Part-4 done (Streamlit)

The image similarity performed here is done using cosine similarity.Including Resnet model and Tensorflow created problems while running on streamlit

app.py

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+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