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"""
building-segmentation
Proof of concept showing effectiveness of a fine tuned instance segmentation model for deteting buildings.
"""
import os
import cv2
os.system("pip install 'git+https://github.com/facebookresearch/detectron2.git'")
from transformers import DetrFeatureExtractor, DetrForSegmentation
from PIL import Image
import gradio as gr
import numpy as np
import torch
import torchvision
import detectron2
# import some common detectron2 utilities
import itertools
import seaborn as sns
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog
from detectron2.checkpoint import DetectionCheckpointer
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file('COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml'))
cfg.MODEL.DEVICE='cpu'
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
cfg.MODEL.WEIGHTS = "model_weights/chatswood_buildings_poc.pth"
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 8
predictor = DefaultPredictor(cfg)
def segment_buildings(im):
outputs = predictor(im)
v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.2)
out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
out_im = out.get_image()[:, :, ::-1]
return out_im
# gradio components
"""
gr_slider_confidence = gr.inputs.Slider(0,1,.1,.7,
label='Set confidence threshold % for masks')
"""
# gradio outputs
inputs = gr.inputs.Image(type="pil", label="Input Image")
outputs = gr.outputs.Image(type="pil", label="Output Image")
title = "Building Segmentation"
description = "An instance segmentation demo for identifying boundaries of buildings in aerial images using DETR (End-to-End Object Detection) model with MaskRCNN-101 backbone"
# Create user interface and launch
gr.Interface(segment_buildings,
inputs = inputs,
outputs = outputs,
title = title,
enable_queue = True,
description = description).launch()
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