The Chooch API is organized around REST. The API recognizes objects and concepts in videos and
images from our pre-trained
models.
The API is compatible with livestreams and live tagging.
The image recognition API is a straightforward REST API conducting classification and object recognition predictions on images. The image URL is passed as a field to the API and the API returns a JSON based response with relevant predictions.
# Image Recognition with Local Image
import requests
import json
url = 'http://api.chooch.ai/predict/image?apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19'
files = {'image': open('local_image.jpg', 'rb')}
response = requests.post(url, files=files)
print(response.content)
var form = new FormData();
form.append("image", "");
var settings = {
"url": "https://api.chooch.ai/predict/image?apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19",
"method": "POST",
"timeout": 0,
"processData": false,
"mimeType": "multipart/form-data",
"contentType": false,
"data": form
};
$.ajax(settings).done(function (response) {
console.log(response);
});
$curl = curl_init();
curl_setopt_array($curl, array(
CURLOPT_URL => "https://api.chooch.ai/predict/image?apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 0,
CURLOPT_FOLLOWLOCATION => false,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => array('image'=> new CURLFILE('')),
));
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
} ?>
curl --location --request POST "https://api.chooch.ai/predict/image?apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19" --form "image=@"
# Image Recognition with Image Url
import requests
import json
url = 'http://api.chooch.ai/predict/image?url=https://s3.amazonaws.com/choochdashboard/base_site/ronaldo_suit.jpg&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19'
response = requests.post(url)
print(response.content)
var form = new FormData();
var settings = {
"url": "https://api.chooch.ai/predict/image?url=https://s3.amazonaws.com/choochdashboard/base_site/ronaldo_suit.jpg&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19",
"method": "GET",
"timeout": 0,
"headers": {
"Content-Type": "application/json"
},
"processData": false,
"mimeType": "multipart/form-data",
"contentType": false,
"data": form
};
$.ajax(settings).done(function (response) {
console.log(response);
});
$curl = curl_init();
curl_setopt_array($curl, array(
CURLOPT_URL => "https://api.chooch.ai/predict/image?url=https://s3.amazonaws.com/choochdashboard/base_site/ronaldo_suit.jpg&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 0,
CURLOPT_FOLLOWLOCATION => false,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "GET",
CURLOPT_HTTPHEADER => array(
"Content-Type: application/json"
),
));
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
} ?>
curl --location --request GET "https://api.chooch.ai/predict/image?url=https://s3.amazonaws.com/choochdashboard/base_site/ronaldo_suit.jpg&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19" --header "Content-Type: application/json"
API input fields are:
The sample JSON response is:
{
"status": "ok",
"url": "https://s3.amazonaws.com/choochdashboard/base_site/ronaldo_suit.jpg",
"predictions": [
{
"class_title": "Cristiano Ronaldo",
"order": "1"
},
{
"class_title": "tom ford style",
"order": "2"
},
{
"class_title": "double-breasted suit, fashion",
"order": "3"
},
{
"class_title": "man",
"order": "6"
},
{
"class_title": "suit",
"order": "7"
},
{
"class_title": "cartoon, sterling archer",
"order": "8"
},
{
"class_title": "icon",
"order": "9"
},
{
"class_title": "man's clothing",
"order": "10"
},
{
"class_title": "face",
"order": "11"
},
{
"class_title": "male",
"order": "12"
},
{
"class_title": "young man",
"order": "13"
},
{
"class_title": "actor, doer",
"order": "14"
},
{
"class_title": "tie",
"order": "15"
},
{
"class_title": "striped tie",
"order": "16"
},
{
"class_title": "windsor tie",
"order": "17"
},
{
"class_title": "four-in-hand tie",
"order": "18"
},
{
"class_title": "cravat",
"order": "19"
}
],
"texts": {
"status": "limited to 15 results",
"predictions": [
],
"summary": [
]
},
"objects": {
"predictions": [
{
"sub_predictions": [
{
