- Overview
- Setup
- Initialization
- Indexing Images
- 4.1 Indexing Your First Images
- 4.2 Image with Metadata
- 4.3 Updating Images
- 4.4 Removing Images
- 4.5 Check Indexing Status
- Solution APIs
- Search Results
- Advanced Search Parameters
- 7.1 Retrieving Metadata
- 7.2 Filtering Results
- 7.3 Result Score
- 7.4 Deduplication
This SDK contains two sets of APIs that provide accurate, reliable and scalable search. It is an open source software to provide easy integration of ViSearch APIs and ProductSearch APIs.
- Current stable version: 1.13.7
- Minimum JDK version: 1.6
Please refer to Product Search & Recommendations API if you are using ViSenze Console (https://console.visenze.com)
| API | Description |
|---|---|
| ViSearch | ViSearch API is a legacy API in old ViSenze dashboard (https://dashboard.visenze.com/) 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. For more details, see ViSearch API Documentation. |
| ProductSearch | ViSenze Discovery Suite (https://console.visenze.com) provides your customers a better and more intuitive product search and discovery experience by helping them search, navigate and interact with products more easily. ViSenze latest Product Search & Recommendations API is included in this SDK. Please refer to online docs for more information. |
Note: please change the version to latest available version.
For Maven projects, include the dependency in pom.xml:
<dependency>
<groupId>com.visenze</groupId>
<artifactId>visearch-java-sdk</artifactId>
<version>1.13.2</version>
</dependency>
For Gradle projects, include this line in your build.gradle dependencies block:
compile 'com.visenze:visearch-java-sdk:1.13.2'
For SBT projects, add the following line to build.sbt:
libraryDependencies += "com.visenze" % "visearch-java-sdk" % "1.13.1"
To start using ViSearch API, initialize ViSearch client with your ViSearch API credentials. Your credentials can be found in ViSearch Dashboard:
// Init ViSearch client with access and secret key
ViSearch client = new ViSearch("access_key", "secret_key");Please init ViSearch client in this way if you connect to another endpoint rather than default (https://visearch.visenze.com)
// Init ViSearch client with custom endpoint, access and secret key
ViSearch client = new ViSearch("https://custom-visearch.yourdomain.com" ,"access_key", "secret_key");ProductSearch must be initialized with an appKey and placementId before it can be used. The app Key and placement ID can be found in [https://console.visenze.com](ViSenze Console) under Search / Recommendations apps' Integration section.
For searches in China, please set the endpoint to https://search.visenze.com.cn.
// Replace APP_KEY, PLACEMENT_ID with your App's key and Placement ID as seen in Console:
ProductSearch api = new ProductSearch.Builder(APP_KEY, PLACEMENT_ID)
.build();
// set CN API endpoint for searches in China
new ProductSearch.Builder(APP_KEY, PLACEMENT_ID).setApiEndPoint("https://search.visenze.com.cn")
.build()
Please note that Indexing APIs only apply for ViSearch API in old ViSenze dashboard. If you are creating an app in ViSenze Console, you need to call Catalog API to insert the products into Catalog which then pushes the data into downstream search or recommendations app.
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 distinct name (im_name) which serves as this image's unique identifier and a publicly downloadable URL (im_url). ViSearch will fetch and index your images from the given URLs. You can check the status of this process using instructions described in Section 4.5. 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
List<Image> images = new ArrayList<Image>();
// the unique identifier of the image 'im_name'
String imName = "red_dress";
// the publicly downloadable url of the image 'im_url'
String imUrl = "http://mydomain.com/images/red_dress.jpg";
images.add(new Image(imName, imUrl));
// calls the insert endpoint to index the image
client.insert(images);Each
insertcall to ViSearch accepts a maximum of 100 images. We recommend indexing your images in batches of 100 for optimized image indexing speed.
Note that error messages may be generated from insert endpoint call, you can check if this happens using the corresponding insert transection.
