Format code with black and isort

scripts/labelbox/upload.py

@@ -1,153 +1,197 @@
 from __future__ import annotations
 
+import argparse
 import uuid
-from typing import Dict, Any
+from pathlib import Path
+from typing import Any, Dict
 
-from labelbox import Client, LabelingFrontend, Dataset, Project
-from labelbox.schema.ontology import OntologyBuilder, Tool
+import pandas as pd
+from labelbox import Client, Dataset, LabelingFrontend, Project
 from labelbox.schema.media_type import MediaType
+from labelbox.schema.ontology import OntologyBuilder, Tool
+from tqdm import tqdm
 
-from pathlib import Path
-import pandas as pd
 import soccertrack
-from soccertrack.logging import show_df # This just makes the df viewable in the notebook.
-from tqdm import tqdm
-import argparse
+from soccertrack.logging import (  # This just makes the df viewable in the notebook.
+    show_df,
+)
 
-def create_ndjson(datarow_id: str, schema_id: str,
-                            segments: Dict[str, Any]) -> Dict[str, Any]:
+
+def create_ndjson(
+    datarow_id: str, schema_id: str, segments: Dict[str, Any]
+) -> Dict[str, Any]:
     return {
         "uuid": str(uuid.uuid4()),
         "schemaId": schema_id,
-        "dataRow": {
-            "id": datarow_id
-        },
-        "segments": segments
+        "dataRow": {"id": datarow_id},
+        "segments": segments,
     }
 
-def _fix_frame(bbdf_tmp, data_row_list, data_row, input_csv_file, SECOND_START_IDX, THRESHOLD_SECOND_START, THRESHOLD_SECOND_END, DATA_ROW_IDX):
-    #データフレームのズレの修正
-    start_second = int(data_row.external_id.split('_')[SECOND_START_IDX])
-    if start_second >= THRESHOLD_SECOND_START & start_second < THRESHOLD_SECOND_END - 30:
+
+def _fix_frame(
+    bbdf_tmp,
+    data_row_list,
+    data_row,
+    input_csv_file,
+    SECOND_START_IDX,
+    THRESHOLD_SECOND_START,
+    THRESHOLD_SECOND_END,
+    DATA_ROW_IDX,
+):
+    # データフレームのズレの修正
+    start_second = int(data_row.external_id.split("_")[SECOND_START_IDX])
+    if (
+        start_second
+        >= THRESHOLD_SECOND_START & start_second
+        < THRESHOLD_SECOND_END - 30
+    ):
         _data_row = list(reversed(data_row_list))[DATA_ROW_IDX + 1]
-        _bbdf_file_name = Path(input_csv_file) / f"{_data_row.external_id.split('.')[0]}.csv"
+        _bbdf_file_name = (
+            Path(input_csv_file) / f"{_data_row.external_id.split('.')[0]}.csv"
+        )
         _bbdf = soccertrack.load_df(_bbdf_file_name)
         bbdf = pd.concat([bbdf_tmp[2:], _bbdf[0:2]], axis=0)
-        
+
     elif start_second >= THRESHOLD_SECOND_END - 30:
         bbdf = bbdf_tmp[2:]
-        
+
     else:
         bbdf = bbdf_tmp
-    bbdf.index = [i+1 for i in range(len(bbdf))]
+    bbdf.index = [i + 1 for i in range(len(bbdf))]
     return bbdf
 
+
 def get_segment(bbdf, KEYFRAME_WINDOW):
     segment = dict()
     for (team_id, player_id), player_df in bbdf.iter_players():
-        
-        if team_id == '3':
-            feature_name = 'BALL'
-        elif team_id == '1' and int(player_id) >= 11:
-            feature_name = team_id + '_' + str(int(player_id) - 11)
-        elif team_id == '0' and player_id == '21':
-            feature_name = '1' + '_' + str(int(player_id) - 11)
-        elif team_id == '0' and player_id == '11':
-            feature_name = '0' + '_' + str(int(player_id) - 11)
-            
+
+        if team_id == "3":
+            feature_name = "BALL"
+        elif team_id == "1" and int(player_id) >= 11:
+            feature_name = team_id + "_" + str(int(player_id) - 11)
+        elif team_id == "0" and player_id == "21":
+            feature_name = "1" + "_" + str(int(player_id) - 11)
+        elif team_id == "0" and player_id == "11":
+            feature_name = "0" + "_" + str(int(player_id) - 11)
+
         else:
-            feature_name = team_id + '_' + str(int(player_id))
+            feature_name = team_id + "_" + str(int(player_id))
 
         key_frames_dict = dict()
         key_frames_dict["keyframes"] = []
-                
+
         for idx, row in player_df.iterrows():
             if idx % KEYFRAME_WINDOW == 0:
                 try:
-                    key_frames_dict["keyframes"].append({
-                        "frame": idx,
-                        "bbox": {
-                            "top": int(row['bb_top']),
-                            "left": int(row['bb_left']),
-                            "height": int(row['bb_height']),
-                            "width": int(row['bb_width'])
+                    key_frames_dict["keyframes"].append(
+                        {
+                            "frame": idx,
+                            "bbox": {
+                                "top": int(row["bb_top"]),
+                                "left": int(row["bb_left"]),
+                                "height": int(row["bb_height"]),
+                                "width": int(row["bb_width"]),
+                            },
                         }
-                    })
+                    )
                 except ValueError:
-                    print('ValueError occured :', feature_name, 'frame_num :', idx)
+                    print("ValueError occured :", feature_name, "frame_num :", idx)
 