"class_title": "tom ford style",
"order": 1
},
{
"class_title": "double breasted suit",
"order": 2
},
{
"class_title": "brown tie",
"order": 3
},
{
"class_title": "double-breasted suit, fashion",
"order": 4
}
],
"object_title": "man",
"coordinates": "0,749,64,982"
},
{
"sub_predictions": [
{
"class_title": "cartoon, sterling archer",
"order": 1
},
{
"class_title": "tom ford style",
"order": 2
},
{
"class_title": "sterling archer",
"order": 3
},
{
"class_title": "double breasted suit",
"order": 4
},
{
"class_title": "brown tie",
"order": 5
},
{
"class_title": "double-breasted suit, fashion",
"order": 6
},
{
"class_title": "icon",
"order": 7
},
{
"class_title": "man's clothing",
"order": 8
}
],
"object_title": "suit",
"coordinates": "25,712,305,995"
},
{
"sub_predictions": [
{
"class_title": "male",
"order": 1
},
{
"class_title": "young man",
"order": 2
},
{
"class_title": "actor, doer",
"order": 3
}
],
"object_title": "face",
"coordinates": "225,504,75,464"
},
{
"sub_predictions": [
{
"class_title": "striped tie",
"order": 1
},
{
"class_title": "windsor tie",
"order": 2
},
{
"class_title": "four-in-hand tie",
"order": 3
},
{
"class_title": "cravat",
"order": 4
}
],
"object_title": "tie",
"coordinates": "320,436,523,875"
}
],
"summary": [
{
"count": "1",
"object_title": "tie"
},
{
"count": "1",
"object_title": "face"
},
{
"count": "1",
"object_title": "suit"
},
{
"count": "1",
"object_title": "man"
}
]
},
"faces": {
"face_count": 1,
"predictions": [
{
"face_name": "Cristiano Ronaldo",
"coordinates": "248,506,192,450"
}
]
},
"predicttype": "dense",
"fileid": "c2f02758f0dd4b8ab703e26f4d70d7c7"
}
The response fields are:
The video recognition API is a REST API indexing videos every second. The API consists of 2 parts: 1) send video and 2) search. The indexing begins by sending the video. After sending the video, the indexed data can be viewed through the search API.
The following is a sample send video URL for indexing to begin:
API input fields are:
The sample JSON response is:
{
url: "https://s3.amazonaws.com/choochvideo/51.mp4",
status: "Success (Video is being indexed)",
predicttype: "video",
fileid: "55de9d9ce1e2469da3a420a7b4de9397"
}
The response fields are:
After the video is indexed, the indexed data can be viewed through the search API by using the File ID of the video. The following is a sample API request URL for viewing indexed data.
API input fields are:
The sample JSON response is:
{
status: "ok",
url: "https://s3.amazonaws.com/choochvideo/51.mp4",
indexstatus: "Success (Video Has Been Indexed)",
seconds: [{
texts: {
status: "limited to 15 results",
predictions: [
{
text_value: "sports",
coordinates: "556,773,379,422"
},
{
text_value: "scenter",
coordinates: "751,1041,376,432"
},
{
text_value: "allaccess",
coordinates: "631,973,458,507"
},
{
text_value: "ronald",
coordinates: "592,994,519,616"
},
{
text_value: "in",
coordinates: "682,735,614,650"
},
{
text_value: "china",
coordinates: "748,922,616,660"
}
],
summary: [
{
count: "1",
text_value: "allaccess"
},
{
count: "1",
text_value: "sports"
},
{
count: "1",
text_value: "ronald"
},
{
count: "1",
text_value: "china"
},
{
count: "1",
text_value: "in"
},
{
count: "1",
text_value: "scenter"
}
]
},
second: 1,
objects: {
predictions: [
{
sub_predictions: [ ],
object_title: "illustration",
coordinates: "211,1138,142,712"
},
{
sub_predictions: [
{
class_title: "victory",
order: 1
},
{
class_title: "joy",
order: 2
}
],
object_title: "man",
coordinates: "190,580,319,712"
},
{
sub_predictions: [ ],
object_title: "face",
coordinates: "279,511,325,658"
},
{
sub_predictions: [ ],
object_title: "person",
coordinates: "978,1089,571,716"
},
{
sub_predictions: [ ],
object_title: "clothing",
coordinates: "971,1097,602,712"
},
{
sub_predictions: [ ],
object_title: "illustration",
coordinates: "211,1138,142,712"
},
{
sub_predictions: [
{
class_title: "victory",
order: 1
},
{
class_title: "joy",
order: 2
}
],
object_title: "man",
coordinates: "190,580,319,712"
},
{
sub_predictions: [ ],
object_title: "face",
coordinates: "279,511,325,658"
},
{
sub_predictions: [ ],
object_title: "person",
coordinates: "978,1089,571,716"
},
{
sub_predictions: [ ],
