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import gradio as gr | |
from PIL import Image,ImageDraw, ImageFont, ImageOps | |
import sys | |
import torch | |
from util import Detection | |
import os | |
if os.environ.get('FACE_MODEL') is not None: | |
face_model = os.environ.get('FACE_MODEL') | |
age_model = os.environ.get('AGE_MODEL') | |
torch.hub.download_url_to_file(face_model, 'face_model.pt') | |
torch.hub.download_url_to_file(age_model, 'age_model.pt') | |
sys.path.append("./") | |
sys.path.append("./yolov5") | |
from yolov5.detect import predict, load_yolo_model | |
# Load Models | |
model, stride, names, pt, jit, onnx, engine = load_yolo_model("face_model.pt", imgsz=[320,320]) | |
age_model_ts = torch.jit.load("age_model.pt") | |
roboto_font = ImageFont.truetype("Roboto-Regular.ttf") | |
def run_yolo(img): | |
img_path = img | |
img0 = Image.open(img_path).convert("RGB") | |
img0 = ImageOps.contain(img0, (720,720)) | |
img0 = ImageOps.exif_transpose(img0) | |
draw = ImageDraw.Draw(img0) | |
predictions = predict(age_model_ts, model, | |
stride, imgsz=[320, 320], | |
conf_thres=0.5, iou_thres=0.45, | |
source=img0 | |
) | |
detections : list[Detection] = [] | |
for k, bbox in enumerate(predictions): | |
det = Detection( | |
(k+1), | |
bbox["xmin"], | |
bbox["ymin"], | |
bbox["xmax"], | |
bbox["ymax"], | |
bbox["conf"], | |
bbox["class"], | |
bbox["class"], | |
img0.size | |
) | |
detections.append(det) | |
draw.rectangle(((det.xmin, det.ymin), (det.xmax, det.ymax)), fill=None, outline=(255,255,255)) | |
draw.rectangle(((det.xmin, det.ymin - 10), (det.xmax, det.ymin)), fill=(255,255,255)) | |
draw.text((det.xmin, det.ymin - 10), det.class_name, fill=(0,0,0), font=roboto_font) | |
return img0 | |
# run_yolo("D:\\Download\\IMG_20220803_153335.jpg") | |
inputs = gr.inputs.Image(type='filepath', label="Input Image") | |
outputs = gr.outputs.Image(type="pil", label="Output Image") | |
title = "AgeGuesser" | |
description = "Guess the age of a person from a facial image!" | |
article = """<p>A fully automated system based on YOLOv5 and EfficientNet to perform face detection and age estimation in real-time.</p> | |
<p><b>Links</b></p> | |
<ul> | |
<li> | |
<a href='https://link.springer.com/chapter/10.1007/978-3-030-89131-2_25'>Springer</a> | |
</li> | |
<li> | |
<a href='https://www.researchgate.net/publication/355777953_Real-Time_Age_Estimation_from_Facial_Images_Using_YOLO_and_EfficientNet'>Paper</a> | |
</li> | |
<li> | |
<a href='https://github.com/ai-hazard/AgeGuesser-train'>Github</a> | |
</li> | |
</ul> | |
<p>Credits to my dear colleague <a href='https://www.linkedin.com/in/nicola-marvulli-904270136/'>Dott. Nicola Marvulli</a>, we've developed AgeGuesser together as part of two university exams. (Computer Vision + Deep Learning)</p> | |
<p>Credits to my dear professors and the <a href='https://sites.google.com/site/cilabuniba/'>CILAB</a> research group</p> | |
<ul> | |
<li> | |
<a href='https://sites.google.com/site/cilabuniba/people/giovanna-castellano'>Prof. Giovanna Castellano</a> | |
</li> | |
<li> | |
<a href='https://sites.google.com/view/gennaro-vessio/home-page'>Prof. Gennaro Vessio</a> | |
</li> | |
</ul> | |
""" | |
examples = [['images/1.jpg'], ['images/2.jpg'], ['images/3.jpg'], ['images/4.jpg'], ['images/5.jpg'], ] | |
gr.Interface(run_yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(enable_queue=True) |