         segment[feature_name] = [key_frames_dict]
     return segment
 
+
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument("--API_key", help="Enter your API key here : ")
     parser.add_argument("--PROJECT_NAME", help="Enter your project name here : ")
     parser.add_argument("--DATASET_NAME", help="Enter your dataset name here : ")
     parser.add_argument("--ONTOLOGY_NAME", help="Enter your ontology name here : ")
-    parser.add_argument("--input_csv_file", help="Enter the name of the csv file that contains the bbdf information :  ")
+    parser.add_argument(
+        "--input_csv_file",
+        help="Enter the name of the csv file that contains the bbdf information :  ",
+    )
 
     args = parser.parse_args()
 
-
-    API_KEY=args.API_key # Add your api key
-    PROJECT_NAME = args.PROJECT_NAME # This is the name of the project you want to upload the data to.
-    DATASET_NAME = args.DATASET_NAME # This is the name of the dataset.
-    ONTOLOGY_NAME = args.ONTOLOGY_NAME # This is the name of the ontology.
+    API_KEY = args.API_key  # Add your api key
+    PROJECT_NAME = (
+        args.PROJECT_NAME
+    )  # This is the name of the project you want to upload the data to.
+    DATASET_NAME = args.DATASET_NAME  # This is the name of the dataset.
+    ONTOLOGY_NAME = args.ONTOLOGY_NAME  # This is the name of the ontology.
 
     client = Client(api_key=API_KEY)
-    #get project information
+    # get project information
     project = next(client.get_projects(where=Project.name == PROJECT_NAME), None)
-    #get dataset information
+    # get dataset information
     dataset = next(client.get_datasets(where=Dataset.name == DATASET_NAME), None)
     # We want to try out a few different tools here.
     ontology_builder = OntologyBuilder(
-    tools=[Tool(tool=Tool.Type.BBOX, name=ONTOLOGY_NAME)]) # This is the name of the label tool you want to use.
+        tools=[Tool(tool=Tool.Type.BBOX, name=ONTOLOGY_NAME)]
+    )  # This is the name of the label tool you want to use.
 
     # When we created a project with the ontology defined above, all of the ids were assigned.
     # So lets reconstruct the ontology builder with all of the ids.
     ontology = ontology_builder.from_project(project)
     # We want all of the feature schemas to be easily accessible by name.
     schema_lookup = {tool.name: tool.feature_schema_id for tool in ontology.tools}
 
-
     KEYFRAME_WINDOW = 1
     SECOND_START_IDX = 3
     THRESHOLD_SECOND_START = 840
     THRESHOLD_SECOND_END = 1800
 
-
     data_row_list = [data_row for data_row in dataset.data_rows()]
-    input_csv_file = args.input_csv_file # This is the name of the csv file that contains the video information.
+    input_csv_file = (
+        args.input_csv_file
+    )  # This is the name of the csv file that contains the video information.
 
     for DATA_ROW_IDX in tqdm(range(len(data_row_list))):
 
         data_row = list(reversed(data_row_list))[DATA_ROW_IDX]
-        bbdf_file_name = Path(input_csv_file) / f"{data_row.external_id.split('.')[0]}.csv"
+        bbdf_file_name = (
+            Path(input_csv_file) / f"{data_row.external_id.split('.')[0]}.csv"
+        )
         try:
             bbdf_tmp = soccertrack.load_df(bbdf_file_name)
-        except FileNotFoundError: # If the file doesn't exist, we'll skip it.
-            print('FileNotFoundError', data_row.external_id)
+        except FileNotFoundError:  # If the file doesn't exist, we'll skip it.
+            print("FileNotFoundError", data_row.external_id)
             continue
-
 
-        #Correction of data frame misalignment
-        bbdf = _fix_frame(bbdf_tmp, data_row_list, data_row, input_csv_file, SECOND_START_IDX, THRESHOLD_SECOND_START, THRESHOLD_SECOND_END, DATA_ROW_IDX)
+        # Correction of data frame misalignment
+        bbdf = _fix_frame(
+            bbdf_tmp,
+            data_row_list,
+            data_row,
+            input_csv_file,
+            SECOND_START_IDX,
+            THRESHOLD_SECOND_START,
+            THRESHOLD_SECOND_END,
+            DATA_ROW_IDX,
+        )
         segment = get_segment(bbdf, KEYFRAME_WINDOW)
 
