41_TFL_RPi_Cifar10_Image_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Classification - Cifar10 model (TFLite)\n",
    "Developed by Marcelo Rovai - 19Aug20\n",
    "\n",
    "Reference: [TensorFlow Lite inference](https://www.tensorflow.org/lite/guide/inference)\n",
    "- Also see: [Python quickstart](https://www.tensorflow.org/lite/guide/python)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The term inference refers to the process of executing a TensorFlow Lite model on-device in order to make predictions based on input data. To perform an inference with a TensorFlow Lite model, you must run it through an interpreter. The TensorFlow Lite interpreter is designed to be lean and fast. The interpreter uses a static graph ordering and a custom (less-dynamic) memory allocator to ensure minimal load, initialization, and execution latency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install just the TensorFlow Lite interpreter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To quickly run TensorFlow Lite models with Python, you can install just the TensorFlow Lite interpreter, instead of all TensorFlow packages."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, if you have Raspberry Pi that's running Raspbian Buster (which has Python 3.7), install the Python wheel as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`$ sudo pip3 install https://dl.google.com/coral/python/tflite_runtime-2.1.0.post1-cp37-cp37m-linux_armv7l.whl`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries and setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import tflite_runtime.interpreter as tflite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Interpreter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_cifar10.tflite\r\n"
     ]
    }
   ],
   "source": [
    "!ls ./models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter = tflite.Interpreter(model_path='./models/model_cifar10.tflite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter.allocate_tensors()\n",
    "input_details = interpreter.get_input_details()\n",
    "output_details = interpreter.get_output_details()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'conv2d_12_input',\n",
       "  'index': 0,\n",
       "  'shape': array([ 1, 32, 32,  3]),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0),\n",
       "  'quantization_parameters': {'scales': array([], dtype=float32),\n",
       "   'zero_points': array([], dtype=int32),\n",
       "   'quantized_dimension': 0}}]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'Identity',\n",
       "  'index': 17,\n",
       "  'shape': array([ 1, 10]),\n",
       "  'dtype': numpy.float32,\n",
       "  'quantization': (0.0, 0),\n",
       "  'quantization_parameters': {'scales': array([], dtype=float32),\n",
       "   'zero_points': array([], dtype=int32),\n",
