Using Azure Cognitive Services to Caption Video

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  • Post category:Python

Using Azure Cognitive Services to Caption Video

In this post we will explore the use of Microsoft Azure Cognitive Services to caption video images caught from a webcam in real time.

We will start by importing some libraries:

  • datetime for displaying the current time on our labels
  • requests to make requests to the Cognitive Services API
  • cv2 (OpenCV) for streaming images from our webcam
In [1]:
import datetime
import requests

import cv2 # opencv

You will need to sign up for an Azure Cognitive Services subscription API key.

In [2]:
subscription_key = # your subscription key here
vision_base_url = ""
vision_analyze_url = vision_base_url + "analyze"

Now we define a function to make a request to Azure Cognitive Services and return a caption with the time of the request that we can use to caption our image.

In [3]:
def get_image_caption(image):
    img = cv2.imencode('.jpg', image)[1].tostring()
    # Request headers
    headers  = {'Content-Type': 'application/octet-stream',
                'Ocp-Apim-Subscription-Key': subscription_key }
    # Request params
    params   = {'visualFeatures': 'Categories,Description,Color'}
    # Make a request to the Azure Cognitive Services Vision Analysis API
    response =,
    # Extract the json from the response
    analysis = response.json()
    # Extract the captions from the output json.
    image_caption = analysis["description"]["captions"][0]["text"].capitalize()
    time_now ="%Y-%m-%d %H:%M:%S")
    # Caption with current time
    return (time_now + ": " + image_caption)

We set a count and each time the count reaches 50, a request is sent to the Cognitive Services API. The caption from the API is overlayed on the webcam image.

Press the ‘q’ button to quit at any point.

In [4]:
stream = cv2.VideoCapture(0)
key = None
count = 0
overlay_text = ""

while True:
    if count % 3 != 0:
        # Read frames from live web cam stream
        (grabbed, frame) =

        # Make copies of the frame for transparency processing
        output = frame.copy()
        # Get caption to overlay
        if count % 50 == 0:
            overlay_text = get_image_caption(output)
        # Overlay caption on image
        cv2.putText(output, overlay_text, (10, 30),
            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)

        # Show the frame
        cv2.imshow("Image", output)
        key = cv2.waitKey(1) & 0xFF
    count +=1
    # Press q to break out of the loop
    if key == ord("q"):

# cleanup

Below are some webcam screenshots with an idea of the kind of output you will see.