Initialer Commit ohne Datasets und Models

.gitignore

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+# IDE Einstellungen
+.idea/
+
+# Datensätze (ganzer Ordner)
+datasets/
+
+# Große Model-Dateien
+*.pt
+
+# Python Cache
+__pycache__/

createDataset.py

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+import json
+import os
+import random
+import boto3
+from urllib.parse import urlparse
+from tqdm.auto import tqdm
+
+
+# --- KONFIGURATION ---
+MINIO_CONFIG = {
+    'endpoint_url': 'https://minio.hgk.ch',
+    'access_key': 'meinAccessKey',
+    'secret_key': 'meinSecretKey',
+    'bucket': 'skiai'
+}
+
+JSON_PATH = 'datasets/skier_pose/labelstudio_export.json'
+OUTPUT_DIR = 'datasets/skier_pose'
+TRAIN_RATIO = 0.8
+
+# Die Reihenfolge MUSS konsistent bleiben
+KP_ORDER = [
+    "leftski_tip", "leftski_tail", "rightski_tip", "rightski_tail",
+    "leftpole_top", "leftpole_bottom", "rightpole_top", "rightpole_bottom"
+]
+
+
+def setup_directories():
+    """Erstellt die Struktur: train/images, train/labels, val/images, val/labels"""
+    for split in ['train', 'val']:
+        os.makedirs(os.path.join(OUTPUT_DIR, split, 'images'), exist_ok=True)
+        os.makedirs(os.path.join(OUTPUT_DIR, split, 'labels'), exist_ok=True)
+
+
+def download_from_minio(s3_path, local_path):
+    parsed = urlparse(s3_path)
+    bucket = MINIO_CONFIG['bucket']
+    # Entfernt 's3://bucketname/' falls vorhanden, sonst nur den slash
+    key = parsed.path.lstrip('/')
+
+    s3 = boto3.client('s3',
+                      endpoint_url=MINIO_CONFIG['endpoint_url'],
+                      aws_access_key_id=MINIO_CONFIG['access_key'],
+                      aws_secret_access_key=MINIO_CONFIG['secret_key'])
+    s3.download_file(bucket, key, local_path)
+
+
+def convert_to_yolo():
+    setup_directories()
+    with open(JSON_PATH, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+
+    random.seed(42)
+    random.shuffle(data)
+    split_idx = int(len(data) * TRAIN_RATIO)
+
+    for i, entry in enumerate(tqdm(data, desc="Importing Images", unit="img")):
+        split = 'train' if i < split_idx else 'val'
+
+        # Dateinamen aus dem 'data'-Feld holen
+        image_s3_path = entry['data']['image']
+        filename = os.path.basename(image_s3_path)
+        base_name = os.path.splitext(filename)[0]
+
+        img_local_path = os.path.join(OUTPUT_DIR, split, 'images', filename)
+        label_local_path = os.path.join(OUTPUT_DIR, split, 'labels', f"{base_name}.txt")
+
+        try:
+            download_from_minio(image_s3_path, img_local_path)
+        except Exception as e:
+            tqdm.write(f"Error treating {filename}: {e}")
+            continue
+
+        yolo_lines = []
+
+        # Sicherstellen, dass Annotationen vorhanden sind
+        if not entry.get('annotations'):
+            continue
+
+        results = entry['annotations'][0].get('result', [])
+
+        # Hilfsmaps für das Matching über IDs
+        kp_map = {}  # ID -> {label, x, y}
+        visibility_map = {}  # ID -> v_status (1 oder 2)
+        bboxes = []  # Liste aller gefundenen BBoxes
+
+        for res in results:
+            res_id = res['id']
+            res_type = res['type']
+            val = res.get('value', {})
+
+            if res_type == 'keypointlabels':
+                kp_map[res_id] = {
+                    'label': val['keypointlabels'][0],
+                    'x': val['x'] / 100.0,
+                    'y': val['y'] / 100.0
+                }
+
+            elif res_type == 'choices':
+                # Matching: Label Studio nutzt die gleiche ID für Keypoint und Choice
+                # Wir prüfen, ob die Checkbox "1" (dein Alias für verdeckt) gewählt wurde
+                if "1" in val.get('choices', []):
+                    visibility_map[res_id] = 1
+
+            elif res_type == 'rectanglelabels':
+                # BBox normalisieren
+                bw = val['width'] / 100.0
+                bh = val['height'] / 100.0
+                bx = (val['x'] / 100.0) + (bw / 2.0)
+                by = (val['y'] / 100.0) + (bh / 2.0)
+                bboxes.append(f"{bx:.6f} {by:.6f} {bw:.6f} {bh:.6f}")
+
+        # Für jede gefundene BBox eine YOLO Zeile generieren
+        # (Hinweis: Aktuell werden alle Keypoints an jede BBox gehängt)
+        for bbox_coords in bboxes:
+            line = f"0 {bbox_coords}"
+
+            for kp_name in KP_ORDER:
+                # Finde die ID des Keypoints mit diesem Namen
+                target_id = next((id for id, d in kp_map.items() if d['label'] == kp_name), None)
+
+                if target_id:
+                    coords = kp_map[target_id]
+                    # Sichtbarkeit: 1 (verdeckt) wenn in visibility_map, sonst 2 (sichtbar)
+                    v = visibility_map.get(target_id, 2)
+                    line += f" {coords['x']:.6f} {coords['y']:.6f} {v}"
+                else:
+                    line += " 0.000000 0.000000 0"
+
+            yolo_lines.append(line)
+
+        with open(label_local_path, 'w', encoding='utf-8') as f:
+            f.write('\n'.join(yolo_lines))
+
+    print(f"Fertig! Daten liegen in: {os.path.abspath(OUTPUT_DIR)}")
+
+if __name__ == "__main__":
+    convert_to_yolo()

main.py

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+from ultralytics import YOLO
+
+# Create a new YOLO model from scratch
+#model = YOLO("yolo11n.yaml")
+
+# Load a pretrained YOLO model (recommended for training)
+model = YOLO("yolo11n-pose.pt")
+
+# Train the model using the 'coco8.yaml' dataset for 3 epochs
+results = model.train(data="datasets/skier_pose/skier_pose.yaml", epochs=200, imgsz=640, device='mps')
+
+# Evaluate the model's performance on the validation set
+results = model.val()
+
+# Perform object detection on an image using the model
+#results = model("bus.jpg", device='cpu')
+
+# Export the model to ONNX format
+#success = model.export(format="onnx")