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app.py
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app.py
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from distutils.command.upload import upload
from keras_vggface.utils import preprocess_input
from keras_vggface.vggface import VGGFace
from tensorflow.python.ops.math_ops import truediv
from src.utils.all_utils import read_yaml, create_directory
import pickle
from sklearn.metrics.pairwise import cosine_similarity
import streamlit as st
from PIL import Image
import os
import cv2
from mtcnn import MTCNN
import numpy as np
config = read_yaml('config/config.yaml')
params = read_yaml('params.yaml')
artifacts = config['artifacts']
artifacts_dir = artifacts['artifacts_dir']
#upload
upload_image_dir = artifacts['upload_image_dir']
upload_path = os.path.join(artifacts_dir, upload_image_dir)
#pickle
pickle_format_data_dir = artifacts['pickle_format_data_dir']
img_pickle_file_name = artifacts['img_pickle_file_name']
raw_local_dir_path = os.path.join(artifacts_dir, pickle_format_data_dir)
pickle_file = os.path.join(raw_local_dir_path, img_pickle_file_name)
#feature path
feature_extraction_dir = artifacts['feature_extraction_dir']
extracted_features_name = artifacts['extracted_features_name']
feature_extraction_path = os.path.join(artifacts_dir, feature_extraction_dir)
feature_name = os.path.join(feature_extraction_path, extracted_features_name)
model_name = params['base']['BASE_MODEL']
include_tops = params['base']['include_top']
poolings = params['base']['pooling']
detector = MTCNN()
model = VGGFace(model = model_name, include_top = include_tops, input_shape=(224, 224, 3), pooling = poolings)
filenames = pickle.load(open(pickle_file, 'rb'))
feature_list = pickle.load(open(feature_name, 'rb'))
#save upload image
def save_upload_image(uploadimage):
try:
create_directory(dirs=[upload_path])
with open(os.path.join(upload_path, uploadimage.name), 'wb') as f:
f.write(uploadimage.getbuffer())
return True
except:
return False
#Extractor features
def extract_feature(img_path, model, detector):
img = cv2.imread(img_path)
results = detector.detect_faces(img)
x, y, width, height = results[0]['box']
face = img[y:y + height, x:x + width]
# extract its features
image = Image.fromarray(face)
image = image.resize((224, 224))
face_array = np.asarray(image)
face_array = face_array.astype('float32')
expanded_img = np.expand_dims(face_array, axis=0)
preprocessed_img = preprocess_input(expanded_img)
result = model.predict(preprocessed_img).flatten()
return result
# recommend image
def recommend(feature_list,features):
similarity = []
for i in range(len(feature_list)):
similarity.append(cosine_similarity(features.reshape(1, -1), feature_list[i].reshape(1, -1))[0][0])
index_pos = sorted(list(enumerate(similarity)), reverse=True, key=lambda x: x[1])[0][0]
return index_pos
#streamlit
st.title('To Whom does your Face Match')
uploadimage = st.file_uploader('Choose an Image')
if uploadimage is not None:
#save the image
if save_upload_image(uploadimage):
#load image
display_image = Image.open(uploadimage)
#extracting features
features = extract_feature(os.path.join(upload_path, uploadimage.name), model, detector)
#recommend
index_pos = recommend(feature_list, features)
predicted_actor = " ".join(filenames[index_pos].split('\\')[1].split('_'))
# display
col1,col2 = st.columns(2)
with col1:
st.header('Your uploaded image')
st.image(display_image)
with col2:
st.header("Seems like " + predicted_actor)
st.image(filenames[index_pos],width=300)