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#IMPORTANT PART ONLY
#!pip install deepface
import gradio as gr
from deepface import DeepFace
#from google.colab import drive
#drive.mount('/content/drive')
#img1_path = '/content/drive/My Drive/Colab Notebooks/DeepLearning/FaceRecognition/PhotoDataSet/10Jan_1.jpeg'
#import zipfile
#with zipfile.ZipFile("PhotoDataSet.zip","r") as zip_ref:
#zip_ref.extractall("db_path")
!curl -O https://huggingface.co/spaces/ipvikas/ImageProcessing/blob/main/PhotoDataSet.zip
!unzip -q PhotoDataSet.zip
!ls
!ls db_path
import os
#db_path='/content/drive/My Drive/Colab Notebooks/DeepLearning/FaceRecognition/PhotoDataSet/'
import pandas as pd
def get_deepface(image):
df = DeepFace.find(img_path=image, db_path=db_path)
#print(df.head())
return DeepFace.analyze(img_path=image)
description = "Deepface is a lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for python. It is a hybrid face recognition framework wrapping state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace and Dlib."
facial_attribute_demo = gr.Interface(
fn=get_deepface,
inputs="image",
outputs=['text'],
title="face recognition and facial attribute analysis",
description=description,
enable_queue=True,
examples=[["10Jan_1.jpeg"]],
cache_examples=False)
facial_attribute_demo .launch() |