use of com.ibm.watson.visual_recognition.v3.model.Classifier in project java-sdk by watson-developer-cloud.
the class VisualRecognition method createClassifier.
/**
* Create a classifier.
*
* <p>Train a new multi-faceted classifier on the uploaded image data. Create your custom
* classifier with positive or negative example training images. Include at least two sets of
* examples, either two positive example files or one positive and one negative file. You can
* upload a maximum of 256 MB per call.
*
* <p>**Tips when creating:**
*
* <p>- If you set the **X-Watson-Learning-Opt-Out** header parameter to `true` when you create a
* classifier, the example training images are not stored. Save your training images locally. For
* more information, see [Data collection](#data-collection).
*
* <p>- Encode all names in UTF-8 if they contain non-ASCII characters (.zip and image file names,
* and classifier and class names). The service assumes UTF-8 encoding if it encounters non-ASCII
* characters.
*
* @param createClassifierOptions the {@link CreateClassifierOptions} containing the options for
* the call
* @return a {@link ServiceCall} with a result of type {@link Classifier}
*/
public ServiceCall<Classifier> createClassifier(CreateClassifierOptions createClassifierOptions) {
com.ibm.cloud.sdk.core.util.Validator.notNull(createClassifierOptions, "createClassifierOptions cannot be null");
RequestBuilder builder = RequestBuilder.post(RequestBuilder.resolveRequestUrl(getServiceUrl(), "/v3/classifiers"));
Map<String, String> sdkHeaders = SdkCommon.getSdkHeaders("watson_vision_combined", "v3", "createClassifier");
for (Entry<String, String> header : sdkHeaders.entrySet()) {
builder.header(header.getKey(), header.getValue());
}
builder.header("Accept", "application/json");
builder.query("version", String.valueOf(this.version));
MultipartBody.Builder multipartBuilder = new MultipartBody.Builder();
multipartBuilder.setType(MultipartBody.FORM);
multipartBuilder.addFormDataPart("name", createClassifierOptions.name());
for (Map.Entry<String, InputStream> entry : createClassifierOptions.positiveExamples().entrySet()) {
String partName = String.format("%s_positive_examples", entry.getKey());
okhttp3.RequestBody part = RequestUtils.inputStreamBody(entry.getValue(), "application/octet-stream");
multipartBuilder.addFormDataPart(partName, entry.getKey() + ".zip", part);
}
if (createClassifierOptions.negativeExamples() != null) {
okhttp3.RequestBody negativeExamplesBody = RequestUtils.inputStreamBody(createClassifierOptions.negativeExamples(), "application/octet-stream");
String negativeExamplesFilename = createClassifierOptions.negativeExamplesFilename();
if (!negativeExamplesFilename.contains(".")) {
negativeExamplesFilename += ".zip";
}
multipartBuilder.addFormDataPart("negative_examples", negativeExamplesFilename, negativeExamplesBody);
}
builder.body(multipartBuilder.build());
ResponseConverter<Classifier> responseConverter = ResponseConverterUtils.getValue(new com.google.gson.reflect.TypeToken<Classifier>() {
}.getType());
return createServiceCall(builder.build(), responseConverter);
}
use of com.ibm.watson.visual_recognition.v3.model.Classifier in project java-sdk by watson-developer-cloud.
the class VisualRecognitionTest method testGetClassifierWOptions.
@Test
public void testGetClassifierWOptions() throws Throwable {
// Schedule some responses.
String mockResponseBody = "{\"classifier_id\": \"classifierId\", \"name\": \"name\", \"owner\": \"owner\", \"status\": \"ready\", \"core_ml_enabled\": false, \"explanation\": \"explanation\", \"created\": \"2019-01-01T12:00:00.000Z\", \"classes\": [{\"class\": \"xClass\"}], \"retrained\": \"2019-01-01T12:00:00.000Z\", \"updated\": \"2019-01-01T12:00:00.000Z\"}";
String getClassifierPath = "/v3/classifiers/testString";
server.enqueue(new MockResponse().setHeader("Content-type", "application/json").setResponseCode(200).setBody(mockResponseBody));
constructClientService();
// Construct an instance of the GetClassifierOptions model
GetClassifierOptions getClassifierOptionsModel = new GetClassifierOptions.Builder().classifierId("testString").build();
// Invoke operation with valid options model (positive test)
Response<Classifier> response = visualRecognitionService.getClassifier(getClassifierOptionsModel).execute();
assertNotNull(response);
Classifier responseObj = response.getResult();
assertNotNull(responseObj);
// Verify the contents of the request
RecordedRequest request = server.takeRequest();
assertNotNull(request);
assertEquals(request.getMethod(), "GET");
// Check query
Map<String, String> query = TestUtilities.parseQueryString(request);
assertNotNull(query);
// Get query params
assertEquals(query.get("version"), "testString");
// Check request path
String parsedPath = TestUtilities.parseReqPath(request);
assertEquals(parsedPath, getClassifierPath);
}
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