Images
Make predictions on image inputs
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.
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
model_id="{THE_MODEL_ID}",
version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(
url="https://samples.clarifai.com/metro-north.jpg"
)
)
)
]
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print("There was an error with your request!")
print("\tCode: {}".format(post_model_outputs_response.outputs[0].status.code))
print("\tDescription: {}".format(post_model_outputs_response.outputs[0].status.description))
print("\tDetails: {}".format(post_model_outputs_response.outputs[0].status.details))
raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]
print("Predicted concepts:")
for concept in output.data.concepts:
print("%s %.2f" % (concept.name, concept.value))<?php
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
///////////////////////////////////////////////////////////////////////////////
// Specifying the Request Data
///////////////////////////////////////////////////////////////////////////////
//
// In the Clarifai platform an image is defined by a special Image object.
// There are several ways in which an Image object can be populated including
// by url and image bytes (base64).
//
$image = new Image([
'url' => 'https://samples.clarifai.com/dog2.jpeg'
]);
//
// After an Image object is created, a Data object is constructed around it.
// The Data object offers a container that contains additional image independent
// metadata. In this particular use case, no other metadata is needed to be
// specified.
//
$data = new Data([
'image' => $image
]);
//
// The Data object is then wrapped in an Input object in order to meet the
// API specification. Additional fields are available to populate in the Input
// object, but for the purposes of this example we can send in just the
// Data object.
//
$input = new Input([
'data' => $data
]);
///////////////////////////////////////////////////////////////////////////////
// Creating the request object
///////////////////////////////////////////////////////////////////////////////
//
// Finally, the request object itself is created. This object carries the request
// along with the request status and other metadata related to the request itself.
// In this example we populate:
// - the `user_app_id` field with the UserAppIDSet constructed above
// - the `model_id` field with the ID of the model we are referencing
// - the `inputs` field with an array of input objects constructed above
//
$request = new PostModelOutputsRequest([
'user_app_id' => $userDataObject, // This is defined above
'model_id' => 'aaa03c23b3724a16a56b629203edc62c', // This is the ID of the publicly available General model.
'inputs' => [$input]
]);
///////////////////////////////////////////////////////////////////////////////
// Making the RPC call
///////////////////////////////////////////////////////////////////////////////
//
// Once the request object is constructed, we can call the actual request to the
// Clarifai platform. This uses the opened gRPC client channel to communicate the
// request and then wait for the response.
//
[$response, $status] = $client->PostModelOutputs(
$request,
$metadata
)->wait();
///////////////////////////////////////////////////////////////////////////////
// Handling the Response
///////////////////////////////////////////////////////////////////////////////
//
// The response is returned and the first thing we do is check the status of it.
// A successful response will have a status code of 0, otherwise there is some
// reported error.
//
if ($status->code !== 0) throw new Exception("Error: {$status->details}");
//
// In addition to the RPC response status, there is a Clarifai API status that
// reports if the operationo was a success or failure (not just that the commuunication)
// was successful.
//
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " .
$response->getStatus()->getDetails());
}
//
// The output of a successful call can be used in many ways. In this example,
// we loop through all of the predicted concepts and print them out along with
// their numerical prediction value (confidence).
//
echo "Predicted concepts:\n";
foreach ($response->getOutputs()[0]->getData()->getConcepts() as $concept) {
echo $concept->getName() . ": " . number_format($concept->getValue(), 2) . "\n";
}
?>import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setModelId("{THE_MODEL_ID}")
.setVersionId("{THE_MODEL_VERSION_ID") // This is optional. Defaults to the latest model version.
