app.py

import cv2
import numpy as np
import gradio as gr
import os

# Install mediapipe if not available
try:
    import mediapipe as mp
except ImportError:
    import pip

    pip.main(['install', 'mediapipe'])
    import mediapipe as mp


def process_face_image(input_image):
    """
    Function processes the image, finds facial landmarks,
    and returns two images: one with landmarks and one with measurements
    """
    # Convert image from gradio to numpy format
    if input_image is None:
        return None, None

    # Face mesh
    mp_face_mesh = mp.solutions.face_mesh
    face_mesh = mp_face_mesh.FaceMesh(static_image_mode=True, max_num_faces=1, min_detection_confidence=0.5)

    # Get image dimensions
    image = input_image.copy()
    height, width, _ = image.shape
    rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Create copies for landmarks and measurements
    image_all_landmarks = image.copy()
    image_with_lines = image.copy()

    # Find landmarks
    result = face_mesh.process(rgb_image)

    # Check if face was detected
    if not result.multi_face_landmarks:
        return image, image, "No face detected"

    # Process the found landmarks
    for facial_landmarks in result.multi_face_landmarks:
        # Draw all landmarks as thin points
        for i in range(0, 468):
            pt1 = facial_landmarks.landmark[i]
            x = int(pt1.x * width)
            y = int(pt1.y * height)
            cv2.circle(image_all_landmarks, (x, y), 1, (100, 100, 0), -1)

            # Add landmark numbers for important points
            if i in [10, 152, 234, 454, 35, 265, 129, 358]:
                cv2.putText(image_all_landmarks, str(i), (x + 2, y + 2),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 255), 1)

        # Face width (points 234 and 454)
        right_face = facial_landmarks.landmark[234]
        left_face = facial_landmarks.landmark[454]
        right_x = int(right_face.x * width)
        right_y = int(right_face.y * height)
        left_x = int(left_face.x * width)
        left_y = int(left_face.y * height)

        # Draw face width line
        cv2.line(image_with_lines, (right_x, right_y), (left_x, left_y), (0, 255, 0), 3)
        face_width = ((left_x - right_x) ** 2 + (left_y - right_y) ** 2) ** 0.5
        cv2.putText(image_with_lines, f"Face width: {face_width:.2f}px",
                    (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)

        # Eye distance (points 35 and 265)
        right_eye = facial_landmarks.landmark[35]
        left_eye = facial_landmarks.landmark[265]
        right_eye_x = int(right_eye.x * width)
        right_eye_y = int(right_eye.y * height)
        left_eye_x = int(left_eye.x * width)
        left_eye_y = int(left_eye.y * height)

        # Draw eye distance line
        cv2.line(image_with_lines, (right_eye_x, right_eye_y), (left_eye_x, left_eye_y), (255, 0, 0), 3)
        eye_distance = ((left_eye_x - right_eye_x) ** 2 + (left_eye_y - right_eye_y) ** 2) ** 0.5
        cv2.putText(image_with_lines, f"Eye distance: {eye_distance:.2f}px",
                    (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 0), 2)

        # Nose width (points 129 and 358)
        right_nose = facial_landmarks.landmark[129]
        left_nose = facial_landmarks.landmark[358]

        right_nose_x = int(right_nose.x * width)
        right_nose_y = int(right_nose.y * height)
        left_nose_x = int(left_nose.x * width)
        left_nose_y = int(left_nose.y * height)

        # Draw nose width line
        cv2.line(image_with_lines, (right_nose_x, right_nose_y), (left_nose_x, left_nose_y), (255, 165, 0), 3)
        nose_width = ((left_nose_x - right_nose_x) ** 2 + (left_nose_y - right_nose_y) ** 2) ** 0.5
        cv2.putText(image_with_lines, f"Nose width: {nose_width:.2f}px",
                    (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 165, 0), 2)

        # Face height (points 10 and 152)
        forehead = facial_landmarks.landmark[10]  # Forehead point
        chin = facial_landmarks.landmark[152]  # Chin point
        forehead_x = int(forehead.x * width)
        forehead_y = int(forehead.y * height)
        chin_x = int(chin.x * width)
        chin_y = int(chin.y * height)

        # Draw face height line
        cv2.line(image_with_lines, (forehead_x, forehead_y), (chin_x, chin_y), (0, 0, 255), 3)
        face_height = ((chin_x - forehead_x) ** 2 + (chin_y - forehead_y) ** 2) ** 0.5
        cv2.putText(image_with_lines, f"Face height: {face_height:.2f}px",
                    (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)

        # Return face ratio
        face_ratio = face_width / face_height if face_height > 0 else 0
        cv2.putText(image_with_lines, f"Face ratio: {face_ratio:.2f}",
                    (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 0, 255), 2)

    # Return both images
    return image_all_landmarks, image_with_lines


# Create Gradio interface
demo = gr.Interface(
    fn=process_face_image,
    inputs=[
        gr.Image(type="numpy", label="Input Image")
    ],
    outputs=[
        gr.Image(type="numpy", label="Face Landmarks"),
        gr.Image(type="numpy", label="Face Measurements")
    ],
    title="Face Analysis with Measurements",
    description="""
    Upload a face image to get:
    1. Image with all landmark points
    2. Image with measurements (face width, eye distance, nose width, face height)
    """,
)

# Add examples from the 'examples' directory if it exists
if os.path.exists("examples"):
    example_list = [["examples/" + example] for example in os.listdir("examples") if
                    example.endswith(('.jpg', '.jpeg', '.png'))]
    if example_list:
        demo = gr.Interface(
            fn=process_face_image,
            inputs=[
                gr.Image(type="numpy", label="Input Image")
            ],
            outputs=[
                gr.Image(type="numpy", label="Face Landmarks"),
                gr.Image(type="numpy", label="Face Measurements")
            ],
            title="Face Analysis with Measurements",
            description="""
            Upload a face image to get:
            1. Image with all landmark points
            2. Image with measurements (face width, eye distance, nose width, face height)
            """,
            examples=example_list
        )

# Launch the interface
if __name__ == "__main__":
    demo.launch(share=True)  # share=True allows you to get a public link