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import argparse
import cv2
import numpy as np
import deep_sort_app
from deep_sort.iou_matching import iou
from application_util import visualization
DEFAULT_UPDATE_MS = 20
def run(sequence_dir, result_file, show_false_alarms=False, detection_file=None,
update_ms=None, video_filename=None):
"""Run tracking result visualization.
Parameters
----------
sequence_dir : str
Path to the MOTChallenge sequence directory.
result_file : str
Path to the tracking output file in MOTChallenge ground truth format.
show_false_alarms : Optional[bool]
If True, false alarms are highlighted as red boxes.
detection_file : Optional[str]
Path to the detection file.
update_ms : Optional[int]
Number of milliseconds between cosecutive frames. Defaults to (a) the
frame rate specifid in the seqinfo.ini file or DEFAULT_UDPATE_MS ms if
seqinfo.ini is not available.
video_filename : Optional[Str]
If not None, a video of the tracking results is written to this file.
"""
seq_info = deep_sort_app.gather_sequence_info(sequence_dir, detection_file)
results = np.loadtxt(result_file, delimiter=',')
if show_false_alarms and seq_info["groundtruth"] is None:
raise ValueError("No groundtruth available. Cannot show false alarms.")
def frame_callback(vis, frame_idx):
print("Frame idx", frame_idx)
image = cv2.imread(
seq_info["image_filenames"][frame_idx], cv2.IMREAD_COLOR)
vis.set_image(image.copy())
if seq_info["detections"] is not None:
detections = deep_sort_app.create_detections(
seq_info["detections"], frame_idx)
vis.draw_detections(detections)
mask = results[:, 0].astype(np.int) == frame_idx
track_ids = results[mask, 1].astype(np.int)
boxes = results[mask, 2:6]
vis.draw_groundtruth(track_ids, boxes)
if show_false_alarms:
groundtruth = seq_info["groundtruth"]
mask = groundtruth[:, 0].astype(np.int) == frame_idx
gt_boxes = groundtruth[mask, 2:6]
for box in boxes:
# NOTE(nwojke): This is not strictly correct, because we don't
# solve the assignment problem here.
min_iou_overlap = 0.5
if iou(box, gt_boxes).max() < min_iou_overlap:
vis.viewer.color = 0, 0, 255
vis.viewer.thickness = 4
vis.viewer.rectangle(*box.astype(np.int))
if update_ms is None:
update_ms = seq_info["update_ms"]
if update_ms is None:
update_ms = DEFAULT_UPDATE_MS
visualizer = visualization.Visualization(seq_info, update_ms)
if video_filename is not None:
visualizer.viewer.enable_videowriter(video_filename)
visualizer.run(frame_callback)
def parse_args():
""" Parse command line arguments.
"""
parser = argparse.ArgumentParser(description="Siamese Tracking")
parser.add_argument(
"--sequence_dir", help="Path to the MOTChallenge sequence directory.",
default=None, required=True)
parser.add_argument(
"--result_file", help="Tracking output in MOTChallenge file format.",
default=None, required=True)
parser.add_argument(
"--detection_file", help="Path to custom detections (optional).",
default=None)
parser.add_argument(
"--update_ms", help="Time between consecutive frames in milliseconds. "
"Defaults to the frame_rate specified in seqinfo.ini, if available.",
default=None)
parser.add_argument(
"--output_file", help="Filename of the (optional) output video.",
default=None)
parser.add_argument(
"--show_false_alarms", help="Show false alarms as red bounding boxes.",
type=bool, default=False)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
run(
args.sequence_dir, args.result_file, args.show_false_alarms,
args.detection_file, args.update_ms, args.output_file) |