> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getlimina.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Processing Image Files

> This guide will get you started with Image deidentification.

Limina supports scanning images for PII and creating de-identified or redacted copies. Limina’s supported entity types function across each file type, with localized variants of different **PII** (Personally Identifiable Information) entities, **PHI** (Protected Health Information) entities, and **PCI** (Payment Card Industry) entities being detected. Our [Supported Languages](/languages) and [Supported Entity Types](/entities) page provides a more detailed look.

<Info>
  If you'd like to try it yourself, please [sign up for an account](https://portal.getlimina.ai/) to get a free API key.
</Info>

## How Images Are Processed

Image files are processed as follows:

1. Non-text PII such as faces and license plates are detected.
2. Additionally the image is run through an OCR system to detect any text present.
3. The output of the OCR system is then passed to a PII detection module.
4. Any PII found in the previous steps is de-identified via blurring or black box redaction.

<Info>
  You can configure the OCR System by setting it as an [Environment Variable](/configuration-and-operations/container-management/environment-variables) or sending it in the request object. Check out our [OCR Guide](/configuration-and-operations/working-with-files/processing-files/ocr-modes) to further understand the OCR modes and their usage.
</Info>

## Constraints

* There is limited support for 8-bit PNG images.
* Multi-page TIFF images are only supported on versions >=4.2.1. Earlier versions will process and return the first page of a multi-page TIFF.

## Supported File Types

| File Type  | Extension       | Content Type                  | Added In |
| ---------- | --------------- | ----------------------------- | -------- |
| JPEG image | `.jpg`, `.jpeg` | `image/jpg`, `image/jpeg`     | 3.0.0    |
| TIFF image | `.tif`, `.tiff` | `image/tif`, `image/tiff`     | 3.0.0    |
| PNG image  | `.png`          | `image/png`                   | 3.4.0    |
| BMP image  | `.bmp`          | `image/bmp`, `image/x-ms-bmp` | 3.4.0    |
| GIF image  | `.gif`          | `image/gif`                   | 4.2.1    |

## Support Matrix

|           | CPU Container | GPU Container | Community API | Professional API |
| --------- | ------------- | ------------- | ------------- | ---------------- |
| Supported | Yes           | Yes           | Up to 10 MiB  | No               |

## Sample Request

<Info>
  [Connect with one of our privacy experts](https://getlimina.ai/contact-us/?utm_source=docs\&utm_medium=website) to run this code.
</Info>

<CodeGroup>
  ```json Request Body wrap lines theme={"theme":"poimandres"}
  {
    "file": {
      "data": "<file_content_base64>",
      "content_type": "image/jpeg"
    },
    "entity_detection": {
      "return_entity": true
    }
  }
  ```

  ```shell cURL wrap lines theme={"theme":"poimandres"}
  echo '{
            "file": {"data": "'$(base64 -w 0 sample.jpeg)'", 
            "content_type": "image/jpeg"}, 
            "entity_detection": {"return_entity": "True"}
        }' \
  | curl --request POST --url 'https://api.private-ai.com/community/v4/process/files/base64' \
         -H 'Content-Type: application/json' \
         -H 'x-api-key: <YOUR KEY HERE>' \
         -d @- \
         | jq -r .processed_file \
         | base64 -d > 'sample.redacted.jpeg'
  ```

  ```python Python wrap lines theme={"theme":"poimandres"}
  import requests
  import base64

  file_url = "https://paidocumentation.blob.core.windows.net/$web/sample.jpeg"
  filename_out = "/path/to/output/sample.redacted.jpeg"
  file_content = requests.get(file_url).content
  file_content_base64 = base64.b64encode(file_content).decode()

  url = "https://api.private-ai.com/community/v4/process/files/base64"

  headers = {"Content-Type": "application/json", "x-api-key": "<INSERT API KEY>"}

  payload = {
    "file":{
      "data": file_content_base64,
      "content_type": "image/jpeg",
    },
    "entity_detection": {
      "return_entity": True
    }
  }

  response = requests.post(url, json=payload, headers=headers)
  with open(filename_out, "wb") as f:
      f.write(base64.b64decode(response.json()["processed_file"]))
  ```

  ```python Python Client wrap lines theme={"theme":"poimandres"}
  from privateai_client import PAIClient
  from privateai_client.objects import request_objects
  import base64

  filename_in = "sample.jpeg"
  filename_out = "sample.redacted.jpeg"

  file_type= "image/jpeg"
  client = PAIClient(url="https://api.private-ai.com/community/v4/", api_key="<YOUR API KEY>")

  with open(filename_in, "rb") as b64_file:
      file_data = base64.b64encode(b64_file.read())
      file_data = file_data.decode("ascii")

  file_obj = request_objects.file_obj(data=file_data, content_type=file_type)
  request_obj = request_objects.file_base64_obj(file=file_obj)
  resp = client.process_files_base64(request_object=request_obj)

  with open(filename_out, 'wb') as redacted_file:
      processed_file = resp.processed_file.encode("ascii")
      processed_file = base64.b64decode(processed_file, validate=True)
      redacted_file.write(processed_file)
  ```
</CodeGroup>

## Sample Response

```json Response wrap lines theme={"theme":"poimandres"}
{
  "processed_file": "Base64 Encoded File Content of the Redacted File",
  "processed_text": "string",
  "entities": "List[Entity]",
  "entities_present": true,
  "languages_detected": {"lang_1": 0.67, "lang_2": 0.74}
}
```