change: made about laz file to a function and set it to the database as accuracy for now

server/data_processing/process_lidar_data.py

@@ -9,22 +9,24 @@ from server.data_processing.area_processing import calculate_corners, define_gri
 lazData_path = ["server/example_lidar_data/ot_N_000005_1.laz", "server/example_lidar_data/ot_N_000033_1.laz"]
 
 # Info about data
-with laspy.open(lazData_path[0]) as fh:
-    # Print metadata properties
-    print("File Version:", fh.header.version)
-    print("Point Count:", fh.header.point_count)
-    print("Scale Factors:", fh.header.scale)
-    print("Offset:", fh.header.offset)
-
-    print('Points from Header:', fh.header.point_count)
-    las = fh.read()
-    print(las)
-    print('Points from data:', len(las.points))
-    ground_pts = las.classification == 2
-    bins, counts = np.unique(las.return_number[ground_pts], return_counts=True)
-    print('Ground Point Return Number distribution:')
-    for r, c in zip(bins, counts):
-        print('    {}:{}'.format(r, c))
+def about_laz_file():
+    with laspy.open(lazData_path[0]) as fh:
+        # Print metadata properties
+        print("File Version:", fh.header.version)
+        print("Point Count:", fh.header.point_count)
+        print("Scale Factors:", fh.header.scale)
+        print("Offset:", fh.header.offset)
+
+        las = fh.read()
+        print(las)
+        print('Points from data:', len(las.points))
+        ground_pts = las.classification == 2
+        bins, counts = np.unique(las.return_number[ground_pts], return_counts=True)
+        print('Ground Point Return Number distribution:')
+        for r, c in zip(bins, counts):
+            print('    {}:{}'.format(r, c))
+
+    return [las.header.version, las.header.point_count, las.header.scale, las.header.offset]
 
 # check if lidar points is within range of the area selected
 def inArea(position, areaRange):

server/map/input_new_data.py

 import json
 from datetime import datetime
-from server.data_processing.process_lidar_data import calculate_area_data
+from server.data_processing.process_lidar_data import calculate_area_data, about_laz_file
 
 # input_new_Lidar_data send new data gathered from the lidar and send it to the database (from the drone, most likely)
 def input_new_Lidar_data(self, cursor, sensorId, bodyOfWater):
@@ -11,13 +11,17 @@ def input_new_Lidar_data(self, cursor, sensorId, bodyOfWater):
 
         total_measurement_average = 0  # the total average of a measurement
 
+        # data about the file read from
+        about_laz = about_laz_file()
+        scale_factor = max(about_laz[2])
+
         # create a new measurement with the time the data is sent, sensor type, where
         # and an estimate of average thickness of ice on water body
         cursor.execute('''
             INSERT INTO Measurement(  SensorID, TimeMeasured, WaterBodyName, 
                                         WholeAverageThickness, CenterLat, CenterLon) VALUES 
                 (?,?,?,?,?,?);
-        ''', (sensorId, datetime.utcnow().replace(microsecond=0), bodyOfWater, 0, latitude, longitude))
+        ''', (sensorId, datetime.utcnow().replace(microsecond=0), bodyOfWater, 0, latitude, longitude, scale_factor))
 
         # auto generate new measurement id
         measurement_id = cursor.lastrowid