Models / Smoking & Tobacco Product Detection

Smoking & Tobacco Product Detection

Overview

The Smoking & Tobacco Product detection model helps you determine if an image or video contains tobacco products and smoking-related situations such as:

people visibly smoking (such as holding a cigarette or puffing smoke)
cigarettes, cigarette butts, cigarette packs and similar paper-based smoking products
cigars
pipes
hookahs, shishas
vapes and vaping products
loose or chewing tobacco

This model focuses on products that are legal in most jurisdictions.

To detect marijuana-related smoking such as joints, cannabis, marijuana leaves, processed marijuana or to detect devices usually related to the consumption of illegal drugs (such as bongs or glass pipes) use the Drug Detection model.

Regular tobacco

The regular_tobacco class is used to flag tobacco products and smoking scenes that do not include any references to marijuana/cannabis or other drugs.

The following situations will be flagged:

Cigarettes & cigarette butts

Typical commercial cigarettes or cigarette butts. This does *not* include joints that are visibly associated with marijuana.

regular_tobacco

People smoking or puffing smoke

Displays of people visibly smoking, such as holding a cigarette, cigar or pipe, puffing smoke, or having their face surrounded by smoke. This does *not* include cases with references to marijuana.

regular_tobacco

Cigarette packs

Typical commercial cigarette packs, as sold in stores.

regular_tobacco

Cigars

Cigars in hand or in cigar boxes.

regular_tobacco

Smoking Pipes

Tobacco pipes, either in hand or not. This does not include glasspipes.

regular_tobacco

Hookahs, shishas or waterpipes

Instruments used for smoking, especially in some European or Middle-Eastern countries. This does not include bongs.

regular_tobacco

Chewing or loose tobacco

Tobacco products that are not smoked: chewing tobacco, snuff, dip and loose tobacco.

regular_tobacco

Vapes and electronic cigarettes

Vapes, vape juice, vape cartridges, unless they have marijuana symbols.

regular_tobacco

Ambiguous tobacco

In some cases, it is not possible to determine if a hand-rolled cigarette contains only regular tobacco or if it also contains marijuana/cannabis/weed. In such cases, the API will return the ambiguous_tobacco tag:

Ambiguous cigarette or joint

Hand-rolled cigarette/joint/blunt/cigarillo where it is unclear whether the content is tobacco or marijuana due to shape, zoom or visibility.

ambiguous_tobacco

If there are additional signs that the content is marijuana-related (such as the color or shape of the joint, the image context, marijuana symbols or references, marijuana-related paraphernalia etc), then this will not be flagged here. To detect such instances, use the Drug Detection model.

Use the model (images)

If you haven't already, create an account to get your own API keys.

Detect Tobacco content

Let's say you want to moderate the following image:

You can either share a URL to the image, or upload the raw binary image.

Option 1: Send image URL

Here's how to proceed if you choose to share the image URL:


curl -X GET -G 'https://api.sightengine.com/1.0/check.json' \
    -d 'models=tobacco' \
    -d 'api_user={api_user}&api_secret={api_secret}' \
    --data-urlencode 'url=https://sightengine.com/assets/img/doc/tobacco/smoking.jpg'


# this example uses requests
import requests
import json

params = {
  'url': 'https://sightengine.com/assets/img/doc/tobacco/smoking.jpg',
  'models': 'tobacco',
  'api_user': '{api_user}',
  'api_secret': '{api_secret}'
}
r = requests.get('https://api.sightengine.com/1.0/check.json', params=params)

output = json.loads(r.text)


$params = array(
  'url' =>  'https://sightengine.com/assets/img/doc/tobacco/smoking.jpg',
  'models' => 'tobacco',
  'api_user' => '{api_user}',
  'api_secret' => '{api_secret}',
);

// this example uses cURL
$ch = curl_init('https://api.sightengine.com/1.0/check.json?'.http_build_query($params));
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
$response = curl_exec($ch);
curl_close($ch);

$output = json_decode($response, true);


// this example uses axios
const axios = require('axios');

axios.get('https://api.sightengine.com/1.0/check.json', {
  params: {
    'url': 'https://sightengine.com/assets/img/doc/tobacco/smoking.jpg',
    'models': 'tobacco',
    'api_user': '{api_user}',
    'api_secret': '{api_secret}',
  }
})
.then(function (response) {
  // on success: handle response
  console.log(response.data);
})
.catch(function (error) {
  // handle error
  if (error.response) console.log(error.response.data);
  else console.log(error.message);
});

See request parameter description

Parameter	Type	Description
media	binary	image to analyze
models	string	comma-separated list of models to apply
api_user	string	your API user id
api_secret	string	your API secret

Option 2: Send raw image

Here's how to proceed if you choose to upload the raw image:


curl -X POST 'https://api.sightengine.com/1.0/check.json' \
    -F 'media=@/path/to/image.jpg' \
    -F 'models=tobacco' \
    -F 'api_user={api_user}' \
    -F 'api_secret={api_secret}'