       "   'quantized_dimension': 0}}]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test the model on random input data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = input_details[0]['shape']\n",
    "input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\n",
    "interpreter.set_tensor(input_details[0]['index'], input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 32, 32, 3)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0.2485008 , 0.40165037, 0.23401776],\n",
       "         [0.28254092, 0.4448997 , 0.5793751 ],\n",
       "         [0.7153931 , 0.17747308, 0.38465196],\n",
       "         ...,\n",
       "         [0.33561796, 0.5863862 , 0.16401479],\n",
       "         [0.820945  , 0.38323897, 0.9691964 ],\n",
       "         [0.516108  , 0.13144143, 0.6415344 ]],\n",
       "\n",
       "        [[0.98503673, 0.7250355 , 0.29269454],\n",
       "         [0.03773272, 0.05092489, 0.73866653],\n",
       "         [0.67219746, 0.7408584 , 0.5536305 ],\n",
       "         ...,\n",
       "         [0.503835  , 0.34604424, 0.02130186],\n",
       "         [0.21136574, 0.99428034, 0.8898311 ],\n",
       "         [0.05437457, 0.714125  , 0.85728145]],\n",
       "\n",
       "        [[0.7083888 , 0.8305931 , 0.32105678],\n",
       "         [0.2686036 , 0.6822804 , 0.24982351],\n",
       "         [0.06242587, 0.7607834 , 0.6515359 ],\n",
       "         ...,\n",
       "         [0.22491947, 0.74090767, 0.70492226],\n",
       "         [0.7554519 , 0.7107994 , 0.50444293],\n",
       "         [0.15059167, 0.28511223, 0.55973244]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[0.41184348, 0.39145857, 0.25238195],\n",
       "         [0.4341274 , 0.6295202 , 0.13398394],\n",
       "         [0.3285305 , 0.99165773, 0.53836805],\n",
       "         ...,\n",
       "         [0.22043778, 0.48216426, 0.7553572 ],\n",
       "         [0.47351336, 0.28497645, 0.07861039],\n",
       "         [0.17409827, 0.5387909 , 0.87978303]],\n",
       "\n",
       "        [[0.28828838, 0.8829057 , 0.35815594],\n",
       "         [0.68287987, 0.70311177, 0.40661684],\n",
       "         [0.30506274, 0.8919979 , 0.9805508 ],\n",
       "         ...,\n",
       "         [0.17897336, 0.21050139, 0.94906896],\n",
       "         [0.53180546, 0.1619843 , 0.09652612],\n",
       "         [0.5236631 , 0.21753618, 0.08100141]],\n",
       "\n",
       "        [[0.5592976 , 0.67660064, 0.6635381 ],\n",
       "         [0.13700165, 0.78218395, 0.5170207 ],\n",
       "         [0.30920416, 0.48465097, 0.8952678 ],\n",
       "         ...,\n",
       "         [0.74381703, 0.4289552 , 0.08178288],\n",
       "         [0.9608494 , 0.6851777 , 0.90447277],\n",
       "         [0.02989549, 0.33917946, 0.40956897]]]], dtype=float32)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpreter.invoke()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00245013 0.06610588 0.00190057 0.00322022 0.00130722 0.00155135\n",
      "  0.8469468  0.00755497 0.00428525 0.0646777 ]]\n"
