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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import glob
import multiprocessing as mp
import os
import time
import cv2
import tqdm
from detectron2.config import get_cfg
from detectron2.data.detection_utils import read_image
from detectron2.utils.logger import setup_logger
from predictor import VisualizationDemo
from centernet.config import add_centernet_config
# constants
WINDOW_NAME = "CenterNet2 detections"
from detectron2.utils.video_visualizer import VideoVisualizer
from detectron2.utils.visualizer import ColorMode, Visualizer
from detectron2.data import MetadataCatalog
def setup_cfg(args):
# load config from file and command-line arguments
cfg = get_cfg()
add_centernet_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
if cfg.MODEL.META_ARCHITECTURE in ['ProposalNetwork', 'CenterNetDetector']:
cfg.MODEL.CENTERNET.INFERENCE_TH = args.confidence_threshold
cfg.MODEL.CENTERNET.NMS_TH = cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST
cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold
cfg.freeze()
return cfg
def get_parser():
parser = argparse.ArgumentParser(description="Detectron2 demo for builtin models")
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", 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")
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(
"--confidence-threshold",
type=float,
default=0.3,
help="Minimum score for instance predictions to be shown",
)
parser.add_argument(
"--opts",
help="Modify config options using the command-line 'KEY VALUE' pairs",
default=[],
nargs=argparse.REMAINDER,
)
return parser
if __name__ == "__main__":
mp.set_start_method("spawn", force=True)
args = get_parser().parse_args()
logger = setup_logger()
logger.info("Arguments: " + str(args))
cfg = setup_cfg(args)
demo = VisualizationDemo(cfg)
output_file = None
if args.input:
if len(args.input) == 1:
args.input = glob.glob(os.path.expanduser(args.input[0]))
files = os.listdir(args.input[0])
args.input = [args.input[0] + x for x in files]
assert args.input, "The input path(s) was not found"
visualizer = VideoVisualizer(
MetadataCatalog.get(
cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
),
instance_mode=ColorMode.IMAGE)
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, visualizer=visualizer)
if 'instances' in predictions:
logger.info(
"{}: detected {} instances in {:.2f}s".format(
path, len(predictions["instances"]), time.time() - start_time
)
)
else:
logger.info(
"{}: detected {} instances in {:.2f}s".format(
path, len(predictions["proposals"]), 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))
visualized_output.save(out_filename)
else:
# assert len(args.input) == 1, "Please specify a directory with args.output"
# out_filename = args.output
if output_file is None:
width = visualized_output.get_image().shape[1]
height = visualized_output.get_image().shape[0]
frames_per_second = 15
output_file = cv2.VideoWriter(
filename=args.output,
# some installation of opencv may not support x264 (due to its license),
# you can try other format (e.g. MPEG)
fourcc=cv2.VideoWriter_fourcc(*"x264"),
fps=float(frames_per_second),
frameSize=(width, height),
isColor=True,
)
output_file.write(visualized_output.get_image()[:, :, ::-1])
else:
# cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL)
cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1])
if cv2.waitKey(1 ) == 27:
break # esc to quit
elif args.webcam:
assert args.input is None, "Cannot have both --input and --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
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 = 15 # video.get(cv2.CAP_PROP_FPS)
num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
basename = os.path.basename(args.video_input)
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] + ".mkv"
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(*"x264"),
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)
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()
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