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setImage(
Image.newBuilder().setUrl("https://samples.clarifai.com/metro-north.jpg")
)
)
)
.build()
);
if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}
// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);
System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
stub.PostModelOutputs(
{
model_id: "{THE_MODEL_ID}",
version_id: "{THE_MODEL_VERSION_ID}", // This is optional. Defaults to the latest model version.
inputs: [
{data: {image: {url: "https://samples.clarifai.com/metro-north.jpg"}}}
]
},
metadata,
(err, response) => {
if (err) {
throw new Error(err);
}
if (response.status.code !== 10000) {
throw new Error("Post model outputs failed, status: " + response.status.description);
}
// Since we have one input, one output will exist here.
const output = response.outputs[0];
console.log("Predicted concepts:");
for (const concept of output.data.concepts) {
console.log(concept.name + " " + concept.value);
}
}
);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/{THE_MODEL_ID}/versions/{THE_MODEL_VERSION_ID}/outputsconst raw = JSON.stringify({
"user_app_id": {
"user_id": "{YOUR_USER_ID}",
"app_id": "{YOUR_APP_ID}"
},
"inputs": [
{
"data": {
"image": {
"url": "https://samples.clarifai.com/metro-north.jpg"
}
}
}
]
});
const requestOptions = {
method: 'POST',
headers: {
'Accept': 'application/json',
'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
},
body: raw
};
// NOTE: MODEL_VERSION_ID is optional, you can also call prediction with the MODEL_ID only
// https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/outputs
// this will default to the latest version_id
fetch("https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/versions/{MODEL_VERSION_ID}/outputs", requestOptions)
.then(response => response.text())
.then(result => console.log(JSON.parse(result, null, 2).outputs[0].data))
.catch(error => console.log('error', error));{
"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.
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
with open("{YOUR_IMAGE_FILE_LOCATION}", "rb") as f:
file_bytes = f.read()
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
model_id="{THE_MODEL_ID}",
version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
image=resources_pb2.Image(
base64=file_bytes
)
)
)
]
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print("There was an error with your request!")
print("\tCode: {}".format(post_model_outputs_response.outputs[0].status.code))
print("\tDescription: {}".format(post_model_outputs_response.outputs[0].status.description))
print("\tDetails: {}".format(post_model_outputs_response.outputs[0].status.details))
raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
# Since we have one input, one output will exist here.
output = post_model_outputs_response.outputs[0]
print("Predicted concepts:")
for concept in output.data.concepts:
print("%s %.2f" % (concept.name, concept.value))<?php
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
//
// For this example, the bytes of an image are needed and can be read in
// using PHP provided functions.
//
$image = "https://samples.clarifai.com/dog2.jpeg";
$imageData = file_get_contents($image); // Get the image data from the URL
///////////////////////////////////////////////////////////////////////////////
// Specifying the Request Data
///////////////////////////////////////////////////////////////////////////////
//
// In the Clarifai platform an image is defined by a special Image object.
// There are several ways in which an Image object can be populated including
// by url and image bytes (base64).
//
$image = new Image([
'base64' => $imageData
]);
//
// After an Image object is created, a Data object is constructed around it.
// The Data object offers a container that contains additional image independent
// metadata. In this particular use case, no other metadata is needed to be
// specified.
//
$data = new Data([
'image' => $image
]);
//
// The Data object is then wrapped in an Input object in order to meet the
// API specification. Additional fields are available to populate in the Input
// object, but for the purposes of this example we can send in just the
// Data object.
//
$input = new Input([
'data' => $data
]);
///////////////////////////////////////////////////////////////////////////////
// Creating the request object
///////////////////////////////////////////////////////////////////////////////
//
// Finally, the request object itself is created. This object carries the request
// along with the request status and other metadata related to the request itself.
// In this example we populate:
// - the `user_app_id` field with the UserAppIDSet constructed above
// - the `model_id` field with the ID of the model we are referencing
// - the `inputs` field with an array of input objects constructed above
//
$request = new PostModelOutputsRequest([
'user_app_id' => $userDataObject, // This is defined above
'model_id' => 'aaa03c23b3724a16a56b629203edc62c', // This is the ID of the publicly available General model.
'inputs' => [$input]
]);
///////////////////////////////////////////////////////////////////////////////
// Making the RPC call
///////////////////////////////////////////////////////////////////////////////
//
// Once the request object is constructed, we can call the actual request to the
// Clarifai platform. This uses the opened gRPC client channel to communicate the
// request and then wait for the response.
