Computer_Vision_Weapon_Detection_System_.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wBBFsy4al6gN"
   },
   "source": [
    "# **Computer-Vision-Weapon-Detection-System** \n",
    "### By: *Mugendi Mugi* and *Samuel Waweru* , Jomo Kenyatta University of Agriculture and Technology , '25 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BqKHqqFqVDWd"
   },
   "source": [
    "# **Importing Tensorflow Libraries and Dependencies** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zwgQNF72l29r",
    "outputId": "8df3206c-49fe-4ea8-9ee8-42d0590439eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow-gpu\n",
      "  Using cached tensorflow_gpu-2.9.1-cp39-cp39-win_amd64.whl (444.0 MB)\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -rotobuf (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -pencv-python (c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages)\n"
     ]
    },
    {
     "name": "stdout",
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      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.10,>=2.9->tensorflow-gpu) (0.4.8)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\hp\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.10,>=2.9->tensorflow-gpu) (3.1.1)\n",
      "Installing collected packages: tensorflow-gpu\n",
      "Successfully installed tensorflow-gpu-2.9.1\n",
      "\n",
      "[notice] A new release of pip available: 22.1.2 -> 22.2.2\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "%pip install tensorflow-gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xKgVdx4fjiRh",
    "outputId": "465299e6-0c69-4fdd-c9a3-c669c1879aa7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\hp\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\scipy\\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9.1\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9vdevZgwkDt4",
    "outputId": "a330228a-aea4-4043-9da1-3072adc5f453"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'models' already exists and is not an empty directory.\n",
      "The filename, directory name, or volume label syntax is incorrect.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/tensorflow/models.git\n",
    "!git@github.com:tensorflow/models.git "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "9MzPWSPjvKh0",
    "outputId": "5fbae6f9-8e78-4dfb-d453-39a79f946481"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s:\\code\\MachineLearningJourney\\Weapon Detection\\training_demo\n"
     ]
    }
   ],
   "source": [
    "!cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1wXOellFwH-7",
    "outputId": "10e728cc-748d-410c-f772-3808c4576be3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The system cannot find the path specified.\n"
     ]
    }
   ],
   "source": [
    "!cd /content/models/research"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "h65v3jbbwW7R",
    "outputId": "33eab970-b9e6-4c8b-aa96-1087b9206283"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S:\\code\\MachineLearningJourney\\Weapon Detection\\training_demo\n"
     ]
    }
   ],
   "source": [
    "!cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "MWYfI6zckl50"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'protoc' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "# From within TensorFlow/models/research/\n",
    "!protoc object_detection/protos/*.proto --python_out=."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Dr8C8rvaugl9",
    "outputId": "415c5b31-8d91-4c3f-9882-e6cf320011f4"
   },
   "outputs": [],
   "source": [
    "!pip install cython\n",
    "!pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-Zg10fw6yw2s"
   },
   "outputs": [],
   "source": [
    "!cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "48QkM_Kzy_nm"
   },
   "outputs": [],
   "source": [
    "!cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I-q1JIHLw1JK",
    "outputId": "be49a49f-23c6-42d4-d684-b7f44eb2c291"
   },
   "outputs": [],
   "source": [
    "# From within TensorFlow/models/research/\n",
    "!cp object_detection/packages/tf2/setup.py .\n",
    "!python -m pip install --use-feature=2020-resolver ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ydtQ52a4yVKK",
    "outputId": "6c4482dd-ec58-47c5-f193-0fc232dc0597"
   },
   "outputs": [],
   "source": [
    "# From within TensorFlow/models/research/\n",
    "!python object_detection/builders/model_builder_tf2_test.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0pCqCxCmzZWc",
    "outputId": "8839646a-f613-4eee-e5a3-9eedba14eebc"
   },
   "outputs": [],
   "source": [
    "!cd /content/training_demo/pre-trained-model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2nczUCT3bvKT",
    "outputId": "e7c14536-ea2c-4aae-c897-648e5de5670d"
   },
   "outputs": [],
   "source": [
    "!wget http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xecqce2Hcvyv",
    "outputId": "19ca3d46-e79f-44f6-8125-20f89032b59a"
   },
   "outputs": [],
   "source": [
