API to emotions and behavior in a few lines of code

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Face detector Emotion recognition

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  • 3
    Add key and API request to your service. That long:
  • api_token = 'path/my.key'
    api_req_base = 'https://api.neurodatalab.com/'
  • Multimodal
    Analyses facial expressions, voice, body pose and physiology
  • Real-time
    All services can handle real-time data flow as fast as pre-recorded data
  • Multiperson
    Get per-person data for up to 7 people present in the frame
  • Precise
    Works with natural data ‘in the wild’ thanks to the biggest affective data set
  • Face
    detector
    0.01 $ per image $ / image
  • Emotion
    recognition
    0.015 $ per image $ / image

Monthly postpayments. Free trial key for 500 images.
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Close
fd
Face detector
DEMO

Choose an image to process:

Soon Fd will work with videos. Be the first to know

Process another image fd
Face detector
DEMO
image 
				See documentation
				
#example code
image = Image.from_file('example1.jpg')
result, status, error_message = face_detector.on_image(image)
See documentation 
				
					
				
				
[
  {
    "h": 278,
    "score": "0.91907316",
    "w": 281,
    "y": 183,
    "x": 132
  },
  {
    "h": 321,
    "score": "0.89893955",
    "w": 301,
    "y": 175,
    "x": 531
  }
]
Process another image fd
Face detector
DEMO
image 
				See documentation
				
#example code
image = Image.from_file('example1.jpg')
result, status, error_message = face_detector.on_image(image)
See documentation 
				
					
				
				
[
  {
    "h": 278,
    "score": "0.91907316",
    "w": 281,
    "y": 183,
    "x": 132
  },
  {
    "h": 321,
    "score": "0.89893955",
    "w": 301,
    "y": 175,
    "x": 531
  }
]
Process another image fd
Face detector
DEMO
image 
				See documentation
				
#example code
image = Image.from_file('example1.jpg')
result, status, error_message = face_detector.on_image(image)
See documentation 
				
					
				
				
[
  {
    "h": 278,
    "score": "0.91907316",
    "w": 281,
    "y": 183,
    "x": 132
  },
  {
    "h": 321,
    "score": "0.89893955",
    "w": 301,
    "y": 175,
    "x": 531
  }
]
Process another image fd
Face detector
DEMO
image 
				See documentation
				
#example code
image = Image.from_file('example1.jpg')
result, status, error_message = face_detector.on_image(image)
See documentation 
				
					
				
				
[
  {
    "h": 278,
    "score": "0.91907316",
    "w": 281,
    "y": 183,
    "x": 132
  },
  {
    "h": 321,
    "score": "0.89893955",
    "w": 301,
    "y": 175,
    "x": 531
  }
]
Close
er
Emotion recognition
DEMO

Choose an image to process:

Soon Er will work with videos. Be the first to know

Process another image er
Emotion recognition
DEMO
image 
					See documentation
					
#example code
image = Image.from_file('example1.jpg')
result, status, error_message = face_detector.on_image(image)
See documentation 
					
						
					
					
[
  {
    "h": 278,
    "emotions": [
      [0.9151450991630554, "Surprise"],
      [0.07965227961540222, "Happiness"],
      [0.004168621730059385, "Anxiety"],
      [0.0005960435955785215, "Anger"],
      [0.00035987311275675893, "Neutral"],
      [7.289356290129945e-05, "Sadness"],
      [5.118385161040351e-06, "Disgust"]
    ],
    "score": "0.91907316",
    "w": 281,
    "y": 183,
    "x": 132
  },
  {
    "h": 321,
    "emotions": [
      [0.8588149547576904, "Anger"],
      [0.0784924104809761, "Sadness"],
      [0.030477698892354965, "Neutral"],
      [0.026959676295518875, "Anxiety"],
      [0.004039310850203037, "Disgust"],
      [0.0010209475876763463, "Surprise"],
      [0.00019492502906359732, "Happiness"]
    ],
    "score": "0.89893955",
    "w": 301,
    "y": 175,
    "x": 531
  }
]
Process another image er
Emotion recognition
DEMO
image 
					See documentation
					
#example code
image = Image.from_file('example1.jpg')
result, status, error_message = face_detector.on_image(image)
See documentation 
					
						
					
					
[
  {
    "h": 278,
    "emotions": [
      [0.9151450991630554, "Surprise"],
      [0.07965227961540222, "Happiness"],
      [0.004168621730059385, "Anxiety"],
      [0.0005960435955785215, "Anger"],
      [0.00035987311275675893, "Neutral"],
      [7.289356290129945e-05, "Sadness"],
      [5.118385161040351e-06, "Disgust"]
    ],
    "score": "0.91907316",
    "w": 281,
    "y": 183,
    "x": 132
  },
  {
    "h": 321,
    "emotions": [
      [0.8588149547576904, "Anger"],
      [0.0784924104809761, "Sadness"],
      [0.030477698892354965, "Neutral"],
      [0.026959676295518875, "Anxiety"],
      [0.004039310850203037, "Disgust"],
      [0.0010209475876763463, "Surprise"],
      [0.00019492502906359732, "Happiness"]
    ],
    "score": "0.89893955",
    "w": 301,
    "y": 175,
    "x": 531
  }
]
Process another image er
Emotion recognition
DEMO
image 
					See documentation
					
#example code
image = Image.from_file('example1.jpg')
result, status, error_message = face_detector.on_image(image)
See documentation 
					
						
					
					
[
  {
    "h": 278,
    "emotions": [
      [0.9151450991630554, "Surprise"],
      [0.07965227961540222, "Happiness"],
      [0.004168621730059385, "Anxiety"],
      [0.0005960435955785215, "Anger"],
      [0.00035987311275675893, "Neutral"],
      [7.289356290129945e-05, "Sadness"],
      [5.118385161040351e-06, "Disgust"]
    ],
    "score": "0.91907316",
    "w": 281,
    "y": 183,
    "x": 132
  },
  {
    "h": 321,
    "emotions": [
      [0.8588149547576904, "Anger"],
      [0.0784924104809761, "Sadness"],
      [0.030477698892354965, "Neutral"],
      [0.026959676295518875, "Anxiety"],
      [0.004039310850203037, "Disgust"],
      [0.0010209475876763463, "Surprise"],
      [0.00019492502906359732, "Happiness"]
    ],
    "score": "0.89893955",
    "w": 301,
    "y": 175,
    "x": 531
  }
]
Process another image er
Emotion recognition
DEMO
image 
					See documentation
					
#example code
image = Image.from_file('example1.jpg')
result, status, error_message = face_detector.on_image(image)
See documentation 
					
						
					
					
[
  {
    "h": 278,
    "emotions": [
      [0.9151450991630554, "Surprise"],
      [0.07965227961540222, "Happiness"],
      [0.004168621730059385, "Anxiety"],
      [0.0005960435955785215, "Anger"],
      [0.00035987311275675893, "Neutral"],
      [7.289356290129945e-05, "Sadness"],
      [5.118385161040351e-06, "Disgust"]
    ],
    "score": "0.91907316",
    "w": 281,
    "y": 183,
    "x": 132
  },
  {
    "h": 321,
    "emotions": [
      [0.8588149547576904, "Anger"],
      [0.0784924104809761, "Sadness"],
      [0.030477698892354965, "Neutral"],
      [0.026959676295518875, "Anxiety"],
      [0.004039310850203037, "Disgust"],
      [0.0010209475876763463, "Surprise"],
      [0.00019492502906359732, "Happiness"]
    ],
    "score": "0.89893955",
    "w": 301,
    "y": 175,
    "x": 531
  }
]