app.py

# import numpy as np
import gradio as gr
import cv2
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

# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()

def detect_objects(input_img):
 # Load image
 im = input_img.copy()

 # Configuration
 cfg = get_cfg()
 cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
 cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5
 cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")

 # Prediction
 predictor = DefaultPredictor(cfg)
 outputs = predictor(im)

 # Visualization
 v = Visualizer(im[:, :, ::-1], MetadataCatalog.get(cfg.DATASETS.TRAIN[0]), scale=1.9)
 out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
 result_image = out.get_image()[:, :, ::-1]

 return result_image

# Interface
image = gr.Image()
output_image = gr.Image()

title = "Object Detection using Mask R-CNN"
description = "This app detects objects in the input image using Mask R-CNN."
examples = [["./input.png"]]

gr.Interface(detect_objects, [image], output_image, title=title, description=description, examples=examples).launch()