--- a
+++ b/MODEL/Validation.py
@@ -0,0 +1,145 @@
+import os
+import cv2
+from ultralytics import YOLO
+import numpy as np
+import pandas as pd
+from sklearn.metrics import classification_report, accuracy_score, f1_score
+from tqdm import tqdm
+
+# Paths to your images and labels folders
+images_folder = r'D:\CSE\Semester - 5\Extra\IIT jammu\YOLO\Code\Data\valid\images'
+labels_folder = r'D:\CSE\Semester - 5\Extra\IIT jammu\YOLO\Code\Data\valid\labels'
+
+# Initialize lists to store results
+true_labels = []
+predicted_labels = []
+
+# Initialize lists to store IoU scores
+iou_scores = []
+
+# Initialize the YOLOv8 model
+model = YOLO("best_75.pt")
+
+# Initialize lists to store detection results
+detection_results = []
+
+def calculate_iou(box1_tensor, box2_list, image_width, image_height):
+    # Extract values from the tensor
+    x1, y1, x2, y2, confidence, class_id = box1_tensor.tolist()
+
+    # Convert values from the list to strings
+    class_id2, x2_gt, y2_gt, w2_gt, h2_gt = map(str, box2_list)
+
+    # Convert coordinates to float
+    x2_gt, y2_gt, w2_gt, h2_gt = float(x2_gt), float(y2_gt), float(w2_gt), float(h2_gt)
+
+    # Convert the model's bounding box coordinates to YOLO format
+    x1, y1, x2, y2 = x1 / image_width, y1 / image_height, x2 / image_width, y2 / image_height
+
+    # Calculate the intersection coordinates
+    xA = max(x1, x2_gt)
+    yA = max(y1, y2_gt)
+    xB = min(x1 + x2, x2_gt + w2_gt)
+    yB = min(y1 + y2, y2_gt + h2_gt)
+
+    # Calculate the intersection area
+    interArea = max(0, xB - xA) * max(0, yB - yA)
+
+    # Calculate the areas of the boxes
+    box1Area = x2 * y2
+    box2Area = w2_gt * h2_gt
+
+    # Calculate the IoU
+    iou = interArea / float(box1Area + box2Area - interArea)
+
+    return iou
+
+# Iterate through image files and corresponding label files
+for image_file in tqdm(os.listdir(images_folder)):
+    if image_file.endswith('.png'):
+        image_path = os.path.join(images_folder, image_file)
+
+        # Determine class (bleeding or non-bleeding) from image file name
+        is_bleeding = image_file.startswith('img-')
+
+        # Load the image
+        image = cv2.imread(image_path)
+
+        # Perform inference
+        results = model.predict(image)
+
+        # Check if bleeding is detected
+        detected_bleeding = results[0].boxes.shape[0] > 0
+
+        # Append true and predicted labels
+        true_labels.append(is_bleeding)
+        predicted_labels.append(detected_bleeding)
+
+        # Extract ground truth bounding boxes from label file
+        label_file_name = os.path.splitext(image_file)[0] + '.txt'
+        label_file_path = os.path.join(labels_folder, label_file_name)
+
+        if os.path.exists(label_file_path):
+            with open(label_file_path, 'r') as label_file:
+                lines = label_file.readlines()
+                ground_truth_boxes = [list(map(float, line.strip().split())) for line in lines]
+
+            # Calculate IoU for each detected box
+            if results[0].boxes.shape[0] > 0:
+                for box in results[0].boxes.boxes:
+                    iou_values = []
+                    for gt_box in ground_truth_boxes:
+                        iou = calculate_iou(box, gt_box, 224, 224)
+                        iou_values.append(iou)
+
+                    max_iou = max(iou_values) if iou_values else 0
+                    iou_scores.append(max_iou)
+            
+            # Append detection results
+            if results[0].boxes.shape[0] > 0:
+                box = results[0].boxes[0]
+                detection_results.append({
+                    'image_path': image_path,
+                    'is_bleeding': is_bleeding,
+                    'detection_confidence': box.conf[0].item()
+                })
+
+# Calculate classification metrics
+accuracy = accuracy_score(true_labels, predicted_labels)
+recall = classification_report(true_labels, predicted_labels, target_names=['Non-Bleeding', 'Bleeding'], output_dict=True)
+f1 = f1_score(true_labels, predicted_labels)
+
+# Create a table of achieved evaluation metrics
+classification_metrics = {
+    'Accuracy': [accuracy],
+    'Recall (Non-Bleeding)': [recall['Non-Bleeding']['recall']],
+    'Recall (Bleeding)': [recall['Bleeding']['recall']],
+    'F1-Score': [f1]
+}
+
+classification_df = pd.DataFrame(classification_metrics)
+print("Classification Metrics:")
+print(classification_df)
+
+print()
+print("Detection Metrics:")
+print("IoU Metrics:")
+# Calculate and print IoU statistics
+iou_scores = np.array(iou_scores)
+iou_mean = np.mean(iou_scores)
+iou_median = np.median(iou_scores)
+iou_75th_percentile = np.percentile(iou_scores, 75)
+print("IoU Mean:", iou_mean)
+print("IoU Median:", iou_median)
+print("IoU 75th Percentile:", iou_75th_percentile)
+
+# Calculate Detection Average Precision (AP) and Mean Average Precision (mAP)
+detection_results_df = pd.DataFrame(detection_results)
+detection_results_df['detection_confidence'] = detection_results_df['detection_confidence'].apply(lambda x: x.item() if isinstance(x, np.float32) else x)
+detection_results_df['is_detected'] = detection_results_df['detection_confidence'] > 0.5  # You can adjust the confidence threshold as needed
+detection_results_df['is_detected'] = detection_results_df['is_detected'].astype(int)
+detection_ap = detection_results_df.groupby('is_bleeding')['is_detected'].apply(lambda x: np.mean(x))
+mAP = detection_ap.mean()
+print("Detection Average Precision (AP):")
+print(detection_ap)
+print("Mean Average Precision (mAP):", mAP)