init commit

models/utils.py

@@ -0,0 +1,106 @@
+from datetime import datetime
+from ultralytics import YOLO
+import numpy as np
+import cv2
+import csv
+import os 
+
+def min_max_normalize(matrix):
+    min_vals = np.min(matrix, axis=0) 
+    max_vals = np.max(matrix, axis=0)
+    range_vals = max_vals - min_vals
+    range_vals[range_vals == 0] = 1
+    normalized_matrix = (matrix - min_vals) / range_vals
+    return normalized_matrix
+
+
+
+def load_dataset(csv_file,unlabeled_ratio=0.15, test_ratio=0.4):
+    
+    data = np.genfromtxt(csv_file, delimiter=",", dtype=str, skip_header=1)
+    class_names = np.unique(data[:, -1])
+    print(f"classes: {class_names[0]} / {class_names[1]}")
+    print(f"dataset samples: {data.shape[0]} / features: {data.shape[1] - 1}")
+    if class_names[0] in np.unique(data[:, -1]) or class_names[1] in np.unique(data[:, -1]):
+        data[:, -1] = np.where(data[:, -1] == class_names[0], 1, -1)
+
+    data = data.astype(np.float32)
+
+    features = min_max_normalize(data[:, :-1])
+    
+
+    np.random.seed(10000)
+    indices = np.random.permutation(len(features))
+
+    split_idx = int(len(features) * (1 - unlabeled_ratio))
+    labeled_test_features = features[indices[:split_idx]]
+    labeled_test_labels = data[indices[:split_idx]][:, -1]
+    U = features[indices[split_idx:]]
+
+    test_split_idx = int(len(labeled_test_features) * (1 - test_ratio))
+    X = labeled_test_features[:test_split_idx]
+    y = labeled_test_labels[:test_split_idx]
+    X_test = labeled_test_features[test_split_idx:]
+    y_test = labeled_test_labels[test_split_idx:]
+    y = y.reshape(y.shape[0], 1)
+    y_test = y_test.reshape(y_test.shape[0], 1)
+
+
+    return X, y, X_test, y_test, U
+
+
+def save_result(model, dataset, accuracy, params, results_file):
+    file_exists = os.path.isfile(results_file)
+    with open(results_file, mode="a", newline="") as f:
+        writer = csv.DictWriter(f, fieldnames=["timestamp", "model", "dataset", "parameters", "accuracy"])
+        if not file_exists:
+            writer.writeheader()
+        writer.writerow({
+            "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+            "model": model,
+            "dataset": dataset,
+            "parameters": params,
+            "accuracy": accuracy
+        })
+
+def load_results(limit, results_file):
+    if not os.path.isfile(results_file):
+        return []
+    with open(results_file, mode="r") as f:
+        reader = list(csv.DictReader(f))
+    return reader[::-1][:limit]
+
+
+
+
+def predict_yolo(image_path, confidence):
+    print("Predicting:", image_path)
+    
+    # Load YOLO model
+    model = YOLO("templates/static/public/files/repair/weights/14_class_best.pt")
+
+    # Run prediction on the input image
+    results = model.predict(
+        source=f"templates/static/public/files/repair/images/{image_path}",
+        save=False,
+        conf=confidence, 
+        device = "cpu",
+        batch=4,
+        imgsz=320
+    )
+
+
+    predicted_img = results[0].plot()  # OpenCV image with boxes drawn
+
+    # Save location (always overwrite the same file)
+    output_dir = "templates/static/public/files/repair/predicted"
+    os.makedirs(output_dir, exist_ok=True)
+    output_filename = "predicted_image.jpg"  # fixed name
+    output_path = os.path.join(output_dir, output_filename)
+    print(f"image {predicted_img} written in : ", output_path)
+
+    # Write image
+    cv2.imwrite(output_path, predicted_img)
+
+    # Return only the filename so template can use it
+    return output_filename