Project Oxford: Computer Vision API example

This Jupyter notebook shows you how to get started with the Project Oxford Computer Vision API in Python, and how to visualize your results.

To use this notebook, you will need to get keys to Computer Vision API. Visit, and then the “Try Computer Vision API” button. On the “Sign in” page, use your Microsoft account to sign in and you will be able to subscribe to Computer Vision API and get free keys (Code of Conduct and TOS). After completing the sign-up process, paste your API key and API region into the variables section below. (Either the primary or the secondary key works.)

In [1]:
import time 
import requests
import cv2
import operator
import numpy as np
from __future__ import print_function

# Import library to display results
import matplotlib.pyplot as plt
%matplotlib inline 
# Display images within Jupyter
In [2]:
# Variables
_url = 'YOUR_ENDPOINT' # Here, paste your full endpoint from the Azure portal
_key = None # Here, paste your primary key
_maxNumRetries = 10

Helper functions

In [3]:
def processRequest( json, data, headers, params ):

    Helper function to process the request to Project Oxford

    json: Used when processing images from its URL. See API Documentation
    data: Used when processing image read from disk. See API Documentation
    headers: Used to pass the key information and the data type request

    retries = 0
    result = None

    while True:

        response = requests.request( 'post', _url, json = json, data = data, headers = headers, params = params )

        if response.status_code == 429: 

            print( "Message: %s" % ( response.json() ) )

            if retries <= _maxNumRetries: 
                retries += 1
                print( 'Error: failed after retrying!' )

        elif response.status_code == 200 or response.status_code == 201:

            if 'content-length' in response.headers and int(response.headers['content-length']) == 0: 
                result = None 
            elif 'content-type' in response.headers and isinstance(response.headers['content-type'], str): 
                if 'application/json' in response.headers['content-type'].lower(): 
                    result = response.json() if response.content else None 
                elif 'image' in response.headers['content-type'].lower(): 
                    result = response.content
            print( "Error code: %d" % ( response.status_code ) )
            print( "Message: %s" % ( response.json() ) )

    return result
In [4]:
def renderResultOnImage( result, img ):
    """Display the obtained results onto the input image"""

    R = int(result['color']['accentColor'][:2],16)
    G = int(result['color']['accentColor'][2:4],16)
    B = int(result['color']['accentColor'][4:],16)

    cv2.rectangle( img,(0,0), (img.shape[1], img.shape[0]), color = (R,G,B), thickness = 25 )

    if 'categories' in result:
        categoryName = sorted(result['categories'], key=lambda x: x['score'])[0]['name']
        cv2.putText( img, categoryName, (30,70), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,0,0), 3 )

Analysis of an image retrieved via URL

In [5]:
# URL direction to image
urlImage = ''

# Computer Vision parameters
params = { 'visualFeatures' : 'Color,Categories'} 

headers = dict()
headers['Ocp-Apim-Subscription-Key'] = _key
headers['Content-Type'] = 'application/json' 

json = { 'url': urlImage } 
data = None

result = processRequest( json, data, headers, params )

if result is not None:
    # Load the original image, fetched from the URL
    arr = np.asarray( bytearray( requests.get( urlImage ).content ), dtype=np.uint8 )
    img = cv2.cvtColor( cv2.imdecode( arr, -1 ), cv2.COLOR_BGR2RGB )

    renderResultOnImage( result, img )

    ig, ax = plt.subplots(figsize=(15, 20))
    ax.imshow( img )