    "!tar xvf ssd_resnet101_v1_fpn_640x640_coco17_tpu-8.tar.gz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "S98C4kp3dBmU",
    "outputId": "ec7a77ac-a7e3-4a41-dc9e-295c5e2c0bf1"
   },
   "outputs": [],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7Fq_QJ48dgNo",
    "outputId": "eda2abb2-c45f-48d2-f582-16939467f709"
   },
   "outputs": [],
   "source": [
    "!cd S:\\code\\MachineLearningJourney\\Weapon Detection\\training_demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D8Ix4z0Reo0I",
    "outputId": "a2ef9d1d-5677-4fe0-ed72-29f15b3902e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"/content/training_demo/pre-trained-model/generate_tfrecord.py\", line 21, in <module>\n",
      "    import pandas as pd\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/pandas/__init__.py\", line 11, in <module>\n",
      "    __import__(dependency)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/numpy/__init__.py\", line 163, in <module>\n",
      "    from . import ma\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/numpy/ma/__init__.py\", line 42, in <module>\n",
      "    from . import core\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/numpy/ma/core.py\", line 1247, in <module>\n",
      "    less = _MaskedBinaryOperation(umath.less)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/numpy/ma/core.py\", line 999, in __init__\n",
      "    super().__init__(mbfunc)\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/numpy/ma/core.py\", line 891, in __init__\n",
      "    self.__doc__ = ufunc.__doc__\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/numpy/core/_internal.py\", line 826, in _ufunc_doc_signature_formatter\n",
      "    in_args = ', '.join(f'x{i+1}' for i in range(ufunc.nin))\n",
      "  File \"/usr/local/lib/python3.7/dist-packages/numpy/core/_internal.py\", line 826, in <genexpr>\n",
      "    in_args = ', '.join(f'x{i+1}' for i in range(ufunc.nin))\n",
      "KeyboardInterrupt\n",
      "2022-08-12 16:16:20.703625: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "Successfully created the TFRecord file: /content/training_demo/annotations/test.record\n"
     ]
    }
   ],
   "source": [
    "# Create train data:\n",
    "!python /content/training_demo/pre-trained-model/generate_tfrecord.py -x /content/training_demo/images/train -l /content/training_demo/annotations/label_map.pbtxt -o /content/training_demo/annotations/train.record\n",
    "\n",
    "# Create test data:\n",
    "!python /content/training_demo/pre-trained-model/generate_tfrecord.py -x /content/training_demo/images/test -l /content/training_demo/annotations/label_map.pbtxt -o /content/training_demo/annotations/test.record\n",
    "\n",
    "# For example\n",
    "# python generate_tfrecord.py -x C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images/train -l C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/label_map.pbtxt -o C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/train.record\n",
    "# python generate_tfrecord.py -x C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/images/test -l C:/Users/sglvladi/Documents/Tensorflow2/workspace/training_demo/annotations/label_map.pbtxt -o C:/Users/sglvladi/Documents/Tensorflow/workspace/training_demo/annotations/test.record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "PWWisBRUhbms",
    "outputId": "33bc737e-58ee-4e10-9dd8-0dafd6e604ab"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'/content/training_demo'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hWRlWDHcbb2J",
    "outputId": "1bba7b0f-aecb-4f9d-9947-3eba0a19018c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0m\u001b[01;34mannotations\u001b[0m/     exporter_main_v2.py  model_main_tf2.py  \u001b[01;34mpre-trained-model\u001b[0m/\n",
      "\u001b[01;34mexported_model\u001b[0m/  \u001b[01;34mimages\u001b[0m/              \u001b[01;34mmodels\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PdaQKgd3bcV8",
    "outputId": "77ad9e98-2543-400e-d345-68c79de1a397"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-08-12 16:19:54.461683: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.\n",
      "W0812 16:19:54.463684 140223796660096 cross_device_ops.py:1386] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.\n",
      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)\n",
      "I0812 16:19:54.468206 140223796660096 mirrored_strategy.py:374] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)\n",
      "INFO:tensorflow:Maybe overwriting train_steps: None\n",
      "I0812 16:19:54.473280 140223796660096 config_util.py:552] Maybe overwriting train_steps: None\n",
      "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
      "I0812 16:19:54.473530 140223796660096 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "rename to distribute_datasets_from_function\n",
      "W0812 16:19:54.509964 140223796660096 deprecation.py:356] From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:564: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "rename to distribute_datasets_from_function\n",
