Object_Detection.ipynb

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   "cell_type": "markdown",
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    "id": "BNm6JThea320"
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   "source": [
    "# Object Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "    !pip install transformers\n",
    "    !pip install gradio\n",
    "    !pip install timm\n",
    "    !pip install inflect\n",
    "    !pip install phonemizer\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:**  `py-espeak-ng` is only available Linux operating systems.\n",
    "\n",
    "To run locally in a Linux machine, follow these commands:\n",
    "```\n",
    "    sudo apt-get update\n",
    "    sudo apt-get install espeak-ng\n",
    "    pip install py-espeak-ng\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kkkJorDBd8oY"
   },
   "source": [
    "### Build the `object-detection` pipeline using 🤗 Transformers Library\n",
    "\n",
    "- This model was release with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) from Carion et al. (2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30,
    "id": "BwlBLWY5dLhB"
   },
   "outputs": [],
   "source": [
    "from helper import load_image_from_url, render_results_in_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Here is some code that suppresses warning messages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 98
   },
   "outputs": [],
   "source": [
    "from transformers.utils import logging\n",
    "logging.set_verbosity_error()\n",
    "\n",
    "from helper import ignore_warnings\n",
    "ignore_warnings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "od_pipe = pipeline(\"object-detection\", \"./models/facebook/detr-resnet-50\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Info about [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore more of the [Hugging Face Hub for more object detection models](https://huggingface.co/models?pipeline_tag=object-detection&sort=trending)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use the Pipeline"
   ]
  },
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    "outputId": "38fc2acf-b935-4698-e154-64aef6d651a7"
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   "outputs": [],
   "source": [
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "height": 47
   },
   "outputs": [],
   "source": [
    "raw_image = Image.open('huggingface_friends.jpg')\n",
    "raw_image.resize((569, 491))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "id": "7fcJtShhfkf1"
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   "source": [
    "pipeline_output = od_pipe(raw_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Return the results from the pipeline using the helper function `render_results_in_image`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "processed_image = render_results_in_image(\n",
    "    raw_image, \n",
    "    pipeline_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "processed_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using `Gradio` as a Simple Interface\n",
    "\n",
    "- Use [Gradio](https://www.gradio.app) to create a demo for the object detection app.\n",
    "- The demo makes it look friendly and easy to use.\n",
    "- You can share the demo with your friends and colleagues as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 132,
    "id": "HPfWT6d8mTEL"
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   "outputs": [],
   "source": [
    "def get_pipeline_prediction(pil_image):\n",
    "    \n",
    "    pipeline_output = od_pipe(pil_image)\n",
    "    \n",
    "    processed_image = render_results_in_image(pil_image,\n",
    "                                            pipeline_output)\n",
    "    return processed_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 132,
    "id": "3onzDMlzl7uj"
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   "source": [
    "demo = gr.Interface(\n",
    "  fn=get_pipeline_prediction,\n",
    "  inputs=gr.Image(label=\"Input image\", \n",
    "                  type=\"pil\"),\n",
    "  outputs=gr.Image(label=\"Output image with predicted instances\",\n",
    "                   type=\"pil\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `share=True` will provide an online link to access to the demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
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    "id": "QcE4p98UmGOc",
    "outputId": "fdf28840-e2c1-488c-da58-8dc605c6f42e"
   },
   "outputs": [],
   "source": [
    "demo.launch(share=True, server_port=int(os.environ['PORT1']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "demo.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "height": 30
   },
   "source": [
    "### Close the app\n",
    "- Remember to call `.close()` on the Gradio app when you're done using it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6LA1Wm6bl724"
   },
   "source": [
    "### Audio Assistant\n",
    "\n",
    "- Combine the object detector with a text-to-speech model that will help dictate what is inside the image.\n",
    "\n",
    "- Inspect the output of the object detection pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "pipeline_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "od_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "raw_image = Image.open('huggingface_friends.jpg')\n",
    "raw_image.resize((284, 245))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from helper import summarize_predictions_natural_language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "text = summarize_predictions_natural_language(pipeline_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Audio Narration of an Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "tts_pipe = pipeline(\"text-to-speech\",\n",
    "                    model=\"./models/kakao-enterprise/vits-ljs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More info about [kakao-enterprise/vits-ljs](https://huggingface.co/kakao-enterprise/vits-ljs)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "narrated_text = tts_pipe(text)"
   ]
  },
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   "source": [
    "### Play the Generated Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from IPython.display import Audio as IPythonAudio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "outputId": "a0bbdce2-216c-40c3-85da-2dd20d5676e8"
   },
   "outputs": [],
   "source": [
    "IPythonAudio(narrated_text[\"audio\"][0],\n",
    "             rate=narrated_text[\"sampling_rate\"])"
   ]
  }
 ],
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