from pathlib import Path
import zipfile
from tqdm import tqdm
import json
import pdal
import time

#======================================================
#%%

KEY_ID = "IRN_JJ"
dirlaz = Path("/home/sabrina/Documents/Datasets/IRN_JJ")
OUT_RESOLUTION = 1.5

#======================================================
#%%
dirdem = Path(dirlaz, "dem")
dirdem.mkdir(exist_ok=True, parents=True)

filelaz = list(dirlaz.glob("*.laz"))
print(f"Found {len(filelaz)} LAZ files.")


#======================================================
def laz_to_dem(key_id, input_laz: Path, output_tif: Path, resolution=1.0):
    """
    Convert a single LAZ file to DEM using PDAL.
    """

    if key_id == "KAZ-AC" :
        pipeline = [ # PLEIADES DATA DO NOT USE SIMPLE MORPHOLOGICAL FILTER (SMRF)
            {
                "type": "readers.las",
                "filename": str(input_laz),
                "spatialreference": "EPSG:32643"
            },
            {
                "type": "writers.gdal",
                "filename": str(output_tif),
                "resolution": resolution,
                "output_type": "max",      
                "data_type": "float32",
                "nodata": -9999,
                "gdalopts": "COMPRESS=DEFLATE|TILED=YES"
            }
        ]
    elif key_id == 'BRA-SP':
        pipeline = [ # AIRBORNE DATA USE SMRF
            {
                "type": "readers.las",
                "filename": str(input_laz)
            },
            {
                "type": "filters.smrf",
                "scalar": 1.25,
                "slope": 0.15,
                "threshold": 0.5,
                "window": 16.0
            },
            {
                "type": "writers.gdal",
                "filename": str(output_tif),
                "resolution": resolution,
                "output_type": "max",       # highest surface elevation per pixel
                "data_type": "float32",
                "nodata": -9999
            }
        ]

    elif key_id == 'CHN-YG':
           pipeline = [
        {
            "type": "readers.las",
            "filename": str(input_laz),
        },
        {
            "type": "filters.range",
            "limits": "Classification![7:7]"
        },
        {
            "type": "filters.outlier",
            # Optional: SfM point clouds often contain isolated spurious points
            # above/below the surface that are not flagged as Class 7.
            # This applies a statistical filter (radius 1.0 m, 6 neighbours).
            "method": "statistical",
            "mean_k": 6,
            "multiplier": 2.0
        },
        {
            "type": "writers.gdal",
            "filename": str(output_tif),
            "resolution": resolution,
            "output_type": "max",          # DSM: highest point per cell
            "data_type": "float32",
            "nodata": -9999,
            "gdalopts": "COMPRESS=DEFLATE|TILED=YES|BIGTIFF=YES",
            "override_srs": "EPSG:32648"
        }
        ]
    elif key_id == 'IRN_JJ':
            pipeline = [
        {
            "type": "readers.las",
            "filename": str(input_laz),
        },
        {
            "type": "filters.assign",
            # The metadata shows Class 0 only (Created, never classified).
            # No noise class exists, so we skip filters.range.
            # This filter is a no-op placeholder for clarity.
            "assignment": "Classification[:]=0"
        },
        {
            "type": "filters.outlier",
            "method": "statistical",
            "mean_k": 6,
            "multiplier": 2.0
        },
        {
            "type": "writers.gdal",
            "filename": str(output_tif),
            "resolution": resolution,
            "output_type": "max",          # DSM: highest point per cell
            "data_type": "float32",
            "nodata": -9999,
            "gdalopts": "COMPRESS=DEFLATE|TILED=YES|BIGTIFF=YES",
        }
        ]
    else:
         print("Worng key id")
         quit

    p = pdal.Pipeline(json.dumps(pipeline))
    p.execute()

#======================================================
def batch_laz_to_dem(input_dir, output_dir, key_id, resolution=1.0):
    input_dir = Path(input_dir)
    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    laz_files = list(input_dir.glob("*.laz")) + list(input_dir.glob("*.las"))

    

    for laz in tqdm(laz_files):
        out_tif = output_dir / f"{laz.stem}.tif"

        if Path(out_tif).exists == True:
            print("File exists")
            continue

        else:
            print(f"Processing: {laz.name}")
            try:
                laz_to_dem(input_laz=laz, output_tif= out_tif, resolution=resolution, key_id= key_id)
            except Exception as e:
                print(f"Error processing {laz.name}: {e}")


#======================================================
batch_laz_to_dem(input_dir = dirlaz,
                output_dir =  dirdem,
                key_id = KEY_ID,
                resolution = OUT_RESOLUTION)