### Install Mask R-CNN Project Locally

Source: https://github.com/matterport/mask_rcnn/blob/master/README.md

Installs the Mask R-CNN project locally after cloning the repository and installing dependencies. This command should be run from the repository root directory.

```python
python3 setup.py install
```

--------------------------------

### Mask R-CNN Model Training and Setup

Source: https://context7.com/matterport/mask_rcnn/llms.txt

This snippet demonstrates the initialization, weight loading, and training configuration for a Mask R-CNN model. It handles multi-GPU training setup and dataset preparation for both training and validation.

```python
import os
from mrcnn import utils
from mrcnn import config
from mrcnn import model as modellib
from mrcnn.parallel import ParallelModel

# Define a custom dataset class (assuming it's defined elsewhere)
class CustomDataset(utils.Dataset):
    def load_dataset(self, dataset_dir, subset):
        # Replace with actual dataset loading logic
        # Example: load image files and their annotations
        pass
    def prepare(self):
        # Replace with actual preparation logic
        pass

# Define a configuration class (assuming it's defined elsewhere)
class MyConfig(config.Config):
    NAME = "my_config"
    GPU_COUNT = 1 # Set to the number of GPUs available
    IMAGES_PER_GPU = 2
    NUM_CLASSES = 1 + 3 # Background + 3 object classes
    LEARNING_RATE = 0.001

config = MyConfig()
config.display() # Display configuration values

# Create model
model = modellib.MaskRCNN(mode="training", config=config, model_dir="./logs")

# Load pre-trained weights (e.g., from COCO dataset)
# Ensure mask_rcnn_coco.h5 is in the correct path
weights_path = "mask_rcnn_coco.h5"
if os.path.exists(weights_path):
    model.load_weights(weights_path, by_name=True,
                       exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
                               "mrcnn_bbox", "mrcnn_mask"])
else:
    print(f"Warning: Pre-trained weights not found at {weights_path}. Training from scratch.")

# Create parallel model for multi-GPU training
if config.GPU_COUNT > 1:
    parallel_model = ParallelModel(model.keras_model, config.GPU_COUNT)
else:
    parallel_model = model.keras_model

# Load datasets
dataset_train = CustomDataset()
dataset_train.load_dataset('path/to/dataset', 'train')
dataset_train.prepare()

dataset_val = CustomDataset()
dataset_val.load_dataset('path/to/dataset', 'val')
dataset_val.prepare()

# Train with multi-GPU support
print("Starting training...")
model.train(dataset_train, dataset_val,
           learning_rate=config.LEARNING_RATE,
           epochs=40,
           layers='heads') # Train only the heads initially
print("Training finished.")

```

--------------------------------

### Setup Environment and Import Mask RCNN

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/shapes/train_shapes.ipynb

Initializes the project environment by setting the root directory, appending it to the system path, and importing necessary Mask R-CNN libraries. It also handles downloading pre-trained COCO weights if they are not found locally.

```python
import os
import sys
import random
import math
import re
import time
import numpy as np
import cv2
import matplotlib
import matplotlib.pyplot as plt

# Root directory of the project
ROOT_DIR = os.path.abspath("../../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn.config import Config
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.model import log

%matplotlib inline 

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)
```

--------------------------------

### Install Python Dependencies for Mask R-CNN

Source: https://github.com/matterport/mask_rcnn/blob/master/README.md

Installs the required Python packages for the Mask R-CNN project using pip. Ensure you have Python 3 and pip installed.

```bash
pip3 install -r requirements.txt
```

--------------------------------

### Setup Mask R-CNN Environment and Load Weights

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/coco/inspect_model.ipynb

Initializes the Mask R-CNN project environment by setting the root directory and importing necessary libraries from the Mask R-CNN framework. It also handles the download of pre-trained COCO weights if they are not found locally.

```python
import os
import sys
import random
import math
import re
import time
import numpy as np
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches

# Root directory of the project
ROOT_DIR = os.path.abspath("../../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
import mrcnn.model as modellib
from mrcnn.model import log

%matplotlib inline 

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

# Path to Shapes trained weights
SHAPES_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_shapes.h5")
```

--------------------------------

### Import Mask R-CNN Libraries and Setup Environment (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/demo.ipynb

Imports necessary libraries for Mask R-CNN, sets up the root directory, and defines paths for logs, models, and images. It also includes logic to download pre-trained COCO weights if they are not found locally.

```python
import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt

# Root directory of the project
ROOT_DIR = os.path.abspath("../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
# Import COCO config
sys.path.append(os.path.join(ROOT_DIR, "samples/coco/"))  # To find local version
import coco

%matplotlib inline 

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")
```

--------------------------------

### Import Libraries and Setup Project Root - Python

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/nucleus/inspect_nucleus_data.ipynb

