| description | sidebar_position |
|---|---|
Make predictions on video inputs |
2 |
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.
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.
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.
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.
:::
import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import CodeBlock from "@theme/CodeBlock";
import CodePythonViaURL from "!!raw-loader!../../../code_snippets/api-guide/predict/python/video_via_url.py"; import CodePythonViaBytes from "!!raw-loader!../../../code_snippets/api-guide/predict/python/video_via_bytes.py";
import CodeJavaScriptViaURL from "!!raw-loader!../../../code_snippets/api-guide/predict/js/video_via_url.html"; import CodeJavaScriptViaBytes from "!!raw-loader!../../../code_snippets/api-guide/predict/js/video_via_bytes.html";
import CodeNodeJSViaURL from "!!raw-loader!../../../code_snippets/api-guide/predict/node/video_via_url.js"; import CodeNodeJSViaBytes from "!!raw-loader!../../../code_snippets/api-guide/predict/node/video_via_bytes.js";
import CodeJavaViaURL from "!!raw-loader!../../../code_snippets/api-guide/predict/java/video_via_url.java"; import CodeJavaViaBytes from "!!raw-loader!../../../code_snippets/api-guide/predict/java/video_via_bytes.java";
import CodePHPViaURL from "!!raw-loader!../../../code_snippets/api-guide/predict/php/video_via_url.php"; import CodePHPViaBytes from "!!raw-loader!../../../code_snippets/api-guide/predict/php/video_via_bytes.php";
import CurlViaURL from "!!raw-loader!../../../code_snippets/api-guide/predict/curl/video_via_url.sh"; import CurlViaBytes from "!!raw-loader!../../../code_snippets/api-guide/predict/curl/video_via_bytes.sh";
import CodeOutputExample1 from "!!raw-loader!../../../code_snippets/api-guide/predict/code_output_examples/video_via_url.txt"; import JSONOutputExample1 from "!!raw-loader!../../../code_snippets/api-guide/predict/code_output_examples/video_via_url.js";
{CodePythonViaURL} {CodeJavaScriptViaURL} {CodeNodeJSViaURL} {CodeJavaViaURL} {CodePHPViaURL} {CurlViaURL}Text Output Example
{CodeOutputExample1}JSON Output Example
{JSONOutputExample1}Below is an example of how you would send the bytes of a video and receive predictions from Clarifai's general-image-recognition model.