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"""Streamlit web app"""
import numpy as np
import streamlit as st
from PIL import Image
from retinaface.pre_trained_models import get_model
from retinaface.utils import vis_annotations
import torch
st.set_option("deprecation.showfileUploaderEncoding", False)
def cached_model():
m = get_model("resnet50_2020-07-20", max_size=1048, device="cpu")
return m
model = cached_model()
st.title("Detect faces and key points")
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
image = np.array(
st.image(image, caption="Before", use_column_width=True)
st.write("Detecting faces...")
with torch.no_grad():
annotations = model.predict_jsons(image)
if not annotations[0]["bbox"]:
st.write("No faces detected")
visualized_image = vis_annotations(image, annotations)
st.image(visualized_image, caption="After", use_column_width=True)