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| 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...") | |
| # extract the features | |
| features = extract_features(uploaded_image.name, model, detector) | |
| # recommend | |
| index_pos = recommend(feature_list,features) | |
| predicted_actor = filenames[index_pos] | |
| # display | |
| display_image_resized = display_image.resize((150,150)) | |
| col1,col2 = st.columns(2) | |
| with col1: | |
| st.subheader('Your uploaded image') | |
| st.image(display_image_resized,width=150, caption='Uploaded Image') | |
| with col2: | |
| st.subheader("Look Like: " + predicted_actor.split("/")[7]) | |
| st.image(filenames[index_pos],width=150, caption='Predicted Look Alike Image') | |