python code for We are looking for a expert in machine learning or AI for image recognitions, training data is about 700 images only. Use case example: An Electrical extension board having six sockets and different appliances are hooked/plugged in this Electrical extension, then Using mobile camera if we hover on this extension board then we need to count how many sockets are free/unplugged and how many are taken/plugged. It is an example, the actual device is similar to this use case but from a different domain.

To implement image recognition for the described use case, you can use Python along with libraries like OpenCV and TensorFlow for image processing and machine learning. Below is a simple Python code to get you started:

python
import cv2
import numpy as np

# Load pre-trained model for object detection
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
    classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]

# Load image
img = cv2.imread("extension_board.jpg")
height, width, channels = img.shape

# Preprocess image for YOLO model
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)

# Process outputs to count sockets
free_sockets = 0
taken_sockets = 0
for out in outs:
    for detection in out:
        scores = detection[5:]
        class_id = np.argmax(scores)
        confidence = scores[class_id]
        if confidence > 0.5:
            center_x = int(detection[0] * width)
            center_y = int(detection[1] * height)
            w = int(detection[2] * width)
            h = int(detection[3] * height)
            x = int(center_x - w / 2)
            y = int(center_y - h / 2)
            if class_id == 84:  # Class ID for socket in COCO dataset
                free_sockets += 1
            elif class_id == 62:  # Class ID for person in COCO dataset (assumed plugged socket)
                taken_sockets += 1

print("Free sockets:", free_sockets)
print("Taken sockets:", taken_sockets)

# Display image with detected objects
cv2.imshow("Extension Board", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code uses the YOLO (You Only Look Once) object detection model to detect objects in an image. You'll need to have the YOLO model files (yolov3.weights, yolov3.cfg, coco.names) in the same directory as your script. Adjust the file paths accordingly.

Make sure to replace "extension_board.jpg" with the path to your input image.

This is a basic example, and you may need to fine-tune the model and preprocessing steps based on your specific use case and dataset. Additionally, training a model with only 700 images may not yield optimal results. If possible, consider using pre-trained models and transfer learning to leverage larger datasets for better accuracy.

 I'd be glad to provide Python code for image recognition of appliance usage in an electrical extension board, incorporating insights from expert feedback and addressing potential issues:

Python

import cv2

import numpy as np

from tensorflow.keras.models import load_model

# Function to preprocess image for model input

def preprocess_image(image):

    # Resize image to a fixed size suitable for your model

    image = cv2.resize(image, (224, 224))  # Example size, adjust based on model

    # Normalize pixel values (typical range: 0-1 or -1 to 1)

    image = image.astype(np.float32) / 255.0

    # Expand dimensions for compatibility with some models (e.g., TensorFlow)

    image = np.expand_dims(image, axis=0)  # Add a new dimension for batch size (1)

    return image

# Load pre-trained model (replace with your trained model path)

model = load_model('path/to/your/model.h5')  # Change to your model file path

# Function to detect and count appliances or free sockets

def detect_appliances(image_path):

    image = cv2.imread(image_path)  # Read image from file

    # Preprocess the image

    preprocessed_image = preprocess_image(image)

    # Make prediction using the model

    prediction = model.predict(preprocessed_image)

    # Decode prediction based on your model's output format (classes or probabilities)

    # Example: Assuming model outputs class probabilities for "plugged" and "unplugged"

    plugged_probability = prediction[0][0]  # Probability of a socket being plugged

    unplugged_probability = prediction[0][1]  # Probability of a socket being unplugged

    # Threshold to determine occupancy (adjust based on your model's behavior)

    plugged_threshold = 0.7  # Example threshold, adjust based on model performance

    unplugged_threshold = 0.7  # Example threshold, adjust based on model performance

    # Count plugged and unplugged sockets based on probabilities

    num_plugged = 0

    num_unplugged = 0

    for i in range(6):  # Assuming 6 sockets, adjust based on your extension board

        if plugged_probability[i] > plugged_threshold:

            num_plugged += 1

        elif unplugged_probability[i] > unplugged_threshold:

            num_unplugged += 1

    # Display results (or return values for mobile app integration)

    print(f"Number of plugged sockets: {num_plugged}")

    print(f"Number of unplugged sockets: {num_unplugged}")

    return num_plugged, num_unplugged  # Example for mobile app integration

# Example usage (replace with your image path)

image_path = 'path/to/your/image.jpg'

num_plugged, num_unplugged = detect_appliances(image_path)

print(f"Number of plugged sockets: {num_plugged}")

print(f"Number of unplugged sockets: {num_unplugged}")

Key Improvements based on Expert Feedback:

• Data Augmentation (Consider if applicable): If you have a limited dataset (700 images), explore data augmentation techniques (e.g., rotations, flips, brightness adjustments) to artificially increase the size and diversity of your training data, potentially improving model performance.
• Model Selection: Choose a pre-trained model (e.g., MobileNet, VGG16) appropriate for mobile deployment and fine-tune it on your specific appliance classification task.
• Transfer Learning: Leverage transfer learning to benefit from pre-trained models' weights and adapt them to your domain (electrical extension board). This can significantly improve performance compared to training from scratch with a small dataset.
• Hyperparameter Tuning: Experiment with different learning rates, optimizers, and other hyperparameters to optimize your model's accuracy.
• Model Evaluation: Use techniques like k-fold cross-validation or a separate validation set to assess model performance on unseen data and avoid overfitting.
• Error Handling: Consider adding error handling mechanisms to gracefully handle potential issues like image loading failures or unexpected prediction formats.
• Mobile App Integration (Optional): If integrating with a mobile app, tailor the code to return the num_plugged and num_unplugged values for display or further processing within the app.

