cheersanimesh / Instance_Segmentation_Mask_RCNN

To perform instance segmentation using Mask R-CNN and Python

Instance_Segmentation_Mask_RCNN

To perform instance segmentation using Mask R-CNN and Python

Overview

I'll be using the Mask R-CNN architecture to perform instance segmentation on images, video and live web-cam feed. The Mask R-CNN architecture is an extension of the Faster R-CNN architecture. It uses ResNet101 as backbone(It also supports ResNet50).

You can find more details about the working and functionality of the code from my article given here.

Arguments

    --image                 ---->   Path to the test image
    --model_path            ---->   Path to the model directory
    --model_name            ---->   Name of the model file 
    --class_names           ---->   Path to the class labels 
    --mrcnn_visualize       ---->   Use the built-in visualize method  
    --instance_segmentation ---->   To toggle between semantic and instance segmentation             
    --draw_bbox             ---->   Draw the bounding box with class labels  
    --camera                ---->   Perform live detection  
    --video                 ---->   Path to video file
    --save_enable           ---->   Enable to save processed image or video  

Usage

• For testing on images
  python mask.py --image <path to image> --save_enable True/False --model_path <path to the model directory> --model_name <name of the model> --class_names <path to the class labels file>

• For testing on videos
  python mask.py --video <path to video file> --save_enable True/False --model_path <path to the model directory> --model_name <name of the model> --class_names <path to the class labels file>

• For testing on web-cam feed
  python mask.py --camera True --model_path <path to the model directory> --model_name <name of the model> --class_names <path to the class labels file>

The program by default looks for the model in the models folder and specifically for the mask_rcnn_coco.h5 file.

Sample Outputs

Notes

• Do note that the code for Mask R-CNN was obtained from Matterport's repository.
• For more details regarding the Mask RCNN implementation visit the link given or read the MRCNN_README file. The files mask.py, mask_colab.py and Mask_On_Colab.ipynb, models, my_images are mine.