Video
Make predictions on video inputs
With a video input, the Predict API response will return a list of predicted concepts for every frame of a video. By default, video is processed at 1 frame per second (but this is configurable in the predict request). This means you will receive a list of concepts for every second of your video.
You can run Predict on your video using a select number of Clarifai Models. The models that are currently supported are: Apparel, Food, General, NSFW, Travel, and Wedding. You make an API call by providing the {model-id} parameter and your data parameter is video instead of image.
Video limits
The Predict API has limits to the length and size it can support. A video, uploaded through URL, can be anywhere up to 80MB in size or 10mins in length. When a video is sent through by bytes, the Predict API can support 10MB in size.
If your video exceeds the limits, please follow our tutorial on how to break up a large video into smaller components, and send those into the Video API. Otherwise, the processing will time out and you will receive an error response.
Via URL
Below is an example of how you would send video URLs and receive back predictions from the general model.
Python
PHP
Java
NodeJS
Python
cURL
Javascript (REST)
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# Insert here the initialization code as outlined on this page:
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# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
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post_model_outputs_response = stub.PostModelOutputs(
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service_pb2.PostModelOutputsRequest(
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model_id="{THE_MODEL_ID}",
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version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
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inputs=[
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resources_pb2.Input(
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data=resources_pb2.Data(
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video=resources_pb2.Video(
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url="https://samples.clarifai.com/beer.mp4"
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)
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)
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)
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]
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),
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metadata=metadata
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)
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if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
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raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
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# Since we have one input, one output will exist here.
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output = post_model_outputs_response.outputs[0]
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# A separate prediction is available for each "frame".
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for frame in output.data.frames:
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print("Predicted concepts on frame " + str(frame.frame_info.time) + ":")
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for concept in frame.data.concepts:
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print("\t%s %.2f" % (concept.name, concept.value))
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<?php
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# Insert here the initialization code as outlined on this page:
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# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
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///////////////////////////////////////////////////////////////////////////////
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// Specifying the Request Data
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///////////////////////////////////////////////////////////////////////////////
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//
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// In the Clarifai platform a video is defined by a special Video object.
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// There are several ways in which an Video object can be populated including
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// by url and video bytes (base64).
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//
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$video = new Video([
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'url' => 'https://samples.clarifai.com/beer.mp4'
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]);
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//
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// After a Video object is created, a Data object is constructed around it.
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// The Data object offers a container that contains additional video independent
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// metadata. In this particular use case, no other metadata is needed to be
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// specified.
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//
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$data = new Data([
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'video' => $video
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]);
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//
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// The Data object is then wrapped in an Input object in order to meet the
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// API specification. Additional fields are available to populate in the Input
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// object, but for the purposes of this example we can send in just the
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// Data object.
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//
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$input = new Input([
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'data' => $data
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]);
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///////////////////////////////////////////////////////////////////////////////
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// Creating the request object
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///////////////////////////////////////////////////////////////////////////////
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//
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// Finally, the request object itself is created. This object carries the request
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// along with the request status and other metadata related to the request itself.
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// In this example we populate:
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// - the `user_app_id` field with the UserAppIDSet constructed above
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// - the `model_id` field with the ID of the model we are referencing
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// - the `inputs` field with an array of input objects constructed above
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//
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$request = new PostModelOutputsRequest([
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'user_app_id' => $userDataObject, // This is defined above
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'model_id' => 'aaa03c23b3724a16a56b629203edc62c', // This is the ID of the publicly available General model.
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'inputs' => [$input]
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]);
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///////////////////////////////////////////////////////////////////////////////
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// Making the RPC call
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///////////////////////////////////////////////////////////////////////////////
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//
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// Once the request object is constructed, we can call the actual request to the
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// Clarifai platform. This uses the opened gRPC client channel to communicate the
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// request and then wait for the response.
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//
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[$response, $status] = $client->PostModelOutputs(
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$request,
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$metadata
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)->wait();
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///////////////////////////////////////////////////////////////////////////////
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// Handling the Response
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///////////////////////////////////////////////////////////////////////////////
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//
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// The response is returned and the first thing we do is check the status of it.
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// A successful response will have a status code of 0, otherwise there is some
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// reported error.
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//
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if ($status->code !== 0) throw new Exception("Error: {$status->details}");
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//
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// In addition to the RPC response status, there is a Clarifai API status that
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// reports if the operationo was a success or failure (not just that the commuunication)
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// was successful.
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//
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if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
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throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " .
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$response->getStatus()->getDetails());
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}
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//
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// The output of a successful call can be used in many ways. In this example,
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// we loop through all of the frames of the video and print out the predicted
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// concepts for each along with their numerical prediction value (confidence).
