Video
Make predictions on video inputs
Input: Video
Output: Concepts
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 (FPS), which is configurable in the predict request. This means you will receive a list of concepts for every second (1000 milliseconds) 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
- Wedding
You can make an API call by providing the MODEL_ID parameter and specifying your data parameter as video instead of image.
Configuring FPS
You can configure the FPS rate in the predict request by setting the sample_ms variable, which is the number of milliseconds between each frame for inference.
The number must range betweeen 100 and 60000.
tip
FPS = 1000/sample_ms
If sample_ms is 1000, then the FPS rate is 1, which is the default.
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 100MB 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.
Predict via URL
Below is an example of how you would send video URLs and receive predictions from Clarifai's general-image-recognition model.
info
The initialization code used in the following examples is outlined in detail on the client installation page.
- Python
- JavaScript (REST)
- NodeJS
- Java
- PHP
- cURL
#######################################################################################################
# In this section, we set the user authentication, user and app ID, model details, URL of the video
# we want as an input, and sample_ms. Change these strings to run your own example.
#######################################################################################################
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = 'YOUR_PAT_HERE'
# Specify the correct user_id/app_id pairings
# Since you're making inferences outside your app's scope
USER_ID = 'clarifai'
APP_ID = 'main'
# Change these to whatever model and video URL you want to use
MODEL_ID = 'general-image-recognition'
MODEL_VERSION_ID = 'aa7f35c01e0642fda5cf400f543e7c40'
VIDEO_URL = 'https://samples.clarifai.com/beer.mp4'
# Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
SAMPLE_MS = 500
############################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
############################################################################
from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)
metadata = (('authorization', 'Key ' + PAT),)
userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)
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=MODEL_ID,
version_id=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=VIDEO_URL
)
)
)
],
model=resources_pb2.Model(
output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(sample_ms=SAMPLE_MS)
)
),
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print(post_model_outputs_response.status)
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))
# Uncomment this line to print the full Response JSON
#print(output)
<!--index.html file-->
<script>
////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, URL of the video
// we want as an input, and sample_ms. Change these strings to run your own example.
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = 'YOUR_PAT_HERE';
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
const USER_ID = 'clarifai';
const APP_ID = 'main';
// Change these to whatever model and video URL you want to use
const MODEL_ID = 'general-image-recognition';
const MODEL_VERSION_ID = 'aa7f35c01e0642fda5cf400f543e7c40';
const VIDEO_URL = 'https://samples.clarifai.com/beer.mp4';
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
const SAMPLE_MS = 500;
///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////
const raw = JSON.stringify({
"user_app_id": {
"user_id": USER_ID,
"app_id": APP_ID
},
"inputs": [
{
"data": {
"video": {
"url": VIDEO_URL
}
}
}
],
"model": {
"output_info": {
"output_config": {
"sample_ms": SAMPLE_MS
}
}
}
});
const requestOptions = {
method: 'POST',
headers: {
'Accept': 'application/json',
'Authorization': 'Key ' + PAT
},
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/" + MODEL_ID + "/versions/" + MODEL_VERSION_ID + "/outputs", requestOptions)
.then(response => response.text())
.then(result => console.log(result))
.catch(error => console.log('error', error));
</script>
//index.js file
/////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, URL of the video
// we want as an input, and sample_ms. Change these strings to run your own example.
////////////////////////////////////////////////////////////////////////////////////////////////////////
// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = "YOUR_PAT_HERE";
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
const USER_ID = "clarifai";
const APP_ID = "main";
// Change these to whatever model and video URL you want to use
const MODEL_ID = "general-image-recognition";
const MODEL_VERSION_ID = "aa7f35c01e0642fda5cf400f543e7c40";
const VIDEO_URL = "https://samples.clarifai.com/beer.mp4";
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
const SAMPLE_MS = 500;
///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////
const { ClarifaiStub, grpc } = require("clarifai-nodejs-grpc");
const stub = ClarifaiStub.grpc();
// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set("authorization", "Key " + PAT);
stub.PostModelOutputs(
{
user_app_id: {
user_id: USER_ID,
app_id: APP_ID,
},
model_id: MODEL_ID,
version_id: MODEL_VERSION_ID, // This is optional. Defaults to the latest model version
inputs: [
{
data: {
video: {
url: VIDEO_URL,
allow_duplicate_url: true,
},
},
},
],
model: {
output_info: {
output_config: {
sample_ms: SAMPLE_MS,
},
},
},
},
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);
}
}
}
);
package com.clarifai.example;
import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;
public class ClarifaiExample {
////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, URL of the video
// we want as an input, and sample_ms. Change these strings to run your own example.