List<Image> images = new ArrayList<Image>();
String imName = "red_dress";
String imUrl = "http://mydomain.com/images/red_dress.jpg";
images.add(new Image(imName, imUrl));
// index the image and get the InsertTrans
InsertTrans trans = client.insert(images);
// check if the insert endpoint reports any errors
if (trans.getErrorList() != null && trans.getErrorList().size > 0) {
System.out.println(trans.getTotal() + " succeed and " + trans.getErrorList().size() + " fail");
System.out.println("Error list: ");
for (int i = 0; i < trans.getErrorList().size(); i++) {
System.out.println(trans.getErrorList().get(i));
}
}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 references for retrieving metadata and filtering search results, see Advanced Search Parameters.
To index your images with metadata, first you need to configure the metadata schema in ViSearch Dashboard. You can add or remove metadata keys, and modify the metadata types to suit your needs.
Let's assume you have the following metadata schema configured:
| Name | Type | Searchable |
|---|---|---|
| title | string | true |
| description | text | true |
| price | float | true |
Then index your image with title, description and price:
List<Image> images = new ArrayList<Image>();
String imName = "vintage_wingtips";
String imUrl = "http://mydomain.com/images/vintage_wingtips.jpg";
// add metadata to your image
Map<String, String> metadata = new HashMap<String, String>();
metadata.put("title", "Vintage Wingtips");
metadata.put("description", "A pair of high quality leather wingtips");
metadata.put("price", "100.0");
images.add(new Image(imName, imUrl, metadata));
client.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 in ViSearch Dashboard 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.
List<Image> images = new ArrayList<Image>();
// the unique identifier 'im_name' of a previously indexed image
String imName = "vintage_wingtips";
// the new url of the image
String imUrl = "http://mydomain.com/images/vintage_wingtips_sale.jpg";
// update metadata of the image
Map<String, Object> metadata = new HashMap<String, Object>();
metadata.put("title", "Vintage Wingtips Sale");
metadata.put("price", "69.99");
images.add(new Image(imName, imUrl, metadata));
client.insert(images);Each
insertcall 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.
// the list of unique identifiers 'im_name' of the images to be removed
List<String> removeList = new ArrayList<String>();
// removing previously indexed image "red_dress"
removeList.add("red_dress");
client.remove(removeList);We recommend calling
removein batches of 100 images for optimized image indexing speed.
The fetching and indexing process take time, and you may only search for images after their indexs are built. If you want to keep track of this process, you can call the insertStatus endpoint with the image's trasaction identifier.
List<Image> images = new ArrayList<Image>();
String imName = "vintage_wingtips";
String imUrl = "http://mydomain.com/images/vintage_wingtips.jpg";
images.add(new Image(imName, imUrl));
// index the image and get the InsertTrans
InsertTrans trans = client.insert(images);
InsertStatus status;
// check the status of indexing process while it is not complete
int percent = 0;
while (percent < 100) {
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
status = client.insertStatus(trans.getTransId());
percent = status.getProcessedPercent();
System.out.println(percent + "% complete");
}
int pageIndex = 1; // error page index always starts with 1
int errorPerPage = 10; // set error page limit
status = client.insertStatus(trans.getTransId(), pageIndex, errorPerPage);
System.out.println("Start time:" + status.getStartTime());
System.out.println("Update time:" + status.getUpdateTime());
System.out.println(status.getTotal() + " insertions with "
+ status.getSuccessCount() + " succeed and "
+ status.getFailCount() + " fail");
// print all the error messages if there are any
if (status.getFailCount() > 0) {
int totPageNumber = (int) Math.ceil(1.0 * status.getFailCount() / status.getErrorLimit());
for (pageIndex = 1; pageIndex <= totPageNumber; pageIndex++) {
status = client.insertStatus(trans.getTransId(), pageIndex, errorPerPage);
List<InsertError> errorList = status.getErrorList();
for (int errorIndex = 0; errorIndex < errorList.size(); errorIndex++) {
System.out.println("failure at page " + pageIndex
+ " with error message: " + errorList.get(errorIndex));
}
}
}Please note that section 5.1 to 5.5 refer to ViSearch API in old ViSenze dashboard. For Console apps, refer to Product Search README link in section 5.6.
