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