Video

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.

gRPC Java
gRPC NodeJS
gRPC Python
js
python
java
csharp
objective-c
php
cURL
gRPC Java
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
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().setVideo(
Video.newBuilder().setUrl("https://samples.clarifai.com/beer.mp4")
)
)
)
.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);
// A separate prediction is available for each "frame".
for (Frame frame : output.getData().getFramesList()) {
System.out.println("Predicted concepts on frame " + frame.getFrameInfo().getTime() + ":");
for (Concept concept : frame.getData().getConceptsList()) {
System.out.printf("\t%s %.2f%n", concept.getName(), concept.getValue());
}
}
gRPC NodeJS
// Insert here the initialization code as outlined on this page:
// https://docs.clarifai.com/api-guide/api-overview
stub.PostModelOutputs(
{
model_id: "{THE_MODEL_ID}",
version_id: "{THE_MODEL_VERSION_ID}", // This is optional. Defaults to the latest model version.
inputs: [
{data: {video: {url: "https://samples.clarifai.com/beer.mp4"}}}
]
},
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]
// A separate prediction is available for each "frame".
for (const frame of output.data.frames) {
console.log("Predicted concepts on frame " + frame.frame_info.time + ":");
for (const concept of frame.data.concepts) {
console.log("\t" + concept.name + " " + concept.value);
}
}
}
);
gRPC Python
from clarifai_grpc.grpc.api import service_pb2, resources_pb2
from clarifai_grpc.grpc.api.status import status_code_pb2
# Insert here the initialization code as outlined on this page:
# https://docs.clarifai.com/api-guide/api-overview
post_model_outputs_response = stub.PostModelOutputs(
service_pb2.PostModelOutputsRequest(
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(
video=resources_pb2.Video(
url="https://samples.clarifai.com/beer.mp4"
)
)
)
]
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
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]
# A separate prediction is available for each "frame".
for frame in output.data.frames:
print("Predicted concepts on frame " + str(frame.frame_info.time) + ":")
for concept in frame.data.concepts:
print("\t%s %.2f" % (concept.name, concept.value))
js
const Clarifai = require('clarifai');
const app = new Clarifai.App({apiKey: 'YOUR_API_KEY'});
app.models.predict(
Clarifai.GENERAL_MODEL,
'https://samples.clarifai.com/beer.mp4',
{video: true, sampleMs: 1000})
.then(response => {
let frames = response['outputs'][0]['data']['frames'];
frames.forEach(frame => {
console.log('Concepts in frame at time: ' + frame['frame_info']['time'] + 'ms');
frame['data']['concepts'].forEach(concept => {
console.log(' ' + concept['name'] + ' ' + concept['value']);
});
});
})
.catch(error => {
console.log('Error status code: ' + error.data['status']['code']);
console.log('Error description: ' + error.data['status']['description']);
if (error.data['status']['details'])
{
console.log('Error details: ' + error.data['status']['details']);
}
});
python
from clarifai.errors import ApiError
from clarifai.rest import ClarifaiApp
app = ClarifaiApp(api_key='YOUR_API_KEY')
m = app.public_models.general_model
try:
response = m.predict_by_url('https://samples.clarifai.com/beer.mp4',
is_video=True,
sample_ms=1000)
except ApiError as e:
print('Error status code: %d' % e.error_code)
print('Error description: %s' % e.error_desc)
if e.error_details:
print('Error details: %s' % e.error_details)
exit(1)
frames = response['outputs'][0]['data']['frames']
for frame in frames:
print('Concepts in frame at time: %d ms' % frame['frame_info']['time'])
for concept in frame['data']['concepts']:
print(' %s %f' % (concept['name'], concept['value']))
java
import clarifai2.api.ClarifaiBuilder;
import clarifai2.api.ClarifaiClient;
import clarifai2.api.ClarifaiResponse;
