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import gradio as gr
from deepface import DeepFace
import numpy as np
import PIL
from PIL import Image, ImageDraw, ImageFont
import time
dbackends = [
'opencv',
'ssd',
'dlib',
'mtcnn',
'retinaface',
'mediapipe',
'yolov8',
'yunet',
'fastmtcnn',
]
annotated_image = gr.AnnotatedImage()
jsontext = gr.Text(label= "deepface extract_faces results",)
def findFaces(imgfile,dbackend):
start_time = time.time()
print(start_time)
face_objs = DeepFace.extract_faces(img_path = imgfile, enforce_detection = False, detector_backend = dbackend)
numberoffaces = len(face_objs)
jsontext = ''
faceannotations = []
for face_obj in face_objs:
face_coordinates = (face_obj["facial_area"]["x"],face_obj["facial_area"]["y"], (face_obj["facial_area"]["x"] + face_obj["facial_area"]["w"]),(face_obj["facial_area"]["y"] + face_obj["facial_area"]["h"]))
face_confidence = "{:.0%}".format(face_obj["confidence"])
face_result=[face_coordinates,face_confidence]
faceannotations.append(face_result)
jsontext=faceannotations
run_time = str(round((time.time() - start_time),2))
results = gr.AnnotatedImage(
label= "Detected " + str(numberoffaces) + " faces via " + dbackend + ' in ' + run_time + ' seconds.',
value=(imgfile, faceannotations)
)
print(run_time)
return(results,jsontext,numberoffaces,run_time)
dbackendchoice = gr.Radio(choices=dbackends,label='Detector Backend:')
demo = gr.Interface(
allow_flagging = "never",
fn=findFaces,
inputs=[gr.Image(value="8428_26_SM.jpg"), dbackendchoice],
outputs=[annotated_image,jsontext],
)
demo.launch(show_error=True)
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