///////////////////////////////////////////////////////////////////////////////////////////////////////
//Your PAT (Personal Access Token) can be found in the portal under Authentication
static final String PAT = "YOUR_PAT_HERE";
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
static final String USER_ID = "clarifai";
static final String APP_ID = "main";
// Change these to whatever model and video URL you want to use
static final String MODEL_ID = "general-image-recognition";
static final String MODEL_VERSION_ID = "aa7f35c01e0642fda5cf400f543e7c40";
static final String VIDEO_URL = "https://samples.clarifai.com/beer.mp4";
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
static final int SAMPLE_MS = 500;
///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////
public static void main(String[] args) {
V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
.withCallCredentials(new ClarifaiCallCredentials(PAT));
MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
.setModelId(MODEL_ID)
.setVersionId(MODEL_VERSION_ID) // This is optional. Defaults to the latest model version
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setVideo(
Video.newBuilder().setUrl(VIDEO_URL)
)
)
)
.setModel(
Model.newBuilder().setOutputInfo(
OutputInfo.newBuilder().setOutputConfig(
OutputConfig.newBuilder().setSampleMs(SAMPLE_MS)
)
)
)
.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());
}
}
}
}
<?php
require __DIR__ . '/vendor/autoload.php';
////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, URL of the video
// we want as an input, and sample_ms. Change these strings to run your own example.
///////////////////////////////////////////////////////////////////////////////////////////////////////
// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = 'YOUR_PAT_HERE';
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
$USER_ID = 'clarifai';
$APP_ID = 'main';
// Change these to whatever model and video URL you want to use
$MODEL_ID = 'general-image-recognition';
$MODEL_VERSION_ID = 'aa7f35c01e0642fda5cf400f543e7c40';
$VIDEO_URL = 'https://samples.clarifai.com/beer.mp4';
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
$SAMPLE_MS = 500;
///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////
use Clarifai\ClarifaiClient;
use Clarifai\Api\Data;
use Clarifai\Api\Video;
use Clarifai\Api\Input;
use Clarifai\Api\PostModelOutputsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;
use Clarifai\Api\Model;
use Clarifai\Api\OutputInfo;
use Clarifai\Api\OutputConfig;
$client = ClarifaiClient::grpc();
$metadata = ['Authorization' => ['Key ' . $PAT ]];
$userDataObject = new UserAppIDSet([
'user_id' => $USER_ID,
'app_id' => $APP_ID
]);
// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client->PostModelOutputs(
// The request object carries the request along with the request status and other metadata related to the request itself
new PostModelOutputsRequest([
'user_app_id' => $userDataObject,
'model_id' => $MODEL_ID,
'version_id' => $MODEL_VERSION_ID, // This is optional. Defaults to the latest model version
'inputs' => [
new Input([ // The Input object wraps the Data object in order to meet the API specification
'data' => new Data([ // The Data object is constructed around the Video object. It offers a container that has additional video independent
// metadata. In this particular use case, no other metadata is needed to be specified
'video' => new Video([ // In the Clarifai platform, a video is defined by a special Video object
'url' => $VIDEO_URL
])
])
])
],
'model' => new Model([
'output_info' => new OutputInfo([
'output_config'=> new OutputConfig([
'sample_ms'=> $SAMPLE_MS
])
])
])
]),
$metadata
)->wait();
// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " .
$response->getStatus()->getDetails());
}
// The output of a successful call can be used in many ways. In this example, we loop through all of the frames of the video and print out the predicted
// concepts for each along with their numerical prediction value (confidence).
