I am mapping a lidar point cloud to 2d image. I have successfully mapped the point cloud to 2d image but there is a shift of 0.5 meter to 1meter in the 2d image from the lidar point cloud when overlaped. This overlapping is important for me as i will work with polygon which is created by the lidar data, The polygon and the image also doesnot overlap properly. The extent of the 2d image is of the lidar data. I cannot figure it out why there is a small misalignment even though they are in the same coordinate system and the image have the same extent as the lidar data.
Here is the data i am working on.
https://drive.google.com/drive/folders/1cBNDJmoh8i3Sb4AoU5FLDqPXjy7OYfaW?usp=sharing
Here is the code that i have tired.
import laspyimport rasterioimport numpy as npfrom PIL import Imagefrom scipy.interpolate import griddataimport matplotlib.pyplot as pltimport geopandas as gpdclass SingleTree: def __init__(self, las_data): self.points = np.vstack((las_data.x, las_data.y, las_data.z, las_data.red, las_data.green, las_data.blue)).T def point_cloud_to_image(self, output_size): # Extract x, y, and color values x = self.points[:, 0] y = self.points[:, 1] red = self.points[:, 3] green = self.points[:, 4] blue = self.points[:, 5] # Normalize coordinates to range [0, 1] x_normalized = (x - x.min()) / (x.max() - x.min()) y_normalized = (y - y.min()) / (y.max() - y.min()) # Scale normalized coordinates to image size, ensuring they are within the bounds x_img = np.clip((x_normalized * (output_size[1] - 1)).astype(int), 0, output_size[1] - 1) y_img = np.clip((y_normalized * (output_size[0] - 1)).astype(int), 0, output_size[0] - 1) # Convert color values to 8-bit r = np.interp(red, (red.min(), red.max()), (0, 255)).astype(np.uint8) g = np.interp(green, (green.min(), green.max()), (0, 255)).astype(np.uint8) b = np.interp(blue, (blue.min(), blue.max()), (0, 255)).astype(np.uint8) # Initialize an empty image img = np.zeros(output_size + (3,), dtype=np.uint8) # Assign colors to image pixels safely for xi, yi, ri, gi, bi in zip(x_img, y_img, r, g, b): img[yi, xi] = [ri, gi, bi] return img def polygon_bound(self, poly_path): # Load the polygon data gdf = gpd.read_file(poly_path) # Assuming you're interested in the first polygon if there are multiple polygon = gdf.geometry[0] # Get the bounding box of the polygon minx, miny, maxx, maxy = polygon.bounds polygon_width = maxx - minx polygon_height = maxy - miny # The top-left corner coordinates top_left_x = minx top_left_y = miny return top_left_x, top_left_y, polygon_width, polygon_heightimport osfrom rasterio.transform import from_origin# Assuming 'path' is the directory containing your '.las' filespath = r"lasdata path"output_path = r"path"number_of_files = len([name for name in os.listdir("directory of lasdata")])discarded_lasdata = []# Loop through your LAS filesfor i in range(1, number_of_files): file = f"tree_i{i}.las" poly_file = "polygons\\poly{}.shp".format(i) image_path = os.path.join(path, file) # Read the LAS file las_data = laspy.read(image_path) # Process the point cloud to generate an image tree = SingleTree(las_data) top_left_x, top_left_y, polygon_width, polygon_height = tree.polygon_bound(poly_file) # Get metadata from LAS file for georeferencing scale = las_data.header.scale offset = las_data.header.offset # Assuming las_data is a LasData object from laspy min_x, min_y, min_z = las_data.header.min max_x, max_y, max_z = las_data.header.max lidar_width = round((max_x - min_x), 0) lidar_height = round((max_y - min_y), 0) if (lidar_width <= 0 or lidar_height <= 0): discarded_lasdata.append(i) continue ltop_left_x = min_x ltop_left_y = min_y resolution = 0.5 image_width_pixels = int((lidar_width * 2 + 5) / resolution) image_height_pixels = int((lidar_height * 2 + 5) / resolution) img = tree.point_cloud_to_image((int(lidar_height*2), int(lidar_width*2))) # Create the geotransform transform = from_origin(ltop_left_x, ltop_left_y, resolution, -resolution) # Define the CRS (coordinate reference system) crs = "EPSG:25832" # Replace with the correct EPSG code for your data # Define the rasterio metadata dictionary meta = {'driver': 'GTiff','dtype': 'uint8','nodata': None,'width': img.shape[1],'height': img.shape[0],'count': 3,'crs': crs,'transform': transform,'compress': 'lzw' } output_filename = os.path.join(output_path, f"lidar_test{i}.tif") # Write the image data and metadata to a GeoTIFF with rasterio.open(output_filename, 'w', **meta) as dst: for k in range(img.shape[2]): dst.write(img[:, :, k], k+1)print("GeoTIFFs have been saved.")You can see that the polygon doesnot properly overlap with image whereas all the lidar point fall inside the polygon
Edit: Upon second look i found that the extent of the lidar data and image is not same and is off by 0 to 1 meter. I think this is because the lidar data width and height is in floating point and we pass an integer as the width and height.But i cannot still figure the solution