ViSearch is an API that provides accurate, reliable and scalable image search. ViSearch API provides endpoints that let developers index their images and perform image searches efficiently. ViSearch API can be easily integrated into your web and mobile applications. More details about ViSearch API can be found in the documentation.

The ViSearch Python SDK is an open source software for easy integration of ViSearch Search API with your application server. It provides three search methods based on the ViSearch Search API - pre-indexed search, color search and upload search. The ViSearch Python SDK also provides an easy integration of the ViSearch Data API which includes data inserting and data removing. For source code and references, visit the github repository.

• Supported on Python 2.7+ and 3.3+

To install visearch, simply:

$ pip install visearch

To start using ViSearch API, initialize ViSearch client with your ViSearch API credentials. Your credentials can be found in ViSearch Dashboard:

from visearch import client

access_key = 'your app access key'
secret_key = 'your app secret key'

api = client.ViSearchAPI(access_key, secret_key)

Built for scalability, ViSearch API enables fast and accurate searches on high volume of images. Before making your first image search, you need to prepare a list of images and index them into ViSearch by calling the /insert endpoint. Each image must have a unique identifier and a publicly downloadable URL. ViSearch will parallelly fetch your images from the given URLs, and index the downloaded for searching. After the image indexes are built, you can start searching for similar images using the unique identifier, using a color, or using another image.

To index your images, prepare a list of images and call the /insert endpoint.

# the list of images to be indexed
# the unique identifier of the image 'im_name', the publicly downloadable url of the image 'im_url'
images = [
    {'im_name': 'red_dress', 'im_url': 'http://mydomain.com/images/red_dress.jpg'},
    {'im_name': 'blue_dress', 'im_url': 'http://mydomain.com/images/blue_dress.jpg'}
]
# calls the /insert endpoint to index the image
response = api.insert(images)

Each insert call to ViSearch accepts a maximum of 100 images. We recommend indexing your images in batches of 100 for optimized image indexing speed.

Images usually come with descriptive text or numeric values as metadata, for example: title, description, category, brand, and price of an online shop listing image caption, tags, geo-coordinates of a photo.

ViSearch combines the power of text search with image search. You can index your images with metadata, and leverage text based query and filtering for even more accurate image search results, for example: limit results within a price range limit results to certain tags, and some keywords in the captions For detailed reference for result filtering, see Advanced Search Parameters.

To index your images with metadata, first you need to configure the metadata schema in ViSearch Dashboard (link to). You can add and remove metadata keys, and modify the metadata types to suit your needs.

Let's assume you have the following metadata schema configured:

Then index your image with title, decription, and price:

images = [{
           'im_name': 'blue_dress',
           'im_url': 'http://mydomain.com/images/blue_dress.jpg',
           'title': 'Blue Dress',
           'description': 'A blue dress',
           'price': 100.0
          },
          ...
         ]
# calls the /insert endpoint to index the image
response = api.insert(images)

Metadata keys are case-sensitive, and metadata without a matching key in the schema will not be processed by ViSearch. Make sure to configure metadata schema for all of your metadata keys.

If you need to update an image or its metadata, call the insert endpoint with the same unique identifier of the image. ViSearch will fetch the image from the updated URL and index the new image, and replace the metadata of the image if provided.

images = [{
           'im_name': 'blue_dress',
           'im_url': 'http://mydomain.com/images/blue_dress.jpg',
           'title': 'Blue Dress',
           'description': 'A blue dress',
           'price': 100.0
          },
          ...
         ]
# calls the /update endpoint to index the image
response = api.update(images)

Each ``insert`` call to ViSearch accepts a maximum of 100 images. We
recommend updating your images in batches of 100 for optimized image
indexing speed.

In case you decide to remove some of the indexed images, you can call the /remove endpoint with the list of unique identifier of the indexed images. ViSearch will then remove the specified images from the index. You will not be able to perform pre-indexed search on this image, and the image will not be found in any search result.

image_names = ["red_dress", "blue_dress"]
response = api.remove(image_names)

We recommend calling remove in batches of 100 images for optimized image indexing speed.

