Updated Readme File

Helmet_Detection/readme.md

@@ -10,17 +10,16 @@ This repository contains the implementation of a Helmet Detection Model using Te
 - [Training](#training)
 - [Evaluation](#evaluation)
 - [Usage](#usage)
-- [Results](#results)
-- [Acknowledgements](#acknowledgements)
+- [Result](#Result)
 
 ## **Introduction**
 
 Helmet detection is crucial for ensuring safety standards are met in various environments, such as construction sites and roadways. This project leverages a Faster R-CNN approach with a ResNet50 backbone to accurately detect helmets in images.
 
 ## **Requirements**
 
-- Python 3.x
-- TensorFlow 2.x
+- Python 
+- TensorFlow 
 - NumPy
 - Matplotlib
 - lxml
@@ -46,7 +45,17 @@ The annotations are in XML format. The `parse_annotation` function extracts obje
 
 ```python
 def parse_annotation(annotation_file):
-    # Parsing logic
+    tree = etree.parse(annotation_file)
+    root = tree.getroot()
+    objects = []
+    for obj in root.findall('object'):
+        obj_struct = {}
+        obj_struct['name'] = obj.find('name').text
+        bbox = obj.find('bndbox')
+        obj_struct['bbox'] = [int(bbox.find('xmin').text), int(bbox.find('ymin').text),
+                              int(bbox.find('xmax').text), int(bbox.find('ymax').text)]
+        objects.append(obj_struct)
+    return objects
 ```
 
 # Loading Data
@@ -55,7 +64,21 @@ The `load_data` function loads and preprocesses the images and annotations for t
 
 ```python
 def load_data(annotations_dir, images_dir):
-    # Loading and preprocessing logic
+    X, y = [], []
+    for annotation_file in os.listdir(annotations_dir):
+        if annotation_file.endswith('.xml'):
+            annotation_path = os.path.join(annotations_dir, annotation_file)
+            objects = parse_annotation(annotation_path)
+            image_path = os.path.join(images_dir, annotation_file.replace('.xml', '.png'))
+            image = load_img(image_path, target_size=(224, 224))
+            image = img_to_array(image)
+            X.append(preprocess_input(image))
+            labels = [0] * len(classes)
+            for obj in objects:
+                label_idx = classes.index(obj['name'])
+                labels[label_idx] = 1
+            y.append(labels)
+    return np.array(X), np.array(y)
 ```
 
 # Model Architecture
@@ -64,7 +87,15 @@ The model is based on Faster R-CNN with a ResNet50 backbone, pre-trained on Imag
 
 ```python
 def create_faster_rcnn():
-    # Model creation logic
+    base_model = ResNet50(include_top=False, weights='imagenet', input_shape=(224, 224, 3))
+    for layer in base_model.layers:
+        layer.trainable = False
+    x = base_model.output
+    x = layers.GlobalAveragePooling2D()(x)
+    x = layers.Dense(128, activation='relu')(x)
+    output = layers.Dense(len(classes), activation='sigmoid')(x)
+    model = models.Model(inputs=base_model.input, outputs=output)
+    return model
 ```
 
 # Training
@@ -89,14 +120,21 @@ The model can be evaluated on a separate test set. Here, we demonstrate how to l
 
 ```python
 def preprocess_image(image_path):
-    # Preprocessing logic
+    image = load_img(image_path, target_size=(224, 224))
+    image = img_to_array(image)
+    image = np.expand_dims(image, axis=0)
+    image = tf.keras.applications.resnet50.preprocess_input(image)
+    return image
 ```
 
 
 ## Interpret Predictions
 ```python
 def interpret_prediction(prediction, threshold=0.6):
-    # Interpretation logic
+   if prediction > threshold:
+        return "With Helmet"
+    else:
+        return "Without Helmet"
 ```
 
 # Usage
@@ -117,7 +155,7 @@ result = interpret_prediction(prediction[0][0])
 display_image_with_prediction(image_path, result)
 ```
 
-# Result and Accuracy
+# Result 
 
 The model is working fine with accuracy of 81% and is able to successfully detect the images With Helmets and Without Helmets.