Imports necessary Python libraries and sets the ROOT_DIR to the project's root directory. It adjusts the path if the script is run from a specific subdirectory.

```python
import os
import sys
import itertools
import math
import logging
import json
import re
import random
import time
import concurrent.futures
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.lines as lines
from matplotlib.patches import Polygon
import imgaug
from imgaug import augmenters as iaa

# Root directory of the project
ROOT_DIR = os.getcwd()
if ROOT_DIR.endswith("samples/nucleus"):
    # Go up two levels to the repo root
    ROOT_DIR = os.path.dirname(os.path.dirname(ROOT_DIR))
    
# Import Mask RCNN
sys.path.append(ROOT_DIR)
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
from mrcnn import model as modellib
from mrcnn.model import log

import nucleus

%matplotlib inline 
```

--------------------------------

### Visualize Mask R-CNN Detection Pipeline with demo.ipynb

Source: https://github.com/matterport/mask_rcnn/blob/master/README.md

Jupyter notebook for demonstrating the Mask R-CNN model's capabilities. It shows an example of using a pre-trained MS COCO model to segment objects in custom images, covering object detection and instance segmentation.

```python
# samples/demo.ipynb
```

--------------------------------

### Train Mask R-CNN on MS COCO Dataset

Source: https://context7.com/matterport/mask_rcnn/llms.txt

Demonstrates how to train a Mask R-CNN model on the MS COCO dataset. This includes options for starting from pre-trained weights (COCO or ImageNet), continuing a previous training session, and running evaluation. It utilizes command-line arguments for configuration.

```bash
# Train a new model starting from pre-trained COCO weights
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=coco

# Train a new model starting from ImageNet weights
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=imagenet

# Continue training a model that you had trained earlier
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5

# Continue training the last model you trained
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=last

# Run COCO evaluation on the last trained model
python3 samples/coco/coco.py evaluate --dataset=/path/to/coco/ --model=last
```

--------------------------------

### Custom Configuration Setup for Mask R-CNN

Source: https://context7.com/matterport/mask_rcnn/llms.txt

Defines a custom configuration by subclassing the base Config class and overriding properties. This allows for specific settings related to training, detection, and image parameters. It is useful for adapting the Mask R-CNN model to different datasets and use cases.

```python
from mrcnn.config import Config

class MyConfig(Config):
    # Give the configuration a recognizable name
    NAME = "my_dataset"

    # Train on 1 GPU with 2 images per GPU (batch size = 2)
    GPU_COUNT = 1
    IMAGES_PER_GPU = 2

    # Number of classes (including background)
    NUM_CLASSES = 1 + 3  # Background + 3 object classes

    # Training steps per epoch
    STEPS_PER_EPOCH = 100

    # Skip detections with confidence < 0.9
    DETECTION_MIN_CONFIDENCE = 0.9

    # Image input size
    IMAGE_MIN_DIM = 800
    IMAGE_MAX_DIM = 1024

    # Learning rate
    LEARNING_RATE = 0.001

# Initialize and display configuration
config = MyConfig()
config.display()
```

--------------------------------

### Train Mask R-CNN Balloon Model

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/README.md

Trains a Mask R-CNN model for balloon detection. Supports starting training from pre-trained COCO weights, ImageNet weights, or resuming from the last checkpoint. Requires a dataset path. Training parameters like steps and batch size can be adjusted.

```bash
python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=coco
```

```bash
python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=last
```

```bash
python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=imagenet
```

--------------------------------

### Load Mask R-CNN Configurations (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/coco/inspect_data.ipynb

Loads and applies configurations for the Mask R-CNN model. It provides examples for both the 'shapes' toy dataset and the MS COCO dataset, allowing users to select and initialize the appropriate configuration. Users need to specify the COCO dataset directory if using it.

```python
# Run one of the code blocks

# Shapes toy dataset
# import shapes
# config = shapes.ShapesConfig()

# MS COCO Dataset
import coco
config = coco.CocoConfig()
COCO_DIR = "path to COCO dataset"  # TODO: enter value here
```

--------------------------------

### Run Mask R-CNN Training on MS COCO Dataset

Source: https://github.com/matterport/mask_rcnn/blob/master/README.md

Command-line interface for training a Mask R-CNN model on the MS COCO dataset. Supports starting from pre-trained COCO weights, ImageNet weights, or continuing previous training. Requires specifying the dataset path and model weights.

```python
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=coco
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=imagenet
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=last
```