Pre-index search is to search similar images based on the your indexed image by its unique identifier (im_name). It should be a valid ID that is used to index your images in the database.

response = api.search("blue_dress")

Color search is to search images with similar color by providing a color code. The color code should be in Hexadecimal and passed to the colorsearch service.

response = api.colorsearch("fa4d4d")

Upload search is used to search similar images by uploading an image or providing an image url. Image class is used to perform the image encoding and resizing. You should construct the Image object and pass it to uploadsearch to start a search.

Using an image from a local file path

image_path = 'blue_dress.jpg'
response = api.uploadsearch(image_path=image_path)

Alternatively, you can pass an image url directly to uploadsearch to start the search.

image_url = 'http://mydomain.com/images/red_dress.jpg'
response = api.uploadsearch(image_url=image_url)

If the object you wish to search for takes up only a small portion of your image, or other irrelevant objects exists in the same image, chances are the search result could become inaccurate. Use the Box parameter to refine the search area of the image to improve accuracy. Noted that the box coordinated is setted with respect to the original size of the image passed, it will be automatically scaled to fit the resized image for uploading:

image_url = 'http://mydomain.com/images/red_dress.jpg'
box = (0,0,10,10)
response = api.uploadsearch(image_url=image_url, box=box)

When performing upload search, you might experience increasing search latency with increasing image file sizes. This is due to the increased time transferring your images to the ViSearch server, and the increased time for processing larger image files in ViSearch.

To reduce upload search latency, by default the uploadSearch method makes a copy of your image file if both of the image dimensions exceed 512 pixels, and resizes the copy to dimensions not exceeding 512x512 pixels. This is the optimized size to lower search latency while not sacrificing search accuracy for general use cases:

# client.uploadSearch(params) is equivalent to using STANDARD resize settings, 512x512 and jpeg 75 quality
image_path = 'blue_dress.jpg'
response = api.uploadsearch(image_path=image_path, resize='STANDARD')

If your image contains fine details such as textile patterns and textures, use the HIGH resize settings to get better search results:

# for images with fine details, use HIGH resize settings 1024x1024 and jpeg 75 quality
image_path = 'blue_dress.jpg'
response = api.uploadsearch(image_path=image_path, resize='HIGH')

Or provide customized resize settings:

# using customized resize settings 800x800 and jpeg 80 quality
image_path = 'blue_dress.jpg'
response = api.uploadsearch(image_path=image_path, resize=(800, 800, 80))

ViSearch returns a maximum number of 1000 most relevant image search results. You can provide pagination parameters to control the paging of the image search results.

Pagination parameters:

page = 1
limit = 25
response = api.uploadsearch(image_url=image_url, page=page, limit=limit)

To retrieve metadata of your image results, provide the list (or tuple) of metadata keys for the metadata value to be returned in the fl (field list) property:

fl = ["price", "brand", "title", "im_url"]  #, or fl = ("price", "brand", "title", "im_url")
response = api.uploadsearch(image_url=image_url, fl=fl)

Only metadata of type string, int, and float can be retrieved from ViSearch. Metadata of type text is not available for retrieval.

To filter search results based on metadata values, provide a dict of metadata key to filter value in the fq (filter query) property:

fq = {"im_cate": "bags", "price": "10,199"}
response = api.uploadsearch(image_url=image_url, fq=fq)

Querying syntax for each metadata type is listed in the following table:

ViSearch image search results are ranked in descending order i.e. from the highest scores to the lowest, ranging from 1.0 to 0.0. By default, the score for each image result is not returned. You can turn on the boolean score property to retrieve the scores for each image result:

score = True
response = api.uploadsearch(image_url=image_url, score=score)

If you need to restrict search results from a minimum score to a maximum score, specify the score_min and/or score_max parameters:

score_min = 0.5
score_max = 0.8
response = api.uploadsearch(image_url=image_url, score_max=score_max, score_min=score_min)

• The image upload.jpg included in the SDK is downloaded from http://pixabay.com/en/boots-shoes-pants-folded-fashion-690502/