Microsoft Cognitive Services API for Machine Learning
This is a thin python wrapper for Microsoft's Microsoft Cognitive Services API focused on collecting image training data for use in machine learning. I have based this library off of another repoisitory created by tristantao called py-ms-cognitive.
Introduction
This module requires that you sign up for Microsoft's Microsoft Cognitive Services and acquire an application key.
Steps
- Visit Microsoft Cognitive Services Signup Page.
- Select the 'Search' tab.
- Click 'Get API Key' on 'Bing Search APIs v7' and agree to both checkboxes.
- Select 'Next' and register using one of the options.
- This will take you to a page where you need to copy either 'Key 1' or 'Key 2' at the bottom of the 'Endpoints' list.
Installation
To install py-ms-cognitive-ml I recommend using virtualenv.
Quick instructions for Python 3.*
pip3 install py-ms-cognitive-mlFull instructions for Python 3.*
- Install Python 3:
sudo apt-get install python3-pip - Install Virtual Environments:
pip install virtualenv - Change to the directory that you want to create your project in and setup an output folder. For example
cd ~/Desktop/ && mkdir bing-api && cd bing-api && mkdir output - Create a Virtual Environment for your project:
virtualenv -p python3 bing-api-env - Install py-ms-cognitive-ml library:
pip3 install py-ms-cognitive-ml
- Note that py-ms-cognitive-ml requires the requests library.
Usage
Remember to set the API_KEY as your own.
Getting Images
# Imports
import requests
import os
from py_ms_cognitive_ml import PyMsCognitiveImageSearch
# Settings
search_terms = ["keys", "cats", "dogs"]
serach_quota_per_term = 5
results = []
total_downloads = 0
if not os.path.exists("output"):
os.mkdir("output")
# Main
for search_term in search_terms:
# Make API call
search_service = PyMsCognitiveImageSearch('API_KEY', search_term)
result_list = search_service.search_all(quota=serach_quota_per_term)
# Scrape images
if not os.path.exists("output/" + str(search_term)):
os.mkdir("output/" + str(search_term))
print("\nDownloading images for term: '" + str(search_term) + "'")
i = 0
urls = []
for i in range(0, len(result_list)):
print ("Downloading image from " + str(result_list[i].name))
# Download the image
try:
if result_list[i].url not in urls:
image_file = requests.get(result_list[i].url, stream=True)
if image_file.status_code == 200:
with open("output/" + search_term + "/" + str(i) + "." + result_list[i].extension, 'wb') as f:
f.write(image_file.content)
urls.append(result_list[i].url)
else:
raise requests.exceptions.RequestException
else:
print("Image already downloaded. Skipping image.")
except requests.exceptions.RequestException:
print("Something went wrong with the request. Skipping image.")
i += 1
results.append(str(len(urls)) + " images downloaded for term '" + str(search_term) + "'.")
total_downloads += len(urls)
print("\n")
for result in results:
print(result)
print(str(total_downloads) + " images based on " + str(len(search_terms)) + " search term(s) downloaded.")