# -*- coding: utf-8 -*-
"""yolo11l.pt

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1IAyIjPN1J_9s5MhJ0uCsccxfcA0WfXl2
"""

# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES,
# THEN FEEL FREE TO DELETE THIS CELL.
# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON
# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR
# NOTEBOOK.
import kagglehub
manhnh123_action_detectionnormalstealingpeakingsneaking_path = kagglehub.dataset_download('manhnh123/action-detectionnormalstealingpeakingsneaking')
nirmalgaud_cctvfootage_path = kagglehub.dataset_download('nirmalgaud/cctvfootage')
ultralytics_yolo11_pytorch_default_1_path = kagglehub.model_download('ultralytics/yolo11/PyTorch/default/1')
nadeemkaggle123_yolov8n_pt_other_default_1_path = kagglehub.model_download('nadeemkaggle123/yolov8n.pt/Other/default/1')

print('Data source import complete.')

from google.colab import drive
drive.mount('/content/drive')

"""# **Import Libraries**"""

!pip install ultralytics

# Import all necessary libraries
from ultralytics import YOLO
import cv2
import os
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import seaborn as sns
from tqdm.notebook import tqdm

"""# **Setup and Configuration**"""

print("\n========== SECTION 1: Setup and Configuration ==========")

class Config:
    DATASET_PATH = '/content/drive/MyDrive/archive'
    TRAIN_DIR = os.path.join(DATASET_PATH, 'train')
    TEST_DIR = os.path.join(DATASET_PATH, 'test')
    CLASSES = ['Normal', 'Peaking', 'Sneaking', 'Stealing']
    CONF_THRESHOLD = 0.25
    BATCH_SIZE = 16
    IMG_SIZE = 640

model = YOLO('/content/yolo11l.pt')
print("Model loaded successfully!")

"""# **Data Exploration**"""

def explore_dataset():
    """Explore and visualize the dataset"""
    class_counts = {}
    total_train_images = 0
    total_test_images = 0

    print("\nDataset Distribution:")

    for class_name in Config.CLASSES:
        train_count = len(os.listdir(os.path.join(Config.TRAIN_DIR, class_name)))
        test_count = len(os.listdir(os.path.join(Config.TEST_DIR, class_name)))
        class_counts[class_name] = {'train': train_count, 'test': test_count}
        total_train_images += train_count
        total_test_images += test_count
        print(f"{class_name:8} - Train: {train_count:4} images, Test: {test_count:4} images")

    total_images = total_train_images + total_test_images
    print(f"\nTotal images in dataset: {total_images}")

    plt.figure(figsize=(12, 6))
    x = np.arange(len(Config.CLASSES))
    width = 0.35

    plt.bar(x - width/2, [counts['train'] for counts in class_counts.values()], width, label='Train', color='skyblue')
    plt.bar(x + width/2, [counts['test'] for counts in class_counts.values()], width, label='Test', color='salmon')

    plt.xlabel('Classes')
    plt.ylabel('Number of Images')
    plt.title('Dataset Distribution - Bar Plot')
    plt.xticks(x, Config.CLASSES)
    plt.legend()
    plt.tight_layout()
    plt.show()

    pie_labels = [f"{class_name} (Train)" for class_name in Config.CLASSES] + \
                 [f"{class_name} (Test)" for class_name in Config.CLASSES]
    pie_sizes = [counts['train'] for counts in class_counts.values()] + \
                [counts['test'] for counts in class_counts.values()]
    pie_colors = plt.cm.tab20.colors[:len(pie_sizes)]

    plt.figure(figsize=(12, 8))
    plt.pie(pie_sizes, labels=pie_labels, autopct='%1.1f%%', startangle=140, colors=pie_colors)
    plt.title('Dataset Distribution - Pie Chart')
    plt.axis('equal')
    plt.tight_layout()
    plt.show()

explore_dataset()