     ]
    }
   ],
   "source": [
    "# The function `get_tensor()` returns a copy of the tensor data.\n",
    "# Use `tensor()` in order to get a pointer to the tensor.\n",
    "output_data = interpreter.get_tensor(output_details[0]['index'])\n",
    "print(output_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test with a image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions based on [TensorFlow Lite Python image classification demo](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/examples/python/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_input_tensor(interpreter, image):\n",
    "    tensor_index = interpreter.get_input_details()[0]['index']\n",
    "    input_tensor = interpreter.tensor(tensor_index)()[0]\n",
    "    input_tensor[:, :] = image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify_image(interpreter, image, top_k=1):\n",
    "    \"\"\"Returns a sorted array of classification results.\"\"\"\n",
    "    set_input_tensor(interpreter, image)\n",
    "    interpreter.invoke()\n",
    "    output_details = interpreter.get_output_details()[0]\n",
    "    output = np.squeeze(interpreter.get_tensor(output_details['index']))\n",
    "\n",
    "    # If the model is quantized (uint8 data), then dequantize the results\n",
    "    if output_details['dtype'] == np.uint8:\n",
    "        scale, zero_point = output_details['quantization']\n",
    "        output = scale * (output - zero_point)\n",
    "\n",
    "    ordered = np.argpartition(-output, top_k)\n",
    "    return [(i, output[i]) for i in ordered[:top_k]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "car_1.jpg  car_3.jpg  car_5.jpg  cat_2.jpg  dog_1.jpg  dog_3.jpg   ship_2.jpg\r\n",
      "car_2.jpg  car_4.jpg  cat_1.jpg  cat_2.png  dog_2.jpg  ship_1.jpg\r\n"
     ]
    }
   ],
   "source": [
    "!ls ./images/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = \"./images/cat_1.jpg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Load input image\n",
    "img = cv2.imread(image_path)\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "img = cv2.resize(img, (32, 32)) \n",
    "plt.imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = img.reshape(1,32,32,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = classify_image(interpreter, image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(5, 1.0)]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes_lst = ['Airplane', 'Automobile', 'Bird', 'Cat',\n",
    "               'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_image(image_path, interpreter, classes_lst):\n",
    "    img = cv2.imread(image_path)\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    img = cv2.resize(img, (32, 32)) \n",
    "    image = img.reshape(1,32,32,3)\n",
    "    pred = classify_image(interpreter, image)[0][0]\n",
    "    print(\"\\n\\t[PREDICTION]: \", classes_lst[pred])\n",
