Spaces:
Running
Running
File size: 3,137 Bytes
5e8395e c3143ea 0347340 4eddd18 4ff0905 1ed5ce5 4915b46 0347340 4915b46 c3143ea 0347340 5e8395e 59ece0e 4915b46 0347340 4915b46 0347340 c3143ea 0347340 54e742e c3143ea 0347340 c3143ea 59ece0e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import gradio as gr
import os
import yaml
from src.face_texture import GetFaceTexture
from src.face_symmetry import GetFaceSymmetry
from src.face_demographics import GetFaceDemographics
from src.face_proportions import GetFaceProportions
def combined_fn(input_image, input_image_2):
demographics_dict = GetFaceDemographics().main(input_image)
golden_ratios_dict, equal_ratios_dict, face_landmarks_image = GetFaceProportions().main(input_image)
face_symmetry_image, symmetry_dict = GetFaceSymmetry().main(input_image)
face_image, face_texture_image, texture_dict = GetFaceTexture().main(input_image)
results = {
"Demographic predictions": demographics_dict,
"Face proportions (golden ratio)": golden_ratios_dict,
"Face proportions (equal ratio)": equal_ratios_dict,
"Face symmetry metrics": symmetry_dict,
"Face texture metrics": texture_dict
}
with open("parameters.yml", 'r') as file:
data = yaml.safe_load(file)
results_interpretation = data["results_interpretation"]
return (results, 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")
jay_z = os.path.join(os.path.dirname(__file__), "data/jay_z.jpg")
iface = gr.Interface(
fn=combined_fn,
inputs=[
gr.Image(type="pil", label="Upload Face 1", value=jay_z),
gr.Image(type="pil", label="Upload Face 2", value=gigi_hadid)
],
outputs=[
gr.JSON(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">
<p><strong>Description:</strong> This tool analyses a facial image to predict age and gender, assess symmetry, evaluate proportions, and examine texture.</p>
<p><strong>Instructions:</strong> For optimal results, upload a clear front-facing image (see example image). To do so, either drag and drop your photo or click on "Upload Face Image", then press 'Submit'.</p>
<p><strong>Interpreting the results:</strong></p>
<p><strong>Other information:</strong></p>
<ul>
<li>No uploaded photo is stored.</li>
<li>The output will take several seconds to compute.</li>
<li>If an error occurs try again or try a different photo or angle.</li>
</ul>
</div>
</body>
</html>
""",
theme=gr.themes.Soft(),
live=False,
)
iface.launch()
|