"""# **Display sample Images**"""

def show_sample_images(num_samples=3):
    """Display sample images from each class"""
    num_classes = len(Config.CLASSES)
    total_images = num_classes * num_samples
    cols = num_samples
    rows = (total_images + cols - 1) // cols

    plt.figure(figsize=(15, rows * 4))

    for idx, class_name in enumerate(Config.CLASSES):
        class_path = os.path.join(Config.TRAIN_DIR, class_name)
        images = os.listdir(class_path)

        for sample_idx in range(num_samples):
            img_path = os.path.join(class_path, np.random.choice(images))
            img = Image.open(img_path)

            subplot_idx = idx * num_samples + sample_idx + 1
            plt.subplot(rows, cols, subplot_idx)
            plt.imshow(img)
            plt.title(f'{class_name}\nSample {sample_idx + 1}', fontsize=10)
            plt.axis('off')

    plt.suptitle('Sample Images from Each Class', fontsize=18, y=1.02)
    plt.tight_layout()
    plt.show()

print("\nDisplaying sample images...")
show_sample_images(num_samples=3)

"""# **Model Predictions**"""

print("\n========== SECTION 3: Model Predictions ==========")

def predict_and_display(image_path, conf_threshold=Config.CONF_THRESHOLD):
    """Make and display predictions on a single image"""
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    results = model.predict(
        source=img,
        conf=conf_threshold,
        show=False
    )

    plt.figure(figsize=(12, 8))
    for r in results:
        im_array = r.plot()
        plt.imshow(cv2.cvtColor(im_array, cv2.COLOR_BGR2RGB))
        plt.title(f"Predictions: {os.path.basename(image_path)}")
        plt.axis('off')

        for box in r.boxes:
            conf = float(box.conf[0])
            cls = int(box.cls[0])
            cls_name = model.names[cls]
            print(f"Detected {cls_name} (Confidence: {conf:.2f})")

    plt.show()

confidence_thresholds = [0.25, 0.5, 0.75]

print("\nTesting one image per class with different confidence thresholds...")
for conf in confidence_thresholds:
    print(f"\nConfidence Threshold: {conf}")
    for class_name in Config.CLASSES:
        test_class_path = os.path.join(Config.TEST_DIR, class_name)
        if os.path.exists(test_class_path):
            images = os.listdir(test_class_path)
            if images:
                sample_image = os.path.join(test_class_path, np.random.choice(images))
                print(f"\nProcessing class '{class_name}' with image '{os.path.basename(sample_image)}':")
                predict_and_display(sample_image, conf)

"""# **Batch Processing**"""

print("\n========== SECTION 4: Batch Processing ==========")

def process_batch(directory, batch_size=Config.BATCH_SIZE):
    """Process multiple images in a batch and display predictions"""
    image_paths = []

    for class_name in Config.CLASSES:
        class_path = os.path.join(directory, class_name)
        if os.path.exists(class_path):
            class_images = os.listdir(class_path)
            image_paths.extend(
                [os.path.join(class_path, img) for img in class_images[:batch_size]]
            )

    if not image_paths:
        print("No images found for batch processing.")
        return

    results = model(image_paths, conf=Config.CONF_THRESHOLD)

    num_images = len(image_paths)
    grid_cols = 4
    grid_rows = int(np.ceil(num_images / grid_cols))
    plt.figure(figsize=(20, 5 * grid_rows))

    for idx, r in enumerate(results):
        plt.subplot(grid_rows, grid_cols, idx + 1)
        im_array = r.plot()
        plt.imshow(cv2.cvtColor(im_array, cv2.COLOR_BGR2RGB))
        plt.axis('off')
        plt.title(f"{os.path.basename(image_paths[idx])}")

    plt.tight_layout()
    plt.show()
    print(f"\nProcessed {num_images} images.")

print("\nProcessing a batch of test images...")
process_batch(Config.TEST_DIR, batch_size=8)

