# Copyright (c) Facebook, Inc. and its affiliates. import argparse import glob import multiprocessing as mp import numpy as np import os import sys import tempfile import time import warnings import cv2 import tqdm sys.path.insert(0, "./") # noqa from demo.predictors import VisualizationDemo from detectron2.checkpoint import DetectionCheckpointer from detectron2.config import LazyConfig, instantiate from detectron2.data.detection_utils import read_image from detectron2.utils.logger import setup_logger # constants WINDOW_NAME = "COCO detections" def setup(args): cfg = LazyConfig.load(args.config_file) cfg = LazyConfig.apply_overrides(cfg, args.opts) return cfg def get_parser(): parser = argparse.ArgumentParser(description="detrex demo for visualizing customized inputs") parser.add_argument( "--config-file", default="projects/dino/configs/dino_r50_4scale_12ep.py", metavar="FILE", help="path to config file", ) parser.add_argument("--webcam", action="store_true", help="Take inputs from webcam.") 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( "--min_size_test", type=int, default=800, help="Size of the smallest side of the image during testing. Set to zero to disable resize in testing.", ) parser.add_argument( "--max_size_test", type=float, default=1333, help="Maximum size of the side of the image during testing.", ) parser.add_argument( "--img_format", type=str, default="RGB", help="The format of the loading images.", ) parser.add_argument( "--metadata_dataset", type=str, default="coco_2017_val", help="The metadata infomation to be used. Default to COCO val metadata.", ) parser.add_argument( "--confidence-threshold", type=float, default=0.5, help="Minimum score for instance predictions to be shown", ) parser.add_argument( "--opts", help="Modify config options using the command-line", default=None, 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(args) model = instantiate(cfg.model) model.to(cfg.train.device) checkpointer = DetectionCheckpointer(model) checkpointer.load(cfg.train.init_checkpoint) model.eval() demo = VisualizationDemo( model=model, min_size_test=args.min_size_test, max_size_test=args.max_size_test, img_format=args.img_format, metadata_dataset=args.metadata_dataset, ) 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): # use PIL, to be consistent with evaluation img = read_image(path, format="BGR") start_time = time.time() predictions, visualized_output = demo.run_on_image(img, args.confidence_threshold) 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!" cam = cv2.VideoCapture(0) 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()