Splitting the SecGuardID gps pathway

index.php

@@ -19,7 +19,7 @@
 
 include_once("GeoDBConnection.php");
 
-$query = "SELECT SecGuardID, Name FROM secguard";
+$query = "SELECT * FROM secguard";
 $result = $conn->query($query);
 
 ?>
@@ -46,34 +46,14 @@
         <!--<img src="" height="250" width="600" class="center">
         <img src="" class="center">-->
         <p>
-            - Select Security Guard's name to track pathway -
+            -  Security Guard's pathway  -
         </p><br>
 
-        <label for="guard">Select Security Guard:</label> 
-        <form action="" method="post">
-            <select required name="guard" id="guard"> 
-                <?php
-                // Check if there are any rows in the result set
-                if ($result->num_rows > 0) {
-                    // Loop through each row and generate an option element
-                    while ($row = $result->fetch_assoc()) {
-                        $id = $row['SecGuardID'];
-                        $name = $row['Name'];
-                        echo "<option value='$id'>$name</option>";
-                    }
-                } 
-                else {
-                    echo "<option value='' disabled>No options available</option>";
-                }
+
 
-                // Close the database connection
-                $conn->close();
-                ?>
-            </select>
-            <input type="submit" value="Submit">
-        </form>
         <p>
-           <!-- <img src="Images/plot.png" alt="Image" width="100%" height="80%"> -->
+          <img src="Images\plot_SecGuardID_1.png" alt="Image" width="100%" height="80%">
+          <img src="Images\plot_SecGuardID_2.png" alt="Image" width="100%" height="80%">
         </p>
         <button onclick="runSeer()"> Run Seer </button>
         <br><br>

python/seer2.py

@@ -1,18 +1,17 @@
 import csv
 import numpy as np
 import cartopy.crs as ccrs
-#%matplotlib
 import matplotlib.pyplot as plt
 import cartopy.io.img_tiles as cimgt
 from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
-import io,time
+import io
 from urllib.request import urlopen, Request
 from PIL import Image
 plt.ion()
 import pandas as pd
 from sqlalchemy import create_engine
-import sys  
-
+import sys
+import os
 
 # Replace the placeholder values with your actual database credentials
 servername = 'localhost'
@@ -23,82 +22,96 @@
 connection_string = f'mysql+mysqlconnector://root:@localhost:3306/geotrackdb'
 engine = create_engine(connection_string)
 qry = 'SELECT * FROM records;'
-df = pd.read_sql_query(qry,engine)
+df = pd.read_sql_query(qry, engine)
 
 # You can use the DataFrame columns to access specific data
 recordid = df['RecordID'].tolist()
-secguard = df['SecGuardID'].tolist()
+secguards = df['SecGuardID'].unique()  # Get unique SecGuardIDs
 date = df['RecordTime'].tolist()
 alts = df['Altitude'].tolist()
 lats = df['Latitude'].tolist()
 lons = df['Longitude'].tolist()
 floors = df['Floor'].tolist()
 
-
 def image_spoof(self, tile): 
-    api_url = self._image_url(tile) # get the url of the street map API
-    req = Request(api_url) # start request
-    req.add_header('User-agent','Anaconda 3') # add user agent to request
-    response = urlopen(req) #Open the URL and get the response
-    im_data = io.BytesIO(response.read()) # Get image data 
-    response.close() # Close the url
-    img = Image.open(im_data) # open image using PIL
+    api_url = self._image_url(tile)  # get the url of the street map API
+    req = Request(api_url)  # start request
+    req.add_header('User-agent', 'Anaconda 3')  # add user agent to request
+    response = urlopen(req)  # Open the URL and get the response
+    im_data = io.BytesIO(response.read())  # Get image data
+    response.close()  # Close the url
+    img = Image.open(im_data)  # open image using PIL
     img = img.convert(self.desired_tile_form)  # Convert the image format
-    return img, self.tileextent(tile), 'lower' # Return the image, tile extent, and vertical alignment
-
-
+    return img, self.tileextent(tile), 'lower'  # Return the image, tile extent, and vertical alignment
 
-cimgt.GoogleTiles.get_image = image_spoof # Spoof the web request for street map
-osm_img = cimgt.GoogleTiles() # Download the spoofed street map using the GoogleTiles class
+cimgt.GoogleTiles.get_image = image_spoof  # Spoof the web request for street map
+osm_img = cimgt.GoogleTiles()  # Download the spoofed street map using the GoogleTiles class
 lat_zoom = 0.0005  # Decreased zoom for latitude
 lon_zoom = 0.0025  # Decreased zoom for longitude
 
