deploy_existing_model.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "\n",
    "role = get_execution_role()\n",
    "sess = sagemaker.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.model import Model\n",
    "from sagemaker.predictor import RealTimePredictor, json_deserializer\n",
    "\n",
    "class ImagePredictor(RealTimePredictor):\n",
    "    def __init__(self, endpoint_name, sagemaker_session):\n",
    "        super().__init__(endpoint_name, sagemaker_session=sagemaker_session, serializer=None, \n",
    "                         deserializer=json_deserializer, content_type='image/jpeg')\n",
    "training_image = get_image_uri(sess.boto_region_name, 'object-detection', repo_version=\"latest\")\n",
    "model_data = 's3://sagemaker-working-bucket/Sweets/output/model.tar.gz'\n",
    "\n",
    "model = Model(role =role,image=training_image,model_data = model_data, predictor_cls=ImagePredictor, sagemaker_session=sess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "object_detector = model.deploy(initial_instance_count = 1, instance_type = 'ml.m4.xlarge')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "def visualize_detection(img_file, dets, classes=[], thresh=0.1):\n",
    "        import random\n",
    "        import matplotlib.pyplot as plt\n",
    "        import matplotlib.image as mpimg\n",
    "\n",
    "        img=mpimg.imread(img_file)\n",
    "        plt.imshow(img)\n",
    "        height = img.shape[0]\n",
    "        width = img.shape[1]\n",
    "        colors = dict()\n",
    "        for det in dets:\n",
    "            (klass, score, x0, y0, x1, y1) = det\n",
    "            if score < thresh:\n",
    "                continue\n",
    "            cls_id = int(klass)\n",
    "            if cls_id not in colors:\n",
    "                colors[cls_id] = (random.random(), random.random(), random.random())\n",
    "            xmin = int(x0 * width)\n",
    "            ymin = int(y0 * height)\n",
    "            xmax = int(x1 * width)\n",
    "            ymax = int(y1 * height)\n",
    "            rect = plt.Rectangle((xmin, ymin), xmax - xmin,\n",
    "                                 ymax - ymin, fill=False,\n",
    "                                 edgecolor=colors[cls_id],\n",
    "                                 linewidth=3.5)\n",
    "            plt.gca().add_patch(rect)\n",
    "            class_name = str(cls_id)\n",
    "            if classes and len(classes) > cls_id:\n",
    "                class_name = classes[cls_id]\n",
    "            plt.gca().text(xmin, ymin - 2,\n",
    "                            '{:s} {:.3f}'.format(class_name, score),\n",
    "                            bbox=dict(facecolor=colors[cls_id], alpha=0.5),\n",
    "                                    fontsize=12, color='white')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'TestData_Sweets/sweet001.png'\n",
    "\n",
    "with open(file_name, 'rb') as image:\n",
    "    f = image.read()\n",
    "    b = bytearray(f)\n",
    "    ne = open('n.txt','wb')\n",
    "    ne.write(b)\n",
    "object_detector.content_type = 'image/jpeg'\n",
    "detections = object_detector.predict(b)\n",
    "\n",
    "print(detections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "object_categories = ['BlackThunder','HomePie','Bisco']\n",
    "\n",
    "threshold = 0.2\n",
    "visualize_detection(file_name, detections['prediction'], object_categories, threshold)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Delete EndPoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker.Session().delete_endpoint(object_detector.endpoint)"
   ]
  }
 ],
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