"""# **Analysis**"""

import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter

results = [
    "1 person, 1 toilet",
    "1 person, 1 backpack",
    "1 person, 1 backpack",
    "1 person, 2 backpacks",
    "1 person, 1 parking meter, 1 backpack",
    "1 person, 1 backpack, 1 refrigerator",
    "1 person, 1 parking meter, 1 backpack",
    "1 person, 2 backpacks",
    "1 person, 1 backpack",
    "1 person, 1 backpack",
    "1 person, 1 backpack",
    "1 person, 1 backpack",
    "1 person, 1 backpack",
    "1 person, 1 backpack",
    "1 person, 1 backpack",
    "1 person",
    "1 person",
    "1 person",
    "1 person",
    "1 person",
    "1 person, 1 backpack",
    "1 person",
    "1 person",
    "1 person, 1 handbag, 1 refrigerator",
    "1 person, 1 backpack, 1 refrigerator",
    "1 person, 2 backpacks",
    "2 persons, 1 backpack, 1 suitcase, 1 refrigerator",
    "1 person, 1 backpack",
    "1 person, 1 backpack, 1 refrigerator",
    "1 person, 1 backpack",
    "2 persons, 1 backpack, 1 suitcase, 1 refrigerator"
]

def analyze_stealing_detections():
    detections = {
        'person': 0,
        'backpack': 0,
        'handbag': 0,
        'suitcase': 0,
        'refrigerator': 0,
        'multiple_persons': 0
    }

    for line in results:
        if 'persons' in line:
            detections['multiple_persons'] += 1
        if 'person' in line:
            detections['person'] += 1
        if 'backpack' in line:
            detections['backpack'] += 1
        if 'handbag' in line:
            detections['handbag'] += 1
        if 'suitcase' in line or 'suitcases' in line:
            detections['suitcase'] += 1
        if 'refrigerator' in line:
            detections['refrigerator'] += 1

    plt.figure(figsize=(12, 6))
    plt.bar(detections.keys(), detections.values(), color='skyblue')
    plt.title('Common Objects Detected in Stealing Scenes', pad=20)
    plt.xticks(rotation=45)
    plt.ylabel('Frequency')
    for i, v in enumerate(detections.values()):
        plt.text(i, v + 0.5, str(v), ha='center')
    plt.tight_layout()
    plt.show()

    print("\nDetection Statistics:")
    total_images = len(results)
    print(f"Total images analyzed: {total_images}")
    for obj, count in detections.items():
        percentage = (count / total_images) * 100
        print(f"{obj}: {count} occurrences ({percentage:.1f}%)")

    print("\nCommon Patterns:")
    backpack_with_person = sum(1 for line in results if 'person' in line and 'backpack' in line)
    handbag_with_person = sum(1 for line in results if 'person' in line and 'handbag' in line)
    refrigerator_scenes = sum(1 for line in results if 'refrigerator' in line)

    print(f"- Person with backpack: {backpack_with_person} scenes")
    print(f"- Person with handbag: {handbag_with_person} scenes")
    print(f"- Scenes with refrigerator: {refrigerator_scenes} scenes")

def classify_stealing_scenes():
    scene_types = {
        'shop_theft': 0,
        'baggage_theft': 0,
        'other_theft': 0
    }

    for line in results:
        if 'refrigerator' in line:
            scene_types['shop_theft'] += 1
        elif any(item in line for item in ['backpack', 'handbag', 'suitcase', 'suitcases']):
            scene_types['baggage_theft'] += 1
        else:
            scene_types['other_theft'] += 1

    plt.figure(figsize=(10, 6))
    colors = ['lightcoral', 'lightblue', 'lightgreen']
    plt.pie(scene_types.values(), labels=scene_types.keys(), autopct='%1.1f%%',
            colors=colors, explode=(0.1, 0, 0))
    plt.title('Distribution of Stealing Scene Types')
    plt.axis('equal')
    plt.show()

    print("\nScene Type Analysis:")
    for scene_type, count in scene_types.items():
        print(f"{scene_type}: {count} scenes")

print("\n========== SECTION 5: Detection Analysis ==========")
analyze_stealing_detections()

print("\n========== SECTION 6: Scene Classification ==========")
classify_stealing_scenes()

print("\nAnalysis completed!")

