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from sentence_transformers import util
from transformers import pipeline
from PIL import Image, ImageDraw
from sentence_transformers import util,SentenceTransformer
import gradio as gr
checkpoint = "google/owlvit-base-patch32"
detector = pipeline(model=checkpoint, task="zero-shot-object-detection")
model = SentenceTransformer('clip-ViT-L-14')
def get_face_image(im1):
predictions = detector(
im1,
candidate_labels=["human face"],
)
max_score = 0
box_area = None
for prediction in predictions:
box = prediction["box"]
label = prediction["label"]
score = prediction["score"]
if score > max_score :
xmin, ymin, xmax, ymax = box.values()
box_area = (xmin, ymin, xmax, ymax)
max_score = score
else:
continue
draw = ImageDraw.Draw(im1)
draw.rectangle(box_area, outline="red", width=1)
#draw.text((xmin, ymin), f"{label}: {round(score,2)}", fill="blue")
crop_img1 = im1.crop(box_area)
#display(crop_img1)
newsize = (256, 256)
face_img1 = crop_img1.resize(newsize)
#display(face_img1)
return face_img1
def predict(im1, im2,inp_sim):
face_image1 = get_face_image(im1)
face_image2 = get_face_image(im2)
img_emb = model.encode([face_image1, face_image2])
sim = util.cos_sim(img_emb[0], img_emb[1])
if sim > inp_sim:
return sim, "SAME PERSON, UNLOCK PHONE"
else:
return sim, "DIFFERENT PEOPLE, DON'T UNLOCK"
description = "An application that can recognize if two faces belong to the same person or not"
title = "Facial Identity Recognition System"
interface = gr.Interface(fn=predict,
inputs= [gr.Image(type="pil", source="webcam"),
gr.Image(type="pil"),
gr.Slider(0, 1, value=0.8, label="Similarity Percentage", info="Choose betwen 0 and 1")],
outputs= [gr.Number(label="Similarity"),
gr.Textbox(label="Message")]
)
interface.launch(debug=True) |