Create ROC_curve_TFlite_Model.py

ROC_curve_TFlite_Model.py

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+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+import xml.etree.ElementTree as ET
+import cv2
+import glob
+import os
+from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score
+
+# Define paths
+test_images_dir = "test_images/"  # Path to test images
+test_annotations_dir = "test_annotations/"  # Path to Pascal VOC XML files
+tflite_model_path = "efficientdet_lite0.tflite"
+
+# Load TFLite model
+interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
+interpreter.allocate_tensors()
+
+# Function to run inference
+def run_inference(interpreter, image):
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    # Preprocess image
+    input_shape = input_details[0]['shape']
+    image = cv2.resize(image, (input_shape[1], input_shape[2]))  # Resize to model input size
+    image = image.astype(np.float32) / 255.0  # Normalize
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+
+    # Set input tensor
+    interpreter.set_tensor(input_details[0]['index'], image)
+    interpreter.invoke()
+
+    # Get output (bounding boxes and scores)
+    output_data = interpreter.get_tensor(output_details[0]['index'])
+    return output_data  # Confidence scores
+
+# Function to parse Pascal VOC XML annotation
+def parse_voc_annotation(xml_file):
+    tree = ET.parse(xml_file)
+    root = tree.getroot()
+
+    objects = root.findall("object")
+    return 1 if objects else 0  # If objects exist, return 1 (object present), else 0
+
+# Load test images and annotations
+image_files = glob.glob(os.path.join(test_images_dir, "*.jpg"))  # Adjust if using .png
+y_scores = []
+y_true = []
+
+for image_file in image_files:
+    # Load image
+    image = cv2.imread(image_file)
+
+    # Get corresponding XML annotation
+    xml_file = os.path.join(test_annotations_dir, os.path.splitext(os.path.basename(image_file))[0] + ".xml")
+    if not os.path.exists(xml_file):
+        continue  # Skip if annotation is missing
+
+    # Get ground truth label (1 = object present, 0 = no object)
+    true_label = parse_voc_annotation(xml_file)
+
+    # Run inference
+    scores = run_inference(interpreter, image)
+    max_score = np.max(scores)  # Get highest confidence score
+
+    # Append results
+    y_scores.append(max_score)
+    y_true.append(true_label)
+
+# Convert to numpy arrays
+y_scores = np.array(y_scores)
+y_true = np.array(y_true)
+
+# Compute ROC curve and AUC
+fpr, tpr, _ = roc_curve(y_true, y_scores)
+roc_auc = auc(fpr, tpr)
+
+# Compute Precision-Recall curve and AP score
+precision, recall, _ = precision_recall_curve(y_true, y_scores)
+average_precision = average_precision_score(y_true, y_scores)
+
+# Plot ROC Curve
+plt.figure(figsize=(8, 6))
+plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC Curve (AUC = {roc_auc:.2f})')
+plt.plot([0, 1], [0, 1], color='gray', linestyle='--')  # Diagonal line
+plt.xlabel("False Positive Rate")
+plt.ylabel("True Positive Rate")
+plt.title("ROC Curve")
+plt.legend()
+plt.show()
+
+# Plot Precision-Recall Curve
+plt.figure(figsize=(8, 6))
+plt.plot(recall, precision, color='green', lw=2, label=f'PR Curve (AP = {average_precision:.2f})')
+plt.xlabel("Recall")
+plt.ylabel("Precision")
+plt.title("Precision-Recall Curve")
+plt.legend()
+plt.show()