echo "Predicted concepts: </br>";
foreach ($response->getOutputs()[0]->getData()->getFrames() as $frame) {
echo "Predicted concepts on frame " . $frame->getFrameInfo()->getTime() . ":";
foreach ($frame->getData()->getConcepts() as $concept) {
echo " " . $concept->getName() . ": " . number_format($concept->getValue(), 2) . "</br>";
}
}
?>
# Model version ID is optional. It defaults to the latest model version, if omitted
curl -X POST "https://api.clarifai.com/v2/users/clarifai/apps/main/models/general-image-recognition/versions/aa7f35c01e0642fda5cf400f543e7c40/outputs" \
-H "Authorization: Key YOUR_PAT_HERE" \
-H "Content-Type: application/json" \
-d '{
"inputs": [
{
"data": {
"video": {
"url": "https://samples.clarifai.com/beer.mp4"
}
}
}
]
}'
Text Output Example
Predicted concepts on frame 250:
glass 1.00
drink 1.00
foam 1.00
beer 0.99
alcohol 0.99
lager 0.99
cold 0.99
bubble 0.98
liquid 0.98
full 0.98
no person 0.97
brewery 0.96
sparkling 0.95
cool 0.95
refreshment 0.95
bar 0.95
wet 0.94
drop 0.94
thirst 0.93
foamy 0.93
Predicted concepts on frame 750:
beer 1.00
glass 1.00
drink 1.00
foam 1.00
lager 1.00
alcohol 0.99
brewery 0.99
cold 0.99
ale 0.98
pint 0.98
liquid 0.98
bar 0.98
full 0.98
pub 0.97
bubble 0.97
mug 0.97
foamy 0.97
no person 0.95
brew 0.95
frosty 0.95
Predicted concepts on frame 1250:
beer 1.00
foam 1.00
glass 1.00
lager 1.00
drink 1.00
alcohol 0.99
brewery 0.99
cold 0.99
bar 0.98
ale 0.98
pub 0.98
liquid 0.97
pint 0.97
full 0.97
foamy 0.97
bubble 0.96
no person 0.96
amber 0.96
intoxicated 0.96
mug 0.95
Predicted concepts on frame 1750:
beer 1.00
glass 1.00
foam 1.00
lager 1.00
drink 1.00
alcohol 1.00
brewery 1.00
pint 0.99
cold 0.99
ale 0.99
pub 0.99
full 0.99
liquid 0.98
bar 0.98
foamy 0.98
intoxicated 0.97
liquor 0.97
amber 0.97
brew 0.97
sketch out 0.97
Predicted concepts on frame 2250:
glass 1.00
drink 1.00
cold 0.98
beer 0.98
foam 0.98
liquid 0.98
alcohol 0.98
full 0.96
no person 0.96
refreshment 0.94
lager 0.94
thirst 0.93
bar 0.92
liquor 0.90
cool 0.90
bubble 0.88
intoxicated 0.88
wet 0.87
pub 0.87
bottle 0.87
Predicted concepts on frame 2750:
glass 1.00
foam 0.99
beer 0.99
drink 0.99
no person 0.99
cold 0.98
alcohol 0.95
liquid 0.95
full 0.94
lager 0.93
thirst 0.91
liquor 0.91
bar 0.91
health 0.90
intoxicated 0.90
wet 0.88
refreshment 0.88
brewery 0.88
pub 0.86
bubble 0.85
Predicted concepts on frame 3250:
glass 1.00
drink 1.00
foam 0.98
beer 0.98
no person 0.97
liquid 0.97
cold 0.97
alcohol 0.97
full 0.95
refreshment 0.92
lager 0.91
gold 0.90
bottle 0.90
food 0.90
drop 0.88
bubble 0.88
health 0.88
vertical 0.87
party 0.87
champagne 0.86
Predicted concepts on frame 3750:
drink 0.98
no person 0.98
glass 0.98
beer 0.98
foam 0.96
alcohol 0.95
liquid 0.94
cold 0.92
gold 0.91
food 0.91
bar 0.90
refreshment 0.89
luxury 0.87
full 0.87
health 0.86
vertical 0.84
desktop 0.83
sparkling 0.82
shining 0.82
wet 0.81
Predicted concepts on frame 4250:
beer 1.00
foam 0.99
no person 0.99
lager 0.98
glass 0.98
drink 0.98
brewery 0.98
pint 0.96
bar 0.96
foamy 0.96
alcohol 0.95
cold 0.94
mug 0.92
full 0.91
ale 0.91
refreshment 0.91
pub 0.89
food 0.89
liquor 0.89
sketch out 0.88
Predicted concepts on frame 4750:
beer 1.00
foam 0.99
no person 0.99
glass 0.98
drink 0.98
lager 0.97
brewery 0.96
bar 0.95
cold 0.94
alcohol 0.94
pint 0.94
foamy 0.92
full 0.92
refreshment 0.90
mug 0.89
food 0.89
pub 0.88
ale 0.88
liquor 0.86
liquid 0.85
Predicted concepts on frame 5250:
beer 1.00
foam 0.99
no person 0.99
glass 0.99