# this example uses requests
import requests
import json

params = {
  'models': 'tobacco',
  'api_user': '{api_user}',
  'api_secret': '{api_secret}'
}
files = {'media': open('/path/to/image.jpg', 'rb')}
r = requests.post('https://api.sightengine.com/1.0/check.json', files=files, data=params)

output = json.loads(r.text)


$params = array(
  'media' => new CurlFile('/path/to/image.jpg'),
  'models' => 'tobacco',
  'api_user' => '{api_user}',
  'api_secret' => '{api_secret}',
);

// this example uses cURL
$ch = curl_init('https://api.sightengine.com/1.0/check.json');
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, $params);
$response = curl_exec($ch);
curl_close($ch);

$output = json_decode($response, true);


// this example uses axios and form-data
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');

data = new FormData();
data.append('media', fs.createReadStream('/path/to/image.jpg'));
data.append('models', 'tobacco');
data.append('api_user', '{api_user}');
data.append('api_secret', '{api_secret}');

axios({
  method: 'post',
  url:'https://api.sightengine.com/1.0/check.json',
  data: data,
  headers: data.getHeaders()
})
.then(function (response) {
  // on success: handle response
  console.log(response.data);
})
.catch(function (error) {
  // handle error
  if (error.response) console.log(error.response.data);
  else console.log(error.message);
});

See request parameter description

Parameter	Type	Description
media	binary	image to analyze
models	string	comma-separated list of models to apply
api_user	string	your API user id
api_secret	string	your API secret

API response

The API will then return a JSON response with the following structure:

                  
                  
{
  "status": "success",
  "request": {
      "id": "req_1OjggusalNb2S7MxwLq2h",
      "timestamp": 1509132120.6988,
      "operations": 1
  },
  "tobacco": {
      "prob": 0.9,
      "classes": {
        "regular_tobacco": 0.9,
        "ambiguous_tobacco": 0.01
      }
  },
  "media": {
      "id": "med_1OjgEqvJtOhqP7sfNe3ga",
      "uri": "https://sightengine.com/assets/img/doc/tobacco/smoking.jpg"
  }
}

Use model (Videos)

Detecting Tobacco content in videos

Option 1: Short video

Here's how to proceed to analyze a short video (less than 1 minute):


curl -X POST 'https://api.sightengine.com/1.0/video/check-sync.json' \
  -F 'media=@/path/to/video.mp4' \
  -F 'models=tobacco' \
  -F 'api_user={api_user}' \
  -F 'api_secret={api_secret}'


# this example uses requests
import requests
import json

params = {
  # specify the models you want to apply
  'models': 'tobacco',
  'api_user': '{api_user}',
  'api_secret': '{api_secret}'
}
files = {'media': open('/path/to/video.mp4', 'rb')}
r = requests.post('https://api.sightengine.com/1.0/video/check-sync.json', files=files, data=params)

output = json.loads(r.text)


$params = array(
  'media' => new CurlFile('/path/to/video.mp4'),
  // specify the models you want to apply
  'models' => 'tobacco',
  'api_user' => '{api_user}',
  'api_secret' => '{api_secret}',
);

// this example uses cURL
$ch = curl_init('https://api.sightengine.com/1.0/video/check-sync.json');
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, $params);
$response = curl_exec($ch);
curl_close($ch);

$output = json_decode($response, true);


// this example uses axios and form-data
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');

data = new FormData();
data.append('media', fs.createReadStream('/path/to/video.mp4'));
// specify the models you want to apply
data.append('models', 'tobacco');
data.append('api_user', '{api_user}');
data.append('api_secret', '{api_secret}');

axios({
  method: 'post',
  url:'https://api.sightengine.com/1.0/video/check-sync.json',
  data: data,
  headers: data.getHeaders()
})
.then(function (response) {
  // on success: handle response
  console.log(response.data);
})
.catch(function (error) {
  // handle error
  if (error.response) console.log(error.response.data);
  else console.log(error.message);
});

See request parameter description

Parameter	Type	Description
media	binary	image to analyze
models	string	comma-separated list of models to apply
interval	float	frame interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional)
api_user	string	your API user id
api_secret	string	your API secret

Option 2: Long video

Here's how to proceed to analyze a long video. Note that if the video file is very large, you might first need to upload it through the Upload API.


curl -X POST 'https://api.sightengine.com/1.0/video/check.json' \
  -F 'media=@/path/to/video.mp4' \
  -F 'models=tobacco' \
  -F 'callback_url=https://yourcallback/path' \
  -F 'api_user={api_user}' \
  -F 'api_secret={api_secret}'


# this example uses requests
import requests
import json

params = {
  # specify the models you want to apply
  'models': 'tobacco',
  # specify where you want to receive result callbacks
  'callback_url': 'https://yourcallback/path',
  'api_user': '{api_user}',
  'api_secret': '{api_secret}'
}
files = {'media': open('/path/to/video.mp4', 'rb')}
r = requests.post('https://api.sightengine.com/1.0/video/check.json', files=files, data=params)

output = json.loads(r.text)


