import gradio as gr import os import yaml import pandas as pd from src.face_texture import GetFaceTexture from src.face_symmetry import GetFaceSymmetry from src.face_demographics import GetFaceDemographics from src.face_proportions import GetFaceProportions from PIL import Image as PILImage from typing import List, Any def get_results(image_input: PILImage.Image) -> List[Any]: demographics_dict = GetFaceDemographics().main(image_input) ( ratios_dict, face_landmarks_image, ) = GetFaceProportions().main(image_input) face_symmetry_image, symmetry_dict = GetFaceSymmetry().main(image_input) face_image, face_texture_image, texture_dict = GetFaceTexture().main(image_input) results = { "Demographic predictions": demographics_dict, "Face proportions": ratios_dict, "Face symmetry metrics": symmetry_dict, "Face texture metrics": texture_dict, } return ( results, face_image, face_landmarks_image, face_symmetry_image, face_texture_image, ) def concatenate_image( image_1: PILImage.Image, image_2: PILImage.Image ) -> PILImage.Image: image ="RGB", (image_1.width + image_2.width, image_1.height)) image.paste(image_1, (0, 0)) image.paste(image_2, (image_1.width, 0)) return image def get_dict_child_data(results_image: dict, image_number: int) -> dict: flattened_data = {"image": f"Face {image_number}"} for key, sub_dict in results_image.items(): for sub_key, value in sub_dict.items(): flattened_data[sub_key] = value return flattened_data def output_fn( image_input_1: PILImage.Image, image_input_2: PILImage.Image ) -> List[Any]: with open("parameters.yml", "r") as file: data = yaml.safe_load(file) results_interpretation = data["results_interpretation"] if image_input_1 is not None and image_input_2 is not None: ( results_image_1, face_image_1, face_landmarks_image_1, face_symmetry_image_1, face_texture_image_1, ) = get_results(image_input_1) ( results_image_2, face_image_2, face_landmarks_image_2, face_symmetry_image_2, face_texture_image_2, ) = get_results(image_input_2) results_image_1, results_image_2 = get_dict_child_data( results_image_1, 1 ), get_dict_child_data(results_image_2, 2) results_df = pd.DataFrame([results_image_1, results_image_2]) face_image = concatenate_image(face_image_1, face_image_2) face_landmarks_image = concatenate_image( face_landmarks_image_1, face_landmarks_image_2 ) face_symmetry_image = concatenate_image( face_symmetry_image_1, face_symmetry_image_2 ) face_texture_image = concatenate_image( face_texture_image_1, face_texture_image_2 ) if image_input_1 == None and image_input_2 is not None: ( results, face_image, face_landmarks_image, face_symmetry_image, face_texture_image, ) = get_results(image_input_2) results_df = pd.DataFrame([get_dict_child_data(results, 2)]) if image_input_2 == None and image_input_1 is not None: ( results, face_image, face_landmarks_image, face_symmetry_image, face_texture_image, ) = get_results(image_input_1) results_df = pd.DataFrame([get_dict_child_data(results, 1)]) return ( results_df, results_interpretation, face_image, face_landmarks_image, face_symmetry_image, face_texture_image, ) gigi_hadid = os.path.join(os.path.dirname(__file__), "data/gigi_hadid.webp") iface = gr.Interface( fn=output_fn, inputs=[ gr.Image(type="pil", label="Upload Face 1", value=gigi_hadid), gr.Image(type="pil", label="Upload Face 2"), ], outputs=[ gr.DataFrame(label="Results"), gr.JSON(label="Results explainer"), gr.Image(type="pil", label="Extracted face"), gr.Image(type="pil", label="Face landmarks"), gr.Image(type="pil", label="Face symmetry"), gr.Image(type="pil", label="Extracted face texture"), ], title="Advanced Facial Feature Detector", description=""" JSON Output in HTML

Turn your selfie into insights! Discover age and gender predictions, symmetry evaluations, and detailed proportions and texture analyses with our app.

Instructions: Upload up to 2 photos. For optimal results, upload a clear front-facing image (see example). To do so, either drag and drop your photo or click Upload Face, then press Submit.

Other information:

""", theme=gr.themes.Soft(), live=False, ) iface.launch()