lager 0.98
drink 0.98
brewery 0.97
pint 0.95
cold 0.95
alcohol 0.95
bar 0.95
full 0.94
foamy 0.93
ale 0.92
mug 0.91
pub 0.91
refreshment 0.88
liquor 0.88
liquid 0.87
food 0.85
Predicted concepts on frame 5750:
beer 1.00
no person 0.99
foam 0.99
glass 0.98
lager 0.98
drink 0.97
brewery 0.97
bar 0.94
pint 0.94
cold 0.93
alcohol 0.93
full 0.93
foamy 0.92
mug 0.89
ale 0.89
pub 0.88
refreshment 0.87
liquor 0.86
vertical 0.85
food 0.85
Predicted concepts on frame 6250:
beer 1.00
foam 0.99
no person 0.99
glass 0.99
lager 0.98
drink 0.98
brewery 0.97
pint 0.95
cold 0.95
bar 0.95
alcohol 0.95
full 0.93
foamy 0.93
ale 0.91
pub 0.90
mug 0.90
refreshment 0.90
liquor 0.87
liquid 0.86
sketch out 0.86
Predicted concepts on frame 6750:
beer 1.00
no person 0.99
foam 0.99
glass 0.98
lager 0.98
drink 0.98
brewery 0.97
cold 0.95
bar 0.95
pint 0.94
alcohol 0.94
full 0.93
foamy 0.91
ale 0.90
pub 0.89
mug 0.89
refreshment 0.88
liquor 0.86
food 0.86
liquid 0.85
Predicted concepts on frame 7250:
beer 1.00
foam 0.99
no person 0.99
glass 0.99
lager 0.98
drink 0.98
brewery 0.97
bar 0.95
cold 0.95
alcohol 0.95
pint 0.95
full 0.93
foamy 0.92
ale 0.91
pub 0.90
mug 0.90
refreshment 0.89
liquid 0.88
liquor 0.86
food 0.86
Predicted concepts on frame 7750:
beer 1.00
foam 0.99
no person 0.99
glass 0.98
lager 0.98
drink 0.98
brewery 0.96
cold 0.96
bar 0.95
full 0.94
pint 0.94
alcohol 0.94
foamy 0.92
ale 0.89
pub 0.88
mug 0.87
liquor 0.87
refreshment 0.87
food 0.86
liquid 0.85
Predicted concepts on frame 8250:
beer 1.00
foam 0.99
no person 0.99
glass 0.98
lager 0.98
drink 0.98
brewery 0.96
cold 0.96
bar 0.95
full 0.94
pint 0.94
alcohol 0.94
foamy 0.92
ale 0.89
pub 0.88
mug 0.87
liquor 0.87
refreshment 0.87
food 0.86
liquid 0.85
JSON Output Example
id: "e79ecf27e849441fa65f4aae88d4b6d7"
status {
code: SUCCESS
description: "Ok"
}
created_at {
seconds: 1701797920
nanos: 263533709
}
model {
id: "general-image-recognition"
name: "Image Recognition"
created_at {
seconds: 1457543499
nanos: 608845000
}
app_id: "main"
model_version {
id: "aa7f35c01e0642fda5cf400f543e7c40"
created_at {
seconds: 1520370624
nanos: 454834000
}
status {
code: MODEL_TRAINED
description: "Model is trained and ready"
}
visibility {
gettable: PUBLIC
}
app_id: "main"
user_id: "clarifai"
metadata {
}
}
user_id: "clarifai"
model_type_id: "visual-classifier"
visibility {
gettable: PUBLIC
}
modified_at {
seconds: 1694180313
nanos: 148401000
}
workflow_recommended {
}
}
input {
id: "dbce4ca63ce04cd69e320756bb788e47"
data {
video {
url: "https://samples.clarifai.com/beer.mp4"
}
}
}
data {
frames {
frame_info {
time: 250
}
data {
concepts {
id: "ai_mCpQg89c"
name: "glass"
value: 0.997752845287323
app_id: "main"
}
concepts {
id: "ai_zJx6RbxW"
name: "drink"
value: 0.9974687099456787
app_id: "main"
}
concepts {
id: "ai_8XGJjH7R"
name: "foam"
value: 0.9957486987113953
app_id: "main"
}
concepts {
id: "ai_TBlp0Pt3"
name: "beer"
value: 0.9945681095123291
app_id: "main"
}
concepts {
id: "ai_drK6ClJR"
name: "alcohol"
value: 0.9880152344703674
app_id: "main"
}
concepts {
id: "ai_3R5pJ6hB"
name: "lager"
value: 0.9860077500343323
app_id: "main"
}
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id: "ai_mCpQg89c"
name: "glass"
value: 0.9825376868247986
app_id: "main"
}
concepts {
id: "ai_3R5pJ6hB"
name: "lager"
value: 0.978348433971405
app_id: "main"
}
concepts {
id: "ai_zJx6RbxW"
name: "drink"
value: 0.9756084084510803
app_id: "main"
}
concepts {
id: "ai_2LWXN7Bn"
name: "brewery"
value: 0.9648955464363098
