Create app.py

app.py

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+import streamlit as st
+import cv2
+import folium
+import geopandas as gpd
+import pandas as pd
+from PIL import Image
+import numpy as np
+from streamlit_folium import st_folium
+
+def process_image(image_path):
+    # Read the image
+    img = cv2.imread(image_path)
+
+    # Apply image processing techniques (e.g., noise reduction, edge detection)
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    blur = cv2.GaussianBlur(gray, (5, 5), 0)
+    edges = cv2.Canny(blur, 100, 200)
+
+    # Dummy analysis results
+    results = {
+        'Topography': 'Mountainous',
+        'Forestry Pattern': 'Dense Forest',
+        'Ocean Pattern': 'Calm',
+        'Geological Features': 'Mountains, Rivers'
+    }
+
+    return img, edges, results
+
+def display_images(images):
+    # Generate captions for each pair of images
+    captions = []
+    for i in range(0, len(images), 2):
+        captions.append(f'Original Image {i//2 + 1}')
+        captions.append(f'Processed Image {i//2 + 1}')
+    
+    # Display images
+    st.image(images, caption=captions, width=300)
+
+def display_results(results):
+    st.write("### Analysis Results")
+    for key, value in results.items():
+        st.write(f"{key}: {value}")
+
+def export_results(results, export_format):
+    results_df = pd.DataFrame(results)
+    if export_format == "Excel (.xlsx)":
+        results_df.to_excel('analysis_results.xlsx', index=False)
+        st.success("Results exported to analysis_results.xlsx")
+    elif export_format == "CSV (.csv)":
+        results_df.to_csv('analysis_results.csv', index=False)
+        st.success("Results exported to analysis_results.csv")
+    elif export_format == "JSON (.json)":
+        results_df.to_json('analysis_results.json', orient='records')
+        st.success("Results exported to analysis_results.json")
+
+def create_map(location_name, latitude, longitude):
+    map = folium.Map(location=[latitude, longitude], zoom_start=10)
+    folium.Marker(location=[latitude, longitude], popup=location_name).add_to(map)
+    return map
+
+def analyze_location(location_name, latitude, longitude):
+    # Dummy function to gather information about the location
+    # In a real application, this would query a geospatial database or API
+    location_info = {
+        'Location Name': location_name,
+        'Latitude': latitude,
+        'Longitude': longitude,
+        'Environmental Impact': 'Moderate',
+        'Geographical Features': 'Mountains, Rivers',
+        'Agricultural Impact': 'High',
+        'Urbanisation Impact': 'Low'
+    }
+    return location_info
+
+# Streamlit app
+st.title("Drone Image Processing")
+
+# Input for location details
+location_name = st.text_input("Location Name")
+latitude_deg = st.number_input("Latitude (degrees)", format="%f")
+latitude_dir = st.selectbox("Latitude Direction", ["N", "S"])
+longitude_deg = st.number_input("Longitude (degrees)", format="%f")
+longitude_dir = st.selectbox("Longitude Direction", ["E", "W"])
+
+# Convert latitude and longitude to decimal degrees
+latitude = latitude_deg if latitude_dir == "N" else -latitude_deg
+longitude = longitude_deg if longitude_dir == "E" else -longitude_deg
+
+# Enter button to find and display the location on the map
+if st.button("Enter"):
+    map = create_map(location_name, latitude, longitude)
+    st_folium(map, width=700, height=500)
+    location_info = analyze_location(location_name, latitude, longitude)
+    st.write("### Location Information")
+    for key, value in location_info.items():
+        st.write(f"{key}: {value}")
+
+# File upload
+uploaded_files = st.file_uploader("Upload Images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
+
+if uploaded_files and location_name and latitude and longitude:
+    images = []
+    results_list = []
+
+    for uploaded_file in uploaded_files:
+        # Save the uploaded file to a temporary location
+        with open(f"temp_{uploaded_file.name}", "wb") as f:
+            f.write(uploaded_file.getbuffer())
+
+        # Process the image
+        original_img, processed_img, results = process_image(f"temp_{uploaded_file.name}")
+
+        # Append images and results
+        images.append(original_img)
+        images.append(processed_img)
+        results_list.append(results)
+
+    # Display images
+    display_images(images)
+    # Display results
+    from typing import Dict, List
+
+    combined_results: Dict[str, List[Dict[str, str]]] = {
+        'Geological Analysis': [],
+        'Agricultural and Environmental Analysis': [], 
+        'Urbanisation and Population Analysis': []
+    }
+    for i, results in enumerate(results_list):
+        st.write(f"### Results for Image {i+1}")
+        display_results(results)
+        combined_results['Geological Analysis'].append({
+            'Topography': results['Topography'],
+            'Geological Features': results['Geological Features']
+        })
+        combined_results['Agricultural and Environmental Analysis'].append({
+            'Forestry Pattern': results['Forestry Pattern'],
+            'Ocean Pattern': results['Ocean Pattern']
+        })
+        combined_results['Urbanisation and Population Analysis'].append({
+            'Urbanisation Impact': 'Not implemented'
+        })
+
+    # Export options
+    st.sidebar.subheader("Export Options")
+    export_format = st.sidebar.radio(
+        "Export Format",
+        ["Excel (.xlsx)", "CSV (.csv)", "JSON (.json)"]
+    )
+
+    # Export results button
+    if st.sidebar.button("Export Results"):
+        export_results(combined_results, export_format)
+
+    # Filter results
+    st.write("### Filter Results")
+    filter_option = st.selectbox("Select Analysis Type", ["Geological Analysis", "Agricultural and Environmental Analysis", "Urbanisation and Population Analysis"])
+
+    if filter_option == "Geological Analysis":
+        st.write("### Geological Analysis Results")
+        for i, results in enumerate(combined_results['Geological Analysis']):
+            st.write(f"### Results for Image {i+1}")
+            st.write(f"Topography: {results['Topography']}")
+            st.write(f"Geological Features: {results['Geological Features']}")
+
+    elif filter_option == "Agricultural and Environmental Analysis":
+        st.write("### Agricultural and Environmental Analysis Results")
+        for i, results in enumerate(combined_results['Agricultural and Environmental Analysis']):
+            st.write(f"### Results for Image {i+1}")
+            st.write(f"Forestry Pattern: {results['Forestry Pattern']}")
+            st.write(f"Ocean Pattern: {results['Ocean Pattern']}")
+
+    elif filter_option == "Urbanisation and Population Analysis":
+        st.write("### Urbanisation and Population Analysis Results")
+        for i, results in enumerate(combined_results['Urbanisation and Population Analysis']):
+            st.write(f"### Results for Image {i+1}")
+            st.write("Urbanisation and Population Analysis not implemented yet.")