    "    plt.imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t[PREDICTION]:  Dog\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"./images/cat_1.jpg\"\n",
    "predict_image(image_path, interpreter, classes_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t[PREDICTION]:  Cat\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"./images/cat_2.jpg\"\n",
    "predict_image(image_path, interpreter, classes_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t[PREDICTION]:  Dog\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"./images/dog_1.jpg\"\n",
    "predict_image(image_path, interpreter, classes_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t[PREDICTION]:  Automobile\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"./images/dog_2.jpg\"\n",
    "predict_image(image_path, interpreter, classes_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t[PREDICTION]:  Dog\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"./images/dog_3.jpg\"\n",
    "predict_image(image_path, interpreter, classes_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t[PREDICTION]:  Automobile\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"./images/car_1.jpg\"\n",
    "predict_image(image_path, interpreter, classes_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t[PREDICTION]:  Automobile\n"
     ]
    },
    {
     "data": {
      "image/png": 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YAOD8PwnLPKWNhvfXPzJKuyQzXBb9s4suoDYm5wHA/I6OYHs+z+dqYoJ/5ppafp3yCEmMTf+SY8LyGhD9uZJpPo7Vq1dR2/HHvYnachPhOUmmI9yzGI4YS0f12YdKrvzfAvCOQPud7n5S+d9+HV8IcWSxX+d398cA8CqaQog3JAdzz3+dma0xs3vNjOdOFkIckRyo898NYBGAkwB0AriDvdHMVpjZKjNblY/IDy+EqC4H5Pzu3u3uBXcvArgHwOkR713p7svcfVkqzRe4hBDV5YCc38z2Xsq9BMDaQzMcIUS1qETqewDA+QDazWwbgFsBnG9mJwFwAFsA8DCzvUhnkpg1N5zvLheRs+6MCy4Mtm/d2Rcxbv4rYyTFP3ZtfR21rVsXzo+Xj5D6EJFvb2ycR7gtO5X+mMJPH+E5917aEZY/B7s30z7rX+BRjplaXp5q4bx51Pad+78TbH/v8vfTPgVSggoAMplGaitGTH+OROhlIuTNqK8zT2Q5ADj15NOo7dFf/YLajj322GB7scglzPb21mB7Mlm51Lffd7r75YHmb1S8ByHEEYme8BMipsj5hYgpcn4hYoqcX4iYIucXIqZUNYFnKpFAy7SwdLSzh0t9RiKYHFx26evvpbbWpjZq6+3m8iGLHjMeuAcaVgYA4Bkwn/nD76jt6EXHUVtHc/hJ6zmtJ9M+ExN8HFERc+MR/c48+9xg+wNf+BLts+HVZ6jt9nseorb2Jj7HF73tkmD71dfzhKZtzbxk29NPP01t7/+zD1Hbr379z9R21hlnBdvHxngkZl//zmB7vsD9aF905Rcipsj5hYgpcn4hYoqcX4iYIucXIqbI+YWIKRaZ/PAQ09re6Be995SgbffuYdrvhmtvC7b39vLaaFf/xVXU9td/fQO1nXgaT46ZmwhHiHmCyyvJRJra4Dy5iUXoh9EJK8PRjImIxI7NTbOobWKc160bH+P151rbwwk8MxFRZ7dfz5OnHnt+KL6sxNlnvIXaxnPh7+aFF1+gfe752tepraUpHJUKAA1N/Lvu7uFz9dOfPRxsTyV4ZGpX945g+7VXX4MNL26IFJ/3oCu/EDFFzi9ETJHzCxFT5PxCxBQ5vxAxpaqBPbXpWpw4Z2nQNtLEV5WTiXBevb+85graZ/FiXh7phz/8KbWdcgbPnTdBAnGKZEUZADwiN2EyxVeHixFBS8WIclKWDNsMXCEYHuRlz668nM9xocCvHal0eMH5gR/8iPb51D/dR23z5i6gti0vb6G2JUvCZbne8uZw3jwA+M63vk1tbvwzd3aFg20A4OF/4YE9LJdjNs2Pj46OOcH2dJrnJtwXXfmFiClyfiFiipxfiJgi5xcipsj5hYgpcn4hYkol5brmAfg2gJkoleda6e53mVkrgO8DWIBSya5L3X139MaAooV3WYjIdcfKOBULvM+SJfOp7fl1PKijWIyQ0YhcNjQ4yLdX4HnuGhuaqa0QIc1lI+ScJFEIkxl+ns/nuRyZTvPyZQsXzqU2WFimuvajH6FdvnbP16itu4uPY8kxXNadJIFJkxHy7A8e+h61DY/wEmtbXnmV2pKT4TyUALB5ezhf49Zt22if1qZwnsHh0X7aZ18qufLnAXzc3ZcCOBPAtWa2FMBNAB5192MAPFr+WwjxBmG/zu/une7++/LrIQDrAcwBsBzAnqcy7gPwvsM1SCHEoed13fOb2QIAJwN4EsBMd9/zaFgXSrcFQog3CBU7v5k1AHgIwA3u/pqbXC9llwjepJrZCjNbZWarRkbGDmqwQohDR0XOb2ZplBz/fnff83B2t5l1lO0dAHpCfd19pbsvc/dl9fW1h2LMQohDwH6d30r5pL4BYL27711u5WEAV5ZfXwngJ4d+eEKIw0UlUX1vBfBhAM+Z2Z56SjcDuB3Ag2Z2FYBXAFy6vw3V1U/DqWdeGLSNRMhla1b9n2B7bZb/knj017+htuXv+wC1ffwvr6e2v//i7cH23bv57cxF7+broMPDPAdhfozPR9eOrdTGIv4sIoffXXfwElozZ4Vz8QHAvNnc9sLGjcH2BfPDUXYAUIiIVrz0A++htsZGXl4rkwp/7hs/9Sna57QzllFba0TexaMXLqG2Yp6X3lr/g+eC7Rde8Hbax0jUZM3riOrbr/O7++PgBefeVvGehBBHFHrCT4iYIucXIqbI+YWIKXJ+IWKKnF+ImFLVBJ7JRAINtQ1hW0Qyy7PPC4sKL2/nCRP7+3lC0N/+5nFqq6/n5Zhuv+3zwfa5R/HotpnTW6gNxmWjNJGoAGD9+nXU9s4//XfhXSV5lOC6deup7c1LeSmso47htp194eiyra+8QvugwJOWLn/f+dT2P/+Zy7p/94XPBdvTKb6vnu1hmRIA+ge4BJsf53LeddffSG1nnBZOxvn0b5+gfZqbwxGhO7uDz9oF0ZVfiJgi5xcipsj5hYgpcn4hYoqcX4iYIucXIqZUVerL5wvYuSuc4zNbyyP0JifD8sqlH3w/7XPXV79ObX968buobfVTv6a2LZs3BdtPOuE42ue2m2+mNidJLgEglYmwpfg5++f/8kiw/dOf/lvaJ5ng+zr33HOo7cnVa6ht2WnnB9sfeuibtE+Rq5E49yz+XZ94PI9+++Id/0jaw7ItAFxx2eXUNjnG5bwceETd+9//IWpL5MIJZVuammifiWIyvK1UuD343orfKYT4o0LOL0RMkfMLEVPk/ELEFDm/EDGlqqv9qVQKM6eTfGvGl3p7iULw8hYeJHLaOW+ltrXPP09tw0M8cKNjdjgA45lnn6V9kkm++trYzFdz8xO8vJNH5JHLjYRXo7965520T0M9L4XVMutoavvg5adT21NPhVWHKLp2bKe2z9z6WWrLRORyLBbDATx/+8lbaJ+ZHfwzv7qZl3qbxqcRp57N8/udf8F1wfae3s5gOwD09YZLgz3xOz6+fdGVX4iYIucXIqbI+YWIKXJ+IWKKnF+ImCLnFyKm7FfqM7N5AL6NUgluB7DS3e8ys9sAfATAnkR6N7v7z6K2lS8W0DcYzu0G55IYk2t6eni+svvuvZfaClbD9zU5TG0dHeHyVHV1M2if4VEeCBJVnqpvgMwTgLa26dTWPxSWRXe/yLc3nstT2z3/jUuEr257mdpmdITLckVJn//45a9Q29joBLVN5iJyIabDQUsvvMjl3s7ObdR28QVnUNt/uv5j1DYRId12rw8HSNVm62mfxa2zgu3ZiFyY+1KJzp8H8HF3/72ZNQJYbWZ7RNw73f2LFe9NCHHEUEmtvk4AneXXQ2a2HkD4aRchxBuG13XPb2YLAJwM4Mly03VmtsbM7jWziBzVQogjjYqd38waADwE4AZ3HwRwN4BFAE5C6ZfBHaTfCjNbZWarBgcGDsGQhRCHgoqc38zSKDn+/e7+IwBw9253L7h7EcA9AIIPerv7Sndf5u7LpkVkJhFCVJf9Or+ZGYBvAFjv7l/aq33vpe9LAKw99MMTQhwuKlntfyuADwN4zsyeKbfdDOByMzsJJflvC4Cr97chgyOdCstKzU1c1hgfCUt9M9t5aa3P/s0nqW3X4Ai11TWGy4kBwPhkLtjuziMS+/r6qK1rB4/aGh7mkuPICB9/Z3dX2BCRIG80Qo7s3bmL2h59YjO1vbjuqWD7F/7uJt5nEy+TlUzzQzVZMGrzfFj6aqjn3/NxixdSWyFCVrz3m9+htve+82JqmxwPl5ZLpnm0YqEYlg7HxniZun2pZLX/cQCh2Y3U9IUQRzZ6wk+ImCLnFyKmyPmFiClyfiFiipxfiJhS1QSePd2d+ModnwvaJsZ41FNDc/jJ4euu+SjtYxkuHbbkhqgtaTzqrLW1Ldje3xeOpAOAwiz+1HPxTVxS8hSXr8bHudzU20ukxSzPLrlxwwZq27TpJWpbfvEJ1JZIhA+tjlkkgSuAo46aR20NNVz2sgy/hjXWNwbbW9vC3yUAzJrBoyY7OmZTW1Mdj6irb+afG7lwxGJLWyvtMq0uLFVmM1m+n33QlV+ImCLnFyKmyPmFiClyfiFiipxfiJgi5xciplhURNqhpqmp3t969tKgraeLR7+xgK4MSc4IRCfHLEUpv/5t5nLhqL5P3Hgj7ZOt4dFjuYipTyW5ZFOX5QptTSZse2kjr2uIiGNgyTG8bl1XRALVnIfneHiER50lIiL3slmedDVR5N/1tMawxFlbGxExN8kTmtbVR0iO/LBCbS2XWgt5MlcR0ZbJZPi6/ed/8VGsf+HFiJH8P3TlFyKmyPmFiClyfiFiipxfiJgi5xcipsj5hYgpVY3qc3daV62pgUfhFcgpitVhA4B8IZz0EwDyRS7l5PK8H4j8dseXvky7GJFkACBhEXJkRFLKqHP2ZZddFmx/y6mn0T7pVIbahka5NDd9No9wc1Kbrm4Bl8qKEckxs1kufeYneUSopcIS4aRHHB81fH6jkqe2RUQKRuRPBRLhsdTWRcibRJ5NJCpS+UrvrfidQog/KuT8QsQUOb8QMUXOL0RMkfMLEVP2u9pvZjUAHgOQLb//h+5+q5ktBPA9AG0AVgP4sLvzZVcAyWQSTdPCq/rbtpIyUwBa2pqD7ZM5vmrPVlABIBERgZE0riAk0+FzZTLN8/5FxU0lI1ZmJ8bCQUQAMDzGV5y/ed99wfbE/d+ifdLpiLxveb4CP1HgY1yyaHGw/QOXfoD22bCO5xI85fRzqG1snOdQzBAlo7EufEwBwO5t2/m+nB9zs1r46nwuQn1K14T79e7gpdJGRsOuNklKyoWo5Mo/AeBCdz8RpXLc7zCzMwF8HsCd7r4YwG4AV1W8VyHElLNf5/cSe6pGpsv/HMCFAH5Ybr8PwPsOywiFEIeFiu75zSxZrtDbA+ARAC8B6Hf/t99A2wDMOTxDFEIcDipyfncvuPtJAOYCOB3AmyrdgZmtMLNVZrZqYqLy+xEhxOHlda32u3s/gF8BOAtAs5ntWTCcCyC4SuLuK919mbsvy2b5YpoQorrs1/nNbLqZNZdf1wK4CMB6lE4CHyy/7UoAPzlcgxRCHHr2m8PPzE5AaUEvidLJ4kF3/6yZHY2S1NcK4A8ArnD3cN2hMtMaa/y0k48iVv6rYHIyvFlL8SCRmjSX0Z743TpqW7xgJrXNmR8OZImSvKLm14pc/imSclelbXL5rUAkwuHh4WA7AGRI3j8ASCe5jBkV5FJD5KvaOr6vQpHbUsbnsRiRwy+TDo/jxptuon2icjxail8vBwcHqa0hIocfiAxYFxGj0zI9fCx+8M+vwdr1GyqK7tmvzu/uawCcHGjfjNL9vxDiDYie8BMipsj5hYgpcn4hYoqcX4iYIucXIqZUtVyXme0EsKduVDuA3qrtnKNxvBaN47W80cYx392nV7LBqjr/a3Zstsrdl03JzjUOjUPj0M9+IeKKnF+ImDKVzr9yCve9NxrHa9E4Xssf7Tim7J5fCDG16Ge/EDFlSpzfzN5hZi+a2SYz4+FVh38cW8zsOTN7xsxWVXG/95pZj5mt3aut1cweMbON5f9bpmgct5nZ9vKcPGNm76rCOOaZ2a/MbJ2ZPW9mf1Vur+qcRIyjqnNiZjVm9pSZPVsex2fK7QvN7Mmy33zfzHidtUpw96r+Qyk0+CUARwPIAHgWwNJqj6M8li0A2qdgv+cBOAXA2r3a/gHATeXXNwH4/BSN4zYAn6jyfHQAOKX8uhHABgBLqz0nEeOo6pwAMAAN5ddpAE8COBPAgwAuK7d/DcDHDmY/U3HlPx3AJnff7KVU398DsHwKxjFluPtjAPbNy7wcpbwJQJUSopJxVB1373T335dfD6GULGYOqjwnEeOoKl7isCfNnQrnnwPg1b3+nsrknw7gF2a22sxWTNEY9jDT3TvLr7sA8Kwih5/rzGxN+bbgsN9+7I2ZLUApf8STmMI52WccQJXnpBpJc+O+4HeOu58C4J0ArjWz86Z6QEDpzI/SiWkquBvAIpRqNHQCuKNaOzazBgAPAbjB3V+TFqeacxIYR9XnxA8iaW6lTIXzbwcwb6+/afLPw427by//3wPgx5jazETdZtYBAOX/e6ZiEO7eXT7wigDuQZXmxMzSKDnc/e7+o3Jz1eckNI6pmpPyvl930txKmQrnfxrAMeWVywyAywDVbvCJAAAA30lEQVQ8XO1BmFm9mTXueQ3gYgBro3sdVh5GKREqMIUJUfc4W5lLUIU5sVLSvG8AWO/uX9rLVNU5YeOo9pxULWlutVYw91nNfBdKK6kvAfibKRrD0SgpDc8CeL6a4wDwAEo/H3Mo3btdhVLNw0cBbATwSwCtUzSO7wB4DsAalJyvowrjOAeln/RrADxT/veuas9JxDiqOicATkApKe4alE40t+x1zD4FYBOAHwDIHsx+9ISfEDEl7gt+QsQWOb8QMUXOL0RMkfMLEVPk/ELEFDm/EDFFzi9ETJHzCxFT/i+pNn4S2ZmzYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"./images/car_2.jpg\"\n",
    "predict_image(image_path, interpreter, classes_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t[PREDICTION]:  Automobile\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_path = \"./images/car_3.jpg\"\n",
    "predict_image(image_path, interpreter, classes_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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  "kernelspec": {
   "display_name": "Tf2_Py37",
   "language": "python",
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   "nbconvert_exporter": "python",
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   "hotkey": "alt-t",
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   "targetLang": "fr",
   "useGoogleTranslate": true
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
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  "varInspector": {
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   "kernels_config": {
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