Screenshots
Detect Screenshot
Detect UI elements and read text in a screenshot.
POST
/
api
/
v1
/
screenshots:detect
Detect Screenshot
curl --request POST \
--url https://argus.axilio.ai/api/v1/screenshots:detect \
--header 'Content-Type: application/json' \
--header 'X-Axilio-Api-Key: <api-key>' \
--data '
{
"image": "<string>",
"confidence_threshold": 0.25,
"inference_type": "combined",
"nms_iou_threshold": 0.45,
"ocr_engine": "free"
}
'import requests
url = "https://argus.axilio.ai/api/v1/screenshots:detect"
payload = {
"image": "<string>",
"confidence_threshold": 0.25,
"inference_type": "combined",
"nms_iou_threshold": 0.45,
"ocr_engine": "free"
}
headers = {
"X-Axilio-Api-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-Axilio-Api-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
image: '<string>',
confidence_threshold: 0.25,
inference_type: 'combined',
nms_iou_threshold: 0.45,
ocr_engine: 'free'
})
};
fetch('https://argus.axilio.ai/api/v1/screenshots:detect', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://argus.axilio.ai/api/v1/screenshots:detect",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'image' => '<string>',
'confidence_threshold' => 0.25,
'inference_type' => 'combined',
'nms_iou_threshold' => 0.45,
'ocr_engine' => 'free'
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"X-Axilio-Api-Key: <api-key>"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://argus.axilio.ai/api/v1/screenshots:detect"
payload := strings.NewReader("{\n \"image\": \"<string>\",\n \"confidence_threshold\": 0.25,\n \"inference_type\": \"combined\",\n \"nms_iou_threshold\": 0.45,\n \"ocr_engine\": \"free\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-Axilio-Api-Key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://argus.axilio.ai/api/v1/screenshots:detect")
.header("X-Axilio-Api-Key", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"image\": \"<string>\",\n \"confidence_threshold\": 0.25,\n \"inference_type\": \"combined\",\n \"nms_iou_threshold\": 0.45,\n \"ocr_engine\": \"free\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://argus.axilio.ai/api/v1/screenshots:detect")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["X-Axilio-Api-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"image\": \"<string>\",\n \"confidence_threshold\": 0.25,\n \"inference_type\": \"combined\",\n \"nms_iou_threshold\": 0.45,\n \"ocr_engine\": \"free\"\n}"
response = http.request(request)
puts response.read_body{
"data": {
"metadata": {
"icon_detections_after_nms": 123,
"icon_detections_before_nms": 123,
"image_height": 123,
"image_width": 123,
"inference_time_ms": 123,
"detect_ns": 123,
"ocr_ns": 123,
"queue_wait_ns": 123
},
"hash_result": {
"ahash": "<string>",
"dhash": "<string>",
"phash": "<string>",
"content_bounds": {
"bottom_y": 123,
"top_y": 123
},
"histogram": [
[
123
]
]
},
"icon_detections": [
{
"bbox": {
"x1": 123,
"x2": 123,
"y1": 123,
"y2": 123
},
"class_id": 123,
"class_name": "<string>",
"confidence": 0.5
}
],
"ocr_results": [
{
"bbox": {
"x1": 123,
"x2": 123,
"y1": 123,
"y2": 123
},
"confidence": 0.5,
"text": "<string>"
}
]
},
"analyzer_type": "all",
"type": "analyzer_result"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}Authorizations
Axilio API key (axl_*).
Body
application/json
Base64 encoded image data
Maximum string length:
16777216Minimum confidence threshold for detections and OCR results
Required range:
0 <= x <= 1Type of inference to perform
Available options:
detect, ocr, combined IoU threshold for Non-Maximum Suppression (NMS)
Required range:
0 <= x <= 1OCR engine to use: 'free' or 'premium'
⌘I
Detect Screenshot
curl --request POST \
--url https://argus.axilio.ai/api/v1/screenshots:detect \
--header 'Content-Type: application/json' \
--header 'X-Axilio-Api-Key: <api-key>' \
--data '
{
"image": "<string>",
"confidence_threshold": 0.25,
"inference_type": "combined",
"nms_iou_threshold": 0.45,
"ocr_engine": "free"
}
'import requests
url = "https://argus.axilio.ai/api/v1/screenshots:detect"
payload = {
"image": "<string>",
"confidence_threshold": 0.25,
"inference_type": "combined",
"nms_iou_threshold": 0.45,
"ocr_engine": "free"
}
headers = {
"X-Axilio-Api-Key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'X-Axilio-Api-Key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({
image: '<string>',
confidence_threshold: 0.25,
inference_type: 'combined',
nms_iou_threshold: 0.45,
ocr_engine: 'free'
})
};
fetch('https://argus.axilio.ai/api/v1/screenshots:detect', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://argus.axilio.ai/api/v1/screenshots:detect",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'image' => '<string>',
'confidence_threshold' => 0.25,
'inference_type' => 'combined',
'nms_iou_threshold' => 0.45,
'ocr_engine' => 'free'
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json",
"X-Axilio-Api-Key: <api-key>"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://argus.axilio.ai/api/v1/screenshots:detect"
payload := strings.NewReader("{\n \"image\": \"<string>\",\n \"confidence_threshold\": 0.25,\n \"inference_type\": \"combined\",\n \"nms_iou_threshold\": 0.45,\n \"ocr_engine\": \"free\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("X-Axilio-Api-Key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://argus.axilio.ai/api/v1/screenshots:detect")
.header("X-Axilio-Api-Key", "<api-key>")
.header("Content-Type", "application/json")
.body("{\n \"image\": \"<string>\",\n \"confidence_threshold\": 0.25,\n \"inference_type\": \"combined\",\n \"nms_iou_threshold\": 0.45,\n \"ocr_engine\": \"free\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://argus.axilio.ai/api/v1/screenshots:detect")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["X-Axilio-Api-Key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"image\": \"<string>\",\n \"confidence_threshold\": 0.25,\n \"inference_type\": \"combined\",\n \"nms_iou_threshold\": 0.45,\n \"ocr_engine\": \"free\"\n}"
response = http.request(request)
puts response.read_body{
"data": {
"metadata": {
"icon_detections_after_nms": 123,
"icon_detections_before_nms": 123,
"image_height": 123,
"image_width": 123,
"inference_time_ms": 123,
"detect_ns": 123,
"ocr_ns": 123,
"queue_wait_ns": 123
},
"hash_result": {
"ahash": "<string>",
"dhash": "<string>",
"phash": "<string>",
"content_bounds": {
"bottom_y": 123,
"top_y": 123
},
"histogram": [
[
123
]
]
},
"icon_detections": [
{
"bbox": {
"x1": 123,
"x2": 123,
"y1": 123,
"y2": 123
},
"class_id": 123,
"class_name": "<string>",
"confidence": 0.5
}
],
"ocr_results": [
{
"bbox": {
"x1": 123,
"x2": 123,
"y1": 123,
"y2": 123
},
"confidence": 0.5,
"text": "<string>"
}
]
},
"analyzer_type": "all",
"type": "analyzer_result"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}{
"code": "<string>",
"detail": "<string>",
"status": 123,
"title": "<string>",
"errors": [
{}
],
"type": "about:blank"
}