{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explicit, suggestive and violent content detection using Amazon Rekognition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "This notebook provides a walkthrough of [content moderation APIs](https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html) in Amazon Rekognition. You can quickly identify inappropriate content in your video and image libraries.\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise Notebook\n",
    "import boto3\n",
    "from IPython.display import Image as IImage, display\n",
    "from IPython.display import HTML, display\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Curent AWS Region. Use this to choose corresponding S3 bucket with sample content\n",
    "\n",
    "mySession = boto3.session.Session()\n",
    "awsRegion = mySession.region_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Init clients\n",
    "rekognition = boto3.client('rekognition')\n",
    "s3 = boto3.client('s3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# S3 bucket that contains sample images and videos\n",
    "\n",
    "# We are providing sample images and videos in this bucket so\n",
    "# you do not have to manually download/upload test images and videos.\n",
    "\n",
    "bucketName = \"aws-workshops-\" + awsRegion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Content moderation in Images\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imageName = \"content-moderation/media/weapon.png\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Call Rekognition to detect objects in the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call Amazon Rekognition to detect unsafe content in the image\n",
    "# https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html\n",
    "\n",
    "detectModerationLabelsResponse = rekognition.detect_moderation_labels(\n",
    "   Image={\n",
    "       'S3Object': {\n",
    "           'Bucket': bucketName,\n",
    "           'Name': imageName,\n",
    "       }\n",
    "   }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "display(IImage(url=s3.generate_presigned_url('get_object', Params={'Bucket': bucketName, 'Key': imageName})))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Review the raw JSON reponse from Rekognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show JSON response returned by Rekognition Moderation API\n",
    "# In the JSON response below, you will see Moderation Labels, confidence score and additional information.\n",
    "\n",
    "display(detectModerationLabelsResponse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display list of detected moderation labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for label in detectModerationLabelsResponse[\"ModerationLabels\"]:\n",
    "    print(\"- {} (Confidence: {})\".format(label[\"Name\"], label[\"Confidence\"]))\n",
    "    print(\"  - Parent: {}\".format(label[\"ParentName\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Content moderation in videos\n",
    "\n",
    "Content Moderation in video is an async operation. \n",
    "https://docs.aws.amazon.com/rekognition/latest/dg/API_StartContentModeration.html\n",
    " - We first start content moderation job which returns a Job Id.\n",
    " - We can then call `get_content_moderation` to get the job status and after job is complete, we can get moderation results.\n",
    " - In production use cases, you would usually use StepFucntion or SNS topic to get notified when job is complete.\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "videoName = \"content-moderation/media/weapon.mov\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Call Rekognition to start a job for content moderation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start content moderation job\n",
    "startModerationLabelDetection = rekognition.start_content_moderation(\n",
    "    Video={\n",
    "        'S3Object': {\n",
    "            'Bucket': bucketName,\n",
    "            'Name': videoName,\n",
    "        }\n",
    "    },\n",
    ")\n",
    "\n",
    "moderationJobId = startModerationLabelDetection['JobId']\n",
    "display(\"Job Id: {0}\".format(moderationJobId))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional (Optional) Request Attributes\n",
    "\n",
    "ClientRequestTokenL\n",
    "https://docs.aws.amazon.com/rekognition/latest/dg/API_StartContentModeration.html#rekognition-StartContentModeration-request-ClientRequestToken\n",
    "\n",
    "JobTag:\n",
    "https://docs.aws.amazon.com/rekognition/latest/dg/API_StartContentModeration.html#rekognition-StartContentModeration-request-JobTag\n",
    "\n",
    "MinConfidence:\n",
    "https://docs.aws.amazon.com/rekognition/latest/dg/API_StartContentModeration.html#rekognition-StartContentModeration-request-MinConfidence\n",
    "\n",
    "NotificationChannel:\n",
    "https://docs.aws.amazon.com/rekognition/latest/dg/API_StartContentModeration.html#rekognition-StartContentModeration-request-NotificationChannel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wait for content moderation job to complete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wait for content moderation job to complete\n",
    "# In production use cases, you would usually use StepFucntion or SNS topic to get notified when job is complete.\n",
    "getContentModeration = rekognition.get_content_moderation(\n",
    "    JobId=moderationJobId,\n",
    "    SortBy='TIMESTAMP'\n",
    ")\n",
    "\n",
    "while(getContentModeration['JobStatus'] == 'IN_PROGRESS'):\n",
    "    time.sleep(5)\n",
    "    print('.', end='')\n",
    " \n",
    "    getContentModeration = rekognition.get_content_moderation(\n",
    "    JobId=moderationJobId,\n",
    "    SortBy='TIMESTAMP')\n",
    "    \n",
    "display(getContentModeration['JobStatus'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Review raw JSON reponse from Rekognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Show JSON response returned by Rekognition Content Moderation API\n",
    "# In the JSON response below, you will see list of detected content\n",
    "# For each detected object, you will see information like Timestamp\n",
    "\n",
    "display(getContentModeration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display list of potentially unsafe content in the video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "theObjects = {}\n",
    "\n",
    "strDetail = \"Moderation labels in video<br>=======================================<br>\"\n",
    "strOverall = \"Moderation labels in the overall video:<br>=======================================<br>\"\n",
    "\n",
    "# Potentially unsafe detected in each frame\n",
    "for obj in getContentModeration['ModerationLabels']:\n",
    "    ts = obj [\"Timestamp\"]\n",
    "    cconfidence = obj['ModerationLabel'][\"Confidence\"]\n",
    "    oname = obj['ModerationLabel'][\"Name\"]\n",
    "    strDetail = strDetail + \"At {} ms: {} (Confidence: {})<br>\".format(ts, oname, round(cconfidence,2))\n",
    "    if oname in theObjects:\n",
    "        cojb = theObjects[oname]\n",
    "        theObjects[oname] = {\"Name\" : oname, \"Count\": 1+cojb[\"Count\"]}\n",
    "    else:\n",
    "        theObjects[oname] = {\"Name\" : oname, \"Count\": 1}\n",
    "\n",
    "# Unique objects detected in video\n",
    "for theObject in theObjects:\n",
    "    strOverall = strOverall + \"Name: {}, Count: {}<br>\".format(theObject, theObjects[theObject][\"Count\"])\n",
    "\n",
    "# Display results\n",
    "display(HTML(strOverall))\n",
    "#display(HTML(strDetail))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show video in the player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Show video in a player\n",
    "\n",
    "s3VideoUrl = s3.generate_presigned_url('get_object', Params={'Bucket': bucketName, 'Key': videoName})\n",
    "\n",
    "videoTag = \"<video controls='controls' autoplay width='640' height='360' name='Video' src='{0}'></video>\".format(s3VideoUrl)\n",
    "\n",
    "videoui = \"<table><tr><td style='vertical-align: top'>{}</td></tr></table>\".format(videoTag)\n",
    "\n",
    "display(HTML(videoui))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "listui = \"<table><tr><td style='vertical-align: top'>{}</td></tr></table>\".format(strDetail)\n",
    "display(HTML(listui))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "### References\n",
    "- https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectModerationLabels.html\n",
    "- https://docs.aws.amazon.com/rekognition/latest/dg/API_StartContentModeration.html\n",
    "- https://docs.aws.amazon.com/rekognition/latest/dg/API_GetContentModeration.html\n",
    "\n",
    "***"
   ]
  }
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