      "INFO:tensorflow:Reading unweighted datasets: ['/content/training_demo/annotations/train.record']\n",
      "I0812 16:19:54.515677 140223796660096 dataset_builder.py:162] Reading unweighted datasets: ['/content/training_demo/annotations/train.record']\n",
      "INFO:tensorflow:Reading record datasets for input file: ['/content/training_demo/annotations/train.record']\n",
      "I0812 16:19:54.515968 140223796660096 dataset_builder.py:79] Reading record datasets for input file: ['/content/training_demo/annotations/train.record']\n",
      "INFO:tensorflow:Number of filenames to read: 1\n",
      "I0812 16:19:54.516086 140223796660096 dataset_builder.py:80] Number of filenames to read: 1\n",
      "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
      "W0812 16:19:54.516204 140223796660096 dataset_builder.py:87] num_readers has been reduced to 1 to match input file shards.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:104: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\n",
      "W0812 16:19:54.520988 140223796660096 deprecation.py:356] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:104: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:236: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.map()\n",
      "W0812 16:19:54.712972 140223796660096 deprecation.py:356] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:236: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.map()\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
      "W0812 16:20:03.236280 140223796660096 deprecation.py:356] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
      "W0812 16:20:06.864904 140223796660096 deprecation.py:356] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "W0812 16:20:08.989618 140223796660096 deprecation.py:356] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:1082: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "/usr/local/lib/python3.7/dist-packages/keras/backend.py:450: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use fn_output_signature instead\n",
      "W0812 16:21:28.840380 140221320644352 deprecation.py:560] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use fn_output_signature instead\n",
      "^C\n"
     ]
    }
   ],
   "source": [
    "!python model_main_tf2.py --model_dir=/content/training_demo/models/my_ssd_resnet101_v1_fpn --pipeline_config_path=/content/training_demo/models/my_ssd_resnet101_v1_fpn/pipeline.config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "id": "JLLQ01Z9cMoC",
    "outputId": "aa8403ed-8083-4cdb-9a70-350f834263e5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'/content/training_demo'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!cd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mSkxrKkuiLu2",
    "outputId": "f1da834c-264b-442c-d421-e574d09d2c9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-08-12 16:23:43.941443: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
      "W0812 16:23:44.265135 140184758941568 deprecation.py:628] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
      "Instead of:\n",
      "results = tf.map_fn(fn, elems, back_prop=False)\n",
      "Use:\n",
      "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
      "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f7ecdaa9450>, because it is not built.\n",
      "W0812 16:24:16.839783 140184758941568 save_impl.py:72] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f7ecdaa9450>, because it is not built.\n",
      "W0812 16:25:05.835531 140184758941568 save.py:238] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalClassHead_layer_call_fn while saving (showing 5 of 329). These functions will not be directly callable after loading.\n",
      "INFO:tensorflow:Assets written to: /content/training_demo/exported_model/my_model/saved_model/assets\n",
      "I0812 16:25:17.138728 140184758941568 builder_impl.py:780] Assets written to: /content/training_demo/exported_model/my_model/saved_model/assets\n",
      "INFO:tensorflow:Writing pipeline config file to /content/training_demo/exported_model/my_model/pipeline.config\n",
      "I0812 16:25:18.371426 140184758941568 config_util.py:254] Writing pipeline config file to /content/training_demo/exported_model/my_model/pipeline.config\n"
     ]
    }
   ],
   "source": [
    "!python exporter_main_v2.py --input_type image_tensor --pipeline_config_path /content/training_demo/models/my_ssd_resnet101_v1_fpn/pipeline.config --trained_checkpoint_dir /content/training_demo/models/my_ssd_resnet101_v1_fpn --output_directory /content/training_demo/exported_model/my_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9fNvGs0kntsx"
   },
   "source": [
    "## **Inferencing my trained model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 287
    },
    "id": "poxCuBRSiy3H",
    "outputId": "20cb9036-08cd-46f0-a965-8e6d3d83e8b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model...Done! Took 27.420327186584473 seconds\n",
