rm old scripts

scripts/cfg.yaml

@@ -1,107 +0,0 @@
-vre:
-  start_frame: ${oc.env:VRE_START_IX}
-  end_frame: ${oc.env:VRE_END_IX,null}
-  export_npy: True
-  export_png: False
-  exception_mode: skip_representation
-
-representations:
-  rgb:
-    type: default/rgb
-    dependencies: []
-    parameters: {}
-
-  hsv:
-    type: default/hsv
-    dependencies: []
-    parameters: {}
-
-  halftone1:
-    type: soft-segmentation/python-halftone
-    dependencies: []
-    parameters:
-      sample: 3
-      scale: 1
-      percentage: 91
-      angles: [0, 15, 30, 45]
-      antialias: False
-      resolution: [240, 426]
-
-  edges_canny:
-    type: edges/canny
-    dependencies: []
-    parameters:
-      threshold1: 100
-      threshold2: 200
-      aperture_size: 3
-      l2_gradient: True
-
-  softseg_gb:
-    type: soft-segmentation/generalized_boundaries
-    dependencies: []
-    parameters:
-      use_median_filtering: True
-      adjust_to_rgb: True
-      max_channels: 3
-
-  edges_dexined:
-    type: edges/dexined
-    dependencies: []
-    parameters: {}
-    batch_size: 15
-    device: ${oc.env:VRE_DEVICE,cpu}
-
-  fastsam(s):
-    type: semantic-segmentation/fastsam
-    dependencies: []
-    parameters:
-      variant: fastsam-s
-      iou: 0.9
-      conf: 0.4
-    device: ${oc.env:VRE_DEVICE,cpu}
-
-  depth_dpt:
-    type: depth/dpt
-    dependencies: []
-    parameters: {}
-    batch_size: 10
-    device: ${oc.env:VRE_DEVICE,cpu}
-
-  # only works if fps is also set (for images) via --frame_rate in cli. For videos, it works just fine.
-  opticalflow_rife:
-    type: optical-flow/rife
-    dependencies: []
-    batch_size: 15
-    parameters:
-      uhd: False
-      compute_backward_flow: False
-    device: ${oc.env:VRE_DEVICE,cpu}
-
-  semantic_mask2former_coco_47429163_0:
-    type: semantic-segmentation/mask2former
-    dependencies: []
-    parameters:
-      model_id: "47429163_0"
-      semantic_argmax_only: True
-    batch_size: 1
-    device: ${oc.env:VRE_DEVICE,cpu}
-
-  semantic_mask2former_mapillary_49189528_0:
-    type: semantic-segmentation/mask2former
-    dependencies: []
-    parameters:
-      model_id: "49189528_0"
-      semantic_argmax_only: True
-    batch_size: 1
-    device: ${oc.env:VRE_DEVICE,cpu}
-
-  depth_marigold:
-    type: depth/marigold
-    dependencies: []
-    parameters:
-      variant: marigold-lcm-v1-0
-      denoising_steps: 4
-      ensemble_size: 1
-      processing_resolution: 768
-    batch_size: 15
-    device: ${oc.env:VRE_DEVICE,cpu}

scripts/convert_m2f_to_dronescapes.py

@@ -1,78 +0,0 @@
-"""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 loggez import loggez_logger as 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())

scripts/count_npz.sh

@@ -1,18 +0,0 @@
-# Usage: bash count_npz npz_540p
-(
-  echo "scene,repr,counts"
-  ls $1 | while read line; do
-    scene_name=$(echo $line | cut -d "_" -f1);
-    ls $1/$line | while read line2; do
-      n_files=$(find $1/$line/"$line2" -name "*.npz" | wc -l);
-      echo "$scene_name","$line2","$n_files";
-    done;
-  done ) | python -c '''
-import sys, pandas as pd;
-df = pd.read_csv(sys.stdin);
-df2 = df.groupby("scene").apply(lambda x: dict(zip(x["repr"], x["counts"])), include_groups=False).reset_index();
-df3 = pd.json_normalize(df2[0]).set_index(df2["scene"]).fillna(0).astype(int);
-df4 = df3.reindex(columns=["rgb", *sorted(x for x in df3.columns if x != "rgb")])
-df4.columns = [f"{x[0:18]}.." if len(x) > 20 else x for x in df4.columns]
-print(df4.to_markdown())
-'''

