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 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 run(sequence_dir, result_file, show_false_alarms=False, detection_file=None,
update_ms=None, video_filename=None, IDnum=0):
"""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.
track_id : currently track id
IDnum : not 0 is foot tracking display
"""
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):
#프레임별로 처리
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) #해당 frame_id인 mask값들 중 [1]들인 id값 추출
boxes = results[mask, 2:6]
vis.draw_groundtruth(track_ids, boxes)
# 발위치 10개 중 y값만 빼기
h_file = os.path.dirname(result_file) # result/text/
with open(h_file + '/ID_h.txt', 'r') as f_hi:
line_splits = [int(l.split(',')[1]) for l in f_hi.read().splitlines()[1:]]
i = 0
#print(line_splits)
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 IDnum != 0: # Tracking 하는 ID만 보여주고 발 표시 !!!!!!!
vis.viewer.circle(
box[0] + box[2] / 2, box[1] + box[3], 3)
#if int(box[1] + box[3]) > line_splits[i] - 3 and int(bbox[1] + bbox[3]) < line_splits[i] + 3:
# vis.viewer.circle(
# box[0] + box[2] / 2, box[1] + box[3], 3)
# i += 1
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)
