added convert script

.gitignore

@@ -12,4 +12,5 @@ __pycache__
 error.txt
 sanity_check.py
 commands.txt
+raw_data/npz_540p_2/
 

README.md

@@ -55,7 +55,25 @@ VRE_DEVICE=cuda CUDA_VISIBLE_DEVICES=1 vre raw_data/videos/slanic_DJI_0956_0957_
 
 ### 1.2.4 Convert Mask2Former from Mapillary classes to segprop8 classes
 
-TODO
+Since we are using pre-trained Mask2Former which has either mapillary or COCO panoptic classes, we need to convert them to dronescapes-compatible (8) classes.
+
+To do this, we use the `scripts/convert_m2f_to_dronescapes.py` script:
+```
+python scripts/convert_m2f_to_dronescapes.py in_dir out_dir mapillary/coco [--overwrite]
+```
+
+```
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/atanasie_DJI_0652_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/atanasie_DJI_0652_full/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/barsana_DJI_0500_0501_combined_sliced_2700_14700/semantic_mask2former_swin_mapillary raw_data/npz_540p/barsana_DJI_0500_0501_combined_sliced_2700_14700/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/comana_DJI_0881_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/comana_DJI_0881_full/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/semantic_mask2former_swin_mapillary raw_data/npz_540p/gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/herculane_DJI_0021_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/herculane_DJI_0021_full/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/semantic_mask2former_swin_mapillary raw_data/npz_540p/jupiter_DJI_0703_0704_0705_combined_sliced_10650_21715/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/norway_210821_DJI_0015_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/norway_210821_DJI_0015_full/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/olanesti_DJI_0416_full/semantic_mask2former_swin_mapillary raw_data/npz_540p/olanesti_DJI_0416_full/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/petrova_DJI_0525_0526_combined_sliced_2850_11850/semantic_mask2former_swin_mapillary raw_data/npz_540p/petrova_DJI_0525_0526_combined_sliced_2850_11850/semantic_mask2former_swin_mapillary_converted mapillary
+python scripts/convert_m2f_to_dronescapes.py raw_data/npz_540p/slanic_DJI_0956_0957_combined_sliced_780_9780/semantic_mask2former_swin_mapillary raw_data/npz_540p/slanic_DJI_0956_0957_combined_sliced_780_9780/semantic_mask2former_swin_mapillary_converted mapillary
+```
 
 ### 1.2.5 Check counts for consistency
 
@@ -198,5 +216,5 @@ python scripts/dronescapes_viewer.py data/test_set_annotated_only/ # or any of t
 
 ## TODOs
 - convert camera normals to world normals
-- mask2former convert
 - add semantics for each representation in a DronescapesReader
+- evaluation script for sseg