scripts/eval_script_old.py

@@ -1,181 +0,0 @@
-"""
-The old evaluation script.
-To run, you first need to split the test scenes data into 3 different directories:
-
-cd /dronescapes/data
-scenes=(comana barsana norway);
-for scene in ${scenes[@]} ; do
-    ls test_set_annotated_only | while read task; do
-        mkdir -p test_set_annotated_only_per_scene/$scene/$task;
-        ls test_set_annotated_only/$task | grep "$scene" | while read line; do
-            cp test_set_annotated_only/$task/$line test_set_annotated_only_per_scene/$scene/$task/$line;
-        done;
-    done
-done
-
-Then run this:
-cd /dronescapes/scripts
-python eval_script_old.py --gt_path ../data/test_set_annotated_only_per_scene/comana/semantic_segprop8/ --pred_path ../data/test_set_annotated_only_per_scene/comana/semantic_mask2former_swin_mapillary_converted/ --class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 --classes land forest residential road little-objects water sky hill -o results/comana --overwrite
-python eval_script_old.py --gt_path ../data/test_set_annotated_only_per_scene/barsana/semantic_segprop8/ --pred_path ../data/test_set_annotated_only_per_scene/barsana/semantic_mask2former_swin_mapillary_converted/ --class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 --classes land forest residential road little-objects water sky hill -o results/barsana --overwrite
-python eval_script_old.py --gt_path ../data/test_set_annotated_only_per_scene/norway/semantic_segprop8/ --pred_path ../data/test_set_annotated_only_per_scene/norway/semantic_mask2former_swin_mapillary_converted/ --class_weights 0.28172092 0.30589653 0.13341699 0.05937348 0.00474491 0.05987466 0.08660721 0.06836531 --classes land forest residential road little-objects water sky hill -o results/norway --overwrite
-"""
-
-from __future__ import annotations
-import os
-import numpy as np
-import pandas as pd
-from natsort import natsorted
-from pathlib import Path
-import shutil
-import tempfile
-from tqdm import tqdm
-
-import argparse
-import warnings
-warnings.filterwarnings("ignore")
-
-def convert_label2multi(label, class_id):
-    out = np.zeros((label.shape[0], label.shape[1]), dtype=np.uint8)
-    data_indices = np.where(np.equal(label, class_id))
-    out[data_indices[0], data_indices[1]] = 1
-    return np.array(out, dtype=bool)
-
-def process_all_video_frames(gt_files: list[Path], pred_files: list[Path], class_id: int):
-    TP, TN, FP, FN = {}, {}, {}, {}
-    for gt_file, pred_file in tqdm(zip(gt_files, pred_files), total=len(gt_files), desc=f"{class_id=}"):
-        gt_label_raw = np.load(gt_file, allow_pickle=True)["arr_0"]
-        net_label_raw = np.load(pred_file, allow_pickle=True)["arr_0"]
-        gt_label = convert_label2multi(gt_label_raw, class_id)
-        net_label = convert_label2multi(net_label_raw, class_id)
-
-        true_positives = np.count_nonzero(gt_label * net_label)
-        true_negatives = np.count_nonzero((gt_label + net_label) == 0)
-        false_positives = np.count_nonzero((np.array(net_label, dtype=int) - np.array(gt_label, dtype=int)) > 0)
-        false_negatives = np.count_nonzero((np.array(gt_label, dtype=int) - np.array(net_label, dtype=int)) > 0)
-        
-        TP[gt_file.name] = true_positives
-        TN[gt_file.name] = true_negatives
-        FP[gt_file.name] = false_positives
-        FN[gt_file.name] = false_negatives
-    df = pd.DataFrame([TP, FP, TN, FN], index=["tp", "fp", "tn", "fn"]).T
-    global_TP, global_TN, global_FP, global_FN = sum(TP.values()), sum(TN.values()), sum(FP.values()), sum(FN.values())
-    global_precision = global_TP / (global_TP + global_FP + np.spacing(1))
-    global_recall = global_TP / (global_TP + global_FN + np.spacing(1))