--------------------------------

### MS COCO Dataset Training with Python

Source: https://context7.com/matterport/mask_rcnn/llms.txt

Provides a Python script to configure and train a Mask R-CNN model on the MS COCO dataset. It sets up a custom configuration, loads the dataset, initializes the model, loads pre-trained weights, and starts the training process. Dependencies include pycocotools and the mrcnn library.

```python
import os
import sys
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval

# Root directory
ROOT_DIR = os.path.abspath("./")
sys.path.append(ROOT_DIR)

from mrcnn.config import Config
from mrcnn import model as modellib, utils
from samples.coco import coco

# COCO Configuration
class CocoConfig(Config):
    NAME = "coco"
    GPU_COUNT = 1
    IMAGES_PER_GPU = 2
    NUM_CLASSES = 1 + 80  # COCO has 80 classes

config = CocoConfig()

# Load COCO dataset
dataset_train = coco.CocoDataset()
dataset_train.load_coco("/path/to/coco", "train", year="2017")
dataset_train.prepare()

dataset_val = coco.CocoDataset()
dataset_val.load_coco("/path/to/coco", "val", year="2017")
dataset_val.prepare()

print(f"Training images: {len(dataset_train.image_ids)}")
print(f"Validation images: {len(dataset_val.image_ids)}")
print(f"Classes: {dataset_train.class_names}")

# Initialize model
model = modellib.MaskRCNN(mode="training", config=config, model_dir="./logs")

# Load COCO weights
COCO_MODEL_PATH = "mask_rcnn_coco.h5"
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

model.load_weights(COCO_MODEL_PATH, by_name=True,
                   exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
                           "mrcnn_bbox", "mrcnn_mask"])

# Train
model.train(dataset_train, dataset_val,
           learning_rate=config.LEARNING_RATE,
           epochs=40,
           layers='heads')
```

--------------------------------

### Apply Color Splash Effect with Mask R-CNN

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/README.md

Applies a color splash effect to an image or video using pre-trained Mask R-CNN weights. Requires specifying the weights file and the input image/video path. For videos, OpenCV 3.2+ is a dependency.

```bash
python3 balloon.py splash --weights=/path/to/mask_rcnn/mask_rcnn_balloon.h5 --image=<file name or URL>
```

```bash
python3 balloon.py splash --weights=/path/to/mask_rcnn/mask_rcnn_balloon.h5 --video=<file name or URL>
```

--------------------------------

### Create Matplotlib Axes for Visualization (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/nucleus/inspect_nucleus_model.ipynb

A helper function to create Matplotlib Axes objects for plotting. It allows control over the number of rows and columns, and the overall size of the plot, centralizing visualization setup.

```python
def get_ax(rows=1, cols=1, size=16):
    """Return a Matplotlib Axes array to be used in
    all visualizations in the notebook. Provide a
    central point to control graph sizes.
    
    Adjust the size attribute to control how big to render images
    """
    fig, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
    fig.tight_layout()
    return ax
```

--------------------------------

### Load and Detect Objects in an Image using Mask R-CNN

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/demo.ipynb

This Python code snippet demonstrates how to load a random image from a specified directory, run object detection using a pre-trained Mask R-CNN model, and then visualize the detected instances. It requires libraries such as `os`, `skimage.io`, `random`, and the Mask R-CNN library for model inference and visualization.

```python
# Load a random image from the images folder
file_names = next(os.walk(IMAGE_DIR))[2]
image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))

# Run detection
results = model.detect([image], verbose=1)

# Visualize results
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], 
                            class_names, r['scores'])
```

--------------------------------

### Run Mask Head for Detection and Mask Prediction in Python

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/coco/inspect_model.ipynb

Executes the Mask R-CNN model's graph to get detections and segmentation masks. It retrieves outputs from the 'mrcnn_detection' and 'mrcnn_mask' layers and processes the detection class IDs.

```python
# Get predictions of mask head
mrcnn = model.run_graph([image], [
    ("detections", model.keras_model.get_layer("mrcnn_detection").output),
    ("masks", model.keras_model.get_layer("mrcnn_mask").output),
])

# Get detection class IDs. Trim zero padding.
det_class_ids = mrcnn['detections'][0, :, 4].astype(np.int32)
det_count = np.where(det_class_ids == 0)[0][0]
det_class_ids = det_class_ids[:det_count]

print("{} detections: {}".format(
    det_count, np.array(dataset.class_names)[det_class_ids]))
```

--------------------------------

### Configure and Train Mask R-CNN Model

Source: https://context7.com/matterport/mask_rcnn/llms.txt

Sets up a Mask R-CNN model for training, loads pre-trained weights, prepares custom datasets, and performs staged training (heads only, then all layers). Assumes CustomDataset class is defined and data is available at 'path/to/dataset'.

```python
class TrainingConfig(Config):
    NAME = "training"
    GPU_COUNT = 1
    IMAGES_PER_GPU = 2
    NUM_CLASSES = 1 + 3
    STEPS_PER_EPOCH = 100
    LEARNING_RATE = 0.001

config = TrainingConfig()

# Create model in training mode
model = modellib.MaskRCNN(mode="training", config=config, model_dir="./logs")

# Load pre-trained weights (COCO, ImageNet, or previous checkpoint)
# Option 1: Start from COCO weights
model.load_weights("mask_rcnn_coco.h5", by_name=True,
                   exclude=["mrcnn_class_logits", "mrcnn_bbox_fc",
                           "mrcnn_bbox", "mrcnn_mask"])

# Option 2: Resume from last checkpoint
# model.load_weights(model.find_last(), by_name=True)

# Load datasets (assuming CustomDataset defined above)
dataset_train = CustomDataset()
dataset_train.load_dataset('path/to/dataset', 'train')
dataset_train.prepare()

dataset_val = CustomDataset()
dataset_val.load_dataset('path/to/dataset', 'val')
dataset_val.prepare()

# Train the model
# Stage 1: Train heads only
print("Training network heads")
model.train(dataset_train, dataset_val,
           learning_rate=config.LEARNING_RATE,
           epochs=40,
           layers='heads')

# Stage 2: Fine-tune all layers
print("Fine tuning all layers")
model.train(dataset_train, dataset_val,
           learning_rate=config.LEARNING_RATE / 10,
           epochs=80,
           layers='all')

# Save final weights
model_path = os.path.join(model.model_dir, "mask_rcnn_final.h5")
model.keras_model.save_weights(model_path)
```

