Images
Via URL
To get predictions for an input, you need to supply an image and the model you'd like to get predictions from. You can supply an image either with a publicly accessible URL or by directly sending bytes. You can send up to 128 images in one API call. You specify the model you'd like to use with the {model-id} parameter.
Below is an example of how you would send image URLs and receive back predictions from the general model.
You can learn all about the different Clarifai Models available later in the guide.
app.models.initModel({id: Clarifai.GENERAL_MODEL, version: "aa7f35c01e0642fda5cf400f543e7c40"})
.then(generalModel => {
return generalModel.predict("@@sampleTrain");
})
.then(response => {
var concepts = response['outputs'][0]['data']['concepts']
})from clarifai.rest import ClarifaiApp
app = ClarifaiApp(api_key='YOUR_API_KEY')
model = app.public_models.general_model
response = model.predict_by_url('@@sampleTrain')ConceptModel model = client.getDefaultModels().generalModel();
ModelVersion modelVersion = model.getVersionByID("the-version").executeSync().get();
ClarifaiResponse<List<ClarifaiOutput<Prediction>>> response = client.predict(model.id())
.withInputs(ClarifaiInput.forImage("@@sampleTrain"))
.withVersion("aa7f35c01e0642fda5cf400f543e7c40")
.executeSync();using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
namespace YourNamespace
{
public class YourClassName
{
public static async Task Main()
{
var Client = new ClarifaiClient("YOUR_API_KEY");
var response = await Client.Predict<Concept>(
Client.PublicModels.GeneralModel.ModelID,
new List<IClarifaiInput>
{
new ClarifaiURLImage("@@sampleTrain"),
new ClarifaiURLImage("the-url-2")
},
"aa7f35c01e0642fda5cf400f543e7c40")
.ExecuteAsync();
}
}
}ClarifaiImage *image = [[ClarifaiImage alloc] initWithURL:@"@@sampleTrain"];
[_app getModelByName:@"general-v1.3" completion:^(ClarifaiModel *model, NSError *error) {
[model predictOnImages:@[image]
completion:^(NSArray<ClarifaiSearchResult *> *outputs, NSError *error) {
NSLog(@"outputs: %@", outputs);
}];
}];use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLImage;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;
$client = new ClarifaiClient('YOUR_API_KEY');
$model = $client->publicModels()->generalModel();
$input = new ClarifaiURLImage("@@sampleTrain");
$response = $model->predict($input)
->withModelVersionID("aa7f35c01e0642fda5cf400f543e7c40")
->executeSync();
if ($response->isSuccessful()) {
/** @var ClarifaiOutput $output */
$output = $response->get();
echo "Predicted concepts:\n";
/** @var Concept $concept */
foreach ($output->data() as $concept) {
echo $concept->name() . ': ' . $concept->value() . "\n";
}
} else {
echo "Response is not successful. Reason: \n";
echo $response->status()->description() . "\n";
echo $response->status()->errorDetails() . "\n";
echo "Status code: " . $response->status()->statusCode();
}curl -X POST
-H 'Authorization: Key YOUR_API_KEY'
-H "Content-Type: application/json"
-d '
{
"inputs": [
{
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
}
]
}'
https://api.clarifai.com/v2/models/aaa03c23b3724a16a56b629203edc62c/versions/aa7f35c01e0642fda5cf400f543e7c40/outputs{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "ea68cac87c304b28a8046557062f34a0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T16:50:25Z",
"model": {
"name": "general-v1.3",
