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| import streamlit as st | |
| from PIL import Image | |
| import os | |
| from deepforest import main | |
| from deepforest import get_data | |
| import matplotlib.pyplot as plt | |
| # from predict import extract_features, predict_similarity, compare_features, extract_features_cp | |
| import os, re | |
| import streamlit as st | |
| import pandas as pd | |
| from PIL import Image | |
| import tempfile | |
| from inference import split_image_from_dataframe | |
| from datetime import datetime | |
| from predict_vit import extract_features, predict_similarity, compare_features, extract_features_cp | |
| from predict_copy import extract_features_with_augmentation, extract_features_with_augmentation_cp | |
| import rasterio | |
| import geopandas as gpd | |
| model = main.deepforest() | |
| model.use_release() | |
| # Set the page configuration | |
| st.set_page_config(page_title="Wise-Vision", page_icon=":deciduous_tree:") | |
| # Title and description | |
| st.title("🌳 Wise-Vision") | |
| st.subheader("AI + Environment Hackathon 2024") | |
| # Sidebar information | |
| st.sidebar.title("About") | |
| st.sidebar.info( | |
| """ | |
| This app is designed for the AI + Environment Hackathon 2024. | |
| Upload a panoramic image and specify a folder path to detect tree species in the image. | |
| Upload a word file to integrate knowledge into the image. | |
| Output will be a panoramic image with identified trees and knowledge symbols. | |
| """ | |
| ) | |
| st.sidebar.title("Contact") | |
| st.sidebar.info( | |
| """ | |
| For more information, contact us at: | |
| [[email protected]] | |
| """ | |
| ) | |
| script_dir = os.path.dirname(os.path.abspath(__file__)) | |
| # Create a new folder within the script directory for storing cropped images | |
| timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') | |
| output_folder_name = f"output_{timestamp}" | |
| output_image_folder = os.path.join(script_dir, output_folder_name) | |
| os.makedirs(output_image_folder, exist_ok=True) | |
| output_image_folder = os.path.abspath(output_image_folder) | |
| # Define paths for the image and Excel file within the new folder | |
| cropped_image_path = os.path.join(output_image_folder, f"panoramic_{timestamp}.png") | |
| excel_output_path = os.path.join(output_image_folder, f"results_{timestamp}.xlsx") | |
| # Input: Upload panoramic image | |
| uploaded_image = st.file_uploader("Upload a panoramic image", type=['png', 'jpeg', 'JPG']) | |
| # Input: Folder path for tree species detection | |
| def extract_treespecies_features(folder_path): | |
| image_files = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith(('png', 'jpg', 'jpeg', '.JPG'))] | |
| species_feature_list = [{"feature": extract_features_with_augmentation(file), "file_name": file} for file in image_files] | |
| return species_feature_list | |
| # print(species_feature_list[:2]) | |
| def perform_inference(cropped_images, species_feature_list, img_df): | |
| st.success("Setting up OPENAI Client:") | |
| client = setup_client() | |
| st.success("Setting up knowledge database & BM25 retriever:") | |
| # retriever = setup_retriever() | |
| st.success("Setting up BM25 Retriever:") | |
| for img_idx, item in enumerate(cropped_images): | |
| image = item["image"] | |
| feature_cp = extract_features_with_augmentation_cp(image) | |
| row_results = [] | |
| species_result = [] | |
| emoji = [] | |
| species_context = [] | |
| for idx, species in enumerate(species_feature_list): | |
| # euclidean_dist, cos_sim = compare_features(feature_cp, species["feature"]) | |
| # print(f'Euclidean Distance: {euclidean_dist}') | |
| # print(f'Cosine Similarity: {cos_sim}') | |
| # Predict similarity | |
| is_similar = predict_similarity(feature_cp, species["feature"], threshold=0.92) | |
| # print(species) | |
| # print(f'Are the images similar? {"Yes" if is_similar else "No"}') | |
| result = "Yes" if is_similar else "No" | |
| if result == "Yes": | |
| item[f"result_{idx}"] = result | |