-    global_f1_score = (2 * global_precision * global_recall) / (global_precision + global_recall + np.spacing(1))
-    global_iou = global_TP / (global_TP + global_FP + global_FN + np.spacing(1))
-    
-    return (global_precision, global_recall, global_f1_score, global_iou)
-
-def join_results(args: argparse.Namespace):
-    out_path = os.path.join(args.out_dir, 'joined_results_' + str(len(args.classes)) + 'classes.txt')
-    out_file = open(out_path, 'w')
-
-    joined_f1_scores_mean = []
-    joined_iou_scores_mean = []
-
-    for CLASS_ID in range(len(args.classes)):
-        RESULT_FILE = os.path.join(args.out_dir, 'evaluation_dronescapes_CLASS_' + str(CLASS_ID) + '.txt')
-        result_file_lines = open(RESULT_FILE, 'r').read().splitlines()
-        for idx, line in enumerate(result_file_lines):
-            if idx != 0:
-                splits = line.split(',')
-                f1_score = float(splits[2])
-                iou_score = float(splits[3])
-                    
-        out_file.write('------------------------- ' + ' CLASS    ' + str(CLASS_ID) + ' - ' + args.classes[CLASS_ID] + '    --------------------------------------------\n')
-        # F1Score
-        out_file.write('F1-Score: ' + str(round(f1_score, 4)) + '\n')
-        # Mean IOU
-        out_file.write('IOU: ' + str(round(iou_score, 4)) + '\n')
-        out_file.write('\n\n')
-        joined_f1_scores_mean.append(f1_score)
-        joined_iou_scores_mean.append(iou_score)
-
-    out_file.write('\n\n')
-    out_file.write('Mean F1-Score all classes: ' + str(round(np.mean(joined_f1_scores_mean), 4)) + '\n')
-    out_file.write('Mean IOU all classes: ' + str(round(np.mean(joined_iou_scores_mean), 4)) + '\n')
-    out_file.write('\n\n')  
-
-    out_file.write('\n\n')
-    out_file.write('Weighted Mean F1-Score all classes: ' + str(round(np.sum(np.dot(joined_f1_scores_mean, args.class_weights)), 4)) + '\n')
-    out_file.write('Weighted Mean IOU all classes: ' + str(round(np.sum(np.dot(joined_iou_scores_mean, args.class_weights)), 4)) + '\n')
-    out_file.write('\n\n')    
-
-    out_file.close()
-    print(f"Written to '{out_path}'")
-
-def compat_old_txt_file(args: Namespace):
-    (tempdir := Path(tempfile.TemporaryDirectory().name)).mkdir()
-    (tempdir / "gt").mkdir()
-    (tempdir / "pred").mkdir()
-    print(f"old pattern detected. Copying files to a temp dir: {tempdir}")
-    test_files = natsorted(open(args.txt_path, "r").read().splitlines())
-    scenes = natsorted(set(([os.path.dirname(x) for x in test_files])))
-    assert len(scenes) == 1, scenes
-    files = natsorted([x for x in test_files if scenes[0] in x])
-    gt_files = [f"{args.gt_path}/{f.split('/')[0]}/segprop{len(args.classes)}/{f.split('/')[1]}.npz" for f in files]
-    pred_files = [f"{args.pred_path}/{f.split('/')[0]}/{int(f.split('/')[1]):06}.npz" for f in files]
-    assert all(Path(x).exists() for x in [*gt_files, *pred_files])
-    for _file in gt_files:
-        os.symlink(_file, tempdir / "gt" / Path(_file).name)
-    for _file in pred_files:
-        os.symlink(_file, tempdir / "pred" / Path(_file).name)
-    args.gt_path = tempdir / "gt"
-    args.pred_path = tempdir / "pred"
-    args.txt_path = None
-
-def main(args: argparse.Namespace):
-    gt_files = natsorted([x for x in args.gt_path.iterdir()], key=lambda x: Path(x).name)
-    pred_files = natsorted([x for x in args.pred_path.iterdir()], key=lambda x: Path(x).name)
-    assert all(Path(x).exists() for x in [*gt_files, *pred_files])
-    global_precision, global_recall, global_f1, global_iou = process_all_video_frames(gt_files, pred_files, args.class_id)
-
