def make_training_example_BBShift_(self,
bbParams,
visualize_example=False):
"""TODO: Docstring for make_training_example_BBShift_.
:returns: TODO
"""
bbox_curr_gt = self.bbox_curr_gt_
bbox_curr_shift = BoundingBox(0, 0, 0, 0)
bbox_curr_shift = bbox_curr_gt.shift(
self.img_curr_, bbParams.lamda_scale, bbParams.lamda_shift,
bbParams.min_scale, bbParams.max_scale, True, bbox_curr_shift)
rand_search_region, rand_search_location, edge_spacing_x, edge_spacing_y = cropPadImage(
bbox_curr_shift, self.img_curr_)
bbox_curr_gt = self.bbox_curr_gt_
bbox_gt_recentered = BoundingBox(0, 0, 0, 0)
bbox_gt_recentered = bbox_curr_gt.recenter(rand_search_location,
edge_spacing_x,
edge_spacing_y,
bbox_gt_recentered)
bbox_gt_recentered.scale(rand_search_region)
bbox_gt_scaled = bbox_gt_recentered
return rand_search_region, self.target_pad_, bbox_gt_scaled
def init(self, image_curr, init_bbox):
""" initializing the first frame in the video """
left = float(init_bbox[0])
top = float(init_bbox[1])
right = float(init_bbox[2])
bottom = float(init_bbox[3])
bbox_gt = BoundingBox(left, top, right, bottom)
self.image_prev = image_curr
self.bbox_prev_tight = bbox_gt
self.bbox_curr_prior_tight = bbox_gt
self.DeltaBox = np.array([0., 0.])
self.lambdaBox = 0.3
self.prevBoxeffect = 0
self.occlusion_flag = 0
target_pad, _, _, _ = cropPadImage(self.bbox_prev_tight,
self.image_prev)
# image, BGR(training type)
target_pad_resize = self.preprocess(target_pad)
# jaehyuk, check hanning windows
hann_1d = np.expand_dims(np.hanning(227), axis=0)
hann_2d = np.transpose(hann_1d) * hann_1d
hann_2d = np.expand_dims(hann_2d, axis=2)
target_pad_resize = target_pad_resize * hann_2d
target_pad_expdim = np.expand_dims(target_pad_resize, axis=0)
self.target_pool5 = sess.run(
[tracknet.target_pool5],
feed_dict={tracknet.target: target_pad_expdim})
self.target_pool5 = np.resize(self.target_pool5, [1, 6, 6, 256])
def reset(self, bbox_curr, bbox_prev, img_curr, img_prev):
"""TODO: to be defined1. """
target_pad, _, _, _ = cropPadImage(bbox_prev, img_prev)
self.img_curr_ = img_curr
self.bbox_curr_gt_ = bbox_curr
self.bbox_prev_gt_ = bbox_prev
self.target_pad_ = target_pad
def track(self, image_curr, tracknet, sess):
"""TODO: Docstring for tracker.
:returns: TODO
"""
target_pad, _, _, _ = cropPadImage(self.bbox_prev_tight,
self.image_prev)
cur_search_region, search_location, edge_spacing_x, edge_spacing_y = cropPadImage(
self.bbox_curr_prior_tight, image_curr)
# image, BGR(training type)
cur_search_region_resize = self.preprocess(cur_search_region)
target_pad_resize = self.preprocess(target_pad)
cur_search_region_expdim = np.expand_dims(cur_search_region_resize,
axis=0)
target_pad_expdim = np.expand_dims(target_pad_resize, axis=0)
fc8 = sess.run(
[tracknet.fc8],
feed_dict={
tracknet.image: cur_search_region_expdim,
tracknet.target: target_pad_expdim
})
bbox_estimate = calculate_box(fc8)
# this box is NMS result, TODO, all bbox check
if not len(bbox_estimate) == 0:
bbox_estimate = BoundingBox(bbox_estimate[0][0],
bbox_estimate[0][1],
bbox_estimate[0][2],
bbox_estimate[0][3])
# Inplace correction of bounding box
bbox_estimate.unscale(cur_search_region)
bbox_estimate.uncenter(image_curr, search_location, edge_spacing_x,
edge_spacing_y)
self.image_prev = image_curr
self.bbox_prev_tight = bbox_estimate
self.bbox_curr_prior_tight = bbox_estimate
else:
bbox_estimate = False
return bbox_estimate
def make_true_example(self):
"""TODO: Docstring for make_true_example.
:returns: TODO
"""
curr_prior_tight = self.bbox_prev_gt_
target_pad = self.target_pad_
curr_search_region, curr_search_location, edge_spacing_x, edge_spacing_y = cropPadImage(curr_prior_tight,
self.img_curr_)
bbox_curr_gt = self.bbox_curr_gt_
bbox_curr_gt_recentered = BoundingBox(0, 0, 0, 0)
bbox_curr_gt_recentered = bbox_curr_gt.recenter(curr_search_location, edge_spacing_x, edge_spacing_y,
bbox_curr_gt_recentered)
bbox_curr_gt_recentered.scale(curr_search_region)
return curr_search_region, target_pad, bbox_curr_gt_recentered
def track(self, image_curr, tracknet, velocity, sess):
"""TODO: Docstring for tracker.
