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import streamlit as st
from transformers import pipeline
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input
from sklearn.neighbors import NearestNeighbors
from PIL import Image
import numpy as np
import glob
import os
resnet_model = ResNet50(weights='imagenet')
st.title("CS634 - Assignment 3")
user_image_input = st.file_uploader("Upload Images", type=["jpg"])
path='lfw/V*'
photos=[]
for fold in glob.glob(path, recursive=True):
for subdir, dirs, files in os.walk(fold):
for file in files:
#st.write(file)
photos.append(os.path.join(subdir, file))
photos.insert(0,"")
celebrity_photo = st.selectbox("Select Photo",photos)
def extract_features(photos, resnet_model):
features = {}
for photo in photos:
if(photo!=""):
img = image.load_img(photo, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
features_vector = resnet_model.predict(x)
features_vector = features_vector.flatten()
features[photo] = features_vector
return features
if(len(celebrity_photo) != 0):
#st.image(user_image_input, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
user_input_image = None
st.write(celebrity_photo)
#st.write(user_image_input.read())
size=len(photos)
#st.write(size)
st.write("Query Image: ")
st.image(celebrity_photo)
features = extract_features(photos, resnet_model)
features_array = np.array(list(features.values()))
nn_model = NearestNeighbors(n_neighbors=11, metric='euclidean')
nn_model.fit(features_array)
query_image_path = photos[size-1]
query_image_feature = features[query_image_path].reshape(1, -1)
distances, indices = nn_model.kneighbors(query_image_feature)
st.write("Similar Images:")
for i in range(1,11):
similar_image_path = photos[indices[0][i]]
similar_image_distance = distances[0][i]
st.write("Similar Image #{}: Distance: {}".format(i, similar_image_distance))
st.image(similar_image_path)
if(user_image_input != None):
celebrity_photo = []
#st.image(user_image_input, caption=None, width=None, use_column_width=None, clamp=False, channels="RGB", output_format="auto")
im = Image.open(user_image_input)
im=im.resize((224,224))
im.save("input_image.jpg", "JPEG")
photos.append("input_image.jpg")
#st.write(user_image_input.read())
size=len(photos)
#st.write(size)
st.write("Query Image: ")
st.image(photos[size-1])
features = extract_features(photos, resnet_model)
features_array = np.array(list(features.values()))
nn_model = NearestNeighbors(n_neighbors=11, metric='euclidean')
nn_model.fit(features_array)
query_image_path = photos[size-1]
query_image_feature = features[query_image_path].reshape(1, -1)
distances, indices = nn_model.kneighbors(query_image_feature)
st.write("Similar Images:")
for i in range(1,11):
similar_image_path = photos[indices[0][i]]
similar_image_distance = distances[0][i]
st.write("Similar Image #{}: Distance: {}".format(i, similar_image_distance))
st.image(similar_image_path)
#else:
# size=len(photos)
# st.write(size)
# st.image(photos[size-1])
# features = extract_features(photos, resnet_model)
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