app.py

import os
# os.system("sudo apt-get update && sudo apt-get install -y git")
# os.system("sudo apt-get -y install pybind11-dev")
# os.system("git clone https://github.com/facebookresearch/detectron2.git")
# os.system("pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html")
os.system("cd detectron2 && pip install detectron2-0.6-cp310-cp310-linux_x86_64.whl")
# os.system("pip3 install torch torchvision torchaudio")
os.system("pip install deepspeed==0.7.0")

import site
from importlib import reload
reload(site)

from PIL import Image
import argparse
import sys
import numpy as np
import cv2
import gradio as gr

from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger

sys.path.insert(0, "third_party/CenterNet2/projects/CenterNet2/")
from centernet.config import add_centernet_config
from grit.config import add_grit_config

from grit.predictor import VisualizationDemo

def get_parser():
    parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs")
    parser.add_argument(
        "--config-file",
        default="configs/GRiT_B_DenseCap_ObjectDet.yaml",
        metavar="FILE",
        help="path to config file",
    )
    parser.add_argument("--cpu", action="store_true", help="Use CPU only.")
    parser.add_argument(
        "--confidence-threshold",
        type=float,
        default=0.5,
        help="Minimum score for instance predictions to be shown",
    )
    parser.add_argument(
        "--test-task",
        type=str,
        default="",
        help="Choose a task to have GRiT perform",
    )
    parser.add_argument(
        "--opts",
        help="Modify config options using the command-line 'KEY VALUE' pairs",
        default=["MODEL.WEIGHTS", "./models/grit_b_densecap_objectdet.pth"],
        nargs=argparse.REMAINDER,
    )
    return parser

def setup_cfg(args):
    cfg = get_cfg()
    if args.cpu:
        cfg.MODEL.DEVICE = "cpu"
    add_centernet_config(cfg)
    add_grit_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    # Set score_threshold for builtin models
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = (
        args.confidence_threshold
    )
    if args.test_task:
        cfg.MODEL.TEST_TASK = args.test_task
    cfg.MODEL.BEAM_SIZE = 1
    cfg.MODEL.ROI_HEADS.SOFT_NMS_ENABLED = False
    cfg.USE_ACT_CHECKPOINT = False
    cfg.freeze()
    return cfg

def predict(image_file):
    image_array = np.array(image_file)[:, :, ::-1]  # BGR
    _, visualized_output = dense_captioning_demo.run_on_image(image_array)
    visualized_output.save(os.path.join(os.getcwd(), "output.jpg"))
    output_image = cv2.imread(os.path.join(os.getcwd(), "output.jpg"))
    output_image = cv2.cvtColor(output_image, cv2.COLOR_BGR2RGB)
    return Image.fromarray(output_image)



args = get_parser().parse_args()
args.test_task = "DenseCap"
setup_logger(name="fvcore")
logger = setup_logger()
logger.info("Arguments: " + str(args))

cfg = setup_cfg(args)

dense_captioning_demo = VisualizationDemo(cfg)

demo = gr.Interface(
    title="Dense Captioning - GRiT",
    fn=predict,
    inputs=gr.Image(type='pil', label="Original Image"),
    outputs=gr.Image(type="pil",label="Output Image"),
    examples=["example_1.jpg", "example_2.jpg"],
)

demo.launch()