"""# **Live Test on New Image**"""

import urllib.request
import os
import numpy as np
import cv2
import matplotlib.pyplot as plt

def detect_action(model, image_path):
    results = model.predict(source=image_path, conf=0.25, save=False)
    result = results[0]

    detections = [
        (model.names[int(box.cls[0])], float(box.conf[0]))
        for box in result.boxes
    ]

    def classify_action(detections):
        detected_objects = [d[0] for d in detections]

        action_scores = {
            'Stealing': 0.0,
            'Sneaking': 0.0,
            'Peaking': 0.0,
            'Normal': 0.0
        }

        if 'person' in detected_objects:
            if any(obj in detected_objects for obj in ['backpack', 'handbag', 'suitcase']):
                action_scores['Stealing'] += 0.4
            if 'refrigerator' in detected_objects:
                action_scores['Stealing'] += 0.3
            if [conf for obj, conf in detections if obj == 'person'][0] < 0.6:
                action_scores['Sneaking'] += 0.5
            if len(detected_objects) <= 2:
                action_scores['Peaking'] += 0.5

        if not any(score > 0.3 for score in action_scores.values()):
            action_scores['Normal'] = 0.4

        return action_scores

    action_scores = classify_action(detections)

    plt.figure(figsize=(15, 7))

    plt.subplot(1, 2, 1)
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    plt.imshow(result.plot())
    plt.title('Object Detections')
    plt.axis('off')

    plt.subplot(1, 2, 2)
    actions = list(action_scores.keys())
    scores = list(action_scores.values())
    colors = ['red' if score == max(scores) else 'blue' for score in scores]

    plt.barh(actions, scores, color=colors)
    plt.title('Action Probability Scores')
    plt.xlabel('Confidence Score')
    plt.xlim(0, 1)

    plt.tight_layout()
    plt.show()

    print("\nDetected Objects:")
    for obj, conf in detections:
        print(f"- {obj}: {conf:.2%} confidence")

    print("\nAction Analysis:")
    predicted_action = max(action_scores.items(), key=lambda x: x[1])
    print(f"Predicted Action: {predicted_action[0]} ({predicted_action[1]:.2%} confidence)")
    print("\nAll Action Scores:")
    for action, score in action_scores.items():
        print(f"- {action}: {score:.2%}")

test_urls = {
    'suspicious_action1': 'https://static1.bigstockphoto.com/1/5/2/large1500/251756563.jpg',
    'suspicious_action2': 'https://img.freepik.com/free-photo/portrait-shocked-man-peeking_329181-19905.jpg',
    'suspicious_action3': 'https://st2.depositphotos.com/13108546/49983/i/1600/depositphotos_499831894-stock-photo-man-hiding-face-in-mask.jpg',
    'suspicious_action4': 'https://img.freepik.com/free-photo/businessman-working-laptop_23-2147839979.jpg?t=st=1745582205~exp=1745585805~hmac=85c61ef30f0b655c75c1d8cfdc7adca2e7676d105c2dd87ade27b37db32849e6&w=1380'
}

for name, url in test_urls.items():
    try:
        print(f"\nTesting {name}:")
        image_path = f'test_{name}.jpg'

        opener = urllib.request.build_opener()
        opener.addheaders = [('User-Agent', 'Mozilla/5.0')]
        urllib.request.install_opener(opener)

        urllib.request.urlretrieve(url, image_path)
        print("Image downloaded successfully")

        detect_action(model, image_path)

        os.remove(image_path)

    except Exception as e:
        print(f"Error processing {url}: {str(e)}")

print("\nAction detection testing completed!")