app_id: "main"
}
concepts {
id: "ai_2gmKZLxp"
name: "cold"
value: 0.9580070972442627
app_id: "main"
}
concepts {
id: "ai_SsmKLB4z"
name: "bar"
value: 0.9467634558677673
app_id: "main"
}
concepts {
id: "ai_54zxXFGL"
name: "full"
value: 0.9425504803657532
app_id: "main"
}
concepts {
id: "ai_QnpbpDLK"
name: "pint"
value: 0.9415781497955322
app_id: "main"
}
concepts {
id: "ai_drK6ClJR"
name: "alcohol"
value: 0.9384997487068176
app_id: "main"
}
concepts {
id: "ai_F547sXrF"
name: "foamy"
value: 0.9242134690284729
app_id: "main"
}
concepts {
id: "ai_qCKzbr0g"
name: "ale"
value: 0.8930195569992065
app_id: "main"
}
concepts {
id: "ai_XNmzgDnF"
name: "pub"
value: 0.8825927376747131
app_id: "main"
}
concepts {
id: "ai_pkvDRSJ1"
name: "mug"
value: 0.8744949698448181
app_id: "main"
}
concepts {
id: "ai_3l2jRv5s"
name: "liquor"
value: 0.8740363121032715
app_id: "main"
}
concepts {
id: "ai_B3MXt5Ng"
name: "refreshment"
value: 0.8686992526054382
app_id: "main"
}
concepts {
id: "ai_3PlgVmlN"
name: "food"
value: 0.864471435546875
app_id: "main"
}
concepts {
id: "ai_5VHsZr8N"
name: "liquid"
value: 0.8527519702911377
app_id: "main"
}
}
id: "06d040abf00bb2329b3089932a4cd242"
}
frames {
frame_info {
index: 16
time: 8250
}
data {
concepts {
id: "ai_TBlp0Pt3"
name: "beer"
value: 0.9981369972229004
app_id: "main"
}
concepts {
id: "ai_8XGJjH7R"
name: "foam"
value: 0.9921506643295288
app_id: "main"
}
concepts {
id: "ai_786Zr311"
name: "no person"
value: 0.9919553399085999
app_id: "main"
}
concepts {
id: "ai_mCpQg89c"
name: "glass"
value: 0.9824104309082031
app_id: "main"
}
concepts {
id: "ai_3R5pJ6hB"
name: "lager"
value: 0.978097140789032
app_id: "main"
}
concepts {
id: "ai_zJx6RbxW"
name: "drink"
value: 0.9754322171211243
app_id: "main"
}
concepts {
id: "ai_2LWXN7Bn"
name: "brewery"
value: 0.964467465877533
app_id: "main"
}
concepts {
id: "ai_2gmKZLxp"
name: "cold"
value: 0.9578704833984375
app_id: "main"
}
concepts {
id: "ai_SsmKLB4z"
name: "bar"
value: 0.9463430643081665
app_id: "main"
}
concepts {
id: "ai_54zxXFGL"
name: "full"
value: 0.9427977204322815
app_id: "main"
}
concepts {
id: "ai_QnpbpDLK"
name: "pint"
value: 0.9410056471824646
app_id: "main"
}
concepts {
id: "ai_drK6ClJR"
name: "alcohol"
value: 0.9379966259002686
app_id: "main"
}
concepts {
id: "ai_F547sXrF"
name: "foamy"
value: 0.9235425591468811
app_id: "main"
}
concepts {
id: "ai_qCKzbr0g"
name: "ale"
value: 0.8916416168212891
app_id: "main"
}
concepts {
id: "ai_XNmzgDnF"
name: "pub"
value: 0.8816885352134705
app_id: "main"
}
concepts {
id: "ai_3l2jRv5s"
name: "liquor"
value: 0.8735255002975464
app_id: "main"
}
concepts {
id: "ai_pkvDRSJ1"
name: "mug"
value: 0.8731899261474609
app_id: "main"
}
concepts {
id: "ai_B3MXt5Ng"
name: "refreshment"
value: 0.8683214783668518
app_id: "main"
}
concepts {
id: "ai_3PlgVmlN"
name: "food"
value: 0.864808976650238
app_id: "main"
}
concepts {
id: "ai_5VHsZr8N"
name: "liquid"
value: 0.8516526818275452
app_id: "main"
}
}
id: "5cff8dcca58754b84ee23e2251eed7fa"
}
}
Predict via Bytes
Below is an example of how you would send the bytes of a video and receive predictions from Clarifai's general-image-recognition model.
- Python
- JavaScript (REST)
- NodeJS
- Java
- PHP
- cURL
############################################################################################################
# In this section, we set the user authentication, user and app ID, model details, location of the video
# we want as an input, and sample_ms. Change these strings to run your own example.
###########################################################################################################