GET /search
Visually Similar Recommendations solution is to search for visually similar images in the image database giving an indexed image’s unique identifier (im_name).
SearchParams params = new SearchParams("vintage_wingtips");
PagedSearchResult searchResult = client.search(params);POST /uploadsearch
Search by image solution is to search similar images by uploading an image or providing an image url.
- Using an image from a local file path:
File imageFile = new File("/path/to/your/image");
UploadSearchParams params = new UploadSearchParams(imageFile);
PagedSearchResult searchResult = client.uploadSearch(params);- Using image url:
String url = "http://mydomain.com/sample_image.jpg";
UploadSearchParams params = new UploadSearchParams(url);
PagedSearchResult searchResult = client.uploadSearch(params);- Using previously searched image id (im_id):
UploadSearchParams params = new UploadSearchParams();
params.setImId("some_im_id");
PagedSearchResult searchResult = client.uploadSearch(params);For optimal results, we recommend images around
1024x1024pixels. Low resolution images may result in unsatisfying search results.
If the image is larger, we recommended to resize the image to1024x1024pixels before sending to API. Too high resolution images may result in timeout.
The maximum file size of an image is 10MB.
If the object you wish to search for takes up only a small portion of your image, or if 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. The box coordinates are set with respect to the original size of the uploading image: (note: if the box coordinates are invalid(negative or exceed the image boundary), this search will be equivalent to the normal Upload Search)
File imageFile = new File("/path/to/your/image");
UploadSearchParams params = new UploadSearchParams(imageFile);
// create the box to refine the area on the searching image
// Box(x1, y1, x2, y2) where (0, 0) is the top-left corner
// of the image, (x1, y1) is the top-left corner of the box,
// and (x2, y2) is the bottom-right corner of the box.
Box box = new Box(50, 50, 200, 200);
params.setBox(box);
PagedSearchResult searchResult = client.uploadSearch(params);POST /discoversearch
Multiple Product Search solution is to search similar images by uploading an image or providing an image url, similar to Search by Image. Multiple Product Search is able to detect all objects in the image and return similar images for each at one time.
- Using an image from a local file path:
File imageFile = new File("/path/to/your/image");
UploadSearchParams params = new UploadSearchParams(imageFile);
PagedSearchResult searchResult = client.discoverSearch(params);- Using image url:
String url = "http://mydomain.com/sample_image.jpg";
UploadSearchParams params = new UploadSearchParams(url);
PagedSearchResult searchResult = client.discoverSearch(params);GET /colorsearch
Search by color solution 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.
Search by single color.
ColorSearchParams.ColorAndWeight color = new ColorSearchParams.ColorAndWeight("000000");
ColorSearchParams params = new ColorSearchParams(Lists.newArrayList(color));
PagedSearchResult searchResult = client.colorSearch(params);Search by multiple colors and relative ratios.
ColorSearchParams.ColorAndWeight colorAndWeight1 = new ColorSearchParams.ColorAndWeight("000000", 50);
ColorSearchParams.ColorAndWeight colorAndWeight2 = new ColorSearchParams.ColorAndWeight("ffffff", 50);
ColorSearchParams params = new ColorSearchParams(Lists.newArrayList(colorAndWeight1, colorAndWeight2));
PagedSearchResult searchResult = client.colorSearch(params);GET /match
Visually Similar Recommendations For Multiple Objects Index Mode solution is to search for all objects which below the visually similar images in the image database giving an indexed image's unique identifier (im_name).
MatchSearchParams params = new MatchSearchParams("im_name");
PagedSearchResult searchResult = client.matchSearch(params);Refer to this Product Search readme.
