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POST
/
v3
/
modules
/
match
/
image
두 이미지 비교
curl --request POST \
  --url https://api.argosidentity.com/v3/modules/match/image \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '{
  "imageSet": {
    "originalImage": "base64_encoded_image_1",
    "compareImage": "base64_encoded_image_2"
  }
}'
{
  "originalHash": "70",
  "compareHash": "71",
  "hammingDistance": 2
}
Image Verifier allows users to input two images and analyze their similarity, providing a result that indicates the level of similarity. This functionality can be applied to various use cases, making it easy for users to adapt it to their specific needs.

​
Request

​
Headers

​
x-api-key
string
required
API key essential for authentication and access control purposes, required for authorization.

​
Body Parameters

​
imageSet
object
required
Object containing the two images to compare.
​
originalImage
string
required
First image to compare in base 64 format.
​
compareImage
string
required
Image to be compared with the original image in base 64 format.

​
Response

​
Success Response (200)

​
originalHash
string
P Hash value of the original image (hexadecimal)
​
compareHash
string
P Hash value of the Compare image (hexadecimal)
​
hammingDistance
number
Hamming Distance between the two hash values, indicating the similarity of the images. A smaller value means the images are more similar.

​
Error Response (400)

​
message
string
Briefly describes the error.

​
Example Request

{
  "imageSet": {
    "originalImage": "base64_encoded_image_1",
    "compareImage": "base64_encoded_image_2"
  }
}

​
Example Response

{
  "originalHash": "70",
  "compareHash": "71",
  "hammingDistance": 2
}

Authorizations

​
x-api-key
string
header
required

Body

application/json
​
imageSet
object
required

Response

모든 것이 예상대로 작동했습니다.

​
originalHash
string

원본 이미지의 P Hash 값 (16진수)

Example:

"70"

​
compareHash
string

비교 이미지의 P Hash 값 (16진수)

Example:

"71"

​
hammingDistance
number

두 해시 값 간의 해밍 거리로, 이미지의 유사도를 나타냅니다. 값이 작을수록 이미지가 더 유사합니다.

Example:

2