scripts/convert_m2f_to_dronescapes.py

@@ -0,0 +1,79 @@
+"""script that converts predictions (.npz) of mask2former (mapillary or coco_panoptic) to dronescapes labels"""
+from argparse import ArgumentParser, Namespace
+from pathlib import Path
+import shutil
+from functools import partial
+import numpy as np
+from tqdm import tqdm
+from loguru import logger
+
+COCO_MAPPING = {
+    "land": ["grass-merged", "dirt-merged", "sand", "gravel", "flower", "playingfield", "snow", "platform"], 
+    "forest": ["tree-merged"], 
+    "residential": ["building-other-merged", "house", "roof", "fence-merged", "wall-other-merged", "wall-brick", "rock-merged", "tent", "bridge", "bench", "window-other", "fire hydrant", "traffic light", "umbrella", "wall-stone", "clock", "chair", "sports ball", 
+    "floor-other-merged", "floor-wood", "stop sign", "door-stuff", "banner", "light", "net", "surfboard", "frisbee", "rug-merged", "potted plant", "parking meter"], 
+    "road": ["road", "railroad", "pavement-merged", "stairs"], 
+    "little-objects": ["truck", "car", "boat", "horse", "person", "train", "elephant", "bus", "bird", "sheep", "cow", "motorcycle", "dog", "bicycle", "airplane", "kite"], 
+    "water": ["river", "water-other", "sea"], 
+    "sky": ["sky-other-merged"], 
+    "hill": ["mountain-merged"]
+}
+
+COCO_CLASSES = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "banner", "blanket", "bridge", "cardboard", "counter", "curtain", "door-stuff", "floor-wood", "flower", "fruit", "gravel", "house", "light", "mirror-stuff", "net", "pillow", "platform", "playingfield", "railroad", "river", "road", "roof", "sand", "sea", "shelf", "snow", "stairs", "tent", "towel", "wall-brick", "wall-stone", "wall-tile", "wall-wood", "water-other", "window-blind", "window-other", "tree-merged", "fence-merged", "ceiling-merged", "sky-other-merged", "cabinet-merged", "table-merged", "floor-other-merged", "pavement-merged", "mountain-merged", "grass-merged", "dirt-merged", "paper-merged", "food-other-merged", "building-other-merged", "rock-merged", "wall-other-merged", "rug-merged"]
+
+MAPILLARY_MAPPING = {
+    "land": ["Terrain", "Sand", "Snow"], 
+    "forest": ["Vegetation"], 
+    "residential": ["Building", "Utility Pole", "Pole", "Fence", "Wall", "Manhole", "Street Light", "Curb", "Guard Rail", "Caravan", "Junction Box", "Traffic Sign (Front)", "Billboard", "Banner", "Mailbox", "Traffic Sign (Back)", "Bench", "Fire Hydrant", "Trash Can", "CCTV Camera", "Traffic Light", "Barrier", "Rail Track", "Phone Booth", "Curb Cut", "Traffic Sign Frame", "Bike Rack"], 
+    "road": ["Road", "Lane Marking - General", "Sidewalk", "Bridge", "Other Vehicle", "Motorcyclist", "Pothole", "Catch Basin", "Car Mount", "Tunnel", "Parking", "Service Lane", "Lane Marking - Crosswalk", "Pedestrian Area", "On Rails", "Bike Lane", "Crosswalk - Plain"], 
+    "little-objects": ["Car", "Person", "Truck", "Boat", "Wheeled Slow", "Trailer", "Ground Animal", "Bicycle", "Motorcycle", "Bird", "Bus", "Ego Vehicle", "Bicyclist", "Other Rider"], 
+    "water": ["Water"], 
+    "sky": ["Sky"], 
+    "hill": ["Mountain"]
+}
+
+MAPILLARY_CLASSES = ["Bird", "Ground Animal", "Curb", "Fence", "Guard Rail", "Barrier", "Wall", "Bike Lane", "Crosswalk - Plain", "Curb Cut", "Parking", "Pedestrian Area", "Rail Track", "Road", "Service Lane", "Sidewalk", "Bridge", "Building", "Tunnel", "Person", "Bicyclist", "Motorcyclist", "Other Rider", "Lane Marking - Crosswalk", "Lane Marking - General", "Mountain", "Sand", "Sky", "Snow", "Terrain", "Vegetation", "Water", "Banner", "Bench", "Bike Rack", "Billboard", "Catch Basin", "CCTV Camera", "Fire Hydrant", "Junction Box", "Mailbox", "Manhole", "Phone Booth", "Pothole", "Street Light", "Pole", "Traffic Sign Frame", "Utility Pole", "Traffic Light", "Traffic Sign (Back)", "Traffic Sign (Front)", "Trash Can", "Bicycle", "Boat", "Bus", "Car", "Caravan", "Motorcycle", "On Rails", "Other Vehicle", "Trailer", "Truck", "Wheeled Slow", "Car Mount", "Ego Vehicle"]
+
+def get_args() -> Namespace:
+    parser = ArgumentParser()
+    parser.add_argument("input_path", type=lambda p: Path(p).absolute())
+    parser.add_argument("output_path", type=lambda p: Path(p).absolute())
+    parser.add_argument("mapping_type", choices=["coco", "mapillary"])
+    parser.add_argument("--overwrite", action="store_true")
+    args = parser.parse_args()
+    assert not args.output_path.exists() or args.overwrite, f"{args.output_path} exists. Use --overwrite"
+    if args.output_path.exists():
+        shutil.rmtree(args.output_path)
+    return args
+
+def do_one(in_out_path: tuple[Path, Path], mapping_type: str):
+    in_path, out_path = in_out_path
+    data = np.load(in_path, allow_pickle=False)
+    data = data if isinstance(data, np.ndarray) else data["arr_0"] # in case on npz, we need this as well
+
+    classes = MAPILLARY_CLASSES if mapping_type == "mapillary" else COCO_CLASSES
+    mapping = MAPILLARY_MAPPING if mapping_type == "mapillary" else COCO_MAPPING
+    mapping_ix = {list(mapping.keys()).index(k): [classes.index(_v) for _v in v] for k, v in mapping.items()}
+    mapping_to_dronescapes = {}
+    for k, v in mapping_ix.items():
+        for _v in v:
+            mapping_to_dronescapes[_v] = k
+    mapped_data = np.vectorize(mapping_to_dronescapes.get)(data).astype(np.uint8)
+    np.savez(out_path, mapped_data)
+    return mapped_data
+
+def main(args: Namespace):
+    in_files = [x for x in args.input_path.iterdir() if x.suffix == ".npz"]
+    out_files = [args.output_path / x.name for x in in_files]
+    args.output_path.mkdir(exist_ok=False, parents=True)
+    assert len(in_files) > 0, "No .npz files found"
+    logger.info(f"In dir: '{args.input_path}'")
+    logger.info(f"Out dir: '{args.output_path}'")
+    logger.info(f"Found {len(in_files)} to convert. Dataset type: '{args.mapping_type}'")
+
+    items = list(zip(in_files, out_files))
+    for item in tqdm(items):
+        do_one(item, mapping_type=args.mapping_type)
+
+if __name__ == "__main__":
+    main(get_args())