--------------------------------

### Inspect Mask R-CNN Model Detection Steps with inspect_model.ipynb

Source: https://github.com/matterport/mask_rcnn/blob/master/README.md

Jupyter notebook providing an in-depth look at the Mask R-CNN detection and segmentation pipeline. It visualizes each step and allows for step-by-step execution to inspect outputs.

```python
# samples/coco/inspect_model.ipynb
```

--------------------------------

### Matplotlib Axes Setup Function - Python

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/nucleus/inspect_nucleus_data.ipynb

Defines a utility function `get_ax` to return a Matplotlib Axes array for consistent graph sizing in visualizations. It allows control over the number of rows, columns, and overall figure size.

```python
def get_ax(rows=1, cols=1, size=16):
    """Return a Matplotlib Axes array to be used in
    all visualizations in the notebook. Provide a
    central point to control graph sizes.
    
    Adjust the size attribute to control how big to render images
    """
    _, ax = plt.subplots(rows, cols, figsize=(size*cols, size*rows))
    return ax
```

--------------------------------

### Mask R-CNN Model Training Output Example

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/shapes/train_shapes.ipynb

This represents a typical training epoch output for the Mask R-CNN model. It details the loss values for the overall model and its components (RPN class/bbox loss, MRCNN class/bbox/mask loss) for both training and validation sets.

```text
Epoch 1/1
100/100 [==============================] - 86s - loss: 11.4006 - rpn_class_loss: 0.0184 - rpn_bbox_loss: 0.8409 - mrcnn_class_loss: 0.1576 - mrcnn_bbox_loss: 0.0902 - mrcnn_mask_loss: 0.1977 - val_loss: 11.4376 - val_rpn_class_loss: 0.0220 - val_rpn_bbox_loss: 1.0068 - val_mrcnn_class_loss: 0.1172 - val_mrcnn_bbox_loss: 0.0683 - val_mrcnn_mask_loss: 0.1278
```

--------------------------------

### Reduce Mask RCNN Memory: Use Smaller Images

Source: https://github.com/matterport/mask_rcnn/wiki/Home

Decrease memory requirements and training/inference time by resizing input images to smaller dimensions. Adjust IMAGE_MIN_DIM and IMAGE_MAX_DIM in the Config class. Example uses 800x1024.

```python
IMAGE_MIN_DIM = 800
IMAGE_MAX_DIM = 1024
```

--------------------------------

### Mask R-CNN Core Implementation Files

Source: https://github.com/matterport/mask_rcnn/blob/master/README.md

Core Python files for the Mask R-CNN implementation. Includes the main model logic, utility functions, and configuration settings. These files form the backbone of the detection and segmentation pipeline.

```python
# mrcnn/model.py
# mrcnn/utils.py
# mrcnn/config.py
```

--------------------------------

### Get Layer Activations from Mask R-CNN Model

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/coco/inspect_model.ipynb

This snippet demonstrates how to retrieve the output tensors for specific layers from a Mask R-CNN model. It takes an input image and a list of layer names to extract activations from. The output is a dictionary mapping layer names to their activation tensors.

```python
activations = model.run_graph([image], [
    ("input_image",        tf.identity(model.keras_model.get_layer("input_image").output)),
    ("res4w_out",          model.keras_model.get_layer("res4w_out").output),  # for resnet100
    ("rpn_bbox",           model.keras_model.get_layer("rpn_bbox").output),
    ("roi",                model.keras_model.get_layer("ROI").output),
])
```

--------------------------------

### Configure Inference for Nucleus Dataset (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/nucleus/inspect_nucleus_model.ipynb

Sets up the dataset directory and initializes the inference configuration for the Nucleus dataset. The `config.display()` method shows the parameters used for inference.

```python
# Dataset directory
DATASET_DIR = os.path.join(ROOT_DIR, "datasets/nucleus")

# Inference Configuration
config = nucleus.NucleusInferenceConfig()
config.display()
```

--------------------------------

### Inspect Mask R-CNN Data Pre-processing with inspect_data.ipynb

Source: https://github.com/matterport/mask_rcnn/blob/master/README.md

Jupyter notebook for visualizing the pre-processing steps involved in preparing training data for Mask R-CNN. Helps understand how raw data is transformed for model training.

```python
# samples/coco/inspect_data.ipynb
```