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:
| Name | Type | Description |
|---|---|---|
| page | Integer | Optional parameter to specify the page of results. The first page of result is 1. Defaults to 1. |
| limit | Integer | Optional parameter to specify the result per page limit. Defaults to 10. |
// building pre-indexed search params
SearchParams params = new SearchParams("vintage_wingtips");
params.setPage(1);
params.setLimit(20);
PagedSearchResult searchResult = client.search(params);
// total number of results
int total = searchResult.getTotal();
// get the list of image search results
List<ImageResult> imageResults = searchResult.getResult();
// iterates through the list and get unique identifiers of the results
for (ImageResult imageResult : imageResults) {
String imName = imageResult.getImName();
// your code follows
}
// if more results available, get the next page of results
params.setPage(2);
PagedSearchResult nextPageOfSearchResult = client.search(params);To retrieve metadata of your image results, provide the list of metadata keys for the metadata value to be returned in the fl (field list) property:
SearchParams params = new SearchParams("vintage_wingtips");
// add fq param to specify the list of metadata to retrieve
List<String> fl = new ArrayList<String>();
fl.add("title");
fl.add("price");
params.setFl(fl);
PagedSearchResult searchResult = client.search(params);
List<ImageResult> imageResults = searchResult.getResult();
for (ImageResult imageResult : imageResults) {
Map<String, String> metadata = imageResult.getMetadata();
// read your metadata here
}To retrieve all metadata of your image results, specify get_all_fl parameter and set it to true:
SearchParams params = new SearchParams("vintage_wingtips");
params.setGetAllFl(true);
PagedSearchResult searchResult = client.search(params);
List<ImageResult> imageResults = searchResult.getResult();
for (ImageResult imageResult : imageResults) {
Map<String, String> metadata = imageResult.getMetadata();
// read your metadata here
}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 map of metadata key to filter value in the fq (filter query) property:
SearchParams params = new SearchParams("vintage_wingtips");
// add fq param to specify the filtering criteria
Map<String, String> fq = new HashMap<String, String>();
// description is metadata type text
fq.put("description", "wingtips");
// price is metadata type float
fq.put("price", "0,199");
params.setFq(fq);
PagedSearchResult searchResult = client.search(params);Querying syntax for each metadata type is listed in the following table:
| Type | FQ |
|---|---|
| string | Metadata value must be exactly matched with the query value, e.g. "Vintage Wingtips" would not match "vintage wingtips" or "vintage" |
| text | Metadata value will be indexed using full-text-search engine and supports fuzzy text matching, e.g. "A pair of high quality leather wingtips" would match any word in the phrase |
| int | Metadata value can be either:
|
| float | Metadata value can be either
|
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 score property to retrieve the scores for each image result:
SearchParams params = new SearchParams("vintage_wingtips");
// return scores for each image result, default is false
params.setScore(true);
PagedSearchResult searchResult = client.search(params);
List<ImageResult> imageResults = searchResult.getResult();
for (ImageResult imageResult : imageResults) {
float score = imageResult.getScore();
// do something with the 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:
| Name | Type | Description |
|---|---|---|
| score_min | Float | Minimum score for the image results. Default is 0.0. |
| score_max | Float | Maximum score for the image results. Default is 1.0. |
SearchParams params = new SearchParams("vintage_wingtips");
params.setScoreMin(0.5f);
params.setScoreMax(0.8f);
// only retrieve search results with scores between 0.5 and 0.8
PagedSearchResult searchResult = client.search(params);ViSearch provide a way to remove any duplicated images in the search response. This is achieved by comparing the confidence score between the search results. Hence you will be able to input a threshold for the system to filter out to how much is difference between the confidence is considered as a duplicate.
The default threshold is 0.0. The default parameter for deduplication is false.
SearchParams params = new SearchParams("vintage_wingtips");
params.setDedup(true);
params.setDedupThreshold(0.001f);
PagedSearchResult searchResult = client.search(params);
List<ImageResult> imageResults = searchResult.getResult();
for (ImageResult imageResult : imageResults) {
// do something with the score
}
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