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detectree2/predictions/predict.ipynb

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 version https://git-lfs.github.com/spec/v1
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+size 4749

detectree2/predictions/predict.py

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+
+# REQUIREMENTS
+"""
+!python -m pip -q install torchvision torch
+!python -m pip -q install rasterio
+!python -m pip -q install git+https://github.com/PatBall1/detectree2.git # in order for this to work, you must have installed gdal
+!python -m pip install opencv-python
+!python -m pip install requests
+"""
+from detectree2.preprocessing.tiling import tile_data
+from detectree2.models.outputs import project_to_geojson, stitch_crowns, clean_crowns
+from detectree2.models.predict import predict_on_data
+from detectree2.models.train import setup_cfg
+from detectron2.engine import DefaultPredictor
+import rasterio
+import os
+import requests
+
+#Somehow this tiles_path where the tilings are stored, only works if the absolute path is provided
+#Do not use relative path
+
+#Make sure that tiles_path ends with '/' otherwise the predict_on_data() will not work later
+
+def create_tiles(input_path, tile_width, tile_height, tile_buffer):
+    img_path = input_path
+    
+    current_directory = os.getcwd()
+    tiles_directory = os.path.join(current_directory, "tiles")
+    if not os.path.exists(tiles_directory):
+        os.makedirs(tiles_directory)
+
+    data = rasterio.open(img_path)
+
+    buffer = tile_buffer
+    tile_width = tile_width
+    tile_height = tile_height
+    tile_data(data, tiles_directory, buffer, tile_width, tile_height, dtype_bool = True)
+
+    return tiles_directory
+
+def download_file(url, local_filename):
+    with requests.get(url, stream=True) as r:
+        r.raise_for_status()
+        with open(local_filename, 'wb') as f:
+            for chunk in r.iter_content(chunk_size=8192): 
+                f.write(chunk)
+    return local_filename
+
+def predict(tile_path, overlap_threshold, confidence_threshold, simplify_value, store_path):
+    url = "https://zenodo.org/records/10522461/files/230103_randresize_full.pth"
+    trained_model = "./230103_randresize_full.pth"
+    
+    download_file(url=url, local_filename=trained_model)
+
+    cfg = setup_cfg(update_model=trained_model)
+    #cfg.MODEL.DEVICE = "cpu"
+    predict_on_data(tile_path, predictor=DefaultPredictor(cfg))
+
+    project_to_geojson(tile_path, tile_path + "predictions/", tile_path + "predictions_geo/")
+    crowns = stitch_crowns(tile_path + "predictions_geo/", 1)
+    clean = clean_crowns(crowns, overlap_threshold, confidence=confidence_threshold)
+    clean = clean.set_geometry(clean.simplify(simplify_value))
+    clean.to_file(store_path + "predicted_delineations.geojson")
+
+def run_detectree2(tif_input_path, tile_width=20, tile_height=20, tile_buffer=20, overlap_threshold=0.35, confidence_threshold=0.2, simplify_value=0.2, store_path='./train_outputs/'):
+    tile_path = create_tiles(input_path=tif_input_path, tile_width=tile_width, tile_height=tile_height, tile_buffer=tile_buffer)
+    predict(tile_path=tile_path, overlap_threshold=overlap_threshold, confidence_threshold=confidence_threshold, simplify_value=simplify_value, store_path=store_path)
+
+run_detectree2(tif_input_path='/Users/jonathanseele/ETH/Hackathons/EcoHackathon/input_dataset/GeoData/TreeCrownVectorDataset.tif')