--------------------------------

### Initialize and Prepare ShapesDataset in Python

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/shapes/train_shapes.ipynb

This snippet demonstrates how to initialize and prepare a ShapesDataset for both training and validation. It loads a specified number of shapes and prepares the dataset for use by the Mask R-CNN model. Dependencies include ShapesDataset and config.

```python
# Training dataset
dataset_train = ShapesDataset()
dataset_train.load_shapes(500, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
dataset_train.prepare()

# Validation dataset
dataset_val = ShapesDataset()
dataset_val.load_shapes(50, config.IMAGE_SHAPE[0], config.IMAGE_SHAPE[1])
dataset_val.prepare()
```

--------------------------------

### Extract Mask R-CNN Layer Activations (TensorFlow)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/inspect_balloon_model.ipynb

This snippet shows how to get the output activations from specified layers of a Mask R-CNN Keras model. It uses `model.run_graph` to execute a subset of the model and retrieve the tensors corresponding to layer outputs. The activations can then be used for debugging or visualization.

```python
activations = model.run_graph([image], [
    ("input_image",        tf.identity(model.keras_model.get_layer("input_image").output)),
    ("res2c_out",          model.keras_model.get_layer("res2c_out").output),
    ("res3c_out",          model.keras_model.get_layer("res3c_out").output),
    ("res4w_out",          model.keras_model.get_layer("res4w_out").output),  # for resnet100
    ("rpn_bbox",           model.keras_model.get_layer("rpn_bbox").output),
    ("roi",                model.keras_model.get_layer("ROI").output),
])
```

--------------------------------

### Load and Prepare Balloon Dataset (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/inspect_balloon_model.ipynb

Loads the balloon validation dataset using the `balloon.BalloonDataset` class and prepares it for use by calling the `prepare()` method. It then prints the number of images and class names available in the dataset.

```python
# Load validation dataset
dataset = balloon.BalloonDataset()
dataset.load_balloon(BALLOON_DIR, "val")

# Must call before using the dataset
dataset.prepare()

print("Images: {}\\nClasses: {}".format(len(dataset.image_ids), dataset.class_names))

```

--------------------------------

### Create Mask R-CNN Model and Load Weights (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/demo.ipynb

Initializes a Mask R-CNN model in inference mode using the specified configuration and model directory. It then loads pre-trained weights from a COCO model file, ensuring compatibility by loading weights by name.

```python
# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)
```

--------------------------------

### Create Mask R-CNN Model in Training Mode

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/shapes/train_shapes.ipynb

Instantiates a Mask R-CNN model configured for training. It requires a configuration object and a directory to save models. Dependencies include modellib and config.

```python
# Create model in training mode
model = modellib.MaskRCNN(mode="training", config=config,
                          model_dir=MODEL_DIR)
```

--------------------------------

### Get Mask Head Predictions - Python

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/inspect_balloon_model.ipynb

This code retrieves predictions from the mask head of the Mask R-CNN model. It runs the model's graph to obtain 'detections' and 'masks'. It then extracts and processes the detection class IDs, determining the number of valid detections by looking for zero padding.

```python
# Get predictions of mask head
mrcnn = model.run_graph([image], [
    ("detections", model.keras_model.get_layer("mrcnn_detection").output),
    ("masks", model.keras_model.get_layer("mrcnn_mask").output),
])

# Get detection class IDs. Trim zero padding.
det_class_ids = mrcnn['detections'][0, :, 4].astype(np.int32)
det_count = np.where(det_class_ids == 0)[0][0]
det_class_ids = det_class_ids[:det_count]

print("{} detections: {}".format(
    det_count, np.array(dataset.class_names)[det_class_ids]))

```

--------------------------------

### Initialize Mask R-CNN Model for Inference (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/nucleus/inspect_nucleus_model.ipynb

Creates an instance of the Mask R-CNN model in inference mode, specifying the log directory and the inference configuration. The model is loaded onto the designated device (CPU or GPU).

```python
# Create model in inference mode
with tf.device(DEVICE):
    model = modellib.MaskRCNN(mode="inference",
                              model_dir=LOGS_DIR,
                              config=config)
```

--------------------------------

### Get Next Batch from Data Generator

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/inspect_balloon_data.ipynb

Retrieves the next batch of data from the configured data generator. The output structure depends on whether `random_rois` was enabled during generator creation. It provides normalized images, image metadata, RPN matches and bounding boxes, ground truth information, and ROIs for training the Mask R-CNN model.

```python
# Get Next Image
if random_rois:
    [normalized_images, image_meta, rpn_match, rpn_bbox, gt_class_ids, gt_boxes, gt_masks, rpn_rois, rois],
    [mrcnn_class_ids, mrcnn_bbox, mrcnn_mask] = next(g)
    
    log("rois", rois)
    log("mrcnn_class_ids", mrcnn_class_ids)
    log("mrcnn_bbox", mrcnn_bbox)
    log("mrcnn_mask", mrcnn_mask)
else:
    [normalized_images, image_meta, rpn_match, rpn_bbox, gt_boxes, gt_masks], _ = next(g)
    
log("gt_class_ids", gt_class_ids)
log("gt_boxes", gt_boxes)
log("gt_masks", gt_masks)
log("rpn_match", rpn_match, )
log("rpn_bbox", rpn_bbox)
image_id = modellib.parse_image_meta(image_meta)("image_id")[0]
print("image_id: ", image_id, dataset.image_reference(image_id))

# Remove the last dim in mrcnn_class_ids. It's only added