import clarifai2.dto.input.ClarifaiInput;
import clarifai2.dto.model.VideoModel;
import clarifai2.dto.model.output.ClarifaiOutput;
import clarifai2.dto.prediction.Concept;
import clarifai2.dto.prediction.Frame;
import java.util.List;
public class YourClassName {
public static void main(String[] args) {
ClarifaiClient client = new ClarifaiBuilder("YOUR_API_KEY")
.buildSync();
VideoModel model = client .getDefaultModels().generalVideoModel();
ClarifaiResponse<List<ClarifaiOutput<Frame>>> response = model.predict()
.withInputs(ClarifaiInput.forVideo("https://samples.clarifai.com/beer.mp4"))
.withSampleMs(1000)
.executeSync();
if (response.isSuccessful()) {
List<Frame> frames = response.get().get(0).data();
for (Frame frame : frames) {
System.out.println("Concepts in frame at time: " + frame.time() + " ms");
for (Concept concept : frame.concepts()) {
System.out.println(" " + concept.name() + " " + concept.value());
}
}
} else {
System.out.println("Error status code: " + response.getStatus().statusCode());
System.out.println("Error description: " + response.getStatus().description());
if (response.getStatus().errorDetails() != null) {
System.out.println("Error details: " + response.getStatus().errorDetails());
}
}
}
}
csharp
using System;
using System.Collections.Generic;
using System.Threading.Tasks;
using Clarifai.API;
using Clarifai.API.Responses;
using Clarifai.DTOs.Inputs;
using Clarifai.DTOs.Models;
using Clarifai.DTOs.Models.Outputs;
using Clarifai.DTOs.Predictions;
namespace YourPackageName
{
public class YourClassName
{
public static async Task Main()
{
var client = new ClarifaiClient("YOUR_API_KEY");
VideoModel model = client.PublicModels.GeneralVideoModel;
ClarifaiResponse<ClarifaiOutput<Frame>> response = await model
.Predict(
input: new ClarifaiURLVideo("https://samples.clarifai.com/beer.mp4"),
sampleMs: 1000
)
.ExecuteAsync();
if (response.IsSuccessful)
{
List<Frame> frames = response.Get().Data;
foreach (Frame frame in frames)
{
Console.WriteLine($"Concepts in frame at time {frame.Time}:");
foreach (Concept concept in frame.Concepts)
{
Console.WriteLine($" {concept.Name} {concept.Value}");
}
}
}
else
{
Console.WriteLine($"Error status code: {response.Status.StatusCode}");
Console.WriteLine($"Error description: {response.Status.Description}");
if (response.Status.ErrorDetails != null)
{
Console.WriteLine($"Error details: {response.Status.ErrorDetails}");
}
}
}
}
}
objective-c
Objective-C client details coming soon
php
use Clarifai\API\ClarifaiClient;
use Clarifai\DTOs\Inputs\ClarifaiURLVideo;
use Clarifai\DTOs\Models\VideoModel;
use Clarifai\DTOs\Outputs\ClarifaiOutput;
use Clarifai\DTOs\Predictions\Concept;
use Clarifai\DTOs\Predictions\Frame;
$client = new ClarifaiClient('YOUR_API_KEY');
/** @var VideoModel $model */
$model = $client->publicModels()->generalVideoModel();
$response = $model->predict(
new ClarifaiURLVideo('https://samples.clarifai.com/beer.mp4'))
->withSampleMs(1000)
->executeSync();
if ($response->isSuccessful()) {
/** @var ClarifaiOutput $output */
$output = $response->get();
/** @var Frame[] $frames */
$frames = $output->data();
foreach ($frames as $frame) {
echo "Concepts in frame at time: {$frame->time()}\n";
/** @var Concept $concept */
foreach ($frame->concepts() as $concept) {
echo " {$concept->name()} {$concept->value()}\n";
}
}
} else {
echo "Error status code: {$response->status()->statusCode()}";
echo "Error description: {$response->status()->description()}";
if ($response->status()->errorDetails()) {
echo "Error details: {$response->status()->errorDetails()}";
}
}
cURL
curl -X POST \
-H "Authorization: Key YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '
{
"inputs": [
{
"data": {
"video": {
"url": "https://samples.clarifai.com/beer.mp4"
}
}
}
]
}'\
https://api.clarifai.com/v2/models/{THE_MODEL_ID}/versions/{THE_MODEL_VERSION_ID}/outputs
