from tensorflow import keras from keras.preprocessing import image 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 #keras.applications.resnet50.ResNet50 #VGGFace(model='resnet50',include_top=False,input_shape=(224,224,3),pooling='avg') #st.text("Hello Welcome") detector = MTCNN() model = keras.applications.resnet50.ResNet50( include_top=False, input_shape=(224,224,3), pooling='avg', weights='imagenet' ) feature_list = pickle.load(open('embedding.pkl', 'rb')) filenames = pickle.load(open('filenames.pkl', 'rb')) filenames = [sub.replace('/kaggle/input/bollywood-celeb-localized-face-dataset/', 'https://technirmitisoftwares.com/img_data/data/') for sub in filenames] def save_uploaded_image(uploaded_image): try: with open(uploaded_image.name, 'wb') as f: f.write(uploaded_image.getbuffer()) return True except: return False def extract_features(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 = keras.applications.resnet50.preprocess_input(expanded_img) result = model.predict(preprocessed_img).flatten() return result 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 st.title('Which bollywood celebrity are you?') uploaded_image = st.file_uploader('Choose an image') if uploaded_image is not None: # save the image in a directory if save_uploaded_image(uploaded_image): display_image = Image.open(uploaded_image) st.header("Image Uploded!, Processing...") #st.image(display_image) # extract the features features = extract_features(uploaded_image.name, model, detector) #st.text(features) #st.text(features.shape) # recommend index_pos = recommend(feature_list,features) predicted_actor = filenames[index_pos] #st.header(predicted_actor) # display col1,col2 = st.columns(2) with col1: st.subheader('Your uploaded image') st.image(display_image,width=150) with col2: st.subheader("Look Like: " + predicted_actor.split("/")[7]) st.image(filenames[index_pos],width=150)