added old evaluation script

.gitignore

@@ -15,4 +15,5 @@ commands.txt
 raw_data/npz_540p_2/
 here.csv
 *.ttf
+scripts/results/
 

scripts/eval_script_old.py

@@ -0,0 +1,189 @@
+"""
+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/ --num_classes 8 -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/ --num_classes 8 -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/ --num_classes 8 -o results/norway --overwrite
+"""
+
+from __future__ import annotations
+import os
+import cv2
+import numpy as np
+import multiprocessing as mp
+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):
+    global_true_positives = 0
+    global_true_negatives = 0
+    global_false_positives = 0
+    global_false_negatives = 0
+    
+    for gt_file, pred_file in tqdm(zip(gt_files, pred_files), total=len(gt_files), desc=f"{class_id=}"):
+        gt_label = np.load(gt_file, allow_pickle=True)["arr_0"]
+        net_label = np.load(pred_file, allow_pickle=True)["arr_0"]
+
+        if gt_label.shape == ():
+            gt_label = gt_label.item()['data']
+        gt_label = convert_label2multi(gt_label, class_id)
+        net_label = convert_label2multi(net_label, 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)
+        
+        global_true_positives += true_positives
+        global_true_negatives += true_negatives
+        global_false_positives += false_positives
+        global_false_negatives += false_negatives
+        
+    global_precision = global_true_positives / (global_true_positives + global_false_positives + np.spacing(1))
+    global_recall = global_true_positives / (global_true_positives + global_false_negatives + np.spacing(1))
+    global_f1_score = (2 * global_precision * global_recall) / (global_precision + global_recall + np.spacing(1))
+    global_iou = global_true_positives / (global_true_positives + global_false_positives + global_false_negatives + np.spacing(1))
+    
+    return (global_precision, global_recall, global_f1_score, global_iou)
+
+def join_results(args: argparse.Namespace):
+    assert args.num_classes in (7, 8, 10), args.num_classes
+    if args.num_classes == 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]
+        #[0.37426183 0.28172092 0.13341699 0.08660721 0.05987466 0.05937348 0.00474491]
+    elif args.num_classes == 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]
+        #[0.30589653 0.28172092 0.13341699 0.08660721 0.06836531 0.05987466 0.05937348 0.00474491]
+    elif args.num_classes == 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]
+        # [0.30589653 0.28172092 0.09954808 0.08660721 0.06836531 0.05987466 0.05937348 0.03386891 0.00445865 0.00028626]
+
+
+    out_path = os.path.join(args.out_dir, 'joined_results_' + str(args.num_classes) + 'classes.txt')
+    out_file = open(out_path, 'w')
+
+    joined_f1_scores_mean = []
+    joined_iou_scores_mean = []
+
+    for CLASS_ID in range(0, len(CLASS_NAMES)):
+        
+        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) + ' - ' + CLASS_NAMES[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, CLASS_WEIGHTS)), 4)) + '\n')
+    out_file.write('Weighted Mean IOU all classes: ' + str(round(np.sum(np.dot(joined_iou_scores_mean, CLASS_WEIGHTS)), 4)) + '\n')
+    out_file.write('\n\n')    
+
+    out_file.close()
+    print(f"Written to '{out_path}'")
+
+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
+    """
+    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("--num_classes", type=int, default=8)
+    parser.add_argument("--txt_path")
+    parser.add_argument("--overwrite", action="store_true")
+    args = parser.parse_args()
+    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:
+        (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{args.num_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
+
+    for class_id in range(args.num_classes):
+        args.class_id = class_id
+        main(args)
+    join_results(args)