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//
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foreach ($output->getData()->getFrames() as $frame) {
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echo "Predicted concepts on frame " . $frame->getFrameInfo()->getTime() . ":";
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foreach ($frame->getData()->getConcepts() as $concept) {
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echo " " . $concept->getName() . ": " . number_format($concept->getValue(), 2) . "\n";
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}
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}
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?>
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import com.clarifai.grpc.api.*;
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import com.clarifai.grpc.api.status.*;
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// Insert here the initialization code as outlined on this page:
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// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
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MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
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PostModelOutputsRequest.newBuilder()
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.setModelId("{THE_MODEL_ID}")
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.setVersionId("{THE_MODEL_VERSION_ID") // This is optional. Defaults to the latest model version.
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.addInputs(
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Input.newBuilder().setData(
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Data.newBuilder().setVideo(
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Video.newBuilder().setUrl("https://samples.clarifai.com/beer.mp4")
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)
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)
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)
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.build()
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);
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if (postModelOutputsResponse.getStatus().getCode() != StatusCode.SUCCESS) {
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throw new RuntimeException("Post model outputs failed, status: " + postModelOutputsResponse.getStatus());
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}
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// Since we have one input, one output will exist here.
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Output output = postModelOutputsResponse.getOutputs(0);
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// A separate prediction is available for each "frame".
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for (Frame frame : output.getData().getFramesList()) {
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System.out.println("Predicted concepts on frame " + frame.getFrameInfo().getTime() + ":");
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for (Concept concept : frame.getData().getConceptsList()) {
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System.out.printf("\t%s %.2f%n", concept.getName(), concept.getValue());
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}
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}
Copied!
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// Insert here the initialization code as outlined on this page:
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// https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
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stub.PostModelOutputs(
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{
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model_id: "{THE_MODEL_ID}",
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version_id: "{THE_MODEL_VERSION_ID}", // This is optional. Defaults to the latest model version.
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inputs: [
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{data: {video: {url: "https://samples.clarifai.com/beer.mp4"}}}
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]
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},
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metadata,
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(err, response) => {
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if (err) {
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throw new Error(err);
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}
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if (response.status.code !== 10000) {
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throw new Error("Post model outputs failed, status: " + response.status.description);
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}
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// Since we have one input, one output will exist here.
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const output = response.outputs[0]
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// A separate prediction is available for each "frame".
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for (const frame of output.data.frames) {
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console.log("Predicted concepts on frame " + frame.frame_info.time + ":");
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for (const concept of frame.data.concepts) {
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console.log("\t" + concept.name + " " + concept.value);
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}
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}
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}
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);
Copied!
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# Insert here the initialization code as outlined on this page:
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# https://docs.clarifai.com/api-guide/api-overview/api-clients#client-installation-instructions
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post_model_outputs_response = stub.PostModelOutputs(
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service_pb2.PostModelOutputsRequest(
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user_app_id=userDataObject, # The userDataObject is created in the overview and is required when using a PAT
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model_id="{THE_MODEL_ID}",
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version_id="{THE_MODEL_VERSION_ID}", # This is optional. Defaults to the latest model version.
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inputs=[
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resources_pb2.Input(
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data=resources_pb2.Data(
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video=resources_pb2.Video(
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url="https://samples.clarifai.com/beer.mp4"
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)
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)
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)
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]
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),
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metadata=metadata
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)
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if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
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print("There was an error with your request!")
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print("\tCode: {}".format(post_model_outputs_response.outputs[0].status.code))
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print("\tDescription: {}".format(post_model_outputs_response.outputs[0].status.description))
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print("\tDetails: {}".format(respopost_model_outputs_responsense.outputs[0].status.details))
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raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
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# Since we have one input, one output will exist here.
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output = post_model_outputs_response.outputs[0]
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# A separate prediction is available for each "frame".
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for frame in output.data.frames:
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print("Predicted concepts on frame " + str(frame.frame_info.time) + ":")
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for concept in frame.data.concepts:
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print("\t%s %.2f" % (concept.name, concept.value))
Copied!
1
curl -X POST \
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-H "Authorization: Key YOUR_API_KEY" \
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-H "Content-Type: application/json" \
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-d '
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{
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"inputs": [
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{
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"data": {
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"video": {
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"url": "https://samples.clarifai.com/beer.mp4"
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}
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}
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}
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]
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}'\
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https://api.clarifai.com/v2/models/{THE_MODEL_ID}/versions/{THE_MODEL_VERSION_ID}/outputs
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# Model version ID is optional. It defaults to the latest model version.
