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Computer Vision
NLP
Objects
Faces
Landmarks
Text
Detect and localize objects in images.
Python
JS
cURL
More

–– CODE language-python ––
import requests\n
url = "https://api.rapyd.ai/v1/vision/localize"\n
payload = {}\n
files = [('file', open('localize_demo.png', 'rb'))]\n
headers = {\n
   'PROVIDER': 'gcp',\n
   'ACCOUNT-ID': 'your-accountid',\n
   'Authorization': 'Bearer your-token'\n
}\n
response = requests.request(\n
   "POST", url, headers=headers, data=payload, files=files)\n
print(response.text.encode('utf8'))\n

–– CODE language-js ––
var myHeaders = new Headers();\n
myHeaders.append("PROVIDER", "gcp");\n
myHeaders.append("ACCOUNT-ID", "your-accountid");\n
myHeaders.append("Authorization", "Bearer your-token");\n
var formdata = new FormData();\n
formdata.append("file", fileInput.files[0], "localize_demo.png");\n
var requestOptions = {\n
 method: 'POST',\n
 headers: myHeaders,\n
 body: formdata,\n
 redirect: 'follow'\n
};\n
fetch("https://api.rapyd.ai/v1/vision/localize", requestOptions)\n
 .then(response => response.text())\n
 .then(result => console.log(result))\n
 .catch(error => console.log('error', error));\n

–– CODE language-shell ––
curl --location --request POST 'https://api.rapyd.ai/v1/vision/localize' \\n
--header 'PROVIDER: gcp' \\n
--header 'ACCOUNT-ID: your-accountid' \\n
--header 'Authorization: Bearer your-token' \\n
--form 'file=localize_demo.png'\n

Find more examples for other languages in our documentation.

>_ View Documentation

Supported Providers: gcp, azure, aws
Inputs: JPEG, PNG, GIF, etc. Maximum file size is 2 MB.  Recommended image size is 640 x 480 pixels (ca. 300k pixels).

Used in the following Use Cases:
Counting People in Images
Analyse images and automatically count the amount of people / persons which can be recognized therein.
Counting Cars in Images
Analyse images and automatically count the amount of cars / vehicles which can be recognized therein.
Detect and analyze faces in images.
Python
JS
cURL
More

–– CODE language-python line-numbers ––
import requests\n
url = "https://api.rapyd.ai/v1/vision/face"\n
payload = {}\n
files = [('file', open('face_demo.png', 'rb'))]\n
headers = {\n
   'PROVIDER': 'gcp',\n
   'ACCOUNT-ID': 'your-accountid',\n
   'Authorization': 'Bearer your-token'\n
}\n
response = requests.request(\n
   "POST", url, headers=headers, data=payload, files=files)\n
print(response.text.encode('utf8'))\n

–– CODE language-js line-numbers ––
var myHeaders = new Headers();\n
myHeaders.append("PROVIDER", "gcp");\n
myHeaders.append("ACCOUNT-ID", "your-accountid");\n
myHeaders.append("Authorization", "Bearer your-token");\n
var formdata = new FormData();\n
formdata.append("file", fileInput.files[0], "face_demo.png");\n
var requestOptions = {\n
 method: 'POST',\n
 headers: myHeaders,\n
 body: formdata,\n
 redirect: 'follow'\n
};\n
fetch("https://api.rapyd.ai/v1/vision/face", requestOptions)\n
 .then(response => response.text())\n
 .then(result => console.log(result))\n
 .catch(error => console.log('error', error));\n

–– CODE language-shell line-numbers ––
curl --location --request POST 'https://api.rapyd.ai/v1/vision/face' \\n
--header 'PROVIDER: gcp' \\n
--header 'ACCOUNT-ID: your-accountid' \\n
--header 'Authorization: Bearer your-token' \\n
--form 'file=face_demo.png'\n

Find more examples for other languages in our documentation.

>_ View Documentation

Supported Providers: gcp, azure, aws
Inputs: JPEG, PNG, GIF, etc. Maximum file size is 2 MB.  The recommended image size is 1600 x 1200 pixels (ca. 1.9M pixels). The larger the faces on the image the better.

Use Case Labs
Anonymize Images by Blurring Out Faces
Protect personal data in images by automatically detecting faces and removing them from the image file.
Detect and localize popular landmarks in images.
Python
JS
cURL
More

–– CODE language-python line-numbers ––
import requests\n
url = "https://api.rapyd.ai/v1/vision/landmark"\n
payload = {}\n
files = [('file', open('landmark_demo.png', 'rb'))]\n
headers = {\n
   'PROVIDER': 'gcp',\n
   'ACCOUNT-ID': 'your-accountid',\n
   'Authorization': 'Bearer your-token'\n
}\n
response = requests.request(\n
   "POST", url, headers=headers, data=payload, files=files)\n
print(response.text.encode('utf8'))\n

