Updated code

Machine Learning/Model/Pet/labels.txt

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+0 DOG
+1 RABBIT
+2 NEUTRAL

Machine Learning/Model/Pet/pet.py

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+from keras.models import load_model  # TensorFlow is required for Keras to work
+import cv2  # Install opencv-python
+import numpy as np
+
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+# Load the model
+model = load_model(r"C:\Users\Midhun Mathew\Desktop\Test\Machine Learning\Model\Pet\keras_model.h5", compile=False)
+
+# Load the labels
+class_names = open(r"C:\Users\Midhun Mathew\Desktop\Test\Machine Learning\Model\Pet\labels.txt", "r").readlines()
+
+# CAMERA can be 0 or 1 based on default camera of your computer
+camera = cv2.VideoCapture(0)
+
+while True:
+    # Grab the webcamera's image.
+    ret, image = camera.read()
+
+    # Resize the raw image into (224-height,224-width) pixels
+    resized_image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)
+
+    # Make the image a numpy array and reshape it to the model's input shape.
+    input_image = np.asarray(resized_image, dtype=np.float32).reshape(1, 224, 224, 3)
+
+    # Normalize the image array
+    input_image = (input_image / 127.5) - 1
+
+    # Predict using the model
+    prediction = model.predict(input_image)
+    index = np.argmax(prediction)
+    class_name = class_names[index].strip()
+    confidence_score = prediction[0][index]
+
+    # Print prediction and confidence score
+    print(f"Class: {class_name}, Confidence Score: {str(np.round(confidence_score * 100))[:-2]}%")
+
+    # Draw the frame and label based on prediction
+    if index == 0:  # DOG
+        cv2.rectangle(image, (0, 0), (image.shape[1], image.shape[0]), (0, 0, 255), 10)  # Red frame
+        cv2.putText(image, "DOG", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 3)  # Red text
+    elif index == 1:  # RABBIT
+        cv2.rectangle(image, (0, 0), (image.shape[1], image.shape[0]), (0, 255, 0), 10)  # Green frame
+        cv2.putText(image, "RABBIT", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 3)  # Green text
+    elif index == 2:  # NEUTRAL
+        pass  # No frame, no text
+
+    # Display the frame with the label
+    cv2.imshow("Webcam Image", image)
+
+    # Listen to the keyboard for presses.
+    keyboard_input = cv2.waitKey(1)
+
+    # 27 is the ASCII for the ESC key.
+    if keyboard_input == 27:
+        break
+
+# Release the camera and close the window
+camera.release()
+cv2.destroyAllWindows()

Machine Learning/Model/face_intruder.py

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+import cv2
+import datetime
+import os
+import time
+
+# Load pre-trained data on face frontals from OpenCV (Haar cascade algorithm)
+face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+
+# Function to trigger an alert and save the photo
+def trigger_alert_and_save(frame):
+    # Print the alert message
+    print("Intrusion Alert! Face Detected!")
+
+    # Get the current time to name the photo uniquely
+    timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
+    # Create a directory to save the photos if it doesn't exist
+    if not os.path.exists('intrusion_photos'):
+        os.makedirs('intrusion_photos')
+
+    # Save the frame as a photo
+    file_name = f"intrusion_photos/intrusion_{timestamp}.jpg"
+    cv2.imwrite(file_name, frame)
+    print(f"Saved photo: {file_name}")
+
+# Start video capture from the default camera (0)
+cap = cv2.VideoCapture(0)
+
+# Variables to track timing and last detected faces
+last_detected_faces = []
+last_photo_time = 0
+photo_interval = 5  # Time interval in seconds between photos
+
+while True:
+    # Read the current frame from the video capture
+    ret, frame = cap.read()
+
+    # Convert the frame to grayscale (Haar cascade requires grayscale)
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces in the frame
+    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
+
+    # Process detected faces
+    current_faces = []
+
+    for idx, (x, y, w, h) in enumerate(faces):
+        current_faces.append((x, y, w, h))
+
+        # Draw a rectangle around the first face (normal detection)
+        if idx == 0:
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)  # Blue box
+            cv2.putText(frame, f"Face detected at {datetime.datetime.now()}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+
+        # If a second face is detected, mark as intruder and use a red box
+        if idx == 1:
+            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)  # Red box for intruder
+            cv2.putText(frame, "Intruder!", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+
+        # Check the time interval to control photo capture frequency
+        current_time = time.time()
+        if (x, y, w, h) not in last_detected_faces and (current_time - last_photo_time > photo_interval):
+            # Trigger intrusion alert and save the photo
+            trigger_alert_and_save(frame)
+            last_photo_time = current_time  # Update the last photo time
+
+    # Update the list of last detected faces
+    last_detected_faces = current_faces
+
+    # Display the frame with detection
+    cv2.imshow('Intrusion Detection', frame)
+
+    # Break the loop if 'q' is pressed
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release the capture and close windows
+cap.release()
+cv2.destroyAllWindows()

my-key.ssh

@@ -0,0 +1 @@
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UserProfile.sshid_rsa

@@ -0,0 +1,49 @@
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UserProfile.sshid_rsa.pub

@@ -0,0 +1 @@
+ssh-rsa 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 Midhun Mathew@Inspiron