Azure AI Vision is a unified service that offers innovative computer vision capabilities. Give your apps the ability to analyze images, read text, and detect faces with prebuilt image tagging, text extraction with optical character recognition (OCR), and responsible facial recognition. Incorporate vision features into your projects with no machine learning experience required.
Extracts text from the provided image using Azure AI Vision OCR. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
The Azure AI Vision API provides powerful image analysis capabilities, enabling you to extract information and insights from your visual data. With this API, you can perform tasks like image classification, object detection, and OCR (Optical Character Recognition) to recognize text within images. Leveraging Pipedream, you can integrate these AI-powered insights into your workflows to create dynamic and automated processes.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
azure_ai_vision: {
type: "app",
app: "azure_ai_vision",
}
},
async run({steps, $}) {
const data = {
"url": `https://images.unsplash.com/photo-1528459199957-0ff28496a7f6`, //e.g. https://images.unsplash.com/photo-1528459199957-0ff28496a7f6
}
return await axios($, {
method: "POST",
url: `${this.azure_ai_vision.$auth.endpoint}computervision/imageanalysis:analyze`,
params: {
"api-version": `2023-02-01-preview`,
features: `read`, //e.g. read
},
headers: {
"Ocp-Apim-Subscription-Key": `${auths.azure_ai_vision.subscription_key}`,
},
data,
})
},
})
Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.
This includes installing PyPI packages, within your code without having to manage a requirements.txt
file or running pip
.
Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:
def handler(pd: "pipedream"):
# Reference data from previous steps
print(pd.steps["trigger"]["context"]["id"])
# Return data for use in future steps
return {"foo": {"test":True}}