INTRODUCTION
Mask R-CNN (Mask Region-based Convolutional Neural Network) is an extension of the R-CNN algorithm designed for object instance segmentation, as well as object detection. This means Mask R-CNN not only identifies objects in an image but also generates masks that segment each object from the background and other objects.
Applications of Mask R-CNN
Instance Segmentation: Unlike semantic segmentation, which classifies every pixel into a category, instance segmentation not only classifies each pixel but also distinguishes individual object instances. For example, in an image with multiple cars, Mask R-CNN will classify and provide a unique mask for each car.
Object Detection: Although it's primarily for segmentation, the bounding box prediction capability of Mask R-CNN also allows for object detection.
Human Pose Estimation: With some modifications, Mask R-CNN can be adapted to predict keypoints on objects (e.g., human joints).
Medical Imaging: It can be used for detecting and precisely segmenting tumors, anomalies, or other regions of interest in medical images.
Robotics: For robots to interact safely and effectively with their environment, understanding objects at the pixel level can be crucial. Mask R-CNN can aid in tasks like object manipulation.
Agriculture: Detecting and segmenting individual fruits in orchards for automated harvesting or monitoring plant health.
Autonomous Vehicles: Precise object detection and segmentation can help autonomous vehicles understand their environment better, e.g., distinguishing between pedestrians.
Video Analysis: Analyzing scenes and objects in videos for surveillance, content creation, or sports analytics.
Augmented Reality (AR): For better object and environment understanding to overlay virtual objects onto the real world.
Fashion and Retail: Automated product categorization, virtual try-ons, or analyzing customer behavior in retail spaces.
Input Image
We will use an image of an elephant as an input image.
Libraries Used
MRNN, Keras, Tensorflow, and Matplotlib
Implementation
class MaskRCNNDetector:
"""
MaskRCNNDector class provides functionality to detect objects using the pretrained COCO dataset with MaskRCNN.
Attributes:
- class_names: A list of class names in the COCO dataset.
- TestConfig: A subclass for MaskRCNN configuration.
- rcnn: The MaskRCNN model instance.
"""
class_names = ['person', 'bicycle', ...., 'car']
class TestConfig(Config):
"""Configuration for MaskRCNN using COCO dataset for inference."""
def __init__(self):
"""
Initializes the COCODetector object and loads the pretrained weights.
"""
pass
@staticmethod
def draw_image_with_boxes(filename, boxes_list):
"""
Draws and displays an image with bounding boxes.
Args:
- filename (str): Path to the image file.
- boxes_list (list): List of bounding boxes.
"""
pass
@staticmethod
def display_instances(image, boxes, masks, ids, names, scores):
"""
Displays the image with bounding boxes, masks, class labels, and scores.
(This method requires an implementation).
Args:
- image (array-like): Input image.
- boxes (list): Bounding boxes.
- masks (array-like): Masks for detected objects.
- ids (list): Class IDs for detected objects.
- names (list): Class names corresponding to class IDs.
- scores (list): Scores for detected objects.
"""
pass
def detect_and_display(self, filename):
"""
Detects objects in an image and displays the results.
Args:
- filename (str): Path to the image file.
"""
pass Class Definition - MaskRCNNDetector: This class is designed to detect objects in images using the Mask R-CNN model trained on the COCO dataset.
class_names: This is a list that stores the names of classes in the COCO dataset. In the given code, it is a shortened example, so only a few names are shown ('person', 'bicycle', ..., 'car').
TestConfig: A nested class inside MaskRCNNDetector for specifying the configuration settings when using the Mask R-CNN model. It's assumed that this class will inherit from a base Config class (presumably from the Mask R-CNN library) to customize the settings.
rcnn: This is mentioned in the class's documentation comment, indicating there will be an attribute that holds the MaskRCNN model instance. However, the actual attribute initialization in the class code is missing.
Methods:
Constructor (__init__): The constructor initializes the object and is expected to load the pretrained weights.
Static Method - draw_image_with_boxes: A static method means it can be called on the class itself without creating an instance. This method is designed to draw and display an image with bounding boxes overlayed on it. However, its actual implementation is missing and has a placeholder (pass).
Static Method - display_instances: Another static method. This method is meant to display an image with various annotations: bounding boxes, masks, class labels, and scores.
Method - detect_and_display: This method intends to detect objects in an input image and then display the results.
# Example usage:
detector = MaskRCNNDetector()
detector.detect_and_display('elephant.jpg')After the class definition, here an example of how to use the MaskRCNNDetector class.
The method detect_and_display is called on the instance with 'elephant.jpg' as an argument, indicating that we want to detect objects in this image and display the results.
As we can see here, we have predicted an elephant with 100 percent accuracy.
We have provided only the code template. For a complete implementation, contact us.
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