$params = array(
  'media' => new CurlFile('/path/to/video.mp4'),
  // specify the models you want to apply
  'models' => 'tobacco',
  // specify where you want to receive result callbacks
  'callback_url' => 'https://yourcallback/path',
  'api_user' => '{api_user}',
  'api_secret' => '{api_secret}',
);

// this example uses cURL
$ch = curl_init('https://api.sightengine.com/1.0/video/check.json');
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_POSTFIELDS, $params);
$response = curl_exec($ch);
curl_close($ch);

$output = json_decode($response, true);


// this example uses axios and form-data
const axios = require('axios');
const FormData = require('form-data');
const fs = require('fs');

data = new FormData();
data.append('media', fs.createReadStream('/path/to/video.mp4'));
// specify the models you want to apply
data.append('models', 'tobacco');
// specify where you want to receive result callbacks
data.append('callback_url', 'https://yourcallback/path');
data.append('api_user', '{api_user}');
data.append('api_secret', '{api_secret}');

axios({
  method: 'post',
  url:'https://api.sightengine.com/1.0/video/check.json',
  data: data,
  headers: data.getHeaders()
})
.then(function (response) {
  // on success: handle response
  console.log(response.data);
})
.catch(function (error) {
  // handle error
  if (error.response) console.log(error.response.data);
  else console.log(error.message);
});

See request parameter description

Parameter	Type	Description
media	binary	image to analyze
callback_url	string	callback URL to receive moderation updates (optional)
models	string	comma-separated list of models to apply
interval	float	frame interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional)
api_user	string	your API user id
api_secret	string	your API secret

Option 3: Live-stream

Here's how to proceed to analyze a live-stream:


curl -X GET -G 'https://api.sightengine.com/1.0/video/check.json' \
    --data-urlencode 'stream_url=https://domain.tld/path/video.m3u8' \
    -d 'models=tobacco' \
    -d 'callback_url=https://your.callback.url/path' \
    -d 'api_user={api_user}' \
    -d 'api_secret={api_secret}'


# if you haven't already, install the SDK with 'pip install sightengine'
from sightengine.client import SightengineClient
client = SightengineClient('{api_user}','{api_secret}')
output = client.check('tobacco').video('https://domain.tld/path/video.m3u8', 'https://your.callback.url/path')


// if you haven't already, install the SDK with 'composer require sightengine/client-php'
use \Sightengine\SightengineClient;
$client = new SightengineClient('{api_user}','{api_secret}');
$output = $client->check(['tobacco'])->video('https://domain.tld/path/video.m3u8', 'https://your.callback.url/path');


// if you haven't already, install the SDK with 'npm install sightengine --save'
var sightengine = require('sightengine')('{api_user}', '{api_secret}');
sightengine.check(['tobacco']).video('https://domain.tld/path/video.m3u8', 'https://your.callback.url/path').then(function(result) {
    // The API response (result)
}).catch(function(err) {
    // Handle error
});

See request parameter description

Parameter	Type	Description
stream_url	string	URL of the video stream
callback_url	string	callback URL to receive moderation updates (optional)
models	string	comma-separated list of models to apply
interval	float	frame interval in seconds, out of 0.5, 1, 2, 3, 4, 5 (optional)
api_user	string	your API user id
api_secret	string	your API secret

Moderation result

The Moderation result will be provided either directly in the request response (for sync calls, see below) or through the callback URL your provided (for async calls).

Here is the structure of the JSON response with moderation results for each analyzed frame under the data.frames array:

            
                  
{
  "status": "success",
    "request": {
    "id": "req_gmgHNy8oP6nvXYaJVLq9n",
    "timestamp": 1717159864.348989,
    "operations": 21
  },
  "data": {
  "frames": [
    {
      "info": {
        "id": "med_gmgHcUOwe41rWmqwPhVNU_1",
        "position": 0
      },
      "tobacco": {
        "prob": 0.9,
        "classes": {
          "regular_tobacco": 0.9,
          "ambiguous_tobacco": 0.01
        }
      },
     },
     ...
    ]
  },
  "media": {
    "id": "med_gmgHcUOwe41rWmqwPhVNU",
    "uri": "yourfile.mp4"
  },
}

You can use the classes under the tobacco object to detect tobacco products and smoking in the video.

Any other needs?

See our full list of Image/Video models for details on other filters and checks you can run on your images and videos. You might also want to check our Text models to moderate text-based content: messages, reviews, comments, usernames...

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MODERATION

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REFERENCE

Smoking & Tobacco Product Detection

Table of contents

Overview

Regular tobacco

Cigarettes & cigarette butts

People smoking or puffing smoke

Cigarette packs

Cigars

Smoking Pipes

Hookahs, shishas or waterpipes

Chewing or loose tobacco

Vapes and electronic cigarettes

Ambiguous tobacco

Ambiguous cigarette or joint

Use the model (images)

Detect Tobacco content

Option 1: Send image URL

Option 2: Send raw image

API response

Use model (Videos)

Detecting Tobacco content in videos

Option 1: Short video

Option 2: Long video

Option 3: Live-stream

Moderation result

Any other needs?