-    out_path = os.path.join(args.out_dir, 'evaluation_dronescapes_CLASS_' + str(args.class_id) + '.txt')
-    out_file = open(out_path, 'w')
-    out_file.write('precision,recall,f1,iou\n')
-    out_file.write('{0:.6f},{1:.6f},{2:.6f},{3:.6f}\n'.format(global_precision, global_recall, global_f1, global_iou))
-    out_file.close()
-    print(f"Written to '{out_path}'")
-
-if __name__ == "__main__":
-    """
-    Barsana: /Date3/hpc/datasets/dronescapes/all_scenes/dataset_splits/20220517_train_on_even_semisup_on_odd_validate_on_last_odd_triplet_journal_split/only_manually_annotated_test_files_36.txt
-    Norce: /Date3/hpc/datasets/dronescapes/all_scenes/dataset_splits/20220810_new_norce_clip/only_manually_annotated_test_files_50.txt
-    Comana: /Date3/hpc/datasets/dronescapes/all_scenes/dataset_splits/20221208_new_comana_clip/only_manually_annotated_test_files_30.txt
-    gt_path: /Date3/hpc/datasets/dronescapes/all_scenes
-    pred_path/Date3/hpc/code/Mask2Former/demo_dronescapes/outputs_dronescapes_compatible/mapillary_sseg
-    NC = 7
-        CLASS_NAMES = ['land', 'forest', 'residential', 'road', 'little-objects', 'water', 'sky']
-        CLASS_WEIGHTS = [0.28172092, 0.37426183, 0.13341699, 0.05937348, 0.00474491, 0.05987466, 0.08660721]
-    NC = 8
-        CLASS_NAMES = ['land', 'forest', 'residential', 'road', 'little-objects', 'water', 'sky', 'hill']
-        CLASS_WEIGHTS = [0.28172092, 0.30589653, 0.13341699, 0.05937348, 0.00474491, 0.05987466, 0.08660721, 0.06836531]
-    NC = 10
-        CLASS_NAMES = ['land', 'forest', 'low-level', 'road', 'high-level', 'cars', 'water', 'sky', 'hill', 'person']
-        CLASS_WEIGHTS = [0.28172092, 0.30589653, 0.09954808, 0.05937348, 0.03386891, 0.00445865, 0.05987466, 0.08660721, 0.06836531, 0.00028626]
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--gt_path", type=Path, required=True)
-    parser.add_argument("--pred_path", type=Path, required=True)
-    parser.add_argument("--out_dir", "-o", required=True, type=Path, default=Path(__file__).parent / "out_dir")
-    parser.add_argument("--classes", nargs="+")
-    parser.add_argument("--class_weights", type=float, nargs="+", required=True)
-    parser.add_argument("--txt_path")
-    parser.add_argument("--overwrite", action="store_true")
-    args = parser.parse_args()
-    if args.classes is None:
-        print("Class names not provided")
-        args.classes = list(map(str, range(len(args.class_weights))))
-    assert len(args.classes) == len(args.class_weights), (args.classes, args.class_weights)
-    assert len(args.classes) in (7, 8, 10), len(args.classes)
-    assert not args.out_dir.exists() or args.overwrite, f"'{args.out_dir}' exists. Use --overwrite"
-    shutil.rmtree(args.out_dir, ignore_errors=True)
-    os.makedirs(args.out_dir, exist_ok=True)
-
-    if args.txt_path is not None:
-        compat_old_txt_file(args)
-
-    for class_id in range(len(args.classes)):
-        args.class_id = class_id
-        main(args)
-    join_results(args)

scripts/txt_files/annotated_and_segprop/val_files_605.txt

@@ -0,0 +1,605 @@
+gradistei_DJI_0787_0788_0789_combined_sliced_3510_13110/8880
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scripts/txt_files/manually_annotated_files/semisup_files_207.txt

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scripts/txt_files/manually_annotated_files/test_files_116.txt

@@ -0,0 +1,116 @@
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scripts/txt_files/manually_annotated_files/train_files_218.txt

@@ -0,0 +1,218 @@
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scripts/txt_files/manually_annotated_files/val_files_15.txt

@@ -0,0 +1,15 @@
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