Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph.
It is a challenging problem that involves building upon methods for object recognition (e.
g.
where are they), object localization (e.
g.
what are their extent), and object classification (e.
g.
what are they).
In recent years, deep learning techniques have achieved state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions.
Most notably is the R-CNN, or Region-Based Convolutional Neural Networks, and the most recent technique called Mask R-CNN that is capable of achieving state-of-the-art results on a range of object detection tasks.
In this tutorial, you will discover how to use the Mask R-CNN model to detect objects in new photographs.
After completing this tutorial, you will know:Let’s get started.
How to Perform Object Detection in Photographs With Mask R-CNN in KerasPhoto by Ole Husby, some rights reserved.
This tutorial is divided into three parts; they are:Take my free 7-day email crash course now (with sample code).
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Download Your FREE Mini-CourseObject detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.
It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized.
An extension of object detection involves marking the specific pixels in the image that belong to each detected object instead of using coarse bounding boxes during object localization.
This harder version of the problem is generally referred to as object segmentation or semantic segmentation.
The Region-Based Convolutional Neural Network, or R-CNN, is a family of convolutional neural network models designed for object detection, developed by Ross Girshick, et al.
There are perhaps four main variations of the approach, resulting in the current pinnacle called Mask R-CNN.
The salient aspects of each variation can be summarized as follows:The Mask R-CNN model introduced in the 2018 paper titled “Mask R-CNN” is the most recent variation of the family models and supports both object detection and object segmentation.
The paper provides a nice summary of the model linage to that point:The Region-based CNN (R-CNN) approach to bounding-box object detection is to attend to a manageable number of candidate object regions and evaluate convolutional networks independently on each RoI.
R-CNN was extended to allow attending to RoIs on feature maps using RoIPool, leading to fast speed and better accuracy.
Faster R-CNN advanced this stream by learning the attention mechanism with a Region Proposal Network (RPN).
Faster R-CNN is flexible and robust to many follow-up improvements, and is the current leading framework in several benchmarks.
— Mask R-CNN, 2018.
The family of methods may be among the most effective for object detection, achieving then state-of-the-art results on computer vision benchmark datasets.
Although accurate, the models can be slow when making a prediction as compared to alternate models such as YOLO that may be less accurate but are designed for real-time prediction.
Mask R-CNN is a sophisticated model to implement, especially as compared to a simple or even state-of-the-art deep convolutional neural network model.
Source code is available for each version of the R-CNN model, provided in separate GitHub repositories with prototype models based on the Caffe deep learning framework.
For example:Instead of developing an implementation of the R-CNN or Mask R-CNN model from scratch, we can use a reliable third-party implementation built on top of the Keras deep learning framework.
The best of breed third-party implementations of Mask R-CNN is the Mask R-CNN Project developed by Matterport.
The project is open source released under a permissive license (i.
e.
MIT license) and the code has been widely used on a variety of projects and Kaggle competitions.
Nevertheless, it is an open source project, subject to the whims of the project developers.
As such, I have a fork of the project available, just in case there are major changes to the API in the future.
The project is light on API documentation, although it does provide a number of examples in the form of Python Notebooks that you can use to understand how to use the library by example.
Two notebooks that may be helpful to review are:There are perhaps three main use cases for using the Mask R-CNN model with the Matterport library; they are:In order to get familiar with the model and the library, we will look at the first example in the next section.
In this section, we will use the Matterport Mask R-CNN library to perform object detection on arbitrary photographs.
Much like using a pre-trained deep CNN for image classification, e.
g.
such as VGG-16 trained on an ImageNet dataset, we can use a pre-trained Mask R-CNN model to detect objects in new photographs.
In this case, we will use a Mask R-CNN trained on the MS COCO object detection problem.
The first step is to install the library.
At the time of writing, there is no distributed version of the library, so we have to install it manually.
The good news is that this is very easy.
Installation involves cloning the GitHub repository and running the installation script on your workstation.
If you are having trouble, see the installation instructions buried in the library’s readme file.
This is as simple as running the following command from your command line:This will create a new local directory with the name Mask_RCNN that looks as follows:The library can be installed directly via pip.
Change directory into the Mask_RCNN directory and run the installation script.
From the command line, type the following:On Linux or MacOS you may need to install the software with sudo permissions; for example, you may see an error such as:In that case, install the software with sudo:The library will then install directly and you will see a lot of successful installation messages ending with the following:This confirms that you installed the library successfully and that you have the latest version, which at the time of writing is version 2.
