lint and so on

Author: TimoK93

README.md

@@ -40,7 +40,6 @@ pip install .
 
 ## Usage
 
-### Validation
 
 The package supports validation, evaluation, and visualization of tracking 
 results. The following examples are shown for an example directory that is 
@@ -108,7 +107,6 @@ Per default, the code is executed using multiple processes with one process per
 available CPU core. Multiprocessing increases the execution time but also 
 the memory consumption. If you need to set the maximal number of processes,
 the number of processes can be specified with the argument 
-
 ```--num-threads``` or ```-n```:
 ```bash
 ctc_evaluate --gt "/ctc/train" --res "/ctc/train" -r -n 4
@@ -182,7 +180,6 @@ print(res["TRA"])
 
 You can visualize your tracking results with the following command:
 
-
 ```bash
 ctc_visualize --img "/ctc/train/challenge_x/01" --res "/ctc/train/challenge_x/01_RES"
 ```
@@ -206,7 +203,6 @@ There are additional arguments that can be used to specify the visualization.
 The following table shows the available arguments:
 
 
-
 | Argument          | Description                                                                              | Default |
 |-------------------|------------------------------------------------------------------------------------------|---------|
 | --img             | The directory to the images **(required)**                                               |         |
@@ -215,7 +211,6 @@ The following table shows the available arguments:
 | --video-name      | The path to the video if a video should be created                                       | None    |
 | --border-width    | The width of the border. Either an integer or a string that describes the challenge name | None    |
 | --show-no-labels  | Print no instance labels to the output as default                                        | False   |
-
 | --show-no-parents | Print no parent labels to the output as default                                          | False   |
 | --ids-to-show     | The IDs of the instances to show. If defined, all others will be ignored.                | None    |
 | --start-frame     | The frame to start the visualization                                                     | 0       |

ctc_metrics/metrics/__init__.py

@@ -12,7 +12,6 @@
 from ctc_metrics.metrics.identity_metrics.idf1 import idf1
 from ctc_metrics.metrics.others.mt_ml import mtml
 from ctc_metrics.metrics.others.faf import faf
-
 from ctc_metrics.metrics.technical.op_ctb import op_ctb
 from ctc_metrics.metrics.technical.op_csb import op_csb
 from ctc_metrics.metrics.biological.bio import bio
@@ -22,4 +21,4 @@
 ALL_METRICS = [
     "Valid", "BC", "CT", "CCA", "TF", "SEG", "TRA", "DET", "MOTA", "HOTA",
     "CHOTA", "IDF1", "MTML", "FAF", "LNK", "OP_CTB", "OP_CSB", "BIO", "OP_CLB"
-
+]

ctc_metrics/metrics/biological/bio.py

@@ -11,7 +11,6 @@ def bio(
     It is the average of the CT, TF, BC, and CCA metrics. If a metric is not
     available, it is not considered in the average.
 
-
     Args:
         ct: The complete tracking metric.
         tf: The track fractions metric.
@@ -20,7 +19,6 @@ def bio(
 
     Returns:
         The BIO metric.
-
     """
     total_metrics = 0
     if ct is not None:
@@ -39,7 +37,5 @@ def bio(
         total_metrics += 1
     else:
         cca = 0
-
     bio_score = (ct + tf + bc + cca) / total_metrics
     return bio_score
-

ctc_metrics/scripts/evaluate.py

@@ -26,7 +26,6 @@ def match_computed_to_reference_masks(
     Args:
         ref_masks: The reference masks. A list of paths to the reference masks.
         comp_masks: The computed masks. A list of paths to the computed masks.
-
         threads: The number of threads to use. If 0, the number of threads
             is set to the number of available CPUs.
 
@@ -75,7 +74,7 @@ def load_data(
         segmentation_data: True,
         threads: int = 0,
 ):
-
+    """
     Load data that is necessary to calculate metrics from the given directories.
 
     Args:
@@ -101,15 +100,13 @@ def load_data(
     assert len(ref_tra_masks) == len(comp_masks), (
         f"{res}: Number of result masks ({len(comp_masks)}) unequal to "
         f"the number of ground truth masks ({len(ref_tra_masks)})!)")
-
     # Match golden truth tracking masks to result masks
     traj = {}
     is_valid = 1
     if trajectory_data:
         traj = match_computed_to_reference_masks(
             ref_tra_masks, comp_masks, threads=threads)
         is_valid = valid(comp_masks, comp_tracks, traj["labels_comp"])
-
     # Match golden truth segmentation masks to result masks
     segm = {}
     if segmentation_data:
@@ -121,7 +118,6 @@ def load_data(
         ]
         segm = match_computed_to_reference_masks(
             ref_seg_masks, _res_masks, threads=threads)
-
     return comp_tracks, ref_tracks, traj, segm, comp_masks, is_valid
 
 
@@ -281,7 +277,6 @@ def evaluate_sequence(
     """
     Evaluates a single sequence.
 
-
     Args:
         res: The path to the results.
         gt: The path to the ground truth.
@@ -366,7 +361,6 @@ def parse_args():
     parser.add_argument('--chota', action="store_true")
     parser.add_argument('--mtml', action="store_true")
     parser.add_argument('--faf', action="store_true")
-
     parser.add_argument('--lnk', action="store_true")
     args = parser.parse_args()
     return args