Additional Considerations:

• Lighting Conditions: Ensure consistent lighting conditions during training and image capture to minimize model performance variations.
• Background Complexity: Aim for clean backgrounds in training images and

I'd be glad to help you with the Python code for image recognition of appliance usage on an electrical extension board, even with a limited dataset of 700 images. Here's a comprehensive approach that incorporates best practices and addresses potential challenges:

1. Data Augmentation (Crucial for Small Datasets):

• Techniques:
  • Random cropping, flipping, rotation, and color jittering to artificially increase the size and diversity of your training data, improving model generalization and robustness.
  • Consider using libraries like imgaug (https://imgaug.readthedocs.io/en/latest/) for efficient data augmentation.

2. Transfer Learning (Leverage Pre-trained Models):

• Rationale: Fine-tune a pre-trained image classification model like MobileNetV2 or EfficientNet that has already learned high-level features from a massive dataset. This significantly reduces training time and improves performance, especially with limited data.
• Implementation:
  • Use libraries like TensorFlow or PyTorch that provide pre-trained models and transfer learning functionalities.
  • Freeze the earlier layers of the pre-trained model to retain their learned features, and train only the final layers on your specific dataset to classify plugged/unplugged sockets.

3. Class Imbalance Handling (Address Potential Bias):

• Challenge: If your dataset has a significant imbalance between plugged and unplugged sockets (e.g., more plugged), the model might prioritize the majority class during training.
• Techniques:
  • Oversampling: Duplicate images of the minority class (unplugged) to create a more balanced dataset.
  • Undersampling: Randomly remove images of the majority class (plugged) to achieve a balance.
  • Weighted Loss Function: Assign higher weights to the loss calculated for the minority class during training, focusing the model's attention on correctly classifying unplugged sockets.

Code Structure (Conceptual):

Python

import tensorflow as tf  # Or PyTorch, depending on your choice

# Load the pre-trained model (e.g., MobileNetV2)
base_model = tf.keras.applications.MobileNetV2(weights='imagenet', include_top=False)

# Freeze the base model layers
base_model.trainable = False

# Create a new classification head for unplugged/plugged sockets
x = base_model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
predictions = tf.keras.layers.Dense(2, activation='softmax')(x)  # Output layer for 2 classes

# Create the final model
model = tf.keras.Model(inputs=base_model.input, outputs=predictions)

# Data augmentation (using imgaug)
from imgaug import augmenters as iaa

def augment_image(image):
  # Define your preferred augmentation pipeline here (e.g., random cropping, flipping)
  augmented_image = iaa.Sequential([
      iaa.Fliplr(0.5),  # 50% chance of horizontal flipping
      iaa.Crop(percent=(0, 0.1))  # Random cropping of up to 10%
  ]).augment_image(image)
  return augmented_image

# Load your dataset (700 images)
train_images, train_labels = load_dataset(...)  # Load training data with plugged/unplugged labels

# Apply data augmentation during training
train_images = [augment_image(img) for img in train_images]

# Compile the model with appropriate optimizer and loss function (consider class imbalance)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train the model with appropriate class weights (if imbalance is present)
class_weights = ...  # Define class weights based on your dataset imbalance
model.fit(train_images, train_labels, epochs=10, class_weight=class_weights)

# Save the model for future use
model.save('socket_usage_classifier.h5')

Use code with caution.

4. Model Deployment and Mobile Integration:

• Save the trained model: Use model.save() to save the trained model in a format suitable for mobile deployment (e.g., TensorFlow Lite for Android/iOS).
• Mobile Integration: Integrate the saved model into your mobile app using libraries like TensorFlow Lite for mobile or PyTorch Mobile.
• Real-time Image Capture and Processing: Use the mobile camera to capture images of the extension board and feed them to the loaded model for prediction.
• Display Results: Display the predicted number of plugged and unplugged sockets on the mobile app's user interface.

Additional Considerations:

• Lighting Conditions: Ensure your training dataset includes images captured under various lighting

Sources

1. github.com/michaelkeller21/MTS-CNN-Project

I'd be glad to provide Python code that addresses the challenges of limited training data, potential object variations, and real-world implementation for image recognition of appliance occupancy in an electrical extension board:

1. Import Libraries:

Python

2. Load and Preprocess Training Images (Addressing Limited Data):

Python

• Explanation:
  • Adjust image paths in the for loop.
  • Resize images to a common size (224x224 is a common choice for CNNs).
  • Extract labels from image filenames (assuming a consistent naming convention).
  • Normalize pixel values between 0 and 1.
  • Consider data augmentation (optional) to create more variations of your limited dataset, helping the model generalize better to unseen images.

3. Build and Train the CNN Model:

Python

• Explanation:
  • Create a simple convolutional neural network (CNN) model with two convolutional layers, max pooling layers, a hidden layer, and an output layer with sigmoid activation for binary classification (occupied/free).
  • Compile the model with binary cross-entropy loss (suitable for binary classification) and Adam optimizer.
  • Train the model using the augmented data generator (if enabled) or the original data. Adjust epochs and validation split based on your dataset size.

4. Load the Trained Model and Perform Image Recognition:

Python

• Explanation:
  • Define a function `count_appliance_occup

Sources

1. sailajakarra.medium.com/deep-learning-using-tensorflow-part3-9c16434b379c
2. pub.towardsai.net/create-a-boolean-image-classifier-fast-with-any-data-set-with-a-brief-explanation-of-the-4b1265b5b33f
3. www.atmosera.com/blog/convolutional-neural-networks/
4. stackoverflow.com/questions/71886524/how-do-solve-data-cardinality-is-ambiguous