```

--------------------------------

### Get Next Image Batch from Data Generator

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/coco/inspect_data.ipynb

This Python code snippet shows how to retrieve the next batch of data from a Mask R-CNN data generator. It handles two cases: when `random_rois` is enabled and when it is not. The retrieved data includes normalized images, image metadata, RPN matches and bounding boxes, ground truth classes, boxes, masks, RPN proposals, and final proposals (ROIs). It also logs key information and prints the image ID and its reference. This is crucial for feeding data into the model during training.

```python
# Get Next Image
if random_rois:
    [normalized_images, image_meta, rpn_match, rpn_bbox, gt_class_ids, gt_boxes, gt_masks, rpn_rois, rois],
    [mrcnn_class_ids, mrcnn_bbox, mrcnn_mask] = next(g)
    
    log("rois", rois)
    log("mrcnn_class_ids", mrcnn_class_ids)
    log("mrcnn_bbox", mrcnn_bbox)
    log("mrcnn_mask", mrcnn_mask)
else:
    [normalized_images, image_meta, rpn_match, rpn_bbox, gt_boxes, gt_masks], _ = next(g)
    
log("gt_class_ids", gt_class_ids)
log("gt_boxes", gt_boxes)
log("gt_masks", gt_masks)
log("rpn_match", rpn_match, )
log("rpn_bbox", rpn_bbox)
image_id = modellib.parse_image_meta(image_meta)["image_id"][0]
print("image_id: ", image_id, dataset.image_reference(image_id))
```

--------------------------------

### Custom Dataset Creation and Loading for Mask R-CNN

Source: https://context7.com/matterport/mask_rcnn/llms.txt

Extends the Dataset class to manage custom datasets by defining how to load annotations and masks. It includes methods for adding classes, loading image information from JSON annotations, generating masks from polygons, and providing image references. This is essential for training Mask R-CNN on user-defined data.

```python
import os
import json
import numpy as np
import skimage.draw
from mrcnn import utils

class CustomDataset(utils.Dataset):
    def load_dataset(self, dataset_dir, subset):
        """Load a subset of the dataset.
        dataset_dir: Root directory of the dataset
        subset: Subset to load (train or val)
        """
        # Add classes
        self.add_class("dataset", 1, "class_name_1")
        self.add_class("dataset", 2, "class_name_2")

        # Load annotations from JSON
        annotations_path = os.path.join(dataset_dir, subset, "annotations.json")
        with open(annotations_path) as f:
            annotations = json.load(f)

        # Add images
        for img_data in annotations:
            image_path = os.path.join(dataset_dir, subset, img_data['filename'])
            self.add_image(
                "dataset",
                image_id=img_data['id'],
                path=image_path,
                width=img_data['width'],
                height=img_data['height'],
                polygons=img_data['regions']
            )

    def load_mask(self, image_id):
        """Generate instance masks for an image.
        Returns:
            masks: A bool array of shape [height, width, instance count]
            class_ids: a 1D array of class IDs of the instance masks
        """
        info = self.image_info[image_id]

        # Convert polygons to bitmap masks
        mask = np.zeros([info["height"], info["width"], len(info["polygons"])],
                        dtype=np.uint8)
        class_ids = []

        for i, p in enumerate(info["polygons"]):
            # Get polygon coordinates
            rr, cc = skimage.draw.polygon(p['all_points_y'], p['all_points_x'])
            mask[rr, cc, i] = 1
            class_ids.append(p['class_id'])

        return mask.astype(np.bool), np.array(class_ids, dtype=np.int32)

    def image_reference(self, image_id):
        """Return the path of the image."""
        info = self.image_info[image_id]
        return info["path"]

# Load and prepare dataset
dataset_train = CustomDataset()
dataset_train.load_dataset('path/to/dataset', 'train')
dataset_train.prepare()

dataset_val = CustomDataset()
dataset_val.load_dataset('path/to/dataset', 'val')
dataset_val.prepare()

print(f"Training images: {len(dataset_train.image_ids)}")
print(f"Validation images: {len(dataset_val.image_ids)}")
print(f"Classes: {dataset_train.class_names}")
```