-
-
         uploads = []
-        for schema_name, schema_id in schema_lookup.items(): 
+        for schema_name, schema_id in schema_lookup.items():
             if schema_name in segment:
-                uploads.append(create_ndjson(data_row.uid, schema_id, segment[schema_name]))
-        upload_task = project.upload_annotations(name=f"upload-job-{uuid.uuid4()}",
-                                                annotations=uploads,
-                                                validate=False)                     
+                uploads.append(
+                    create_ndjson(data_row.uid, schema_id, segment[schema_name])
+                )
+        upload_task = project.upload_annotations(
+            name=f"upload-job-{uuid.uuid4()}", annotations=uploads, validate=False
+        )
         # Wait for upload to finish (Will take up to five minutes)
         upload_task.wait_until_done()
         # Review the upload status
-        print('Done!', ' Video_name : ' ,data_row.external_id, ': Errors', upload_task.errors)
+        print(
+            "Done!",
+            " Video_name : ",
+            data_row.external_id,
+            ": Errors",
+            upload_task.errors,
+        )
+
 
 if __name__ == "__main__":
-    main()
+    main()

scripts/yolov5/build_dataset.py

@@ -3,11 +3,11 @@
 """
 import argparse
 from pathlib import Path
-from joblib import Parallel, delayed
 
 import cv2
 import numpy as np
 import yaml
+from joblib import Parallel, delayed
 
 import soccertrack
 from soccertrack import Camera
@@ -60,10 +60,11 @@ def move_files_to_folder(files, folder):
         def _parallel_imwrite(frame_num, frame):
             file_path = f"{save_dir}/{path_to_csv.stem}/{frame_num:06d}.png"
             cv2.imwrite(file_path, frame)
-        
+
         res = Parallel(n_jobs=-1)(
-            delayed(_parallel_imwrite)(frame_num, frame) for frame_num, frame in enumerate(tqdm(cam.iter_frames()))
-            )
+            delayed(_parallel_imwrite)(frame_num, frame)
+            for frame_num, frame in enumerate(tqdm(cam.iter_frames()))
+        )
         break
 
     # Partition the dataset

soccertrack/__init__.py

@@ -1,6 +1,6 @@
-from contextlib import contextmanager
 import logging
 import warnings
+from contextlib import contextmanager
 
 
 @contextmanager
@@ -18,7 +18,7 @@ def all_logging_disabled(highest_level=logging.CRITICAL):
     warnings.simplefilter("ignore")
     from vidgear.gears import WriteGear, CamGear
 
-from soccertrack.dataframe import CoordinatesDataFrame, BBoxDataFrame
-from soccertrack.io import load_df
 import soccertrack.datasets  # noqa
 from soccertrack.camera import Camera
+from soccertrack.dataframe import BBoxDataFrame, CoordinatesDataFrame
+from soccertrack.io import load_df

soccertrack/cli.py

@@ -1,14 +1,13 @@
-from glob import glob
 import os
+from glob import glob
 from typing import Optional
 
 import numpy as np
-
 from fire import Fire
 
-from soccertrack.utils.camera import find_intrinsic_camera_parameters, Camera
-from soccertrack.utils.utils import make_video
 from soccertrack.utils import logger, set_log_level
+from soccertrack.utils.camera import Camera, find_intrinsic_camera_parameters
+from soccertrack.utils.utils import make_video
 
 
 class CLI:

soccertrack/dataframe/bboxdataframe.py

@@ -1,15 +1,15 @@
 from __future__ import annotations
 
 from hashlib import md5
+from pathlib import Path
 from typing import Any, Optional, Type
 
 import cv2
 import numpy as np
 import pandas as pd
-from pathlib import Path
 
-from soccertrack.utils import make_video
 from soccertrack.logger import tqdm
+from soccertrack.utils import make_video
 
 from ..logger import logger
 from ..utils import MovieIterator, get_fps

soccertrack/dataframe/gpsdataframe.py

@@ -1,4 +1,5 @@
 import pandas as pd
+
 from .base import SoccerTrackMixin
 
 

soccertrack/datasets/__init__.py

@@ -1,8 +1,9 @@
 import os
+from pathlib import Path
+from typing import Optional
 
 from soccertrack.logger import logger
-from typing import Optional
-from pathlib import Path
+
 from .downloader import KaggleDownloader
 
 __all__ = ["available", "get_path", "KaggleDownloader"]

soccertrack/datasets/labelbox_uploader.py

@@ -1,18 +1,18 @@
 from __future__ import annotations
 
 import uuid
-from typing import Dict, Any
+from pathlib import Path
+from typing import Any, Dict
 
-from labelbox import Client, LabelingFrontend, Dataset, Project
-from labelbox.schema.ontology import OntologyBuilder, Tool
+from labelbox import Client, Dataset, LabelingFrontend, Project
 from labelbox.schema.media_type import MediaType
+from labelbox.schema.ontology import OntologyBuilder, Tool
+from tqdm import tqdm
 
-from pathlib import Path
 import soccertrack
-from soccertrack.logging import (
+from soccertrack.logging import (  # This just makes the df viewable in the notebook.
     show_df,
-)  # This just makes the df viewable in the notebook.
-from tqdm import tqdm
+)
 
 
 def bbdf2ndjson(