import streamlit as st
import cv2
from PIL import Image
import numpy as np
import os
import matplotlib.pyplot as plt
# Define a function to search for similar images
def search_similar_images(img_path, num_results=10):
# Load the query image
query_image = cv2.imread(img_path)
# Convert the query image to grayscale
query_image_gray = cv2.cvtColor(query_image, cv2.COLOR_BGR2GRAY)
# Resize the query image to a fixed size
query_image_resized = cv2.resize(query_image_gray, (300, 300))
# Calculate the histogram of the query image
query_hist = cv2.calcHist([query_image_resized], [0], None, [256], [0, 256])
# Normalize the histogram
query_hist_norm = cv2.normalize(query_hist, query_hist, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
# Load all images from the 'images' directory
images_dir = "images"
image_files = os.listdir(images_dir)
images = []
for image_file in image_files:
if image_file.endswith(".jpg") or image_file.endswith(".png"):
image_path = os.path.join(images_dir, image_file)
image = cv2.imread(image_path)
images.append(image)
# Calculate the histograms and similarities for each image
similarities = []
for image in images:
# Convert the image to grayscale
image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Resize the image to a fixed size
image_resized = cv2.resize(image_gray, (300, 300))
# Calculate the histogram of the image
image_hist = cv2.calcHist([image_resized], [0], None, [256], [0, 256])
# Normalize the histogram
image_hist_norm = cv2.normalize(image_hist, image_hist, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
# Calculate the correlation between the histograms
similarity = cv2.compareHist(query_hist_norm, image_hist_norm, cv2.HISTCMP_CORREL)
# Add the similarity to the list
similarities.append(similarity)
# Get the indices of the top num_results most similar images
indices = np.argsort(similarities)[-num_results:]
# Create a list of the top num_results most similar images
results = []
for index in indices:
results.append(images[index])
# Return the list of results
return results
def display_results(similar_images):
# Create a figure with a grid of 10 subplots
fig, axs = plt.subplots(2, 5, figsize=(10, 4))
# Loop through the similar images and display each one in a subplot
for i,img in enumerate(similar_images):
# Load the image using OpenCV
# Convert to RGB for displaying with matplotlib
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Calculate the row and column index for the current subplot
row = i // 5
col = i % 5
# Display the image in the current subplot
axs[row, col].imshow(img)
axs[row, col].axis("off")
# Adjust the spacing between subplots and display the figure
plt.subplots_adjust(wspace=0.05, hspace=0.05)
st.pyplot(fig)
def app():
# Set the page title and icon
st.set_page_config(page_title="Similar Images Search", page_icon=":mag:")
# Add a title to the app
st.title("Find Similar Images")
# Add a description to the app
st.markdown("This app allows you to search for similar images.")
# Allow the user to choose between "Browse a single image" and "Select from test dataset"
option = st.radio("Select an option:", ("Browse a single image", "Select from test dataset"))
# If the user selects "Browse a single image"
if option == "Browse a single image":
# Allow the user to upload an image
uploaded_file = st.file_uploader("Choose an image:", type=["jpg", "jpeg", "png"])
# If the user has uploaded an image
if uploaded_file is not None:
# Load the image
image = Image.open(uploaded_file).convert("RGB")
# Display the uploaded image
st.image(image, caption="Uploaded Image", use_column_width=True)
# Search for similar images
# print("Uploaded file ==> ",uploaded_file)
# Save the image to a temporary file
temp_file_path = "temp.jpg"
image.save(temp_file_path)
results = search_similar_images(temp_file_path)
# Display the results as a grid of images
st.markdown("---")
st.subheader("Similar Images")
display_results(results)
# If the user selects "Select from test dataset"
else:
# Get a list of all test dataset image files
test_dataset_dir = "images"
test_dataset_files = os.listdir(test_dataset_dir)
test_dataset_images = []
# Display the list of test dataset image files in a sidebar
st.sidebar.title("Test Dataset")
for test_dataset_file in test_dataset_files:
if test_dataset_file.endswith(".jpg") or test_dataset_file.endswith(".png"):
test_dataset_image_path = os.path.join(test_dataset_dir, test_dataset_file)
test_dataset_image = Image.open(test_dataset_image_path)
test_dataset_images.append(test_dataset_image)
# Display the file name as a text instead of an image
st.sidebar.write(test_dataset_file)
# Allow the user to select an image from the test dataset
selected_image_path = st.sidebar.selectbox("Select an image:", test_dataset_files)
# Load the selected image
selected_image_path = os.path.join(test_dataset_dir, selected_image_path)
selected_image = Image.open(selected_image_path)
# Display the selected image
st.image(selected_image, caption="Selected Image", use_column_width=True)
# Search for similar images
results = search_similar_images(selected_image_path)
# Display the results as a grid of images
if selected_image_path != os.path.join(test_dataset_dir, test_dataset_files[0]):
st.markdown("---")
st.subheader("Similar Images")
display_results(results)
# for result in results:
# st.image(result, caption="Similar Image", use_column_width=True)
# print("result==> ",result)
# Add a footer to the app
st.markdown("---")
st.markdown("Created by Pruthul")
app()
#### == Image feature extraction using Resnet == (Failed when run using streamlit) ###
# from sklearn.neighbors import NearestNeighbors
# from tensorflow.keras.preprocessing.image import load_img, img_to_array
# from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input
# def extract_features(image_path, model):
# # Load and preprocess the image
# image = load_img(image_path, target_size=(224, 224))
# image_array = img_to_array(image)
# image_array = preprocess_input(image_array)
# # Extract the features using the ResNet50 model
# features = model.predict(image_array.reshape(1, 224, 224, 3))
# # Flatten the features and return them as a 1D array
# features = features.flatten()
# return features
# # Define a function to search for similar images
# def search_similar_images(img_path, num_results=10):
# #load feature dictionary
# with open("features.npy", "rb") as f:
# image_dict = np.load(f, allow_pickle=True).item()
# # Fit a nearest neighbor model on the features
# nn_model = NearestNeighbors(n_neighbors=num_results, metric='cosine')
# # Convert the dictionary to a matrix of feature vectors
# features_list = np.array(list(image_dict.values()))
# # Fit the model to the feature matrix
# nn_model.fit(features_list)
# # Define the file path to the test image
# test_image_path = img_path
# # Load all images from the 'images' directory
# images_dir = "images"
# image_files = os.listdir(images_dir)
# images = []
# for image_file in image_files:
# if image_file.endswith(".jpg") or image_file.endswith(".png"):
# image_path = os.path.join(images_dir, image_file)
# image = cv2.imread(image_path)
# images.append(image)
# model = ResNet50(weights='imagenet', include_top=False)
# # Extract features from the test image
# test_image_features = extract_features(test_image_path, model)
# # Reshape the test image features to match the shape of the feature vectors
# test_image_features = test_image_features.reshape(1, -1)
# # # Find the 10 most similar images to the test image
# distances, indices = nn_model.kneighbors(test_image_features)
# # Create a list of the top num_results most similar images
# results = []
# for index in indices[0]:
# results.append(images[index])
# # Return the list of results
# return results