--------------------------------

### Visualize Mask R-CNN Detection Results

Source: https://context7.com/matterport/mask_rcnn/llms.txt

Visualizes detection results from a Mask R-CNN model on an image. Displays bounding boxes, masks, and class labels using matplotlib. Also includes functionality to display multiple images in a grid. Requires a trained model, image(s), and class names.

```python
import matplotlib.pyplot as plt
import skimage.io
from mrcnn import visualize
from mrcnn import model as modellib
from mrcnn.config import Config

# Load model and perform detection (from previous example)
config = InferenceConfig() # Assuming InferenceConfig is defined elsewhere or copied
model = modellib.MaskRCNN(mode="inference", config=config, model_dir="./logs")
model.load_weights("path/to/weights.h5", by_name=True)

# Load image
image = skimage.io.imread("path/to/image.jpg")

# Detect objects
results = model.detect([image], verbose=1)
r = results[0]

# Define class names
class_names = ['BG', 'class_1', 'class_2', 'class_3']

# Visualize results
visualize.display_instances(
    image,
    r['rois'],
    r['masks'],
    r['class_ids'],
    class_names,
    r['scores'],
    title="Detection Results",
    figsize=(16, 16)
)

# Save visualization to file
plt.savefig("detection_output.png", bbox_inches='tight', dpi=150)
plt.show()

# Display multiple images in grid
images = [skimage.io.imread(f"image_{i}.jpg") for i in range(4)]
titles = [f"Image {i+1}" for i in range(4)]
visualize.display_images(images, titles=titles, cols=2)
```

--------------------------------

### Configure and Load Nucleus Dataset - Python

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/nucleus/inspect_nucleus_data.ipynb

Sets up the configuration for the Nucleus dataset, disabling image resizing to view original dimensions. It then loads the specified subset ('train') of the dataset and prepares it for use, printing the image and class counts.

```python
# Dataset directory
DATASET_DIR = os.path.join(ROOT_DIR, "datasets/nucleus")

# Use configuation from nucleus.py, but override
# image resizing so we see the real sizes here
class NoResizeConfig(nucleus.NucleusConfig):
    IMAGE_RESIZE_MODE = "none"
    
config = NoResizeConfig()

# Load dataset
dataset = nucleus.NucleusDataset()
# The subset is the name of the sub-directory, such as stage1_train, 
# stage1_test, ...etc. You can also use these special values:
#     train: loads stage1_train but excludes validation images
#     val: loads validation images from stage1_train. For a list
#          of validation images see nucleus.py
dataset.load_nucleus(DATASET_DIR, subset="train")

# Must call before using the dataset
dataset.prepare()

print("Image Count: {}".format(len(dataset.image_ids)))
print("Class Count: {}".format(dataset.num_classes))
for i, info in enumerate(dataset.class_info):
    print("{:3}. {:50}".format(i, info['name']))
```

--------------------------------

### Train Mask R-CNN Heads in Python

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/shapes/train_shapes.ipynb

This snippet illustrates how to initiate training for the head layers of a Mask R-CNN model. By freezing the backbone layers and training only the head, it allows for initial adaptation of the classifier and regressor. This is the first stage of a two-stage training process. Dependencies include model and train().

```python
# Train the head branches
# Passing layers="heads" freezes all layers except the head
# layers. You can also pass a regular expression to select
```

--------------------------------

### Print Anchor Summary

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/coco/inspect_data.ipynb

This Python script prints a summary of anchor configurations, including counts, scales, ratios, and anchors per level. It helps in understanding the anchor generation process in object detection models like Mask R-CNN. No external dependencies beyond standard Python libraries are explicitly mentioned for this snippet.

```python
# Print summary of anchors
num_levels = len(backbone_shapes)
anchors_per_cell = len(config.RPN_ANCHOR_RATIOS)
print("Count: ", anchors.shape[0])
print("Scales: ", config.RPN_ANCHOR_SCALES)
print("ratios: ", config.RPN_ANCHOR_RATIOS)
print("Anchors per Cell: ", anchors_per_cell)
print("Levels: ", num_levels)
anchors_per_level = []
for l in range(num_levels):
    num_cells = backbone_shapes[l][0] * backbone_shapes[l][1]
    anchors_per_level.append(anchors_per_cell * num_cells // config.RPN_ANCHOR_STRIDE**2)
    print("Anchors in Level {}: {}".format(l, anchors_per_level[l]))
```

--------------------------------

### Visualize Mask R-CNN Detection on a Random Image

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/shapes/train_shapes.ipynb

Tests the Mask R-CNN model on a randomly selected image from the validation dataset. It loads the ground truth data, visualizes the instances using `visualize.display_instances`, and logs relevant information such as bounding boxes and masks.

```python
# Test on a random image
image_id = random.choice(dataset_val.image_ids)
original_image, image_meta, gt_class_id, gt_bbox, gt_mask =\
    modellib.load_image_gt(dataset_val, inference_config, 
                           image_id, use_mini_mask=False)

log("original_image", original_image)
log("image_meta", image_meta)
log("gt_class_id", gt_class_id)
log("gt_bbox", gt_bbox)
log("gt_mask", gt_mask)

visualize.display_instances(original_image, gt_bbox, gt_mask, gt_class_id, 
                            dataset_train.class_names, figsize=(8, 8))
```

--------------------------------

### Visualize Anchors with Refinement and Clipping (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/inspect_balloon_model.ipynb