object_title: "clothing",
coordinates: "971,1097,602,712"
}
],
summary: [
{
count: "2",
object_title: "person"
},
{
count: "2",
object_title: "face"
},
{
count: "2",
object_title: "clothing"
},
{
count: "2",
object_title: "illustration"
},
{
count: "2",
object_title: "man"
}
]
},
predictions: [
{
class_title: "cristiano ronaldo",
order: "1"
},
{
class_title: "illustration",
order: "2"
},
{
class_title: "man",
order: "3"
},
{
class_title: "victory",
order: "4"
},
{
class_title: "joy",
order: "5"
},
{
class_title: "face",
order: "6"
},
{
class_title: "sports",
order: "7"
},
{
class_title: "scenter",
order: "8"
},
{
class_title: "allaccess",
order: "9"
},
{
class_title: "ronald",
order: "10"
},
{
class_title: "in",
order: "11"
},
{
class_title: "china",
order: "12"
}
],
faces: {
face_count: 1,
predictions: [
{
face_name: "Cristiano Ronaldo",
coordinates: "212,390,292,472"
}
]
}
},
{
texts: {
status: "limited to 15 results",
predictions: [
{
text_value: "scenter",
coordinates: "712,1023,279,339"
},
{
text_value: "sorts",
coordinates: "512,745,302,345"
},
{
text_value: "allfaccess",
coordinates: "583,937,366,417"
},
{
text_value: "onald",
coordinates: "545,970,428,530"
},
{
text_value: "i",
coordinates: "626,683,534,572"
},
{
text_value: "china",
coordinates: "690,906,529,582"
},
{
text_value: "mra",
coordinates: "1042,1084,568,584"
},
{
text_value: "s",
coordinates: "957,993,652,671"
}
],
summary: [
{
count: "1",
text_value: "allfaccess"
},
{
count: "1",
text_value: "i"
},
{
count: "1",
text_value: "mra"
},
{
count: "1",
text_value: "s"
},
{
count: "1",
text_value: "china"
},
{
count: "1",
text_value: "sorts"
},
{
count: "1",
text_value: "onald"
},
{
count: "1",
text_value: "scenter"
}
]
},
second: 2,
objects: {
predictions: [
{
sub_predictions: [
{
class_title: "maybe olympics",
order: 1
}
],
object_title: "man",
coordinates: "78,547,267,712"
},
{
sub_predictions: [
{
class_title: "psycho joker",
order: 1
}
],
object_title: "face",
coordinates: "241,455,271,582"
},
{
sub_predictions: [
{
class_title: "forward",
order: 1
},
{
class_title: "back",
order: 2
}
],
object_title: "man",
coordinates: "965,1150,467,712"
},
{
sub_predictions: [ ],
object_title: "person",
coordinates: "981,1145,459,707"
},
{
sub_predictions: [ ],
object_title: "clothing",
coordinates: "987,1150,503,697"
},
{
sub_predictions: [
{
class_title: "maybe olympics",
order: 1
}
],
object_title: "man",
coordinates: "78,547,267,712"
},
{
sub_predictions: [
{
class_title: "psycho joker",
order: 1
}
],
object_title: "face",
coordinates: "241,455,271,582"
},
{
sub_predictions: [
{
class_title: "forward",
order: 1
},
{
class_title: "back",
order: 2
}
],
object_title: "man",
coordinates: "965,1150,467,712"
},
{
sub_predictions: [ ],
object_title: "person",
coordinates: "981,1145,459,707"
},
{
sub_predictions: [ ],
object_title: "clothing",
coordinates: "987,1150,503,697"
}
],
summary: [
{
count: "4",
object_title: "man"
},
{
count: "2",
object_title: "person"
},
{
count: "2",
object_title: "face"
},
{
count: "2",
object_title: "clothing"
}
]
},
predictions: [
{
class_title: "cristiano ronaldo",
order: "1"
},
{
class_title: "man - 1",
order: "2"
},
{
class_title: "maybe olympics",
order: "3"
},
{
class_title: "face",
order: "4"
},
{
class_title: "psycho joker",
order: "5"
},
{
class_title: "man - 2",
order: "6"
},
{
class_title: "forward",
order: "7"
},
{
class_title: "back",
order: "8"
},
{
class_title: "scenter",
order: "9"
},
{
class_title: "sorts",
order: "10"
},
{
class_title: "allfaccess",
order: "11"
},
{
class_title: "onald",
order: "12"
},
{
class_title: "i",
order: "13"
},
{
class_title: "china",
order: "14"
},
{
class_title: "mra",
order: "15"
},
{
class_title: "s",
order: "16"
}
],
faces: {
face_count: 1,
predictions: [
{
face_name: "Cristiano Ronaldo",
coordinates: "192,342,260,408"
}
]
}
},
],
index_status: "Success (Video Has Been Indexed)",
fileid: "55de9d9ce1e2469da3a420a7b4de9397"
}
The response fields are:
The FTP drop function was developed on top of the Chooch API for image and video professionals who want to tag their images and videos autonomously. For every client a personal FTP host is issued together with the API.