# Your PAT (Personal Access Token) can be found in the Account's Security section
PAT = 'YOUR_PAT_HERE'
# Specify the correct user_id/app_id pairings
# Since you're making inferences outside your app's scope
USER_ID = 'clarifai'
APP_ID = 'main'
# Change these to whatever model and video input you want to use
MODEL_ID = 'general-image-recognition'
MODEL_VERSION_ID = 'aa7f35c01e0642fda5cf400f543e7c40'
VIDEO_FILE_LOCATION = 'YOUR_VIDEO_FILE_LOCATION_HERE'
# Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
SAMPLE_MS = 500
############################################################################
# YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
############################################################################
from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
from clarifai_grpc.grpc.api.status import status_code_pb2
channel = ClarifaiChannel.get_grpc_channel()
stub = service_pb2_grpc.V2Stub(channel)
metadata = (('authorization', 'Key ' + PAT),)
userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)
with open(VIDEO_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=MODEL_ID,
version_id=MODEL_VERSION_ID, # This is optional. Defaults to the latest model version
inputs=[
resources_pb2.Input(
data=resources_pb2.Data(
video=resources_pb2.Video(
base64=file_bytes
)
)
)
],
model=resources_pb2.Model(
output_info=resources_pb2.OutputInfo(
output_config=resources_pb2.OutputConfig(sample_ms=SAMPLE_MS)
)
),
),
metadata=metadata
)
if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
print(post_model_outputs_response.status)
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))
# Uncomment this line to print the full Response JSON
#print(output)
<!--index.html file-->
<script>
///////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, bytes of the
// video we want as an input, and sample_ms. Change these strings to run your own example.
//////////////////////////////////////////////////////////////////////////////////////////////////
// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = 'YOUR_PAT_HERE';
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
const USER_ID = 'clarifai';
const APP_ID = 'main';
// Change these to whatever model and video input you want to use
const MODEL_ID = 'general-image-recognition';
const MODEL_VERSION_ID = 'aa7f35c01e0642fda5cf400f543e7c40';
const VIDEO_BYTES_STRING = 'YOUR_BYTES_STRING_HERE';
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
const SAMPLE_MS = 500;
///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////
const raw = JSON.stringify({
"user_app_id": {
"user_id": USER_ID,
"app_id": APP_ID
},
"inputs": [
{
"data": {
"video": {
"base64": VIDEO_BYTES_STRING
}
}
}
],
"model": {
"output_info": {
"output_config": {
"sample_ms": SAMPLE_MS
}
}
}
});
const requestOptions = {
method: 'POST',
headers: {
'Accept': 'application/json',
'Authorization': 'Key ' + PAT
},
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/" + MODEL_ID + "/versions/" + MODEL_VERSION_ID + "/outputs", requestOptions)
.then(response => response.text())
.then(result => console.log(result))
.catch(error => console.log('error', error));
</script>
//index.js file
////////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, location of the video
// we want as an input, and sample_ms. Change these strings to run your own example.