-if lons and lats:
-    extent = [np.min(lons) - lon_zoom, np.max(lons) + lon_zoom, np.min(lats) - lat_zoom, np.max(lats) + lat_zoom]
+# Create a directory for individual plots
+individual_plots_directory = 'Images/Individual_Plots'
+os.makedirs(individual_plots_directory, exist_ok=True)
+
+for secguard in secguards:
+    secguard_df = df[df['SecGuardID'] == secguard]
+
+    if lons and lats:
+        extent = [
+            np.min(secguard_df['Longitude']) - lon_zoom,
+            np.max(secguard_df['Longitude']) + lon_zoom,
+            np.min(secguard_df['Latitude']) - lat_zoom,
+            np.max(secguard_df['Latitude']) + lat_zoom
+        ]
+
+        fig, ax1 = plt.subplots(figsize=(14, 12), facecolor='#FCFCFC', subplot_kw={'projection': osm_img.crs})
+        ax1.set_title(f'GPS Tracker Map - SecGuardID {secguard}', fontsize=16)
+
+        # Set the extents of the map
+        ax1.set_extent(extent)
 
-    fig = plt.figure(figsize=(14, 12), facecolor='#FCFCFC')
-    ax1 = plt.axes(projection=osm_img.crs)
-    ax1.set_title('GPS Tracker Map', fontsize=16)
+        # Set longitude and latitude indicators
+        ax1.set_xticks(np.linspace(extent[0], extent[1], 7), crs=ccrs.PlateCarree())
+        ax1.set_yticks(np.linspace(extent[2], extent[3], 7)[1:], crs=ccrs.PlateCarree())
 
-    # Set the extents of the map
-    ax1.set_extent(extent)
+        # Format the longitude and latitude tick labels
+        lon_format = LongitudeFormatter(number_format='0.1f', degree_symbol='', dateline_direction_label=True)
+        lat_format = LatitudeFormatter(number_format='0.1f', degree_symbol='')
 
-    # Set longitude and latitude indicators
-    ax1.set_xticks(np.linspace(extent[0], extent[1], 7), crs=ccrs.PlateCarree())
-    ax1.set_yticks(np.linspace(extent[2], extent[3], 7)[1:], crs=ccrs.PlateCarree())
+        # Set the major formatters for longitude and latitude
+        ax1.xaxis.set_major_formatter(lon_format)
+        ax1.yaxis.set_major_formatter(lat_format)
 
-    # Format the longitude and latitude tick labels
-    lon_format = LongitudeFormatter(number_format='0.1f', degree_symbol='', dateline_direction_label=True)
-    lat_format = LatitudeFormatter(number_format='0.1f', degree_symbol='')
+        # Set tick label font sizes
+        ax1.xaxis.set_tick_params(labelsize=14)
+        ax1.yaxis.set_tick_params(labelsize=14)
 
-    # Set the major formatters for longitude and latitude
-    ax1.xaxis.set_major_formatter(lon_format)
-    ax1.yaxis.set_major_formatter(lat_format)
+        # Empirical calculation of scale based on zoom
+        scale = np.ceil(-np.sqrt(2) * np.log(np.divide((extent[1] - extent[0]) / 2.0, 350.0)))
 
-    # Set tick label font sizes
-    ax1.xaxis.set_tick_params(labelsize=14)
-    ax1.yaxis.set_tick_params(labelsize=14)
+        # Ensure scale does not exceed 19
+        scale = (scale < 20) and scale or 19
 
-    # Empirical calculation of scale based on zoom
-    scale = np.ceil(-np.sqrt(2) * np.log(np.divide((extent[1] - extent[0]) / 2.0, 350.0)))
+        # Add the spoofed OSM image to the map with the specified zoom level
+        ax1.add_image(osm_img, int(scale + 1))
 
-    # Ensure scale does not exceed 19
-    scale = (scale < 20) and scale or 19
+        for index in range(0, len(secguard_df['Longitude']), 5):
+            ax1.plot(secguard_df['Longitude'].iloc[index], secguard_df['Latitude'].iloc[index], markersize=10,
+                     marker='o', linestyle='', color='Black', transform=ccrs.PlateCarree(), label='GPS Point')  # plot points
 
-    # Add the spoofed OSM image to the map with the specified zoom level
-    ax1.add_image(osm_img, int(scale + 1))
+            annotation_text = f'Alt: {secguard_df["Altitude"].iloc[index]:.2f} meters\nFloor: {secguard_df["Floor"].iloc[index]}'
+            ax1.text(secguard_df['Longitude'].iloc[index], secguard_df['Latitude'].iloc[index], annotation_text,
+                     fontsize=10, color='red', transform=ccrs.PlateCarree(), ha='right', va='bottom')
 
+            plt.pause(0.1)
+        else:
+            print(f"Error: No data for SecGuardID {secguard}")
 
-for index in range(0, len(lons), 5):
-        ax1.plot(lons[index], lats[index], markersize=10, marker='o', linestyle='',
-                 color='Black', transform=ccrs.PlateCarree(), label='GPS Point')  # plot points
+        # Save individual plots
+        plt.savefig(f'{individual_plots_directory}/plot_SecGuardID_{secguard}.png')
+        plt.show()
 
-        annotation_text = f'Alt: {alts[index]:.2f} meters\nFloor: {floors[index]}'
-        ax1.text(lons[index], lats[index], annotation_text,
-                 fontsize=10, color='red', transform=ccrs.PlateCarree(), ha='right', va='bottom')
+    else:
+        print("Error: lons or lats list is empty.")
 
-        plt.pause(0.01)
-else:
-    print("Error: lons or lats list is empty.")
 
-plt.savefig('Images/plot.png')