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import cv2
import numpy as np
import gradio as gr
from dataclasses import dataclass
from mivolo.predictor import Predictor
from utils import *
import warnings
warnings.filterwarnings("ignore")
@dataclass
class Cfg:
detector_weights: str
checkpoint: str
device: str = "cpu"
with_persons: bool = True
disable_faces: bool = False
draw: bool = True
class ValidImgDetector:
predictor = None
def __init__(self):
# Use a pipeline as a high-level helper
detector_file = "yolov8x_person_face.pt"
age_gender_file = "model_imdb_cross_person_4.22_99.46.pth.tar"
detector_path = f"./model/{detector_file}"
age_gender_path = f"./model/{age_gender_file}"
domain = "https://huggingface.co/monet-joe/human-detector/resolve/main/"
download_file(f"{domain}{detector_file}", detector_path)
download_file(f"{domain}{age_gender_file}", age_gender_path)
predictor_cfg = Cfg(detector_path, age_gender_path)
self.predictor = Predictor(predictor_cfg)
def _detect(
self,
image: np.ndarray,
score_threshold: float,
iou_threshold: float,
mode: str,
predictor: Predictor
) -> np.ndarray:
# input is rgb image, output must be rgb too
predictor.detector.detector_kwargs['conf'] = score_threshold
predictor.detector.detector_kwargs['iou'] = iou_threshold
if mode == "Use persons and faces":
use_persons = True
disable_faces = False
elif mode == "Use persons only":
use_persons = True
disable_faces = True
elif mode == "Use faces only":
use_persons = False
disable_faces = False
predictor.age_gender_model.meta.use_persons = use_persons
predictor.age_gender_model.meta.disable_faces = disable_faces
# image = image[:, :, ::-1] # RGB -> BGR
detected_objects, out_im = predictor.recognize(image)
has_child, has_female, has_male = False, False, False
if len(detected_objects.ages) > 0:
has_child = min(detected_objects.ages) < 18
has_female = 'female' in detected_objects.genders
has_male = 'male' in detected_objects.genders
return out_im[:, :, ::-1], has_child, has_female, has_male
def valid_img(self, img_path):
image = cv2.imread(img_path)
return self._detect(
image,
0.4,
0.7,
"Use persons and faces",
self.predictor
)
def inference(photo):
detector = ValidImgDetector()
return detector.valid_img(photo)
iface = gr.Interface(
fn=inference,
inputs=gr.Image(label='Upload photo', type='filepath'),
outputs=[
gr.Image(label='Detection result', type='numpy'),
gr.Textbox(label='Has child'),
gr.Textbox(label='Has female'),
gr.Textbox(label='Has male')
],
examples=get_jpg_files('./examples')
)
iface.launch()
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