adding apaar's files

Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/CSV Converter.py

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+# -*- coding: utf-8 -*-
+"""Untitled25.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1_lpSJ727oRCSAGy86MTC3AN7Zrg6nSMi
+"""
+
+from __future__ import annotations
+import json
+from pathlib import Path
+from typing import Any, Iterable, Optional, Tuple, Dict
+
+import pandas as pd
+
+from google.colab import files
+
+
+def _get(d: dict, path: Iterable[str], default=None):
+    cur = d
+    for k in path:
+        if not isinstance(cur, dict) or k not in cur:
+            return default
+        cur = cur[k]
+    return cur
+
+def is_tracking_schema(obj: Any) -> bool:
+    """Heuristic for AFL tracking schema: dict -> 'tracking_results' (list) -> frame dicts with 'players' list."""
+    if not isinstance(obj, dict):
+        return False
+    trs = obj.get("tracking_results")
+    if not isinstance(trs, list) or not trs:
+        return False
+    for fr in trs:
+        if isinstance(fr, dict) and isinstance(fr.get("players"), list):
+            return True
+    return False
+
+def parse_tracking_json_to_df(json_obj: dict, conf_min: float = 0.0) -> pd.DataFrame:
+    rows = []
+    for fr in json_obj.get("tracking_results", []):
+        if not isinstance(fr, dict):
+            continue
+        fno = fr.get("frame_number")
+        ts  = fr.get("timestamp")
+        for p in fr.get("players", []):
+            if not isinstance(p, dict):
+                continue
+
+            conf = p.get("confidence")
+            if conf is None:
+                continue
+            try:
+                if float(conf) < conf_min:
+                    continue
+            except Exception:
+                pass
+
+            x1 = _get(p, ["bbox","x1"]); y1 = _get(p, ["bbox","y1"])
+            x2 = _get(p, ["bbox","x2"]); y2 = _get(p, ["bbox","y2"])
+            cx = _get(p, ["center","x"]); cy = _get(p, ["center","y"])
+
+            w  = p.get("width",  (x2 - x1) if (x1 is not None and x2 is not None) else None)
+            h  = p.get("height", (y2 - y1) if (y1 is not None and y2 is not None) else None)
+
+            rows.append([fno, p.get("player_id"), ts, x1, y1, x2, y2, cx, cy, w, h, conf])
+
+    df = pd.DataFrame(rows, columns=[
+        "frame_id","player_id","timestamp_s",
+        "x1","y1","x2","y2","cx","cy","w","h","confidence"
+    ])
+    if df.empty:
+        return df
+
+    for c in ["frame_id","player_id"]:
+        df[c] = pd.to_numeric(df[c], errors="coerce").astype("Int64")
+    for c in ["timestamp_s","x1","y1","x2","y2","cx","cy","w","h","confidence"]:
+        df[c] = pd.to_numeric(df[c], errors="coerce")
+    return df.sort_values(["frame_id","player_id"]).reset_index(drop=True)
+
+
+def _guess_record_path(obj: Any) -> Optional[Tuple[str, list]]:
+    if isinstance(obj, list) and all(isinstance(x, dict) for x in obj):
+        return ("<TOP_LIST>", [])
+    if isinstance(obj, dict):
+        list_keys = [k for k, v in obj.items() if isinstance(v, list)]
+        if len(list_keys) == 1 and all(isinstance(x, dict) for x in obj[list_keys[0]]):
+            meta_fields = [k for k in obj.keys() if k != list_keys[0]]
+            return (list_keys[0], meta_fields)
+    return None
+
+def normalize_json_to_df(
+    json_obj: Any,
+    record_key: Optional[str] = None,
+    meta: Optional[list[str]] = None,
+    sep: str = ".",
+) -> pd.DataFrame:
+    guess = _guess_record_path(json_obj) if record_key is None else None
+    if record_key is None and guess:
+        record_key, guessed_meta = guess
+        if meta is None:
+            meta = guessed_meta
+
+    if record_key == "<TOP_LIST>":
+        data = json_obj
+        return pd.json_normalize(data, sep=sep, max_level=None)
+
+    if record_key:
+        if not isinstance(json_obj, dict) or record_key not in json_obj:
+            raise ValueError(f"record_key '{record_key}' not found in root JSON object")
+        data = json_obj[record_key]
+        return pd.json_normalize(data, sep=sep, meta=meta or [], max_level=None)
+
+    return pd.json_normalize(json_obj, sep=sep, max_level=None)
+
+
+def colab_json2csv(
+    mode: str = "auto",
+    conf_min: float = 0.0,
+    record_key: Optional[str] = None,
+    meta: Optional[list[str]] = None,
+    sep: str = ".",
+    suffix: str = "_parsed",
+    download: bool = True,
+    preview_rows: int = 5,
+) -> Dict[str, pd.DataFrame]:
+
+    uploaded = files.upload()
+    if not uploaded:
+        print("No files uploaded.")
+        return {}
+
+    results: Dict[str, pd.DataFrame] = {}
+
+    for fname, content in uploaded.items():
+        try:
+            obj = json.loads(content.decode("utf-8"))
+        except Exception as e:
+            print(f"[ERROR] Could not parse JSON in {fname}: {e}")
+            continue
+
+        chosen = mode
+        if mode == "auto":
+            chosen = "tracking" if is_tracking_schema(obj) else "normalize"
+
+        try:
+            if chosen == "tracking":
+                df = parse_tracking_json_to_df(obj, conf_min=conf_min)
+            else:
+                df = normalize_json_to_df(obj, record_key=record_key, meta=meta, sep=sep)
+        except Exception as e:
+            print(f"[ERROR] Failed to convert {fname} in {chosen} mode: {e}")
+            continue
+
+        stem = Path(fname).stem
+        out_csv = f"{stem}{suffix}.csv"
+        df.to_csv(out_csv, index=False)
+        results[fname] = df
+
+        nrows = len(df)
+        print(f"[OK] {fname} → {out_csv} ({nrows} rows)  |  mode={chosen}")
+
+        if nrows and preview_rows:
+            display(df.head(preview_rows))
+
+        if download:
+            try:
+                files.download(out_csv)
+            except Exception as e:
+                print(f"(Download skipped for {out_csv}: {e})")
+
+    return results
+
+
+dfs = colab_json2csv()
+