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const raw = JSON.stringify({
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"user_app_id": {
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"user_id": "{YOUR_USER_ID}",
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"app_id": "{YOUR_APP_ID}"
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},
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"inputs": [
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{
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"data": {
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"video": {
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"url": "https://samples.clarifai.com/beer.mp4"
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}
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}
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}
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]
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});
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const requestOptions = {
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method: 'POST',
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headers: {
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'Accept': 'application/json',
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'Authorization': 'Key {YOUR_PERSONAL_TOKEN}'
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},
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body: raw
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};
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// NOTE: MODEL_VERSION_ID is optional, you can also call prediction with the MODEL_ID only
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// https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/outputs
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// this will default to the latest version_id
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fetch("https://api.clarifai.com/v2/models/{YOUR_MODEL_ID}/versions/{MODEL_VERSION_ID}/outputs", requestOptions)
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.then(response => response.text())
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.then(result => console.log(result))
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.catch(error => console.log('error', error));
Copied!
Response JSON
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{
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"status": {
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"code": 10000,
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"description": "Ok"
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},
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"outputs": [
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{
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"id": "d8234da5d1f04ca8a2e13e34d51f9b85",
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"status": {
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"code": 10000,
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"description": "Ok"
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},
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"created_at": "2017-06-28T14:58:41.835370141Z",
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"model": {
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"id": "aaa03c23b3724a16a56b629203edc62c",
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"name": "general-v1.3",
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"created_at": "2016-03-09T17:11:39.608845Z",
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"app_id": "main",
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"output_info": {
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"message": "Show output_info with: GET /models/{model_id}/output_info",
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"type": "concept",
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"type_ext": "concept"
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},
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"model_version": {
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"id": "aa9ca48295b37401f8af92ad1af0d91d",
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"created_at": "2016-07-13T01:19:12.147644Z",
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"status": {
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"code": 21100,
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"description": "Model trained successfully"
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}
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}
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},
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"input": {
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"id": "f0fc1a005f124d389da4d80823a3125b",
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"data": {
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"video": {
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"url": "https://samples.clarifai.com/beer.mp4"
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}
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}
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},
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"data": {
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"frames": [
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{
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"frame_info": {
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"index": 0,
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"time": 0
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},
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"data": {
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"concepts": [
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{
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"id": "ai_zJx6RbxW",
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"name": "drink",
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"value": 0.98658466,
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"app_id": "main"
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},
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{
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"id": "ai_mCpQg89c",
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"name": "glass",
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"value": 0.97975093,
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"app_id": "main"
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},
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{
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"id": "ai_drK6ClJR",
