Datasets:
File size: 2,755 Bytes
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Convert shapefile annotations to a format suitable for training a deep learning model.
'''
#==============================================================================
#%% IMPORTS
#==============================================================================
from pathlib import Path
import geopandas as gpd
from shapely.geometry import Point
from shapely.geometry import box
import rasterio
import json
import numpy as np
import cv2
from tqdm.auto import tqdm
#==============================================================================
#%% CONFIGURATION
#==============================================================================
KEY_ID = "BRA_SP"
SHAPEFILE_PATH = Path('/home/sabrina/Documents/Tese/00_Dados/01_Keypoints_SHP/pontos_liberdadefinal.shp')
IMG_DIR = Path(f'/home/sabrina/Documents/Tese/05_Dataset/MatchGeo-DEM-v1/data/{KEY_ID}/tiles')
ANNO_DIR = Path(f'/home/sabrina/Documents/Tese/05_Dataset/MatchGeo-DEM-v1/data/{KEY_ID}/annotation')
ANNO_DIR.mkdir(exist_ok=True)
#==============================================================================
#%% CONVERT SHAPEFILE TO JSON ANNOTATIONS
#==============================================================================
gdf = gpd.read_file(SHAPEFILE_PATH)
img_files = sorted(IMG_DIR.glob('*.tif'))
print(f"Found {len(img_files)} images to process.")
for img_file in tqdm(img_files, desc="Processing images"):
with rasterio.open(img_file) as src:
profile = src.profile
transform = src.transform
# Check if there are point inf the gdf inside the image bounds
img_bounds = rasterio.transform.array_bounds(profile['height'], profile['width'], transform)
img_poly = box(*img_bounds)
if not gdf.intersects(img_poly).any():
print(f"No keypoints found in {img_file.stem}, skipping.")
continue
# Intersect the GeoDataFrame with the image bounds to get only relevant keypoints
gdf_img = gdf[gdf.intersects(img_poly)]
# Convert to pixel coordinates
keypoints = []
for idx, row in gdf_img.iterrows():
geom = row.geometry
if isinstance(geom, Point):
x, y = geom.x, geom.y
# Convert to pixel coordinates
col, row = ~transform * (x, y)
keypoints.append((int(col)/profile['width'], int(row)/profile['height'])) # Normalize to [0, 1]
# Create annotation dictionary
anno = {
"image": img_file.name,
"keypoints": keypoints
}
# Save annotation as JSON
anno_file = ANNO_DIR / f"{img_file.stem}.json"
with open(anno_file, 'w') as f:
json.dump(anno, f, indent=4)
# %%
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