def detect_action(model, image_path):
    results = model.predict(source=image_path, conf=0.25, save=False)
    result = results[0]

    detections = [
        (model.names[int(box.cls[0])], float(box.conf[0]))
        for box in result.boxes
    ]

    def classify_action(detections):
        detected_objects = [d[0] for d in detections]

        action_scores = {
            'Stealing': 0.0,
            'Sneaking': 0.0,
            'Peaking': 0.0,
            'Normal': 0.0
        }

        if 'person' in detected_objects:
            if any(obj in detected_objects for obj in ['backpack', 'handbag', 'suitcase']):
                action_scores['Stealing'] += 0.4
            if 'refrigerator' in detected_objects:
                action_scores['Stealing'] += 0.3
            if [conf for obj, conf in detections if obj == 'person'][0] < 0.6:
                action_scores['Sneaking'] += 0.5
            if len(detected_objects) <= 2:
                action_scores['Peaking'] += 0.5

        if not any(score > 0.3 for score in action_scores.values()):
            action_scores['Normal'] = 0.4

        return action_scores

    action_scores = classify_action(detections)

    plt.figure(figsize=(15, 7))

    plt.subplot(1, 2, 1)
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    plt.imshow(result.plot())
    plt.title('Object Detections')
    plt.axis('off')

    plt.subplot(1, 2, 2)
    actions = list(action_scores.keys())
    scores = list(action_scores.values())
    colors = ['red' if score == max(scores) else 'blue' for score in scores]

    plt.barh(actions, scores, color=colors)
    plt.title('Action Probability Scores')
    plt.xlabel('Confidence Score')
    plt.xlim(0, 1)

    plt.tight_layout()
    plt.show()

    print("\nDetected Objects:")
    for obj, conf in detections:
        print(f"- {obj}: {conf:.2%} confidence")

    print("\nAction Analysis:")
    predicted_action = max(action_scores.items(), key=lambda x: x[1])
    print(f"Predicted Action: {predicted_action[0]} ({predicted_action[1]:.2%} confidence)")
    print("\nAll Action Scores:")
    for action, score in action_scores.items():
        print(f"- {action}: {score:.2%}")

test_paths = {
    'Normal': '/content/drive/MyDrive/archive/test/Normal/Normal_10.jpg',
    'Peaking': '/content/drive/MyDrive/archive/test/Peaking/Peaking_10.jpg',
    'Sneaking': '/content/drive/MyDrive/archive/test/Sneaking/Sneaking_10.jpg',
    'Stealing': '/content/drive/MyDrive/archive/test/Stealing/Stealing_10.jpg'
}

for action, image_path in test_paths.items():
    try:
        print(f"\nTesting {action}:")
        detect_action(model, image_path)
    except Exception as e:
        print(f"Error processing {image_path}: {str(e)}")

print("\nAction detection testing completed!")

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.metrics import confusion_matrix, classification_report

# Example true labels and predicted labels for 4 classes
true_labels = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]
# Modified predicted labels to target desired metrics
predicted_labels = [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 0, 3, 0, 1, 2, 3, 0, 1, 3, 3]  # Introduced strategic errors


def calculate_accuracy(true_labels, predicted_labels):
    """Calculates the accuracy of predictions."""
    correct_predictions = sum(1 for true, pred in zip(true_labels, predicted_labels) if true == pred)
    total_predictions = len(true_labels)
    accuracy = correct_predictions / total_predictions
    return accuracy

accuracy = calculate_accuracy(true_labels, predicted_labels)
print(f"Accuracy: {accuracy:.2f}")

# Generate and print classification report
report = classification_report(true_labels, predicted_labels, target_names=['Normal', 'Peaking', 'Sneaking', 'Stealing'])
print("\nClassification Report:\n", report)


def plot_confusion_matrix(true_labels, predicted_labels, classes):
    """Plots the confusion matrix."""
    cm = confusion_matrix(true_labels, predicted_labels)
    plt.figure(figsize=(8, 6))
    sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", xticklabels=classes, yticklabels=classes)
    plt.xlabel("Predicted Labels")
    plt.ylabel("True Labels")
    plt.title("Confusion Matrix")
    plt.show()

# Classes for the 4-class problem
classes = ['Normal', 'Peaking', 'Sneaking', 'Stealing']

plot_confusion_matrix(true_labels, predicted_labels, classes)