demo.py

# Copyright (c) Facebook, Inc. and its affiliates.
'''
    Modifications Copyright (c) 2024-present NAVER Corp, Apache License v2.0
    original source: https://github.com/facebookresearch/Detic/blob/main/demo.py
'''
import argparse
import glob
import multiprocessing as mp
import numpy as np
import os
import tempfile
import time
import warnings
import cv2
import tqdm
import sys
import mss

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

sys.path.insert(0, 'third_party/CenterNet2/')
from centernet.config import add_centernet_config
from proxydet.config import add_proxydet_config

from proxydet.predictor import VisualizationDemo

# Fake a video capture object OpenCV style - half width, half height of first screen using MSS
class ScreenGrab:
    def __init__(self):
        self.sct = mss.mss()
        m0 = self.sct.monitors[0]
        self.monitor = {'top': 0, 'left': 0, 'width': m0['width'] / 2, 'height': m0['height'] / 2}

    def read(self):
        img =  np.array(self.sct.grab(self.monitor))
        nf = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)
        return (True, nf)

    def isOpened(self):
        return True
    def release(self):
        return True


# constants
WINDOW_NAME = "ProxyDet"

def setup_cfg(args):
    cfg = get_cfg()
    if args.cpu:
        cfg.MODEL.DEVICE="cpu"
    add_centernet_config(cfg)
    add_proxydet_config(cfg)
    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    # Set score_threshold for builtin models
    cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold
    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold
    cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
    cfg.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_PATH = 'rand' # load later
    if not args.pred_all_class:
        cfg.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL = True
    cfg.freeze()
    return cfg


def get_parser():
    parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs")
    parser.add_argument(
        "--config-file",
        default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml",
        metavar="FILE",
        help="path to config file",
    )
    parser.add_argument("--webcam", help="Take inputs from webcam.")
    parser.add_argument("--cpu", action='store_true', help="Use CPU only.")
    parser.add_argument("--video-input", help="Path to video file.")
    parser.add_argument(
        "--input",
        nargs="+",
        help="A list of space separated input images; "
        "or a single glob pattern such as 'directory/*.jpg'",
    )
    parser.add_argument(
        "--output",
        help="A file or directory to save output visualizations. "
        "If not given, will show output in an OpenCV window.",
    )
    parser.add_argument(
        "--vocabulary",
        default="lvis",
        choices=['lvis', 'openimages', 'objects365', 'coco', 'custom'],
        help="",
    )
    parser.add_argument(
        "--custom_vocabulary",
        default="",
        help="",
    )
    parser.add_argument(
        "--zeroshot_weight_path",
        default=None,
        help="zeroshot text embedding path used during training",
    )
    parser.add_argument("--pred_all_class", action='store_true')
    parser.add_argument(
        "--confidence-threshold",
        type=float,
        default=0.5,
        help="Minimum score for instance predictions to be shown",
    )
    parser.add_argument(
        "--base-cat-threshold",
        type=float,
        default=0.9,
        help="Minimum score for similarity with trained base categories",
    )
    parser.add_argument(
        "--opts",
        help="Modify config options using the command-line 'KEY VALUE' pairs",
        default=[],
        nargs=argparse.REMAINDER,
    )
    return parser


def test_opencv_video_format(codec, file_ext):
    with tempfile.TemporaryDirectory(prefix="video_format_test") as dir:
        filename = os.path.join(dir, "test_file" + file_ext)
        writer = cv2.VideoWriter(
            filename=filename,
            fourcc=cv2.VideoWriter_fourcc(*codec),
            fps=float(30),
            frameSize=(10, 10),
            isColor=True,
        )
        [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)]
        writer.release()
        if os.path.isfile(filename):
            return True
        return False


if __name__ == "__main__":
    mp.set_start_method("spawn", force=True)
    args = get_parser().parse_args()
    setup_logger(name="fvcore")
    logger = setup_logger()
    logger.info("Arguments: " + str(args))

    cfg = setup_cfg(args)
    print(_highlight(cfg.dump(), ".yaml"))

    demo = VisualizationDemo(cfg, args)

    if args.input:
        if len(args.input) == 1:
            args.input = glob.glob(os.path.expanduser(args.input[0]))
            assert args.input, "The input path(s) was not found"
        for path in tqdm.tqdm(args.input, disable=not args.output):
            img = read_image(path, format="BGR")
            start_time = time.time()
            predictions, visualized_output = demo.run_on_image(img)
            logger.info(
                "{}: {} in {:.2f}s".format(
                    path,
                    "detected {} instances".format(len(predictions["instances"]))
                    if "instances" in predictions
                    else "finished",
                    time.time() - start_time,
                )
            )

            if args.output:
                if os.path.isdir(args.output):
                    assert os.path.isdir(args.output), args.output
                    out_filename = os.path.join(args.output, os.path.basename(path))
                else:
                    assert len(args.input) == 1, "Please specify a directory with args.output"
                    out_filename = args.output
                visualized_output.save(out_filename)
            else:
                cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
                cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])
                if cv2.waitKey(0) == 27:
                    break  # esc to quit
    elif args.webcam:
        assert args.input is None, "Cannot have both --input and --webcam!"
        assert args.output is None, "output not yet supported with --webcam!"
        if args.webcam == "screen":
            cam = ScreenGrab()
        else:
            cam = cv2.VideoCapture(int(args.webcam))
        for vis in tqdm.tqdm(demo.run_on_video(cam)):
            cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
            cv2.imshow(WINDOW_NAME, vis)
            if cv2.waitKey(1) == 27:
                break  # esc to quit
        cam.release()
        cv2.destroyAllWindows()
    elif args.video_input:
        video = cv2.VideoCapture(args.video_input)
        width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
        frames_per_second = video.get(cv2.CAP_PROP_FPS)
        num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
        basename = os.path.basename(args.video_input)
        codec, file_ext = (
            ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4")
        )
        if codec == ".mp4v":
            warnings.warn("x264 codec not available, switching to mp4v")
        if args.output:
            if os.path.isdir(args.output):
                output_fname = os.path.join(args.output, basename)
                output_fname = os.path.splitext(output_fname)[0] + file_ext
            else:
                output_fname = args.output
            assert not os.path.isfile(output_fname), output_fname
            output_file = cv2.VideoWriter(
                filename=output_fname,
                # some installation of opencv may not support x264 (due to its license),
                # you can try other format (e.g. MPEG)
                fourcc=cv2.VideoWriter_fourcc(*codec),
                fps=float(frames_per_second),
                frameSize=(width, height),
                isColor=True,
            )
        assert os.path.isfile(args.video_input)
        for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames):
            if args.output:
                output_file.write(vis_frame)
            else:
                cv2.namedWindow(basename, cv2.WINDOW_NORMAL)
                cv2.imshow(basename, vis_frame)
                if cv2.waitKey(1) == 27:
                    break  # esc to quit
        video.release()
        if args.output:
            output_file.release()
        else:
            cv2.destroyAllWindows()