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Object detection is a computer vision task that involves both localizing one or more objects within an image and classifies each object in the image. It deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.
Why: Problem statement
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). This also 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.
How: Solution description
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method called Mask R-CNN, add a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The block diagram has shown bellow.
We can also use a pre-trained Mask R-CNN model to detect objects. The first step is to install the library. Download the weights for the pre-trained model, specifically a Mask R-CNN trained on the MS Coco dataset. Download the model weights to a file with the name ‘mask_rcnn_coco.h5‘ in your current working directory. 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:
‘rois‘: The bound boxes or regions-of-interest (ROI) for detected objects.
‘masks‘: The masks for the detected objects.
‘class_ids‘: The class integers for the detected objects.
‘scores‘: The probability or confidence for each predicted class.
We can draw each box detected in the image by first getting the dictionary for the first image (e.g. results), 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 image. We can use these coordinates to create a Rectangle() from the matplotlib API and draw each rectangle over the top of our image. We can now tie all of this together and load the pre-trained model and use it to detect objects.
We can find the coordinates of each object by using the below code.
We use pillow and OpenCV library to detect the coordinates of the objects. First, it will divide our place into 4 equal parts and tell us the coordinates of the required object.
Inorder to pass this code to robotic arm, we have to make serial communication for object detection code with robotic arm. The serial communication code can be found below.
This code combines the object detection code with the robotic arm. Now, the robotic arm will pick and place the object according to the user instructions.
How is it different from competition
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 display_instances() function is flexible, allowing you to only draw the mask or only the bounding boxes.
Who are your customers
We can deploy this object detection into robots or we can use it through the web application. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.