Image Stats API

This project provides an API to extract statistical information from images, including EXIF data and color analysis (average and dominant colors).

Features

• EXIF Data Extraction: Retrieves standard EXIF (Exchangeable image file format) data from images.
  • Processes and validates GPS information, filtering out empty or default values.
  • Handles non-serializable EXIF values by converting them to string representations.
• Color Analysis:
  • Average Color: Calculates the average color of an image. Supports multiple averaging methods:
    • arithmetic (default)
    • geometric (Configurable via the AVERAGING_METHOD environment variable).
  • Dominant Color: Identifies the most prominent color in an image using HSV color space clustering.
• Image Processing:
  • Handles image input via file upload or URL.
  • Resizes large images (max dimension > 512px) to a thumbnail for faster processing while preserving aspect ratio.
  • Converts images to RGBA format and filters out transparent pixels before color analysis.
• LitServe Integration: Built using litserve for efficient serving.

Running the Server

The server is implemented in server.py.

Using uv (recommended via Makefile):

The makefile provides a convenient way to run the server:

make run

This command uses uv run server.py.

Manually:

You can also run it directly using a Python interpreter capable of running the dependencies (e.g., after setting up a virtual environment and installing packages):

python server.py

Environment Variables:

The server can be configured using the following environment variables:

• PORT: Port to run the server on (default: 8010).
• LOG_LEVEL: Logging level (e.g., INFO, DEBUG; default: INFO).
• AVERAGING_METHOD: Method for calculating average color (arithmetic, harmonic, geometric; default: arithmetic).
• NUM_API_SERVERS: Number of API server instances (default: 1).
• WORKERS_PER_DEVICE: Number of worker threads per device (default: 1).

API Endpoint

POST /stats

• Description: Analyzes an image and returns its EXIF data and color statistics.

• Request:

  • Method: POST
  • Content-Type: multipart/form-data (for file uploads) or application/json (for URL inputs).
  • Body:
    • For file upload: Form data with a file field named content.
    • For URL input: JSON object with a key content whose value is the image URL string (e.g., {"content": "http://example.com/image.jpg"}).
      • Note: The server includes a hack to replace localhost:3210 with backend:3210 in URLs, which might be relevant for specific deployment scenarios.

• Response: application/json

  • Success (200 OK):

    {
      "exif_data": {
        // ... various EXIF tags and values ...
        "GPSInfo": { /* GPS data if valid, otherwise omitted */ }
      },
      "color_data": {
        "avg_color": {
          "rgb": [0.R, 0.G, 0.B], // RGB values (0-1 range)
          "hex": "#RRGGBB",       // Hex color code
          "method": "method_used"
        },
        "dominant_color": {
          "rgb": [0.R, 0.G, 0.B], // RGB values (0-1 range)
          "hex": "#RRGGBB"        // Hex color code
        }
      }
    }

    If color_data could not be determined (e.g., image has no valid pixels), it might be null. If exif_data could not be extracted, it might be empty or partially filled.

Client Examples

1. Python Client (client.py)

The client.py script provides a simple command-line interface to send an image to the API and print the response.

Usage:

python client.py [path_to_your_image]

If no image path is provided, it defaults to trying to send snowman.png from the current directory.

The script will:

1. Check if the image file exists.
2. Send a POST request to http://127.0.0.1:8001/stats with the image file.
3. Print the HTTP status code and the JSON response from the server.

Example (using uv via Makefile):

First, ensure you have an image (e.g., snowman.png, which can be downloaded using make snowman.png). Then:

make client-test

This will run uv run client.py snowman.png (implicitly, as snowman.png is the default if no argument is given to client.py after uv run).

To test with a different image:

uv run client.py my_image.jpg

2. cURL Example

The makefile includes a curl-test target that demonstrates how to interact with the API using curl.

Command from Makefile:

curl -X POST -F "content=@snowman.png" http://127.0.0.1:8001/stats | jq

Explanation:

• curl -X POST: Sends a POST request.
• -F "content=@snowman.png": Sends the file snowman.png as part of a multipart/form-data request. The @ symbol tells curl to read the content from the specified file. The field name is content.
• http://127.0.0.1:8001/stats: The URL of the API endpoint.
• | jq: Pipes the JSON output to jq for pretty-printing.

Usage (via Makefile):

make curl-test

This target first ensures snowman.png is downloaded if it doesn't exist (due to the snowman.png dependency in the Makefile rule).

Makefile Targets

The makefile provides several useful targets:

• run: Runs the server using uv run server.py.
• snowman.png: Downloads a sample image (snowman.png) from Hugging Face if it doesn't already exist in the current directory. This is used for testing.
• curl-test: Tests the API endpoint using curl with snowman.png. It depends on the snowman.png target. The output is piped to jq for readability.
• client-test: Tests the API endpoint using the client.py script with snowman.png (by default). It runs uv run client.py.

Dependencies

Key dependencies include:

• litserve: For the API server framework.
• Pillow (PIL): For image manipulation.
• numpy: For numerical operations, especially on pixel data.
• requests: Used by server.py to fetch images from URLs and by client.py to interact with the API.
• uv: Used in the Makefile for running python scripts and managing environments (implied).

uv run will ensure dependencies are installed. If you need a requirements.txt file, the command

uv pip compile pyproject.toml -o requirements.txt

can be used to gnerate one.