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import cv2 as cv
import numpy as np
import gradio as gr
from huggingface_hub import hf_hub_download
from yunet import YuNet
from ediffiqa import eDifFIQA
# Download face detection model (YuNet)
model_path_yunet = hf_hub_download(
repo_id="opencv/face_detection_yunet",
filename="face_detection_yunet_2023mar.onnx"
)
# Download face quality assessment model (eDifFIQA Tiny)
model_path_quality = hf_hub_download(
repo_id="opencv/face_image_quality_assessment_ediffiqa",
filename="ediffiqa_tiny_jun2024.onnx"
)
# Backend and target
backend_id = cv.dnn.DNN_BACKEND_OPENCV
target_id = cv.dnn.DNN_TARGET_CPU
# Initialize YuNet for face detection
face_detector = YuNet(
modelPath=model_path_yunet,
inputSize=[320, 320],
confThreshold=0.9,
nmsThreshold=0.3,
topK=5000,
backendId=backend_id,
targetId=target_id
)
# Initialize eDifFIQA for quality assessment
quality_model = eDifFIQA(
modelPath=model_path_quality,
inputSize=[112, 112]
)
quality_model.setBackendAndTarget(
backendId=backend_id,
targetId=target_id
)
REFERENCE_FACIAL_POINTS = np.array([
[38.2946 , 51.6963 ],
[73.5318 , 51.5014 ],
[56.0252 , 71.7366 ],
[41.5493 , 92.3655 ],
[70.729904, 92.2041 ]
], dtype=np.float32)
def align_image(image, detection_data):
src_pts = np.float32(detection_data[0][4:-1]).reshape(5, 2)
tfm, _ = cv.estimateAffinePartial2D(src_pts, REFERENCE_FACIAL_POINTS, method=cv.LMEDS)
face_img = cv.warpAffine(image, tfm, (112, 112))
return face_img
def assess_face_quality(input_image):
bgr_image = cv.cvtColor(input_image, cv.COLOR_RGB2BGR)
h, w, _ = bgr_image.shape
face_detector.setInputSize([w, h])
detections = face_detector.infer(bgr_image)
if detections is None or len(detections) == 0:
return "No face detected.", input_image
aligned_face = align_image(bgr_image, detections)
score = np.squeeze(quality_model.infer(aligned_face)).item()
output_image = aligned_face.copy()
cv.putText(output_image, f"{score:.3f}", (0, 20), cv.FONT_HERSHEY_DUPLEX, 0.8, (0, 0, 255), 2)
output_image = cv.cvtColor(output_image, cv.COLOR_BGR2RGB)
return f"Quality Score: {score:.3f}", output_image
# Gradio Interface
demo = gr.Interface(
fn=assess_face_quality,
inputs=gr.Image(type="numpy", label="Upload Face Image"),
outputs=[
gr.Text(label="Quality Score"),
gr.Image(type="numpy", label="Aligned Face with Score")
],
title="Face Image Quality Assessment (eDifFIQA + YuNet)",
allow_flagging="never",
description="Upload a face image. The app detects and aligns the face, then evaluates image quality using the eDifFIQA model."
)
if __name__ == "__main__":
demo.launch()
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