1.
It is always a good idea to confirm that the library was installed correctly.
You can confirm that the library was installed correctly by querying it via the pip command; for example:You should see output informing you of the version and installation location; for example:We are now ready to use the library.
We are going to use a pre-trained Mask R-CNN model to detect objects on a new photograph.
First, download the weights for the pre-trained model, specifically a Mask R-CNN trained on the MS Coco dataset.
The weights are available from the project GitHub project and the file is about 250 megabytes.
Download the model weights to a file with the name ‘mask_rcnn_coco.
h5‘ in your current working directory.
We also need a photograph in which to detect objects.
We will use a photograph from Flickr released under a permissive license, specifically a photograph of an elephant taken by Mandy Goldberg.
Download the photograph to your current working directory with the filename ‘elephant.
jpg‘.
Elephant (elephant.
jpg)Taken by Mandy Goldberg, some rights reserved.
First, the model must be defined via an instance MaskRCNN class.
This class requires a configuration object as a parameter.
The configuration object defines how the model might be used during training or inference.
In this case, the configuration will only specify the number of images per batch, which will be one, and the number of classes to predict.
You can see the full extent of the configuration object and the properties that you can override in the config.
py file.
We can now define the MaskRCNN instance.
We will define the model as type “inference” indicating that we are interested in making predictions and not training.
We must also specify a directory where any log messages could be written, which in this case will be the current working directory.
The next step is to load the weights that we downloaded.
Now we can make a prediction for our image.
First, we can load the image and convert it to a NumPy array.
We can then make a prediction with the model.
Instead of calling predict() as we would on a normal Keras model, will call the detect() function and pass it the single image.
The result contains a dictionary for each image that we passed into the detect() function, in this case, a list of a single dictionary for the one image.
The dictionary has keys for the bounding boxes, masks, and so on, and each key points to a list for multiple possible objects detected in the image.
The keys of the dictionary of note are as follows:We can draw each box detected in the image by first getting the dictionary for the first image (e.
g.
results[0]), and then retrieving the list of bounding boxes (e.
g.
[‘rois’]).
Each bounding box is defined in terms of the bottom left and top right coordinates of the bounding box in the imageWe can use these coordinates to create a Rectangle() from the matplotlib API and draw each rectangle over the top of our image.
To keep things neat, we can create a function to do this that will take the filename of the photograph and the list of bounding boxes to draw and will show the photo with the boxes.
We can now tie all of this together and load the pre-trained model and use it to detect objects in our photograph of an elephant, then draw the photograph with all detected objects.
The complete example is listed below.
Running the example loads the model and performs object detection.
More accurately, we have performed object localization, only drawing bounding boxes around detected objects.
In this case, we can see that the model has correctly located the single object in the photo, the elephant, and drawn a red box around it.
Photograph of an Elephant With All Objects Localized With a Bounding BoxNow that we know how to load the model and use it to make a prediction, let’s update the example to perform real object detection.
That is, in addition to localizing objects, we want to know what they are.
The Mask_RCNN API provides a function called display_instances() that will take the array of pixel values for the loaded image and the aspects of the prediction dictionary, such as the bounding boxes, scores, and class labels, and will plot the photo with all of these annotations.
One of the arguments is the list of predicted class identifiers available in the ‘class_ids‘ key of the dictionary.
The function also needs a mapping of ids to class labels.
The pre-trained model was fit with a dataset that had 80 (81 including background) class labels, helpfully provided as a list in the Mask R-CNN Demo, Notebook Tutorial, listed below.
We can then provide the details of the prediction for the elephant photo to the display_instances() function; for example:The display_instances() function is flexible, allowing you to only draw the mask or only the bounding boxes.
You can learn more about this function in the visualize.
py source file.
The complete example with this change using the display_instances() function is listed below.
Running the example shows the photograph of the elephant with the annotations predicted by the Mask R-CNN model, specifically:The result is very impressive and sparks many ideas for how such a powerful pre-trained model could be used in practice.
Photograph of an Elephant With All Objects Detected With a Bounding Box and MaskThis section provides more resources on the topic if you are looking to go deeper.
In this tutorial, you discovered how to use the Mask R-CNN model to detect objects in new photographs.
Specifically, you learned:Do you have any questions?.Ask your questions in the comments below and I will do my best to answer.
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