–– CODE language-js line-numbers ––
var myHeaders = new Headers();\n
myHeaders.append("PROVIDER", "gcp");\n
myHeaders.append("ACCOUNT-ID", "your-accountid");\n
myHeaders.append("Authorization", "Bearer your-token");\n
var formdata = new FormData();\n
formdata.append("file", fileInput.files[0], "landmark_demo.png");\n
var requestOptions = {\n
 method: 'POST',\n
 headers: myHeaders,\n
 body: formdata,\n
 redirect: 'follow'\n
};\n
fetch("https://api.rapyd.ai/v1/vision/landmark", requestOptions)\n
 .then(response => response.text())\n
 .then(result => console.log(result))\n
 .catch(error => console.log('error', error));\n

–– CODE language-shell line-numbers ––
curl --location --request POST 'https://api.rapyd.ai/v1/vision/landmark' \\n
--header 'PROVIDER: gcp' \\n
--header 'ACCOUNT-ID: your-accountid' \\n
--header 'Authorization: Bearer your-token' \\n
--form 'file=landmark_demo.png'\n

Find more examples for other languages in our documentation.

>_ View Documentation

Supported Providers: gcp, azure
Inputs: JPEG, PNG, GIF, etc. Maximum file size is 2 MB.  Recommended image size is 640 x 480 pixels (ca. 300k pixels).

Use Case Labs
Show Wikipedia Info for Tourist Attractions
Provide context and explanations for pictures taken by tourists, so they can get even more out of their trip.
Extract text and text structures from images.
Python
JS
cURL
More

–– CODE language-python line-numbers ––
import requests\n
url = "https://api.rapyd.ai/v1/vision/text"\n
payload = {}\n
files = [('file', open('text_demo.png', 'rb'))]\n
headers = {\n
   'PROVIDER': 'gcp',\n
   'ACCOUNT-ID': 'your-accountid',\n
   'Authorization': 'Bearer your-token'\n
}\n
response = requests.request(\n
   "POST", url, headers=headers, data=payload, files=files)\n
print(response.text.encode('utf8'))\n

–– CODE language-js line-numbers ––
var myHeaders = new Headers();\n
myHeaders.append("PROVIDER", "gcp");\n
myHeaders.append("ACCOUNT-ID", "your-accountid");\n
myHeaders.append("Authorization", "Bearer your-token");\n
var formdata = new FormData();\n
formdata.append("file", fileInput.files[0], "text_demo.png");\n
var requestOptions = {\n
 method: 'POST',\n
 headers: myHeaders,\n
 body: formdata,\n
 redirect: 'follow'\n
};\n
fetch("https://api.rapyd.ai/v1/vision/text", requestOptions)\n
 .then(response => response.text())\n
 .then(result => console.log(result))\n
 .catch(error => console.log('error', error));\n

–– CODE language-shell line-numbers ––
curl --location --request POST 'https://api.rapyd.ai/v1/vision/text' \\n
--header 'PROVIDER: gcp' \\n
--header 'ACCOUNT-ID: your-accountid' \\n
--header 'Authorization: Bearer your-token' \\n
--form 'file=text_demo.png'\n

Find more examples for other languages in our documentation.

>_ View Documentation

Supported Providers: aws, azure, gcp
Inputs: JPEG, PNG, GIF, etc. Maximum file size is 2 MB. The recommended image size is 1024 x 768 pixels (ca. 786k pixels).

Use Case Labs
We are currently working on use cases for this category. Please check back later or message us at hello@rapyd.ai for use case suggestions.
Sentiment
Keyphrases
Entities
Analyse opinions expressed in a piece of text.
Python
JS
cURL
More

–– CODE language-python line-numbers ––
import requests\n\n
headers = {\n
 'ACCOUNT-ID': 'your-accountid',\n
 'Authorization': 'Bearer your-token',\n
 'Content-Type': 'application/json'\n
}\n\n
payload = {\n
 "text": "I don't like old cabin cruisers.", \n
 "provider": "aws",\n
 "language": "auto"\n
 }\n\n
url = "https://api.rapyd.ai/v1/nlp/sentiment"\n
response = requests.request("POST", url, headers=headers, json = payload)\n\n
print(response.text.encode('utf8'))\n

–– CODE language-js line-numbers ––
var myHeaders = new Headers();\n
myHeaders.append("ACCOUNT-ID", "your-accountid");\n
myHeaders.append("Content-Type", "application/json");\n
myHeaders.append("Authorization", "Bearer your-token");\n
var raw = JSON.stringify({\n
 "text": "I don't like old cabin cruisers.",\n
 "provider": "aws",\n
 "language": "auto"\n
});\n
var requestOptions = {\n
 method: 'POST',\n
 headers: myHeaders,\n
 body: raw,\n
 redirect: 'follow'\n
};\n
fetch("https://api.rapyd.ai/v1/nlp/sentiment", requestOptions)\n
 .then(response => response.text())\n
 .then(result => console.log(result))\n
 .catch(error => console.log('error', error));\n

–– CODE language-shell line-numbers ––
curl --location --request POST 'https://api.rapyd.ai/v1/nlp/sentiment' \ \n
--header 'ACCOUNT-ID: your-accountid' \ \n
--header 'Content-Type: application/json' \ \n
--header 'Authorization: Bearer your-token' \ \n
--data-raw '{\n
   "text": "I don'\''t like old cabin cruisers.",\n
   "provider":"aws",\n
   "language": "auto"\n
}'\n

Find more examples for other languages in our documentation.