"id": "aaa03c23b3724a16a56b629203edc62c",
"created_at": "2016-03-09T17:11:39Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "ea68cac87c304b28a8046557062f34a0",
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_HLmqFqBf",
"name": "train",
"app_id": null,
"value": 0.9989112
},
{
"id": "ai_fvlBqXZR",
"name": "railway",
"app_id": null,
"value": 0.9975532
},
{
"id": "ai_Xxjc3MhT",
"name": "transportation system",
"app_id": null,
"value": 0.9959158
},
{
"id": "ai_6kTjGfF6",
"name": "station",
"app_id": null,
"value": 0.992573
},
{
"id": "ai_RRXLczch",
"name": "locomotive",
"app_id": null,
"value": 0.992556
},
{
"id": "ai_VRmbGVWh",
"name": "travel",
"app_id": null,
"value": 0.98789215
},
{
"id": "ai_SHNDcmJ3",
"name": "subway system",
"app_id": null,
"value": 0.9816359
},
{
"id": "ai_jlb9q33b",
"name": "commuter",
"app_id": null,
"value": 0.9712483
},
{
"id": "ai_46lGZ4Gm",
"name": "railroad track",
"app_id": null,
"value": 0.9690325
},
{
"id": "ai_tr0MBp64",
"name": "traffic",
"app_id": null,
"value": 0.9687052
},
{
"id": "ai_l4WckcJN",
"name": "blur",
"app_id": null,
"value": 0.9667078
},
{
"id": "ai_2gkfMDsM",
"name": "platform",
"app_id": null,
"value": 0.9624243
},
{
"id": "ai_CpFBRWzD",
"name": "urban",
"app_id": null,
"value": 0.960752
},
{
"id": "ai_786Zr311",
"name": "no person",
"app_id": null,
"value": 0.95864904
},
{
"id": "ai_6lhccv44",
"name": "business",
"app_id": null,
"value": 0.95720303
},
{
"id": "ai_971KsJkn",
"name": "track",
"app_id": null,
"value": 0.9494642
},
{
"id": "ai_WBQfVV0p",
"name": "city",
"app_id": null,
"value": 0.94089437
},
{
"id": "ai_dSCKh8xv",
"name": "fast",
"app_id": null,
"value": 0.9399334
},
{
"id": "ai_TZ3C79C6",
"name": "road",
"app_id": null,
"value": 0.93121606
},
{
"id": "ai_VSVscs9k",
"name": "terminal",
"app_id": null,
"value": 0.9230834
}
]
}
}
]
}Via bytes
Below is an example of how you would send the bytes of an image and receive back predictions from the general model.
app.models.predict(Clarifai.GENERAL_MODEL, {base64: "G7p3m95uAl..."}).then(
function(response) {
// do something with response
},
function(err) {
// there was an error
}
);from clarifai.rest import ClarifaiApp
app = ClarifaiApp(api_key='YOUR_API_KEY')
model = app.public_models.general_model
response = model.predict_by_filename('/home/user/image.jpeg')
# You could also use model.predict_by_bytes or model.predict_by_base64client.getDefaultModels().generalModel().predict()
.withInputs(ClarifaiInput.forImage(new File("/home/user/image.jpeg")))
.executeSync();using System.IO;
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.DTOs.Inputs;
namespace YourNamespace
{
public class YourClassName
{
public static async Task Main()
{
var client = new ClarifaiClient("YOUR_API_KEY");
await client.PublicModels.GeneralModel.Predict(
new ClarifaiFileImage(File.ReadAllBytes("/home/user/image.jpeg")))
.ExecuteAsync();
}
}
}UIImage *image = [UIImage imageNamed:@"dress.jpg"];
ClarifaiImage *clarifaiImage = [[ClarifaiImage alloc] initWithImage:image];
[_app getModelByName:@"general-v1.3" completion:^(ClarifaiModel *model, NSError *error) {
[model predictOnImages:@[clarifaiImage]
completion:^(NSArray<ClarifaiSearchResult *> *outputs, NSError *error) {
NSLog(@"outputs: %@", outputs);
}];
}];use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiFileImage;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;