| item[f"file_name_{idx}"] = species["file_name"] | |
| row_results.append(species["file_name"]) | |
| # Regular expression to match the tree species name | |
| species_pattern = r'identified_species\\([^\\]+) -' | |
| # Search for the pattern in the file path | |
| match = re.search(species_pattern, species["file_name"]) | |
| # Extract and print the tree species name if found | |
| if match: | |
| tree_species = match.group(1) | |
| # species_info = retriever.invoke(f"Scientific name:{tree_species}") | |
| # ans = generate_image(species_info, client) | |
| # emoji.append(ans) | |
| # text_context = [doc.page_content for doc in species_info] | |
| # text_context = ", ".join(text_context) | |
| # species_context.append(text_context) | |
| # print(ans) | |
| # species_result.append(tree_species) | |
| else: | |
| print("Tree species name not found.") | |
| img_df.at[img_idx, "species_identified"] = ", ".join(species_result) if species_result else "No similar species found" | |
| img_df.at[img_idx, "result_file_path"] = ", ".join(row_results) if row_results else "" | |
| # img_df.at[img_idx, "emoji"] = ", ".join(emoji) if emoji else "" | |
| # img_df.at[img_idx, "retreived context"] = ", ".join(species_context) if species_context else "" | |
| return cropped_images | |
| # Function to simulate tree species detection | |
| # Display uploaded image and detected tree species | |
| if uploaded_image is not None: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix='.JPG') as temp_file: | |
| temp_file.write(uploaded_image.read()) | |
| temp_file_path = temp_file.name | |
| # Open and display the image | |
| # image = Image.open(uploaded_image) | |
| sample_image_path = get_data(temp_file_path) | |
| boxes = model.predict_image(path=sample_image_path, return_plot=False) | |
| img_actual = model.predict_image(path=sample_image_path, return_plot=True, color=(137, 0, 0), thickness=9) | |
| st.image(img_actual, caption='Segmented Panoramic Image', channels ='RGB', use_column_width=True) | |
| st.success("Sample Dataframe:") | |
| st.dataframe(boxes.head()) | |
| plt.imshow(img_actual[:,:,::-1]) | |
| # plt.show(img[:,:,::-1]) | |
| plt.savefig(cropped_image_path) | |
| # if st.button("Next Step"): | |
| accuracy_threshold = st.slider("Accuracy threshold for cropping images:",min_value=0.1, max_value=1.0, value=0.4) | |
| images_list = split_image_from_dataframe(boxes, temp_file_path, output_folder_name) | |
| image_width = 200 | |
| st.success("Sample Images:") | |
| # Display the images in a row | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.image(images_list[3]["image"], caption="Sample 1", width=image_width) | |
| with col2: | |
| st.image(images_list[4]["image"], caption="Sample 2", width=image_width) | |
| with col3: | |
| st.image(images_list[5]["image"], caption="Sample 3", width=image_width) | |
| folder_path = 'D:/Downloads/image/plant_images/plant_images/drone_igapo_flooded_forest/identified_species' | |
| species_feature_list = extract_treespecies_features(folder_path) | |
| final_result = perform_inference(images_list, species_feature_list, boxes) | |
| st.success("Final Data:") | |
| st.dataframe(boxes) | |
| boxes.to_excel(excel_output_path) | |
| for index, row in boxes.iterrows(): | |
| species_identified = row['species_identified'] | |
| if species_identified !="No similar species found": | |
| cropped_image_path = row['cropped_image_path'] | |
| result_file_path = row['result_file_path'] | |
| if type(result_file_path) == list: | |
| result_file_path = result_file_path[0] | |
| result_file_path = result_file_path.split(',')[0] | |
| st.write(species_identified) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.image(cropped_image_path, caption='Cropped Image') | |
| with col2: | |
| st.image(result_file_path, caption='Species Match') | |
| # Detect tree species | |
| # detected_species = detect_tree_species(image, folder_path) | |
| # Display detected tree species | |
| # st.write("### Detected Tree Species:") | |
| # for species in detected_species: | |
| # st.write(f"- {species}") | |