# Model version ID is optional. It defaults to the latest model version.
Response JSON
Response JSON
{
"status": {
"code": 10000,
"description": "Ok"
},
"outputs": [
{
"id": "d8234da5d1f04ca8a2e13e34d51f9b85",
"status": {
"code": 10000,
"description": "Ok"
},
"created_at": "2017-06-28T14:58:41.835370141Z",
"model": {
"id": "aaa03c23b3724a16a56b629203edc62c",
"name": "general-v1.3",
"created_at": "2016-03-09T17:11:39.608845Z",
"app_id": "main",
"output_info": {
"message": "Show output_info with: GET /models/{model_id}/output_info",
"type": "concept",
"type_ext": "concept"
},
"model_version": {
"id": "aa9ca48295b37401f8af92ad1af0d91d",
"created_at": "2016-07-13T01:19:12.147644Z",
"status": {
"code": 21100,
"description": "Model trained successfully"
}
}
},
"input": {
"id": "f0fc1a005f124d389da4d80823a3125b",
"data": {
"video": {
"url": "https://samples.clarifai.com/beer.mp4"
}
}
},
"data": {
"frames": [
{
"frame_info": {
"index": 0,
"time": 0
},
"data": {
"concepts": [
{
"id": "ai_zJx6RbxW",
"name": "drink",
"value": 0.98658466,
"app_id": "main"
},
{
"id": "ai_mCpQg89c",
"name": "glass",
"value": 0.97975093,
"app_id": "main"
},
{
"id": "ai_drK6ClJR",
"name": "alcohol",
"value": 0.9783862,
"app_id": "main"
},
{
"id": "ai_8XGJjH7R",
"name": "foam",
"value": 0.97157896,
"app_id": "main"
},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
"value": 0.969543,
"app_id": "main"
},
{
"id": "ai_SsmKLB4z",
"name": "bar",
"value": 0.96628696,
"app_id": "main"
},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
"value": 0.95581007,
"app_id": "main"
},
{
"id": "ai_Lq00FggW",
"name": "desktop",
"value": 0.92861253,
"app_id": "main"
},
{
"id": "ai_7vR9zv7l",
"name": "bubble",
"value": 0.9082134,
"app_id": "main"
},
{
"id": "ai_B3MXt5Ng",
"name": "refreshment",
"value": 0.9020835,
"app_id": "main"
},
{
"id": "ai_7Xg5SQRW",
"name": "luxury",
"value": 0.8990605,
"app_id": "main"
},
{
"id": "ai_786Zr311",
"name": "no person",
"value": 0.89708906,
"app_id": "main"
},
{
"id": "ai_3R5pJ6hB",
"name": "lager",
"value": 0.8938055,
"app_id": "main"
},
{
"id": "ai_7qwGxLch",
"name": "gold",
"value": 0.8892093,
"app_id": "main"
},
{
"id": "ai_wmbvr5TG",
"name": "celebration",
"value": 0.88606626,
"app_id": "main"
},
{
"id": "ai_4lvjn8qv",
"name": "closeup",
"value": 0.881963,
"app_id": "main"
},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
"value": 0.8674431,
"app_id": "main"
},
{
"id": "ai_12dz73B9",
"name": "bottle",
"value": 0.86288416,
"app_id": "main"
},
{
"id": "ai_zFnPQdgB",
"name": "wood",
"value": 0.86252767,
"app_id": "main"
},
{
"id": "ai_8LWlDfFD",
"name": "table",
"value": 0.86069393,
"app_id": "main"
}
]
}
},
{
"frame_info": {
"index": 1,
"time": 1000
},
"data": {
"concepts": [
{
"id": "ai_zJx6RbxW",
"name": "drink",
"value": 0.98658466,
"app_id": "main"
},
{
"id": "ai_mCpQg89c",
"name": "glass",
"value": 0.97975093,
"app_id": "main"
},
{
"id": "ai_drK6ClJR",
"name": "alcohol",
"value": 0.9783862,
"app_id": "main"
},
{
"id": "ai_8XGJjH7R",
"name": "foam",
"value": 0.97157896,
"app_id": "main"
},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
"value": 0.969543,
"app_id": "main"
},
{
"id": "ai_SsmKLB4z",
"name": "bar",
"value": 0.96628696,
"app_id": "main"
},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
"value": 0.95581007,
"app_id": "main"
},
{
"id": "ai_7vR9zv7l",
"name": "bubble",
"value": 0.9082134,
"app_id": "main"
},
{
"id": "ai_B3MXt5Ng",
"name": "refreshment",
"value": 0.9020835,
"app_id": "main"
},
{
"id": "ai_786Zr311",
"name": "no person",
"value": 0.89708906,
"app_id": "main"
},
{
"id": "ai_3R5pJ6hB",
"name": "lager",
"value": 0.8938055,
"app_id": "main"
},
{
"id": "ai_7qwGxLch",
"name": "gold",
"value": 0.8834515,
"app_id": "main"
},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
"value": 0.8674431,
"app_id": "main"
},
{
"id": "ai_b01mhdxB",
"name": "party",
"value": 0.8603341,