:returns: TODO
"""
target_pad, _, _, _ = cropPadImage(self.bbox_prev_tight,
self.image_prev)
cur_search_region, search_location, edge_spacing_x, edge_spacing_y = cropPadImage(
self.bbox_curr_prior_tight, image_curr)
# image, BGR(training type)
cur_search_region_resize = self.preprocess(cur_search_region)
target_pad_resize = self.preprocess(target_pad)
cur_search_region_expdim = np.expand_dims(cur_search_region_resize,
axis=0)
target_pad_expdim = np.expand_dims(target_pad_resize, axis=0)
re_fc4_image, fc4_adj = sess.run(
[tracknet.re_fc4_image, tracknet.fc4_adj],
feed_dict={
tracknet.image: cur_search_region_expdim,
tracknet.target: target_pad_expdim
})
bbox_estimate, object_bool, objectness = calculate_box(
re_fc4_image, fc4_adj)
print('objectness_s is: ', objectness)
########### original method ############
# this box is NMS result, TODO, all bbox check
if not len(bbox_estimate) == 0:
bbox_estimate = BoundingBox(bbox_estimate[0][0],
bbox_estimate[0][1],
bbox_estimate[0][2],
bbox_estimate[0][3])
# Inplace correction of bounding box
bbox_estimate.unscale(cur_search_region)
bbox_estimate.uncenter(image_curr, search_location, edge_spacing_x,
edge_spacing_y)
# self.image_prev = image_curr
# self.bbox_prev_tight = bbox_estimate
self.bbox_curr_prior_tight = bbox_estimate
else:
# self.image_prev = self.image_prev
# self.bbox_prev_tight = self.bbox_prev_tight
self.bbox_curr_prior_tight = self.bbox_curr_prior_tight
bbox_estimate = self.bbox_curr_prior_tight
########### original method ############
############ trick method ############
# if object_bool:
# # if not len(bbox_estimate) == 0:
# # current_box_wh = np.array([(bbox_estimate.[0][2] - bbox_estimate.[0][0]), (bbox_estimate.[0][3] - bbox_estimate.[0][1])], dtype=np.float32)
# # prev_box_wh = np.array([5., 5.], dtype=np.float32)
#
# bbox_estimate = BoundingBox(bbox_estimate[0][0], bbox_estimate[0][1], bbox_estimate[0][2], bbox_estimate[0][3])
#
# # relative distance from center point [5. 5.]
# relative_current_box = np.array([(bbox_estimate.x2 + bbox_estimate.x1) / 2,
# (bbox_estimate.y2 + bbox_estimate.y1) / 2],
# dtype=np.float32)
# relative_distance = np.linalg.norm(relative_current_box - np.array([5., 5.]))
#
# # Inplace correction of bounding box
# bbox_estimate.unscale(cur_search_region)
# bbox_estimate.uncenter(image_curr, search_location, edge_spacing_x, edge_spacing_y)
#
# # image's width height , center point
# current_box = np.array([(bbox_estimate.x2 + bbox_estimate.x1) / 2, (bbox_estimate.y2 + bbox_estimate.y1) / 2], dtype=np.float32)
# prev_box = np.array([(self.bbox_curr_prior_tight.x2 + self.bbox_curr_prior_tight.x1) / 2, (self.bbox_curr_prior_tight.y2 + self.bbox_curr_prior_tight.y1) / 2],
# dtype=np.float32)
#
# if relative_distance < 2:
# self.DeltaBox = self.lambdaBox * (current_box - prev_box) + (1 - self.lambdaBox) * self.DeltaBox
#
#
# self.image_prev = image_curr
# self.bbox_prev_tight = bbox_estimate
# self.bbox_curr_prior_tight = bbox_estimate
# print(self.DeltaBox)
# else:
# # under prev img, box block is no update
# self.image_prev = self.image_prev
# self.bbox_prev_tight = self.bbox_prev_tight
# # self.bbox_curr_prior_tight = self.bbox_prev_tight
# self.bbox_curr_prior_tight = BoundingBox(self.bbox_curr_prior_tight.x1 + self.DeltaBox[0],
# self.bbox_curr_prior_tight.y1 + self.DeltaBox[1],
# self.bbox_curr_prior_tight.x2 + self.DeltaBox[0],
# self.bbox_curr_prior_tight.y2 + self.DeltaBox[1])
# bbox_estimate = self.bbox_curr_prior_tight
# print('distance is {:>3}'.format(relative_distance))
# print(self.DeltaBox)
# else:
# # under prev img, box block is no update
# self.image_prev = self.image_prev
# self.bbox_prev_tight = self.bbox_prev_tight
# # self.bbox_curr_prior_tight = self.bbox_prev_tight
# self.bbox_curr_prior_tight = BoundingBox(self.bbox_curr_prior_tight.x1 + self.DeltaBox[0],
# self.bbox_curr_prior_tight.y1 + self.DeltaBox[1],
# self.bbox_curr_prior_tight.x2 + self.DeltaBox[0],
# self.bbox_curr_prior_tight.y2 + self.DeltaBox[1])
# bbox_estimate = self.bbox_curr_prior_tight
# print('occlusion is detected')
# print(self.DeltaBox)
#
# ############ trick method ############
left_x = bbox_estimate.x1
left_y = bbox_estimate.y1
width = bbox_estimate.x2 - bbox_estimate.x1
height = bbox_estimate.y2 - bbox_estimate.y1
return vot.Rectangle(left_x, left_y, width, height)
