Upload 6 files

.gitattributes

@@ -1,35 +1,35 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,17 @@
+<<<<<<< HEAD
 ---
 license: mit
 ---
+=======
+# CVAT Model Repository
+
+Dieser Modell-Repository ist für die Verwendung in CVAT konzipiert.
+
+## Modell-Eigenschaften
+- Aufgabe: Objekt-Erkennung/Segmentierung
+- Framework: PyTorch
+- Eingabetyp: Bild
+
+## Verwendung
+Bitte konsultieren Sie die Dokumentation für die korrekte Integration in CVAT.
+>>>>>>> c4c4716 (Initial commit with model files)

config.json

@@ -0,0 +1,9 @@
+{
+    "name": "Ihr-Modell-Name",
+    "framework": "torch",
+    "labels": ["object"],  // Liste Ihrer Klassen
+    "type": "detector",    // Oder "segmentation", "classifier", etc.
+    "params": {
+        "threshold": 0.5
+    }
+}

cvat-config.json

@@ -0,0 +1,13 @@
+{
+    "task_type": "segmentation",  // oder "detection", "classification"
+    "input_type": "image",        // oder "video"
+    "output_type": "mask",        // oder "rectangle", "polygon", etc.
+    "model_config": {
+        "type": "segmentation",
+        "labels": ["object"],
+        "framework": "pytorch",
+        "model_params": {
+            "threshold": 0.5
+        }
+    }
+}

inference.py

@@ -0,0 +1,68 @@
+import torch
+from transformers import Pipeline
+from segment_anything import sam_model_registry, SamPredictor
+import numpy as np
+
+class CVATWrapper:
+    def __init__(self, model_path):
+        # Modell laden
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        
+        # Für SAM:
+        self.model = sam_model_registry["vit_h"](checkpoint=model_path)
+        self.model.to(self.device)
+        self.predictor = SamPredictor(self.model)
+        
+        # Für eigene Modelle:
+        # self.model = torch.load(model_path)
+        # self.model.eval()
+
+    def preprocess(self, image):
+        # Bildvorverarbeitung
+        if isinstance(image, str):
+            # Falls Bildpfad übergeben wird
+            image = cv2.imread(image)
+            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        return image
+
+    def predict(self, image):
+        # Vorverarbeitung
+        image = self.preprocess(image)
+        
+        # Für SAM:
+        self.predictor.set_image(image)
+        masks, scores, logits = self.predictor.predict()
+        
+        # Für eigene Modelle:
+        # output = self.model(processed_image)
+        # masks = process_output(output)  # Je nach Modellausgabe
+        
+        # Konvertierung ins CVAT-Format
+        results = []
+        for mask, score in zip(masks, scores):
+            if score > 0.5:  # Konfidenz-Schwellenwert
+                results.append({
+                    "confidence": float(score),
+                    "label": "object",
+                    "points": mask_to_polygons(mask),  # Hilfsfunktion unten
+                    "type": "polygon"
+                })
+        
+        return results
+
+def mask_to_polygons(mask):
+    """Konvertiert eine binäre Maske in CVAT-Polygon-Format"""
+    import cv2
+    contours, _ = cv2.findContours(
+        mask.astype(np.uint8), 
+        cv2.RETR_EXTERNAL, 
+        cv2.CHAIN_APPROX_SIMPLE
+    )
+    polygons = []
+    for contour in contours:
+        # Vereinfache die Konturen
+        epsilon = 0.005 * cv2.arcLength(contour, True)
+        approx = cv2.approxPolyDP(contour, epsilon, True)
+        if len(approx) >= 3:  # Mindestens 3 Punkte für ein Polygon
+            polygons.append(approx.reshape(-1, 2).tolist())
+    return polygons[0] if polygons else []

requirements.txt

@@ -0,0 +1,5 @@
+torch>=1.7.0
+segment-anything  # Für SAM
+opencv-python
+numpy
+transformers