"app_id": "main"
},
{
"id": "ai_XNmzgDnF",
"name": "pub",
"value": 0.85809004,
"app_id": "main"
},
{
"id": "ai_2gmKZLxp",
"name": "cold",
"value": 0.85319245,
"app_id": "main"
},
{
"id": "ai_Lq00FggW",
"name": "desktop",
"value": 0.8506696,
"app_id": "main"
},
{
"id": "ai_54zxXFGL",
"name": "full",
"value": 0.84634554,
"app_id": "main"
},
{
"id": "ai_zFnPQdgB",
"name": "wood",
"value": 0.8446485,
"app_id": "main"
},
{
"id": "ai_wmbvr5TG",
"name": "celebration",
"value": 0.8383831,
"app_id": "main"
}
]
}
},
{
"frame_info": {
"index": 2,
"time": 2000
},
"data": {
"concepts": [
{
"id": "ai_zJx6RbxW",
"name": "drink",
"value": 0.9856042,
"app_id": "main"
},
{
"id": "ai_mCpQg89c",
"name": "glass",
"value": 0.97975093,
"app_id": "main"
},
{
"id": "ai_8XGJjH7R",
"name": "foam",
"value": 0.9755833,
"app_id": "main"
},
{
"id": "ai_786Zr311",
"name": "no person",
"value": 0.9733174,
"app_id": "main"
},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
"value": 0.969543,
"app_id": "main"
},
{
"id": "ai_SsmKLB4z",
"name": "bar",
"value": 0.94170487,
"app_id": "main"
},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
"value": 0.92778283,
"app_id": "main"
},
{
"id": "ai_2gmKZLxp",
"name": "cold",
"value": 0.9227257,
"app_id": "main"
},
{
"id": "ai_3PlgVmlN",
"name": "food",
"value": 0.9179274,
"app_id": "main"
},
{
"id": "ai_drK6ClJR",
"name": "alcohol",
"value": 0.90887475,
"app_id": "main"
},
{
"id": "ai_B3MXt5Ng",
"name": "refreshment",
"value": 0.9045203,
"app_id": "main"
},
{
"id": "ai_3R5pJ6hB",
"name": "lager",
"value": 0.8938055,
"app_id": "main"
},
{
"id": "ai_WbwL0pPL",
"name": "breakfast",
"value": 0.87420183,
"app_id": "main"
},
{
"id": "ai_54zxXFGL",
"name": "full",
"value": 0.8699659,
"app_id": "main"
},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
"value": 0.8674431,
"app_id": "main"
},
{
"id": "ai_XNmzgDnF",
"name": "pub",
"value": 0.85809004,
"app_id": "main"
},
{
"id": "ai_Lq00FggW",
"name": "desktop",
"value": 0.8506696,
"app_id": "main"
},
{
"id": "ai_zFnPQdgB",
"name": "wood",
"value": 0.8446485,
"app_id": "main"
},
{
"id": "ai_qNxqNBWN",
"name": "cream",
"value": 0.844169,
"app_id": "main"
},
{
"id": "ai_7D0mdp1W",
"name": "delicious",
"value": 0.8397074,
"app_id": "main"
}
]
}
},
{
"frame_info": {
"index": 3,
"time": 3000
},
"data": {
"concepts": [
{
"id": "ai_8XGJjH7R",
"name": "foam",
"value": 0.996614,
"app_id": "main"
},
{
"id": "ai_zJx6RbxW",
"name": "drink",
"value": 0.9794438,
"app_id": "main"
},
{
"id": "ai_786Zr311",
"name": "no person",
"value": 0.9733174,
"app_id": "main"
},
{
"id": "ai_TBlp0Pt3",
"name": "beer",
"value": 0.9645849,
"app_id": "main"
},
{
"id": "ai_mCpQg89c",
"name": "glass",
"value": 0.94761443,
"app_id": "main"
},
{
"id": "ai_pkvDRSJ1",
"name": "mug",
"value": 0.92864025,
"app_id": "main"
},
{
"id": "ai_2gmKZLxp",
"name": "cold",
"value": 0.9227257,
"app_id": "main"
},
{
"id": "ai_5VHsZr8N",
"name": "liquid",
"value": 0.91797745,
"app_id": "main"
},
{
"id": "ai_3PlgVmlN",
"name": "food",
"value": 0.9179274,
"app_id": "main"
},
{
"id": "ai_WbwL0pPL",
"name": "breakfast",
"value": 0.904904,
"app_id": "main"
},
{
"id": "ai_B3MXt5Ng",
"name": "refreshment",
"value": 0.9045203,
"app_id": "main"
},
{
"id": "ai_54zxXFGL",
"name": "full",
"value": 0.889248,
"app_id": "main"
},
{
"id": "ai_BrnHNkt0",
"name": "coffee",
"value": 0.8689867,
"app_id": "main"
},
{
"id": "ai_7D0mdp1W",
"name": "delicious",
"value": 0.86591685,
"app_id": "main"
},
{
"id": "ai_SsmKLB4z",
"name": "bar",
"value": 0.8546975,
"app_id": "main"
},
{
"id": "ai_mZ2tl6cW",
"name": "health",
"value": 0.8544879,
"app_id": "main"
},
{
"id": "ai_cHsR7RS8",
"name": "milk",
"value": 0.852397,
"app_id": "main"
},
{
"id": "ai_zFnPQdgB",
"name": "wood",
"value": 0.8446485,
"app_id": "main"
},
{
"id": "ai_qNxqNBWN",
"name": "cream",
"value": 0.844169,
"app_id": "main"
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