def get_sample(self, idx):
"""
Returns sample without transformation for visualization.
Sample consists of resized previous and current frame with target
which is passed to the network. Bounding box values are normalized
between 0 and 1 with respect to the target frame and then scaled by
factor of 10.
"""
opts_curr = {}
curr_sample = {}
curr_img = self.get_orig_sample(idx, 1)['image']
currbb = self.get_orig_sample(idx, 1)['bb']
prevbb = self.get_orig_sample(idx, 0)['bb']
bbox_curr_shift = BoundingBox(prevbb[0], prevbb[1], prevbb[2],
prevbb[3])
(rand_search_region, rand_search_location, edge_spacing_x,
edge_spacing_y) = cropPadImage(bbox_curr_shift, curr_img)
bbox_curr_gt = BoundingBox(currbb[0], currbb[1], currbb[2], currbb[3])
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)
# get larger context
bbox_curr_shift.kContextFactor = 4
(rand_search_region_x2, rand_search_location_x2, edge_spacing_x_x2,
edge_spacing_y_x2) = cropPadImage(bbox_curr_shift, curr_img)
curr_sample['image'] = rand_search_region
curr_sample['image_x2'] = rand_search_region_x2
curr_sample['bb'] = bbox_gt_recentered.get_bb_list()
# additional options for visualization
opts_curr['edge_spacing_x'] = edge_spacing_x
opts_curr['edge_spacing_y'] = edge_spacing_y
opts_curr['search_location'] = rand_search_location
opts_curr['search_region'] = rand_search_region
# build prev sample
prev_sample = self.get_orig_sample(idx, 0)
prev_sample_x2 = self.get_orig_sample(idx, 0)
prev_sample, opts_prev = crop_sample(prev_sample)
prev_sample_x2, opts_prev_x2 = crop_sample(prev_sample_x2, 4)
prev_sample['image_x2'] = prev_sample_x2['image']
# scale
scale = Rescale((self.input_size, self.input_size))
scaled_curr_obj = scale(curr_sample, opts_curr)
scaled_prev_obj = scale(prev_sample, opts_prev)
training_sample = {
'previmg': scaled_prev_obj['image'],
'currimg': scaled_curr_obj['image'],
'previmg_x2': scaled_prev_obj['image_x2'],
'currimg_x2': scaled_curr_obj['image_x2'],
'currbb': scaled_curr_obj['bb']
}
return training_sample, opts_curr
def _get_rect(self, sample):
"""
Performs forward pass through the GOTURN network to regress
bounding box coordinates in the original image dimensions.
"""
x1, x2 = sample['previmg'], sample['currimg']
x1 = x1.unsqueeze(0).to(self.device)
x2 = x2.unsqueeze(0).to(self.device)
y = self.net(x1, x2)
bb = y.data.cpu().numpy().transpose((1, 0))
bb = bb[:, 0]
bbox = BoundingBox(bb[0], bb[1], bb[2], bb[3])
# inplace conversion
bbox.unscale(self.opts['search_region'])
bbox.uncenter(self.curr_img, self.opts['search_location'],
self.opts['edge_spacing_x'], self.opts['edge_spacing_y'])
return bbox.get_bb_list()
def show_sample_no_wait(self, idx):
"""
Helper function to display sample, which is passed to GOTURN.
Shows previous frame and current frame with bounding box.
"""
x, _ = self.get_sample(idx)
prev_image = x['previmg']
curr_image = x['currimg']
bb = x['currbb']
bbox = BoundingBox(bb[0], bb[1], bb[2], bb[3])
bbox.unscale(curr_image)
bb = bbox.get_bb_list()
bb = [int(val) for val in bb]
prev_image = cv2.cvtColor(prev_image, cv2.COLOR_RGB2BGR)
curr_image = cv2.cvtColor(curr_image, cv2.COLOR_RGB2BGR)
curr_image = cv2.rectangle(curr_image, (bb[0], bb[1]), (bb[2], bb[3]),
(0, 255, 0), 2)
concat_image = np.hstack((prev_image, curr_image))
cv2.imshow('imagenet dataset sample', concat_image)
cv2.waitKey(150)