The FTP drop sends the image to the API. The API predicts the image and places the results in the keywords field of the IPTC metadata file. Once the metadata has been successfully placed into the keywords fields of the IPTC file, it is sent to the destination directed by the user.
Chooch provides a Facial Recognition Perception training platform to train your own face images. Below is a summary on how to train and test a Facial Recognition Perception.
https://app.chooch.ai
If you don’t have an account simply sign
up.
After logging into the Chooch Dashboard click on the Training tab. The Training options
will appear as seen below:
There are 3 options of training on the screen: Facial, Image and Object.
Select “Facial”. The Facial Recognition Perceptions screen will
appear as seen below:
The other options will be available soon.
You can create and manage your Facial Recognition Perceptions from this screen.
Let’s create a new perception named “Famous People”. We click on “Create Perception” and
type in “Famous People” as seen below:
After the Perception is created the Perception Detail Screen will appear as seen below:
Let’s add a Person to the perception.
As seen below we add “George Clooney” to the perception.
After we add a person the “Person Detail” page will appear. We recommend at least 5
face photos for each person in order to get high quality results. But you can use less
photos, minimum one photo is needed.
Let’s add some images to the Person (George Clooney). We can select and upload multiple images
as seen below.
Now that we added our images we can test our Perception. Let’s click on back and go to the
Perception detail screen.
After we enabled our Perception and added a person we can start testing.
Just click on “Predict via Upload” and select and image. Below we uploaded an image of
“George Clooney” that was not in the dataset we uploaded. We got a similarity score of
0.80 which means this person is most likely George Clooney. Anything above 0.70
is considered a hit.
There is also a search function to search people in the Perceptions.
On the Settings tab of the Dashboard you will see your API key. Please refer to
the Facial Recognition API documentation on how to predict faces using the Chooch API.
Chooch Face Recognition consists of Perception > People > Images
You can search and tag a face in the Chooch API by feeding the Chooch API a Face Image. Below
is a
sample post of an image url.
The fields are:
You can also post an image through the image field. Below are 2 separate sample posts in python.
import requests
import json
import time
url = 'https://api.chooch.ai/predict/face?url=https://choochdashboard.s3.amazonaws.com/base_site/1461123713-esq050116cover001s.jpg&person_id_filter=-1&model_id=14&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19'
response = requests.post(url)
json_data = json.loads(response.content)
print(json_data)
var form = new FormData();
var settings = {
"url": "https://api.chooch.ai/predict/face?url=https://choochdashboard.s3.amazonaws.com/base_site/1461123713-esq050116cover001s.jpg&person_id_filter=-1&model_id=14&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19",
"method": "POST",
"mimeType": "multipart/form-data",
"contentType": false,
"data": form
};
$.ajax(settings).done(function (response) {
console.log(response);
});
$curl = curl_init();
curl_setopt_array($curl, array(
CURLOPT_URL => "https://api.chooch.ai/predict/face?url=https://choochdashboard.s3.amazonaws.com/base_site/1461123713-esq050116cover001s.jpg&person_id_filter=-1&model_id=14&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 0,
CURLOPT_FOLLOWLOCATION => false,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
));
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
} ?>
curl --location --request POST "https://api.chooch.ai/predict/face?url=https://choochdashboard.s3.amazonaws.com/base_site/1461123713-esq050116cover001s.jpg&person_id_filter=-1&model_id=14&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19"
import requests
import json
import time
url = 'https://api.chooch.ai/predict/face?person_id_filter=7&model_id=5&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19'
files = {'image': open('local_image.jpg', 'rb')}
response = requests.post(url, files=files)
response = requests.post(url)
json_data = json.loads(response.content)
print(json_data)
var form = new FormData();
form.append("image", "");
var settings = {
"url": "https://api.chooch.ai/predict/face?person_id_filter=-1&model_id=14&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19",
"method": "POST",
"timeout": 0,
"processData": false,
"mimeType": "multipart/form-data",
"contentType": false,
"data": form
};
$.ajax(settings).done(function (response) {
console.log(response);
});
$curl = curl_init();
curl_setopt_array($curl, array(
CURLOPT_URL => "https://api.chooch.ai/predict/face?person_id_filter=-1&model_id=14&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 0,
CURLOPT_FOLLOWLOCATION => false,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => array('image'=> new CURLFILE('')),
));
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
} ?>
curl --location --request POST "https://api.chooch.ai/predict/face?person_id_filter=-1&model_id=14&apikey=346g5717-1sd3-35h6-9104-b8h5c819dn19" --form "image=@"
The example JSON response is as below. In this example, we got a hit with a similarity of 0.80 percent. The face_recog_hit is True when we get a hit and False when we don’t.
