    plt.show()\n",
    "\n",
    "w.interact(show_similar_images, \n",
    "    start_image_idx=w.IntSlider(max=len(images)-1, continuous_update=False),\n",
    "    n_neighbors=w.IntSlider(min=2, value=5, max=10, continuous_update=False),\n",
    ")"
   ]
  }
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         "text/plain": "<Figure size 1000x500 with 6 Axes>"
        },
        "metadata": {},
        "output_type": "display_data"
       }
      ]
     }
    },
    "fa09ae24fa314069be4aaad4a4cfbf26": {
     "model_module": "@jupyter-widgets/controls",
     "model_module_version": "1.5.0",
     "model_name": "SliderStyleModel",
     "state": {
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      "_model_module_version": "1.5.0",
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      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
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  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}