>_ View Documentation

Supported Providers: aws, azure, gcp
Inputs: Raw text with max. 4.000 characters. All major languages are supported.

Use Case Labs
Analyse Twitter Topic Sentiment By Region
Compare Twitter sentiment by regions to discover differences and reveal meaningful insights.
Analyse Twitter Sentiment Trends Over Time
Compare Twitter sentiment over time to discover trends and reveal meaningful insights.
Find Negative Viral Tweets on Twitter
Identify negative mentions early and react on them before they turn into a major crisis.
Identify key words and key phrases in texts.
Python
JS
cURL
More

–– CODE language-python line-numbers ––
import requests\n\n
headers = {\n
 'ACCOUNT-ID': 'your-accountid',\n
 'Authorization': 'Bearer your-token',\n
 'Content-Type': 'application/json'\n
}\n\n
payload = {\n
 "text": "Seems it never rains in Southern California.", \n
 "provider": "aws",\n
 "language": "auto"\n
 }\n\n
url = "https://api.rapyd.ai/v1/nlp/keyphrase"\n
response = requests.request("POST", url, headers=headers, json = payload)\n\n
print(response.text.encode('utf8'))\n

–– CODE language-js line-numbers ––
var myHeaders = new Headers();\n
myHeaders.append("ACCOUNT-ID", "your-accountid");\n
myHeaders.append("Content-Type", "application/json");\n
myHeaders.append("Authorization", "Bearer your-token");\n
var raw = JSON.stringify({\n
 "text": "Seems it never rains in Southern California.",\n
 "provider": "aws",\n
 "language": "auto"\n
});\n
var requestOptions = {\n
 method: 'POST',\n
 headers: myHeaders,\n
 body: raw,\n
 redirect: 'follow'\n
};\n
fetch("https://api.rapyd.ai/v1/nlp/keyphrase", requestOptions)\n
 .then(response => response.text())\n
 .then(result => console.log(result))\n
 .catch(error => console.log('error', error));\n

–– CODE language-shell line-numbers ––
curl --location --request POST 'https://api.rapyd.ai/v1/nlp/keyphrase' \ \n
--header 'ACCOUNT-ID: your-accountid' \ \n
--header 'Content-Type: application/json' \ \n
--header 'Authorization: Bearer your-token' \ \n
--data-raw '{\n
   "text": "Seems it never rains in Southern California.",\n
   "provider":"aws",\n
   "language": "auto"\n
}'\n

Find more examples for other languages in our documentation.

>_ View Documentation

Supported Providers: aws, azure
Inputs: Raw text with max. 4.000 characters. All major languages are supported.

Use Case Labs
We are currently working on use cases for this category. Please check back later or message us at hello@rapyd.ai for use case suggestions.
Recognize known entities in texts.
Python
JS
cURL
More

–– CODE language-python line-numbers ––
import requests\n\n
headers = {\n
 'ACCOUNT-ID': 'your-accountid',\n
 'Authorization': 'Bearer your-token',\n
 'Content-Type': 'application/json'\n
}\n\n
payload = {\n
 "text": "Next summer I want to visit Barcelona and Lion.", \n
 "provider": "aws",\n
 "language": "auto"\n
 }\n\n
url = "https://api.rapyd.ai/v1/nlp/entities"\n
response = requests.request("POST", url, headers=headers, json = payload)\n\n
print(response.text.encode('utf8'))\n

–– CODE language-js line-numbers ––
var myHeaders = new Headers();\n
myHeaders.append("ACCOUNT-ID", "your-accountid");\n
myHeaders.append("Content-Type", "application/json");\n
myHeaders.append("Authorization", "Bearer your-token");\n
var raw = JSON.stringify({\n
 "text": "Next summer I want to visit Barcelona and Lion.",\n
 "provider": "aws",\n
 "language": "auto"\n
});\n
var requestOptions = {\n
 method: 'POST',\n
 headers: myHeaders,\n
 body: raw,\n
 redirect: 'follow'\n
};\n
fetch("https://api.rapyd.ai/v1/nlp/entities", requestOptions)\n
 .then(response => response.text())\n
 .then(result => console.log(result))\n
 .catch(error => console.log('error', error));\n

–– CODE language-shell line-numbers ––
curl --location --request POST 'https://api.rapyd.ai/v1/nlp/entities' \ \n
--header 'ACCOUNT-ID: your-accountid' \ \n
--header 'Content-Type: application/json' \ \n
--header 'Authorization: Bearer your-token' \ \n
--data-raw '{\n
   "text": "Next summer I want to visit Barcelona and Lion.",\n
   "provider":"aws",\n
   "language": "auto"\n
}'\n

Find more examples for other languages in our documentation.

>_ View Documentation

Supported Providers: aws, azure, gcp
Inputs: Raw text with max. 4.000 characters. All major languages are supported.

Use Case Labs
Create Content Labels With Entities
Content organization can be complex and challenging as the number of documents grows. Entity recognition can help assigning content labels.

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Use Cases

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