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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,93 @@ | ||
| import os | ||
| import geopandas as gpd | ||
| from deepforest import utilities | ||
| import pandas as pd | ||
| import rasterio | ||
| from PIL import Image | ||
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| # Directory containing the shapefiles and tif files | ||
| directory = "/orange/ewhite/DeepForest/Alejandro_Chile/alejandro" | ||
|
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| # List to store all the GeoDataFrames | ||
| gdfs = [] | ||
|
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| # Iterate over all files in the directory | ||
| for filename in os.listdir(directory): | ||
| if filename.endswith(".shp"): | ||
| # Load the shapefile | ||
| shapefile_path = os.path.join(directory, filename) | ||
| gdf = gpd.read_file(shapefile_path) | ||
|
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| # Extract the base name (without extension) | ||
| base_name = os.path.splitext(filename)[0].lower() | ||
|
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| # Construct the corresponding tif file name | ||
| tif_filename = f"mos_{base_name}.tif" | ||
| tif_path = os.path.join(directory, tif_filename) | ||
|
|
||
| # Check if the corresponding tif file exists | ||
| if os.path.exists(tif_path): | ||
| # Append the GeoDataFrame to the list | ||
| gdf["image_path"] = os.path.basename(tif_path) | ||
| # remove multi-polygons | ||
| gdf = gdf[~gdf.geometry.type.isin(["MultiPolygon"])] | ||
| gdf["label"] = "Tree" | ||
| image_annotations = utilities.read_file(gdf, root_dir=directory) | ||
| # Just keep image_path, geometry, label columns | ||
| image_annotations = image_annotations[["image_path", "geometry", "label"]] | ||
| gdfs.append(image_annotations) | ||
| else: | ||
| print(f"Corresponding tif file for {filename} not found.") | ||
|
|
||
| # Concatenate all GeoDataFrames | ||
| all_annotations = gpd.GeoDataFrame(pd.concat(gdfs, ignore_index=True)) | ||
|
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||
| # Remove any annotations with negative coordinates | ||
| # Remove any annotations with negative coordinates | ||
| all_annotations = all_annotations[all_annotations.geometry.apply(lambda geom: geom.bounds[0] >= 0 and geom.bounds[1] >= 0)] | ||
|
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| # Save the concatenated annotations to a CSV file | ||
| annotations_csv_path = os.path.join(directory, "annotations.csv") | ||
| # Ensure the output directory exists | ||
| output_directory = os.path.join(directory, "png_images") | ||
| os.makedirs(output_directory, exist_ok=True) | ||
|
|
||
| # Process each unique image path | ||
| unique_image_paths = all_annotations["image_path"].unique() | ||
| for image_path in unique_image_paths: | ||
| # Open the tif file | ||
| with rasterio.open(os.path.join(directory,image_path)) as src: | ||
| # Read the data matrix | ||
| data = src.read() | ||
| png_path = os.path.join(output_directory, os.path.basename(image_path).replace(".tif", ".png")) | ||
| # Normalize the bands to 0-255 | ||
| bands = data.astype('float32') | ||
| for i in range(bands.shape[0]): | ||
| band_min, band_max = bands[i].min(), bands[i].max() | ||
| bands[i] = 255 * (bands[i] - band_min) / (band_max - band_min) | ||
| bands = bands.astype('uint8') | ||
|
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||
| transform = src.transform | ||
|
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||
| # Save the normalized image as PNG | ||
| with rasterio.open( | ||
| png_path, | ||
| 'w', | ||
| driver='PNG', | ||
| height=src.height, | ||
| width=src.width, | ||
| count=3, | ||
| dtype='uint8', | ||
| transform=transform, | ||
| ) as dst: | ||
| dst.write(bands[0], 1) | ||
| dst.write(bands[1], 2) | ||
| dst.write(bands[2], 3) | ||
|
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| # Replace .tif with .png for the image_paths | ||
| all_annotations["image_path"] = all_annotations["image_path"].str.replace(".tif", ".png") | ||
|
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||
| # Make full image_path | ||
| all_annotations["image_path"] = output_directory + "/" + all_annotations["image_path"] | ||
| all_annotations["source"] = "Alejandro_Miranda" | ||
| all_annotations.to_csv("/orange/ewhite/DeepForest/Alejandro_Chile/alejandro/annotations.csv", index=False) |
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