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   "source": [
    "<span style =\" color : green ;font - weight : bold \">ImageAI for Object Detection</span>\n",
    "http://imageai.org/#features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet.\n",
    "</p>\n",
    "ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. The model implementations provided include RetinaNet, YOLOv3 and TinyYOLOv3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 80 possible objects that you can detect with the\n",
    "ObjectDetection class, and they are as seen below.\n",
    "\n",
    "    person,   bicycle,   car,   motorcycle,   airplane,\n",
    "    bus,   train,   truck,   boat,   traffic light,   fire hydrant,   stop_sign,\n",
    "    parking meter,   bench,   bird,   cat,   dog,   horse,   sheep,   cow,   elephant,   bear,   zebra,\n",
    "    giraffe,   backpack,   umbrella,   handbag,   tie,   suitcase,   frisbee,   skis,   snowboard,\n",
    "    sports ball,   kite,   baseball bat,   baseball glove,   skateboard,   surfboard,   tennis racket,\n",
    "    bottle,   wine glass,   cup,   fork,   knife,   spoon,   bowl,   banana,   apple,   sandwich,   orange,\n",
    "    broccoli,   carrot,   hot dog,   pizza,   donot,   cake,   chair,   couch,   potted plant,   bed,\n",
    "    dining table,   toilet,   tv,   laptop,   mouse,   remote,   keyboard,   cell phone,   microwave,\n",
    "    oven,   toaster,   sink,   refrigerator,   book,   clock,   vase,   scissors,   teddy bear,   hair dryer,\n",
    "    toothbrush."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>requirements：</p>\n",
    "<p>tensorflow==1.15.0</p>\n",
    "<p>numpy==1.19.5</p>\n",
    "<p>scipy==1.4.1</p>\n",
    "<p>keras==2.1.0</p>\n",
    "<p>imageai==2.0.2</p>\n",
    "\n",
    "<p>Or update to newest version, see https://github.com/OlafenwaMoses/ImageAI</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the RetinaNet model file for object detection\n",
    "\n",
    "https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_coco_best_v2.0.1.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imageai.Detection import ObjectDetection\n",
    "import matplotlib.pyplot as plt\n",
    "import skimage.io\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "execution_path = os.getcwd()\n",
    "\n",
    "detector = ObjectDetection()\n",
    "detector.setModelTypeAsRetinaNet()\n",
    "detector.setModelPath(os.path.join(execution_path, \"resnet50_coco_best_v2.0.1.h5\"))\n",
    "detector.loadModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "detections = detector.detectObjectsFromImage(\n",
    "    input_image=os.path.join(execution_path, \"image.jpg\"),\n",
    "    output_image_path=os.path.join(execution_path, \"imagenew.jpg\"),\n",
    ")\n",
    "\n",
    "for eachObject in detections:\n",
    "    print(eachObject[\"name\"], \" : \", eachObject[\"percentage_probability\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = skimage.io.imread(\"image.jpg\")\n",
    "imagenew = skimage.io.imread(\"imagenew.jpg\")\n",
    "\n",
    "_, axis = plt.subplots(1, 2)\n",
    "axis[0].imshow(image, cmap=\"gray\")\n",
    "axis[1].imshow(imagenew, cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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