{
status:"ok",
person_id_filter:-1,
post_type:"face_search",
face_recog_hit:true,
faces:[
{
person_id:22,
person_name:"George Clooney",
coordinates:"1402,158,1657,506",
similarity:0.80
}
]
}
You can create a person through the Chooch API. Below is a sample Python based usage of the create_person command.
# Create Person Python Code
import requests
import json
url = 'https://api.chooch.ai/predict/face?person_name=Tom Bill&model_id=5&apikey=cx065e5d-51wc87-9df6-163e-8b65a78k51e3&command=create_person'
response = requests.post(url)
json_data = json.loads(response.content)
print(json_data)
var form = new FormData();
var settings = {
"url": "https://api.chooch.ai/predict/face?person_name=Tom Bill&model_id=5&apikey=cx065e5d-51wc87-9df6-163e-8b65a78k51e3&command=create_person",
"method": "POST",
"processData": false,
"mimeType": "multipart/form-data",
"contentType": false,
"data": form
};
$.ajax(settings).done(function (response) {
console.log(response);
});
$curl = curl_init();
curl_setopt_array($curl, array(
CURLOPT_URL => "https://api.chooch.ai/predict/face?person_name=Tom%20Bill&model_id=5&apikey=cx065e5d-51wc87-9df6-163e-8b65a78k51e3&command=create_person",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 0,
CURLOPT_FOLLOWLOCATION => false,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
));
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
} ?>
curl --location --request POST "https://api.chooch.ai/predict/face?person_name=Tom%20Bill&model_id=5&apikey=cx065e5d-51wc87-9df6-163e-8b65a78k51e3&command=create_person"
{
person_id: 37,
status: 37,
status_description: success,
post_type: create_person
}
A face image can be added to an existing person using the person_id. Below is a sample Python based usage of the insert_person_image command.
# Add face image to person with person_id_filter (person_id) Python Code
import requests
import json
url = 'https://api.chooch.ai/predict/face?person_id_filter=37&apikey=cx065e5d-51wc87-9df6-163e-8b65a78k51e3&command=insert_person_image'
files = {'image': open('your_image.jpg', 'rb')}
response = requests.post(url, files=files)
json_data = json.loads(response.content)
print(json_data)
var form = new FormData();
form.append("image", "");
var settings = {
"url": "https://api.chooch.ai/predict/face?person_id_filter=37&apikey=cx065e5d-51wc87-9df6-163e-8b65a78k51e3&command=insert_person_image",
"method": "POST",
"processData": false,
"mimeType": "multipart/form-data",
"contentType": false,
"data": form
};
$.ajax(settings).done(function (response) {
console.log(response);
});
$curl = curl_init();
curl_setopt_array($curl, array(
CURLOPT_URL => "https://api.chooch.ai/predict/face?person_id_filter=37&apikey=cx065e5d-51wc87-9df6-163e-8b65a78k51e3&command=insert_person_image",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 0,
CURLOPT_FOLLOWLOCATION => false,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => array('image'=> new CURLFILE('')),
));
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
} ?>
curl --location --request POST "https://api.chooch.ai/predict/face?person_id_filter=37&apikey=cx065e5d-51wc87-9df6-163e-8b65a78k51e3&command=insert_person_image" --form "image=@"
{
status: 210,
status_description: success,
post_type: insert_person_image
}
More coming soon.
