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Shrikrishna
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Commit
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ec77159
1
Parent(s):
0b6cffd
Upload 4 files
Browse files- app.py +99 -0
- embedding.pkl +3 -0
- filenames.pkl +3 -0
- requirements.txt +10 -0
app.py
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from tensorflow import keras
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from keras.preprocessing import image
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import pickle
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from sklearn.metrics.pairwise import cosine_similarity
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import streamlit as st
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from PIL import Image
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import os
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import cv2
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from mtcnn import MTCNN
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import numpy as np
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#keras.applications.resnet50.ResNet50
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#VGGFace(model='resnet50',include_top=False,input_shape=(224,224,3),pooling='avg')
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#st.text("Hello Welcome")
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detector = MTCNN()
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model = keras.applications.resnet50.ResNet50(
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include_top=False,
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input_shape=(224,224,3),
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pooling='avg',
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weights='imagenet'
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)
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feature_list = pickle.load(open('embedding.pkl', 'rb'))
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filenames = pickle.load(open('filenames.pkl', 'rb'))
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filenames = [sub.replace('/kaggle/input/bollywood-celeb-localized-face-dataset/', 'https://technirmitisoftwares.com/img_data/data/') for sub in filenames]
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def save_uploaded_image(uploaded_image):
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try:
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with open(uploaded_image.name, 'wb') as f:
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f.write(uploaded_image.getbuffer())
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return True
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except:
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return False
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def extract_features(img_path, model, detector):
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img = cv2.imread(img_path)
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results = detector.detect_faces(img)
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x, y, width, height = results[0]['box']
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face = img[y:y + height, x:x + width]
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# extract its features
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image = Image.fromarray(face)
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image = image.resize((224, 224))
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face_array = np.asarray(image)
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face_array = face_array.astype('float32')
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expanded_img = np.expand_dims(face_array, axis=0)
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preprocessed_img = keras.applications.resnet50.preprocess_input(expanded_img)
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result = model.predict(preprocessed_img).flatten()
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return result
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def recommend(feature_list,features):
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similarity = []
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for i in range(len(feature_list)):
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similarity.append(cosine_similarity(features.reshape(1, -1), feature_list[i].reshape(1, -1))[0][0])
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index_pos = sorted(list(enumerate(similarity)), reverse=True, key=lambda x: x[1])[0][0]
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return index_pos
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st.title('Which bollywood celebrity are you?')
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uploaded_image = st.file_uploader('Choose an image')
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if uploaded_image is not None:
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# save the image in a directory
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if save_uploaded_image(uploaded_image):
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display_image = Image.open(uploaded_image)
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st.header("Image Uploded!, Processing...")
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#st.image(display_image)
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# extract the features
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features = extract_features(uploaded_image.name, model, detector)
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#st.text(features)
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#st.text(features.shape)
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# recommend
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index_pos = recommend(feature_list,features)
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predicted_actor = filenames[index_pos]
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#st.header(predicted_actor)
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# display
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col1,col2 = st.columns(2)
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with col1:
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st.header('Your uploaded image')
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st.image(display_image,width=150)
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with col2:
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st.header("Look Like: " + predicted_actor.split("/")[7])
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st.image(filenames[index_pos],width=150)
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embedding.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:c7f487b833163517d66a9a234f933104d4e4e10a5e9db757aa3098fc81b8407a
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size 71477400
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filenames.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:96eb16a7402a2c5dc1c7bd55fef34d672cb25c0cd9f0e8538bca606a50453d32
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size 1309928
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requirements.txt
ADDED
@@ -0,0 +1,10 @@
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1 |
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mtcnn
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tensorflow
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keras
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Keras
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keras-vggface
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Keras_Vggface
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keras_applications
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Keras_Applications
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numpy
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scikit-learn
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