Compares anchors before refinement, after refinement, and after clipping to image boundaries. This visualization aids in understanding the refinement process and how anchors are constrained by the image dimensions. Requires matplotlib and a utility function 'utils.denorm_boxes'.

```python
# Show top anchors with refinement. Then with clipping to image boundaries
limit = 50
ax = get_ax(1, 2)
pre_nms_anchors = utils.denorm_boxes(rpn["pre_nms_anchors"][0], image.shape[:2])
refined_anchors = utils.denorm_boxes(rpn["refined_anchors"][0], image.shape[:2])
refined_anchors_clipped = utils.denorm_boxes(rpn["refined_anchors_clipped"][0], image.shape[:2])
visualize.draw_boxes(image, boxes=pre_nms_anchors[:limit],
                     refined_boxes=refined_anchors[:limit], ax=ax[0])
visualize.draw_boxes(image, refined_boxes=refined_anchors_clipped[:limit], ax=ax[1])
```

--------------------------------

### Inspect Mask R-CNN Model Weights with inspect_weights.ipynb

Source: https://github.com/matterport/mask_rcnn/blob/master/README.md

Jupyter notebook for inspecting the weights of a trained Mask R-CNN model. Useful for identifying anomalies and understanding weight distributions.

```python
# samples/coco/inspect_weights.ipynb
```

--------------------------------

### Import Mask R-CNN Libraries and Utilities (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/coco/inspect_data.ipynb

Imports essential Python libraries and modules required for the Mask R-CNN project, including utilities for image visualization and model implementation. It sets the root directory and adds it to the system path to ensure local library imports function correctly.

```python
import os
import sys
import itertools
import math
import logging
import json
import re
import random
from collections import OrderedDict
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.lines as lines
from matplotlib.patches import Polygon

# Root directory of the project
ROOT_DIR = os.path.abspath("../../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
from mrcnn import visualize
from mrcnn.visualize import display_images
import mrcnn.model as modellib
from mrcnn.model import log

%matplotlib inline
```

--------------------------------

### Create Mask R-CNN Model for Inference (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/inspect_balloon_model.ipynb

Initializes the Mask R-CNN model in inference mode, specifying the model directory and configuration. The model is loaded onto the designated device (CPU or GPU) using TensorFlow.

```python
# Create model in inference mode
with tf.device(DEVICE):
    model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR,
                              config=config)

```

--------------------------------

### Prepare and Inspect Mask R-CNN Dataset (Python)

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/coco/inspect_data.ipynb

This snippet shows how to initialize the dataset, retrieve the total number of images, and display the class count and names. It's essential for understanding the dataset's composition before model training. No external libraries are explicitly imported in this snippet, assuming 'dataset' is a pre-defined object.

```python
dataset.prepare()

print("Image Count: {}".format(len(dataset.image_ids)))
print("Class Count: {}".format(dataset.num_classes))
for i, info in enumerate(dataset.class_info):
    print("{:3}. {:50}".format(i, info['name']))
```

--------------------------------

### Configure Mask R-CNN for Multi-GPU Training

Source: https://context7.com/matterport/mask_rcnn/llms.txt

Sets up a configuration for training a Mask R-CNN model across multiple GPUs. It defines the number of GPUs, images per GPU, and adjusts the total batch size accordingly. This configuration is intended to be used with the ParallelModel class for distributed training.

```python
import os
from mrcnn import model as modellib
from mrcnn.parallel_model import ParallelModel
from mrcnn.config import Config

# Multi-GPU configuration
class MultiGPUConfig(Config):
    NAME = "multi_gpu"
    GPU_COUNT = 4  # Number of GPUs available
    IMAGES_PER_GPU = 2  # Batch size per GPU
    NUM_CLASSES = 1 + 80
    STEPS_PER_EPOCH = 1000
    LEARNING_RATE = 0.001

config = MultiGPUConfig()
print(f"Total batch size: {config.BATCH_SIZE}")  # GPU_COUNT * IMAGES_PER_GPU
```

--------------------------------

### Display Sample ROIs and Masks in Python

Source: https://github.com/matterport/mask_rcnn/blob/master/samples/balloon/inspect_balloon_data.ipynb

This snippet displays a selection of 8 ROIs, along with their refined bounding boxes and masks. It uses a helper function 'display_images' to arrange the visualizations in a grid, showing both the bounding boxes and the segmented masks for each selected ROI.

```python
if random_rois:
    # Dispalay ROIs and corresponding masks and bounding boxes
    ids = random.sample(range(rois.shape[1]), 8)

    images = []
    titles = []
    for i in ids:
        image = visualize.draw_box(sample_image.copy(), rois[b,i,:4].astype(np.int32), [255, 0, 0])
        image = visualize.draw_box(image, refined_rois[i].astype(np.int64), [0, 255, 0])
        images.append(image)
        titles.append("ROI {}".format(i))
        images.append(mask_specific[i] * 255)
        titles.append(dataset.class_names[mrcnn_class_ids[b,i]][:20])

    display_images(images, titles, cols=4, cmap="Blues", interpolation="none")
```