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"name": "alcohol",
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"value": 0.9783862,
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"app_id": "main"
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},
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{
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"id": "ai_8XGJjH7R",
70
"name": "foam",
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"value": 0.97157896,
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"app_id": "main"
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},
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{
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"id": "ai_TBlp0Pt3",
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"name": "beer",
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"value": 0.969543,
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"app_id": "main"
79
},
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{
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"id": "ai_SsmKLB4z",
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"name": "bar",
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"value": 0.96628696,
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"app_id": "main"
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},
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{
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"id": "ai_5VHsZr8N",
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"name": "liquid",
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"value": 0.95581007,
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"app_id": "main"
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},
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{
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"id": "ai_Lq00FggW",
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"name": "desktop",
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"value": 0.92861253,
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"app_id": "main"
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},
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{
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"id": "ai_7vR9zv7l",
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"name": "bubble",
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"value": 0.9082134,
102
"app_id": "main"
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},
104
{
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"id": "ai_B3MXt5Ng",
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"name": "refreshment",
107
"value": 0.9020835,
108
"app_id": "main"
109
},
110
{
111
"id": "ai_7Xg5SQRW",
112
"name": "luxury",
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"value": 0.8990605,
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"app_id": "main"
115
},
116
{
117
"id": "ai_786Zr311",
118
"name": "no person",
119
"value": 0.89708906,
120
"app_id": "main"
121
},
122
{
123
"id": "ai_3R5pJ6hB",
124
"name": "lager",
125
"value": 0.8938055,
126
"app_id": "main"
127
},
128
{
129
"id": "ai_7qwGxLch",
130
"name": "gold",
131
"value": 0.8892093,
132
"app_id": "main"
133
},
134
{
135
"id": "ai_wmbvr5TG",
136
"name": "celebration",
137
"value": 0.88606626,
138
"app_id": "main"
139
},
140
{
141
"id": "ai_4lvjn8qv",
142
"name": "closeup",
143
"value": 0.881963,
144
"app_id": "main"
145
},
146
{
147
"id": "ai_pkvDRSJ1",
148
"name": "mug",
149
"value": 0.8674431,
150
"app_id": "main"
151
},
152
{
153
"id": "ai_12dz73B9",
154
"name": "bottle",
155
"value": 0.86288416,
156
"app_id": "main"
157
},
158
{
159
"id": "ai_zFnPQdgB",
160
"name": "wood",
161
"value": 0.86252767,
162
"app_id": "main"
163
},
164
{
165
"id": "ai_8LWlDfFD",
166
"name": "table",
167
"value": 0.86069393,
168
"app_id": "main"
169
}
170
]
171
}
172
},
173
{
174
"frame_info": {
175
"index": 1,
176
"time": 1000
177
},
178
"data": {
179
"concepts": [
180
{
181
"id": "ai_zJx6RbxW",
182
"name": "drink",
183
"value": 0.98658466,
184
"app_id": "main"
185
},
186
{
187
"id": "ai_mCpQg89c",
188
"name": "glass",
189
"value": 0.97975093,
190
"app_id": "main"
191
},
192
{
193
"id": "ai_drK6ClJR",
194
"name": "alcohol",
195
"value": 0.9783862,
196
"app_id": "main"
197
},
198
{
199
"id": "ai_8XGJjH7R",
200
"name": "foam",
201
"value": 0.97157896,
202
"app_id": "main"
203
},
204
{
205
"id": "ai_TBlp0Pt3",
206
"name": "beer",
207
"value": 0.969543,
208
"app_id": "main"
209
},
210
{
211
"id": "ai_SsmKLB4z",
212
"name": "bar",
213
"value": 0.96628696,
214
"app_id": "main"
215
},
216
{
217
"id": "ai_5VHsZr8N",
218
"name": "liquid",
219
"value": 0.95581007,
220
"app_id": "main"
221
},
222
{
223
"id": "ai_7vR9zv7l",
224
"name": "bubble",
225
"value": 0.9082134,
226
"app_id": "main"
227
},
228
{
229
"id": "ai_B3MXt5Ng",
230
"name": "refreshment",
231
"value": 0.9020835,
232
"app_id": "main"
233
},
234
{
235
"id": "ai_786Zr311",
236
"name": "no person",
237
"value": 0.89708906,
238
"app_id": "main"
239
},
240
{
241
"id": "ai_3R5pJ6hB",
242
"name": "lager",
243
"value": 0.8938055,
244
"app_id": "main"
245
},
246
{
247
"id": "ai_7qwGxLch",
248
"name": "gold",
249
"value": 0.8834515,
250
"app_id": "main"
251
},
252
{
253
"id": "ai_pkvDRSJ1",
254
"name": "mug",
255
"value": 0.8674431,
256
"app_id": "main"
257
},
258
{
259
"id": "ai_b01mhdxB",
260
"name": "party",
261
"value": 0.8603341,
262
"app_id": "main"
263
},
264
{
265
"id": "ai_XNmzgDnF",
266
"name": "pub",
267
"value": 0.85809004,
268
"app_id": "main"
269
},
270
{
271
"id": "ai_2gmKZLxp",
272
"name": "cold",
273
"value": 0.85319245,
274
"app_id": "main"
275
},
276
{
277
"id": "ai_Lq00FggW",
278
"name": "desktop",
279
"value": 0.8506696,
280
"app_id": "main"
281
},
282
{
283
"id": "ai_54zxXFGL",
284
"name": "full",
285
"value": 0.84634554,
286
"app_id": "main"
287
},
288
{
289
"id": "ai_zFnPQdgB",
290
"name": "wood",
291
"value": 0.8446485,
292
"app_id": "main"
293
},
294
{
295
"id": "ai_wmbvr5TG",
296
"name": "celebration",
297
"value": 0.8383831,
298
"app_id": "main"
299
}
300
]
301
}
302
},
303
{
304
"frame_info": {
305
"index": 2,
306
"time": 2000
307
},
308
"data": {
309
"concepts": [
310
{
311
"id": "ai_zJx6RbxW",
312
"name": "drink",
313
"value": 0.9856042,
314
"app_id": "main"
315
},
316
{
317
"id": "ai_mCpQg89c",
318
"name": "glass",
319
"value": 0.97975093,
320
"app_id": "main"
321
},
322
{
323
"id": "ai_8XGJjH7R",
324
"name": "foam",
325
"value": 0.9755833,
326
"app_id": "main"
327
},
328
{
329
"id": "ai_786Zr311",
330
"name": "no person",
331
"value": 0.9733174,
332
"app_id": "main"
333
},
334
{
335
"id": "ai_TBlp0Pt3",
336
"name": "beer",
337
"value": 0.969543,
338
"app_id": "main"
339
},
340
{
341
"id": "ai_SsmKLB4z",
342
"name": "bar",
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