////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Your PAT (Personal Access Token) can be found in the Account's Security section
const PAT = "YOUR_PAT_HERE";
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
const USER_ID = "clarifai";
const APP_ID = "main";
// Change these to whatever model and video input you want to use
const MODEL_ID = "general-image-recognition";
const MODEL_VERSION_ID = "aa7f35c01e0642fda5cf400f543e7c40";
const VIDEO_FILE_LOCATION = "YOUR_VIDEO_FILE_LOCATION_HERE";
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
const SAMPLE_MS = 500;
///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////
const { ClarifaiStub, grpc } = require("clarifai-nodejs-grpc");
const stub = ClarifaiStub.grpc();
// This will be used by every Clarifai endpoint call
const metadata = new grpc.Metadata();
metadata.set("authorization", "Key " + PAT);
const fs = require("fs");
const videoBytes = fs.readFileSync(VIDEO_FILE_LOCATION);
stub.PostModelOutputs(
{
user_app_id: {
user_id: USER_ID,
app_id: APP_ID,
},
model_id: MODEL_ID,
version_id: MODEL_VERSION_ID, // This is optional. Defaults to the latest model version.
inputs: [
{
data: {
video: {
base64: videoBytes,
},
},
},
],
model: {
output_info: {
output_config: {
sample_ms: SAMPLE_MS,
},
},
},
},
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);
}
}
}
);
package com.clarifai.example;
import com.clarifai.grpc.api.*;
import com.clarifai.channel.ClarifaiChannel;
import com.clarifai.credentials.ClarifaiCallCredentials;
import com.clarifai.grpc.api.status.StatusCode;
import java.io.File;
import java.io.IOException;
import java.nio.file.Files;
import com.google.protobuf.ByteString;
public class ClarifaiExample {
////////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, location of the video
// we want as an input, and sample_ms. Change these strings to run your own example.
////////////////////////////////////////////////////////////////////////////////////////////////////////////
//Your PAT (Personal Access Token) can be found in the portal under Authentication
static final String PAT = "YOUR_PAT_HERE";
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
static final String USER_ID = "clarifai";
static final String APP_ID = "main";
// Change these to whatever model and video input you want to use
static final String MODEL_ID = "general-image-recognition";
static final String MODEL_VERSION_ID = "aa7f35c01e0642fda5cf400f543e7c40";
static final String VIDEO_FILE_LOCATION = "YOUR_VIDEO_FILE_LOCATION_HERE";
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
static final int SAMPLE_MS = 500;
///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////
public static void main(String[] args) throws IOException {
V2Grpc.V2BlockingStub stub = V2Grpc.newBlockingStub(ClarifaiChannel.INSTANCE.getGrpcChannel())
.withCallCredentials(new ClarifaiCallCredentials(PAT));
MultiOutputResponse postModelOutputsResponse = stub.postModelOutputs(
PostModelOutputsRequest.newBuilder()
.setUserAppId(UserAppIDSet.newBuilder().setUserId(USER_ID).setAppId(APP_ID))
.setModelId(MODEL_ID)
.setVersionId(MODEL_VERSION_ID) // This is optional. Defaults to the latest model version
.addInputs(
Input.newBuilder().setData(
Data.newBuilder().setVideo(
Video.newBuilder()
.setBase64(ByteString.copyFrom(Files.readAllBytes(
new File(VIDEO_FILE_LOCATION).toPath()
)))
)
)
)
.setModel(
Model.newBuilder().setOutputInfo(
OutputInfo.newBuilder().setOutputConfig(
OutputConfig.newBuilder().setSampleMs(SAMPLE_MS)
)
)
)
.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());
}
}
}
}
<?php
require __DIR__ . '/vendor/autoload.php';
//////////////////////////////////////////////////////////////////////////////////////////////////////////////
// In this section, we set the user authentication, user and app ID, model details, location of the video
// we want as an input, and sample_ms. Change these strings to run your own example.
