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26/11/2018, · $ tree --dirsfirst . ├── ,mask,-,rcnn,-coco │ ├── frozen_inference_graph.pb │ ├── ,mask,_,rcnn,_inception_v2_coco_2018_01_28.pbtxt │ └── object_detection_classes_coco.txt └── instance_segmentation.py 1 directory, 4 files Our project includes one directory (consisting of three files) and one Python script:
Run the ,OpenCV, code and visualize object segmentation on an image; Here is a commands you can use to execute the ,OpenCV, code above and generate a visualization of the image: $ python ,mask,_,rcnn,.py --,mask,-,rcnn mask,-,rcnn,-coco --image images/example_01.jpg. An example of the output:
image Matrix of the type CV_8U containing an image where objects are detected.; objects Vector of rectangles where each rectangle contains the detected object.; scaleFactor Parameter specifying how much the image size is reduced at each image scale.; minNeighbors Parameter specifying how many neighbors each candidate rectangle should have to retain it.; flags Parameter with the same meaning ...
We will be using the ,mask rcnn, framework created by the Data scientists and researchers at Facebook AI Research (FAIR). Let’s have a look at the steps which we will follow to perform image segmentation using ,Mask R-CNN,. Step 1: Clone the repository. First, we will clone the ,mask rcnn, repository which