      "Running inference for /content/testimage1.jpg... Done\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=377x235 at 0x7F7175363690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Object Detection (On Image) From TF2 Saved Model\n",
    "=====================================\n",
    "\"\"\"\n",
    "\n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)\n",
    "import pathlib\n",
    "import tensorflow as tf\n",
    "import cv2\n",
    "import argparse\n",
    "from google.colab.patches import cv2_imshow\n",
    "\n",
    "# Enable GPU dynamic memory allocation\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)\n",
    "\n",
    "# PROVIDE PATH TO IMAGE DIRECTORY\n",
    "IMAGE_PATHS = 'S:\\code\\MachineLearningJourney\\Weapon Detection\\training_demo\\testimage1.jpg'\n",
    "\n",
    "\n",
    "# PROVIDE PATH TO MODEL DIRECTORY\n",
    "PATH_TO_MODEL_DIR = '/content/training_demo/exported_model/my_model'\n",
    "\n",
    "# PROVIDE PATH TO LABEL MAP\n",
    "PATH_TO_LABELS = '/content/training_demo/annotations/label_map.pbtxt'\n",
    "\n",
    "# PROVIDE THE MINIMUM CONFIDENCE THRESHOLD\n",
    "MIN_CONF_THRESH = float(0.60)\n",
    "\n",
    "# LOAD THE MODEL\n",
    "\n",
    "import time\n",
    "from object_detection.utils import label_map_util\n",
    "from object_detection.utils import visualization_utils as viz_utils\n",
    "\n",
    "PATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + \"/saved_model\"\n",
    "\n",
    "print('Loading model...', end='')\n",
    "start_time = time.time()\n",
    "\n",
    "# LOAD SAVED MODEL AND BUILD DETECTION FUNCTION\n",
    "detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)\n",
    "\n",
    "end_time = time.time()\n",
    "elapsed_time = end_time - start_time\n",
    "print('Done! Took {} seconds'.format(elapsed_time))\n",
    "\n",
    "# LOAD LABEL MAP DATA FOR PLOTTING\n",
    "\n",
    "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n",
    "                                                                    use_display_name=True)\n",
    "\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')   # Suppress Matplotlib warnings\n",
    "\n",
    "def load_image_into_numpy_array(path):\n",
    "    \"\"\"Load an image from file into a numpy array.\n",
    "    Puts image into numpy array to feed into tensorflow graph.\n",
    "    Note that by convention we put it into a numpy array with shape\n",
    "    (height, width, channels), where channels=3 for RGB.\n",
    "    Args:\n",
    "      path: the file path to the image\n",
    "    Returns:\n",
    "      uint8 numpy array with shape (img_height, img_width, 3)\n",
    "    \"\"\"\n",
    "    return np.array(Image.open(path))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print('Running inference for {}... '.format(IMAGE_PATHS), end='')\n",
    "\n",
    "image = cv2.imread(IMAGE_PATHS)\n",
    "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "image_expanded = np.expand_dims(image_rgb, axis=0)\n",
    "\n",
    "# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.\n",
    "input_tensor = tf.convert_to_tensor(image)\n",
    "# The model expects a batch of images, so add an axis with `tf.newaxis`.\n",
    "input_tensor = input_tensor[tf.newaxis, ...]\n",
    "\n",
    "# input_tensor = np.expand_dims(image_np, 0)\n",
    "detections = detect_fn(input_tensor)\n",
    "\n",
    "# All outputs are batches tensors.\n",
    "# Convert to numpy arrays, and take index [0] to remove the batch dimension.\n",
    "# We're only interested in the first num_detections.\n",
    "num_detections = int(detections.pop('num_detections'))\n",
    "detections = {key: value[0, :num_detections].numpy()\n",
    "               for key, value in detections.items()}\n",
    "detections['num_detections'] = num_detections\n",
    "\n",
    "# detection_classes should be ints.\n",
    "detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
    "\n",
    "image_with_detections = image.copy()\n",
    "\n",
    "# SET MIN_SCORE_THRESH BASED ON YOU MINIMUM THRESHOLD FOR DETECTIONS\n",
    "viz_utils.visualize_boxes_and_labels_on_image_array(\n",
    "      image_with_detections,\n",
    "      detections['detection_boxes'],\n",
    "      detections['detection_classes'],\n",
    "      detections['detection_scores'],\n",
    "      category_index,\n",
    "      use_normalized_coordinates=True,\n",
    "      max_boxes_to_draw=200,\n",
    "      min_score_thresh=0.5,\n",
    "      agnostic_mode=False)\n",
    "\n",
    "print('Done')\n",
    "# DISPLAYS OUTPUT IMAGE\n",
    "cv2_imshow(image_with_detections)\n",
    "# CLOSES WINDOW ONCE KEY IS PRESSED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qlOQeroWpelI"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Computer-Vision-Weapon-Detection-System .ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3.9.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "b671c20432fcd147198c92e7f072af9e705f087eb990bee22b07f08caab9f630"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}