Instance Segmentation MASK R-CNN | with Python and Opencv
In this lesson, we will talk about an interesting algorithm. Mask R-CNN is an instant segmentation algorithm which means that it can detect the object in the image but also a mask on each object. This means that on a person you not only have the box but the coordinates surrounding the person.
Mask R-CNN was developed in 2017, which means that it is old, in computer vision software development is very fast, but still today it is an excellent algorithm to be used even in commercial projects. For those interested, I also leave the link to the official research https://arxiv.org/abs/1703.06870 .
In this tutorial we will see how to use mask rcnn in a very simple way with OpenCV.
How to use Mask R-CNN with OpenCV
First of all you have to make sure you have OpenCV installed, if not run this command from the terminal:
pip install opencv-python
If everything is installed correctly, you can download the files for the dnn modules from this site
When we have all the material to proceed we set up the python file in the usual way by calling the opencv library
import cv2 import numpy as np ... cv2.waitKey(0)
and we’re ready to go ahead and upload the model.
Load the model
To make the model work with OpenCV on our python file we use the function cv2.dnn.readNetFromTensorflow being careful to enter the correct path of the files to be load.
# Loading Mask RCNN net = cv2.dnn.readNetFromTensorflow("dnn/frozen_inference_graph_coco.pb", "dnn/mask_rcnn_inception_v2_coco_2018_01_28.pbtxt")
Detect object (draw the box)
We need to convert the image to model-readable format.
# Detect objects blob = cv2.dnn.blobFromImage(img, swapRB=True) net.setInput(blob)
The image size must be scaled, the color format must change, and much more, otherwise, you will get errors.
We put everything in the network and the model is already working
boxes, masks = net.forward(["detection_out_final", "detection_masks"]) detection_count = boxes.shape
We have all the coordinates of the objects and all that remains is to do the extraction. Being an array we use a simple for loop and as a limit the total number of objects found detection_count
for i in range(detection_count): box = boxes[0, 0, i] class_id = box score = box if score < 0.5: continue # Get box Coordinates x = int(box * width) y = int(box * height) x2 = int(box * width) y2 = int(box * height) roi = black_image[y: y2, x: x2] roi_height, roi_width, _ = roi.shape
This is a first result of the box extraction applied to the image
Detect object (take out the mask)
For each box, in the same position, there is an associated mask that perfectly overlaps the object. All coordinate data has been extracted and is now contained in the arrays.
The first step is to take the mask. For this we use the function of opencv Threshold and there is an example of use on this article Identify objects moving on a conveyor belt using Opencv with Python
# Get the mask mask = masks[i, int(class_id)] mask = cv2.resize(mask, (roi_width, roi_height)) _, mask = cv2.threshold(mask, 0.5, 255, cv2.THRESH_BINARY) cv2.rectangle(img, (x, y), (x2, y2), (255, 0, 0), 3)
We must now draw the coordinates with the function fillPoly()
# Get mask coordinates contours, _ = cv2.findContours(np.array(mask, np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) color = colors[int(class_id)] for cnt in contours: cv2.fillPoly(roi, [cnt], (int(color), int(color), int(color)))
If we now try to apply all these operations to the image, we get the result below. Mask R-CNN without too many elaborations allows us to obtain this first result.
This was the basis of instant image segmentation, obviously, this is a basis on which improvements and improvements can be applied.
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