app.py

"""
building-segmentation
Proof of concept showing effectiveness of a fine tuned instance segmentation model for deteting buildings.
"""
import os, json, cv2, random
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

cfg = get_cfg()
cfg.MODEL.DEVICE='cpu'
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 
cfg.MODEL.WEIGHTS = "model_weights/chatswood_buildings_poc.pth"  

def segment_buildings(input_image):

    im = cv2.imread(input_image.name)
    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"))
    return Image.fromarray(np.uint8(out.get_image())).convert('RGB')

# gradio components -inputs
gr_image_input = gr.inputs.Image(type="file")
"""
gr_slider_confidence = gr.inputs.Slider(0,1,.1,.7,
                                        label='Set confidence threshold % for masks')
"""
# gradio outputs
gr_image_output = gr.outputs.Image(type="pil") 

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(predict_building_mask, 
                inputs = gr_image_input,
                outputs = gr_image_output,
                title = title,
                enable_queue = True,
                description = description).launch()