/////////////////////////////////////////////////////////////////////////////////////////////////////////////
// Your PAT (Personal Access Token) can be found in the Account's Security section
$PAT = 'YOUR_PAT_HERE';
// Specify the correct user_id/app_id pairings
// Since you're making inferences outside your app's scope
$USER_ID = 'clarifai';
$APP_ID = 'main';
// Change these to whatever model and video input you want to use
$MODEL_ID = 'general-image-recognition';
$MODEL_VERSION_ID = 'aa7f35c01e0642fda5cf400f543e7c40';
$VIDEO_FILE_LOCATION = 'YOUR_VIDEO_FILE_LOCATION_HERE';
// Change this to configure the FPS rate (If it's not configured, it defaults to 1 FPS)
$SAMPLE_MS = 500;
///////////////////////////////////////////////////////////////////////////////////
// YOU DO NOT NEED TO CHANGE ANYTHING BELOW THIS LINE TO RUN THIS EXAMPLE
///////////////////////////////////////////////////////////////////////////////////
use Clarifai\ClarifaiClient;
use Clarifai\Api\Data;
use Clarifai\Api\Video;
use Clarifai\Api\Input;
use Clarifai\Api\PostModelOutputsRequest;
use Clarifai\Api\Status\StatusCode;
use Clarifai\Api\UserAppIDSet;
use Clarifai\Api\Model;
use Clarifai\Api\OutputInfo;
use Clarifai\Api\OutputConfig;
$client = ClarifaiClient::grpc();
$metadata = ['Authorization' => ['Key ' . $PAT ]];
$userDataObject = new UserAppIDSet([
'user_id' => $USER_ID,
'app_id' => $APP_ID
]);
$videoData = file_get_contents($VIDEO_FILE_LOCATION); // Get the video bytes data from the location
// Let's make a RPC call to the Clarifai platform. It uses the opened gRPC client channel to communicate a
// request and then wait for the response
[$response, $status] = $client->PostModelOutputs(
// The request object carries the request along with the request status and other metadata related to the request itself
new PostModelOutputsRequest([
'user_app_id' => $userDataObject,
'model_id' => $MODEL_ID,
'version_id' => $MODEL_VERSION_ID, // This is optional. Defaults to the latest model version
'inputs' => [
new Input([ // The Input object wraps the Data object in order to meet the API specification
'data' => new Data([ // The Data object is constructed around the Video object. It offers a container that has additional video independent
// metadata. In this particular use case, no other metadata is needed to be specified
'video' => new Video([ // In the Clarifai platform, a video is defined by a special Video object
'base64' => $videoData
])
])
])
],
'model' => new Model([
'output_info' => new OutputInfo([
'output_config'=> new OutputConfig([
'sample_ms'=> $SAMPLE_MS
])
])
])
]),
$metadata
)->wait();
// A response is returned and the first thing we do is check the status of it
// A successful response will have a status code of 0; otherwise, there is some error
if ($status->code !== 0) {
throw new Exception("Error: {$status->details}");
}
// In addition to the RPC response status, there is a Clarifai API status that reports if the operation was a success or failure
// (not just that the communication was successful)
if ($response->getStatus()->getCode() != StatusCode::SUCCESS) {
throw new Exception("Failure response: " . $response->getStatus()->getDescription() . " " .
$response->getStatus()->getDetails());
}
// The output of a successful call can be used in many ways. In this example, we loop through all of the frames of the video and print out the predicted
// concepts for each along with their numerical prediction value (confidence).
echo "Predicted concepts: </br>";
foreach ($response->getOutputs()[0]->getData()->getFrames() as $frame) {
echo "Predicted concepts on frame " . $frame->getFrameInfo()->getTime() . ":";
foreach ($frame->getData()->getConcepts() as $concept) {
echo " " . $concept->getName() . ": " . number_format($concept->getValue(), 2) . "</br>";
}
}
?>
# Model version ID is optional. It defaults to the latest model version, if omitted
curl -X POST "https://api.clarifai.com/v2/users/clarifai/apps/main/models/general-image-recognition/versions/aa7f35c01e0642fda5cf400f543e7c40/outputs" \
-H "Authorization: Key YOUR_PAT_HERE" \
-H "Content-Type: application/json" \
-d '{
"inputs": [
{
"data": {
"video": {
"base64": "YOUR_BYTES_STRING_HERE"
}
}
}
]
}'