$client = new ClarifaiClient('YOUR_API_KEY');
$response = $client->publicModels()->generalModel()->predict(
new ClarifaiFileImage(file_get_contents('/home/user/image.jpeg')))
->executeSync();
if ($response->isSuccessful()) {
/** @var ClarifaiOutput $output */
$output = $response->get();
echo "Predicted concepts:\n";
/** @var Concept $concept */
foreach ($output->data() as $concept) {
echo $concept->name() . ': ' . $concept->value() . "\n";
}
} else {
echo "Response is not successful. Reason: \n";
echo $response->status()->description() . "\n";
echo $response->status()->errorDetails() . "\n";
echo "Status code: " . $response->status()->statusCode();
}// Smaller files (195 KB or less)
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '
{
"inputs": [
{
"data": {
"image": {
"base64": "'"$(base64 /home/user/image.jpeg)"'"
}
}
}
]
}'\
https://api.clarifai.com/v2/models/{model-id}/outputs
// Larger Files (Greater than 195 KB)
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d @- https://api.clarifai.com/v2/models/{model-id}/outputs << FILEIN
{
"inputs": [
{
"data": {
"image": {
"base64": "$(base64 /home/user/image.png)"
}
}
}
]
}
FILEIN{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "e1cf385843b94c6791bbd9f2654db5c0",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2016-11-22T16:59:23Z",
"model": {
"name": "general-v1.3",
"id": "aaa03c23b3724a16a56b629203edc62c",
"created_at": "2016-03-09T17:11:39Z",
"app_id": null,
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "e1cf385843b94c6791bbd9f2654db5c0",
"data": {
"image": {
"url": "https://s3.amazonaws.com/clarifai-api/img/prod/b749af061d564b829fb816215f6dc832/e11c81745d6d42a78ef712236023df1c.jpeg"
}
}
},
"data": {
"concepts": [
{
"id": "ai_l4WckcJN",
"name": "blur",
"app_id": null,
"value": 0.9973569
},
{
"id": "ai_786Zr311",
"name": "no person",
"app_id": null,
"value": 0.98865616
},
{
"id": "ai_JBPqff8z",
"name": "art",
"app_id": null,
"value": 0.986006
},
{
"id": "ai_5rD7vW4j",
"name": "wallpaper",
"app_id": null,
"value": 0.9722556
},
{
"id": "ai_sTjX6dqC",
"name": "abstract",
"app_id": null,
"value": 0.96476805
},
{
"id": "ai_Dm5GLXnB",
"name": "illustration",
"app_id": null,
"value": 0.922542
},
{
"id": "ai_5xjvC0Tj",
"name": "background",
"app_id": null,
"value": 0.8775655
},
{
"id": "ai_tBcWlsCp",
"name": "nature",
"app_id": null,
"value": 0.87474406
},
{
"id": "ai_rJGvwlP0",
"name": "insubstantial",
"app_id": null,
"value": 0.8196385
},
{
"id": "ai_2Bh4VMrb",
"name": "artistic",
"app_id": null,
"value": 0.8142488
},
{
"id": "ai_mKzmkKDG",
"name": "Christmas",
"app_id": null,
"value": 0.7996079
},
{
"id": "ai_RQccV41p",
"name": "woman",
"app_id": null,
"value": 0.7955615
},
{
"id": "ai_20SCBBZ0",
"name": "vector",
"app_id": null,
"value": 0.7775099
},
{
"id": "ai_4sJLn6nX",
"name": "dark",
"app_id": null,
"value": 0.7715479
},
{
"id": "ai_5Kp5FMJw",
"name": "still life",
"app_id": null,
"value": 0.7657637
},
{
"id": "ai_LM64MDHs",
"name": "shining",
"app_id": null,
"value": 0.7542407
},
{
"id": "ai_swtdphX8",
"name": "love",
"app_id": null,
"value": 0.74926054
},
{
"id": "ai_h45ZTxZl",
"name": "square",
"app_id": null,
"value": 0.7449074
},
{
"id": "ai_cMfj16kJ",
"name": "design",
"app_id": null,
"value": 0.73926914
},
{
"id": "ai_LxrzLJmf",
"name": "bright",
"app_id": null,
"value": 0.73790145
}
]
}
}
]
}Last updated