paresh95
PS | Add face comparison feature
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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 = PILImage.new("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="""
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>JSON Output in HTML</title>
<style>
.section {
margin-bottom: 20px;
}
</style>
</head>
<body>
<div class="section">
<font size="3">
<h3><center>Turn your selfie into insights! Discover age and gender predictions, symmetry evaluations, and detailed proportions and texture analyses with our app.</center></h3>
<hr style="margin-top: 20px; margin-bottom: 20px;">
<p><strong>Instructions:</strong> 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 <i>Upload Face</i>, then press <i>Submit</i>.</p>
<p><strong>Other information:</strong></p>
<ul>
<li>The output computation requires approximately 5 to 30 seconds.</li>
<li>No uploaded photo is stored.</li>
<li>If an error occurs try again or try a different photo or angle.</li>
<li>Once submitted, a section detailing the results and associated images will be displayed.</li>
</ul>
</font>
</div>
</body>
</html>
""",
theme=gr.themes.Soft(),
live=False,
)
iface.launch()