def init(self, imgs, bbox):
"""
args:
imgs(np.ndarray): batch of BGR image
batch of bbox: (x, y, w, h) bbox
"""
bbox = get_min_max_bbox(bbox)
bbox = cxy_wh_2_rect(bbox)
self.center_pos = np.array([bbox[:, 0]+(bbox[:, 2])/2.0,
bbox[:, 1]+(bbox[:, 3])/2.0])
self.center_pos = self.center_pos.transpose()
self.size = np.array([bbox[:, 2], bbox[:, 3]])
self.size = self.size.transpose()
w_z = self.size[:, 0] + CONTEXT_AMOUNT * np.sum(self.size, 1)
h_z = self.size[:, 1] + CONTEXT_AMOUNT * np.sum(self.size, 1)
s_z = np.round(np.sqrt(w_z * h_z))
self.channel_average = []
for img in imgs:
self.channel_average.append(np.mean(img, axis=(0, 1)))
self.channel_average = np.array(self.channel_average)
z_crop = []
for i, img in enumerate(imgs):
z_crop.append(self.get_subwindow(img, self.center_pos[i],
EXEMPLAR_SIZE,
s_z[i],
self.channel_average[i],
ind=0))
z_crop = torch.cat(z_crop) # print(z_crop)
self.model.template(z_crop)
self.cnt = 0
def visualise_transformed_data_point(x, y):
img_t = x[0]
img_i = x[1]
bbox_t = x[2][np.newaxis, :]
bbox_t = get_min_max_bbox(bbox_t)
bbox_t = cxy_wh_2_rect(bbox_t)
center_pos = np.array([
bbox_t[0, 0] + (bbox_t[0, 2] - 1) / 2.0,
bbox_t[0, 1] + (bbox_t[0, 3] - 1) / 2.0
])
size = np.array([bbox_t[0, 2], bbox_t[0, 3]])
# calculate z crop size
w_z = size[0] + CONTEXT_AMOUNT * np.sum(size)
h_z = size[1] + CONTEXT_AMOUNT * np.sum(size)
s_z = np.round(np.sqrt(w_z * h_z))
scale_z = EXEMPLAR_SIZE / s_z
s_x = np.round(s_z * (INSTANCE_SIZE / EXEMPLAR_SIZE))
# print("Track centre = ", center_pos)
channel_average = np.mean(img_t, axis=(0, 1))
# get crop
# cv2.imwrite('/home/sudeep/Desktop/img1.jpg', img)
# print(img.shape)
# print("Init centre = ", center_pos)
img_t = get_subwindow(img_t, center_pos, INSTANCE_SIZE, s_x,
channel_average)
sz = EXEMPLAR_SIZE
sx = INSTANCE_SIZE
centre = np.array([(sx / 2.0), (sx / 2.0)])
xmin = centre[0] - (sz / 2.0)
xmax = centre[0] + (sz / 2.0)
t_quad = np.array([xmin, xmax, xmin, xmin, xmax, xmin, xmax,
xmax]) #inclusive
img_i = get_subwindow(img_i, center_pos, INSTANCE_SIZE, s_x,
channel_average)
i_quad = y
imgt_box = draw_bbox(img_t, t_quad)
imgi_box = draw_bbox(img_i, i_quad)
vis = np.concatenate((imgt_box, imgi_box), axis=1)
return vis
def transform_to_gt(x, y):
img_t = x[0]
img_i = x[1]
bbox_t = x[2][np.newaxis, :]
bbox_t = get_min_max_bbox(bbox_t)
bbox_t = cxy_wh_2_rect(bbox_t)
y = get_min_max_bbox(y[np.newaxis, :])[0]
center_pos = np.array([
bbox_t[0, 0] + (bbox_t[0, 2] - 1) / 2.0,
bbox_t[0, 1] + (bbox_t[0, 3] - 1) / 2.0
])
size = np.array([bbox_t[0, 2], bbox_t[0, 3]])
# calculate z crop size
w_z = size[0] + CONTEXT_AMOUNT * np.sum(size)
h_z = size[1] + CONTEXT_AMOUNT * np.sum(size)
s_z = np.round(np.sqrt(w_z * h_z))
scale_z = EXEMPLAR_SIZE / s_z
y[0] -= (INSTANCE_SIZE / 2)
y[1] -= (INSTANCE_SIZE / 2)
y[2] -= (EXEMPLAR_SIZE)
y[3] -= (EXEMPLAR_SIZE)
y = y / scale_z
# print("Bounding box shape = ", bbox.shape)
# lr = penalty[best_idx] * score[best_idx] * cfg.TRACK.LR
cx = y[0] + center_pos[0]
cy = y[1] + center_pos[1]
# bbox2 = [x.detach() for x in bbox]
# # smooth bbox
# print(size, bbox)
width = size[0] * (1 - TRANSITION_LR) + (size[0] + y[2]) * TRANSITION_LR
height = size[1] * (1 - TRANSITION_LR) + (size[1] + y[3]) * TRANSITION_LR
cx, cy, width, height = _bbox_clip(cx, cy, width, height, img_i.shape[:2])
bbox = np.array([cx - width / 2, cy - height / 2, width, height])
quad_num = get_region_from_corner(bbox[np.newaxis, :])[0]
return quad_num