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))
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))
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)