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| import gradio as gr | |
| from PIL import Image,ImageDraw, ImageFont | |
| import sys | |
| import os | |
| import torch | |
| from util import Detection | |
| 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") | |
| age_model_ts = torch.jit.load("age_model.pt") | |
| from yolov5.detect import predict, load_yolo_model | |
| # Model | |
| model, stride, names, pt, jit, onnx, engine = load_yolo_model("face_model.pt", imgsz=[320,320]) | |
| def run_yolo(img): | |
| #img0 = Image.open(img.name).convert("RGB") | |
| img_path = img.name # ["name"] | |
| img0 = Image.open(img_path).convert("RGB") | |
| draw = ImageDraw.Draw(img0) | |
| predictions = predict(age_model_ts, model, stride, names, pt, jit, onnx, engine, imgsz=[320, 320], conf_thres=0.5, iou_thres=0.45, save_conf=True, | |
| exist_ok=True, nosave=True, save_txt=False, source=img_path, project=None, name=None) | |
| detections : list[Detection] = [] | |
| for k, (bboxes, img) in enumerate(predictions): | |
| #print(bboxes) | |
| # exp.imgs.append(img_info) | |
| for i, bbox in enumerate(bboxes): | |
| det = Detection( | |
| (k+1)*(i+1), | |
| bbox["xmin"], | |
| bbox["ymin"], | |
| bbox["xmax"], | |
| bbox["ymax"], | |
| bbox["conf"], | |
| bbox["class"], | |
| bbox["class"], | |
| img0.size | |
| ) | |
| same = list(filter(lambda x: x.xmin == det.xmin and x.ymin == det.ymin or ( det.xmin > x.xmin and det.ymin > x.ymin and det.xmax < x.xmax and det.ymax < x.ymax ) or ( det.xmin < x.xmin and det.ymin < x.ymin and det.xmax > x.xmax and det.ymax > x.ymax ) or Detection.get_iou(det, x) > 0.6, detections)) | |
| if len(same) == 0: | |
| 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=ImageFont.truetype("Roboto-Regular.ttf")) | |
| return img0 | |
| inputs = gr.inputs.Image(type='file', label="Input Image") | |
| outputs = gr.outputs.Image(type="pil", label="Output Image") | |
| title = "AgeGuesser" | |
| description = "Guess the age of a person from his/her face!" | |
| article = """A fully automated system based on YOLOv5 and EfficientNet to perform face detection and age estimation in real-time. | |
| Links: | |
| <ul> | |
| <li> | |
| <a href='https://link.springer.com/chapter/10.1007/978-3-030-89131-2_25'>Paper</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> | |
| """ | |
| 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) |