AI NEXUS Facial Edema Analysis

Welcome to the AI Nexus Edema Analysis App. Follow these simple steps to analyze facial swelling:

1. Grant Camera Access

This allows you to capture images directly from your webcam.

2. Capture or Upload Your Baseline Input

This is your "before" photo or mesh file (`.npy`). It is needed for comparison.

3. Capture or Upload Your Current Face Input

This is your "after" photo or mesh file (`.npy`).

4. Process Your Inputs

Click the "Process Images" button to run the analysis. The results, including key metrics, will be displayed below.

For Best Results (with images)

For the most accurate analysis, please ensure images meet the following criteria:

The subject is facing straight ahead (frontal face, looking at the camera).
The facial expression is neutral and relaxed.
The face is not tightly cropped. The image should include some background area around the head to ensure better face detection.

Programmatic API Usage

You can also use the API directly for automated analysis. The endpoint accepts both image files (e.g., `.jpg`, `.png`) and pre-calculated mesh files (`.npy`).

Using `curl`:


curl -X POST \
    -F "baseline_input=@/path/to/your/baseline.jpg" \
    -F "current_input=@/path/to/your/current.npy" \
    https://AI-Nexus-server-url.com/api/analyze

Using Python with `requests`:

import requests
API_URL = "https://AI-Nexus-server-url.com/api/analyze"
with open("baseline.jpg", "rb") as f1, open("current.jpg", "rb") as f2:
files = {'baseline_input': f1, 'current_input': f2}
response = requests.post(API_URL, files=files)
results = response.json()
print(results)

API Response Fields

The `ratios` and `scores` keys in the JSON output correspond to the fields from the server's `FaceMetrics` data structure. The following fields are returned:

#Bilateral (Right and Left)
eyebrow_fulness_right_ratio: float
eyebrow_fulness_left_ratio: float
cheek_fulness_right_ratio: float
cheek_fulness_left_ratio: float
mid_cheek_fulness_right_ratio: float
mid_cheek_fulness_left_ratio: float
jaw_fulness_right_ratio: float
jaw_fulness_left_ratio: float
cheek_curvature_right_ratio: float
cheek_curvature_left_ratio: float
cheek_area_right_ratio: float
cheek_area_left_ratio: float
periorbital_volume_right: float
periorbital_volume_left: float
cheek_volume_right_ratio: float
cheek_volume_left_ratio: float

#Global (Singular)
lip_fulness_ratio: float
face_aspect_ratio: float
lower_to_upper_face_ratio: float

#Note: The `scores` dictionary is only populated for the 'current_results' object.
       Conversely, the 'baseline_results' object will have an empty `scores` dictionary.
       This is shown in the example JSON output below.

Example JSON Output:

{
"success": true,
"baseline_results": {
    "ratios": {
    "eyebrow_fulness_right_ratio": 0.512,
    "eyebrow_fulness_left_ratio": 0.515,
    "cheek_area_right_ratio": 0.834,
    "...": "..."
    },
    "scores": {}
},
"current_results": {
    "ratios": {
    "eyebrow_fulness_right_ratio": 0.531,
    "eyebrow_fulness_left_ratio": 0.533,
    "cheek_area_right_ratio": 0.866,
    "...": "..."
    },
    "scores": {
    "eyebrow_fulness_right_ratio": 1.037,
    "eyebrow_fulness_left_ratio": 1.035,
    "cheek_area_right_ratio": 1.038,
    "...": "..."
    }
}
}

How to use the AI Nexus Facial Edema App