Player_Tracking/afl_player_tracking_and_crowd_monitoring/player_tracking_logic/Sprint Detection.py

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+# -*- coding: utf-8 -*-
+"""Untitled27.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1KUJ2_CipSaGbrTYK5_OFHtxEvfXOEDDt
+"""
+
+import pandas as pd
+
+df = pd.read_csv("players_over_25kmh.csv")
+
+SPRINT_THRESHOLD = 7.0
+
+df["is_sprint"] = df["speed_kmh"] > SPRINT_THRESHOLD
+
+segments = []
+for player_id, player_data in df.groupby("player_id"):
+    sprinting = False
+    start_frame = None
+
+    for idx, row in player_data.iterrows():
+        if row["is_sprint"] and not sprinting:
+            sprinting = True
+            start_frame = row["frame_id"]
+        elif not row["is_sprint"] and sprinting:
+            sprinting = False
+            end_frame = row["frame_id"] - 1
+            segments.append([player_id, start_frame, end_frame])
+
+    if sprinting:
+        segments.append([player_id, start_frame, player_data["frame_id"].iloc[-1]])
+
+sprint_segments = pd.DataFrame(segments, columns=["player_id", "start_frame", "end_frame"])
+
+sprint_segments.to_csv("flagged_sprints.csv", index=False)
+
+print("Flagged sprint segments saved to flagged_sprints.csv")
+print(sprint_segments.head())
+