//
[$response, $status] = $client->PostModelOutputs(
$request,
$metadata
)->wait();
///////////////////////////////////////////////////////////////////////////////
// Handling the Response
///////////////////////////////////////////////////////////////////////////////
//
// The response is returned and the first thing we do is check the status of it.
// A successful response will have a status code of 0, otherwise there is some
// reported error.
//
if ($status->code !== 0) throw new Exception("Error: {$status->details}");
//
// In addition to the RPC response status, there is a Clarifai API status that
// reports if the operationo was a success or failure (not just that the commuunication)
// was successful.
//
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " .
$response->getStatus()->getDetails());
}
//
// The output of a successful call can be used in many ways. In this example,
// we loop through all of the predicted concepts and print them out along with
// their numerical prediction value (confidence).
//
echo "Predicted concepts:\n";
foreach ($response->getOutputs()[0]->getData()->getConcepts() as $concept) {
echo $concept->getName() . ": " . number_format($concept->getValue(), 2) . "\n";
}
?>import com.clarifai.grpc.api.*;
import com.clarifai.grpc.api.status.*;
import com.google.protobuf.ByteString;
import java.io.File;
import java.nio.file.Files;
// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setModelId("{THE_MODEL_ID}")
.setVersionId("{THE_MODEL_VERSION_ID") // This is optional. Defaults to the latest model version.
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setImage(
Image.newBuilder()
.setBase64(ByteString.copyFrom(Files.readAllBytes(
new File("{YOUR_IMAGE_FILE_LOCATION}").toPath()
)))
)
)
)
.build()
);
if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
}
// Since we have one input, one output will exist here.
Output output = postModelOutputsResponse.getOutputs(0);
System.out.println("Predicted concepts:");
for (Concept concept : output.getData().getConceptsList()) {
System.out.printf("%s %.2f%n", concept.getName(), concept.getValue());
}// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
const fs = require("fs");
const imageBytes = fs.readFileSync("{YOUR_IMAGE_FILE_LOCATION}");
stub.PostModelOutputs(
{
model_id: "{THE_MODEL_ID}",
version_id: "{THE_MODEL_VERSION_ID}", // This is optional. Defaults to the latest model version.
inputs: [
{data: {image: {base64: imageBytes}}}
]
},
metadata,
(err, response) => {
if (err) {
throw new Error(err);
}
if (response.status.code !== 10000) {
throw new Error("Post model outputs failed, status: " + response.status.description);
}
// Since we have one input, one output will exist here.
const output = response.outputs[0];
console.log("Predicted concepts:");
for (const concept of output.data.concepts) {
console.log(concept.name + " " + concept.value);
}
}
);// 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": "'"$(base64 /home/user/image.jpeg)"'"
}
}
}
]
}'\
https://api.clarifai.com/v2/models/{THE_MODEL_ID}/outputs
// Larger Files (Greater than 195 KB)
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d @- https://api.clarifai.com/v2/models/{model-id}/outputs << FILEIN
{
"inputs": [
{
"data": {
"image": {
"base64": "$(base64 /home/user/image.png)"
}
}
}
]
}
FILEINconst raw = JSON.stringify({
"user_app_id": {
"user_id": "{YOUR_USER_ID}",
"app_id": "{YOUR_APP_ID}"
},
"inputs": [
{
"data": {
"image": {
"base64": "{BYTES_STRING}"
}
}
}
]
});
const requestOptions = {
method: 'POST',
headers: {
'Accept': 'application/json',
'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
},
body: raw
};
// NOTE: MODEL_VERSION_ID is optional, you can also call prediction with the MODEL_ID only
// https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/outputs
// this will default to the latest version_id
fetch("https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/versions/{MODEL_VERSION_ID}/outputs", requestOptions)
.then(response => response.text())
.then(result => console.log(result))
.catch(error => console.log('error', error));{
"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
},
{
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