cav.heatmap(np.array(labels * 255 / np.max(labels),
dtype=np.uint8)))
print(np.sum(tcl_mask[:, :, 1]))
t0 = time.time()
for bbox_idx in range(1, ret):
bbox_mask = labels == bbox_idx
text_map = tcl_mask[:, :, 0] * bbox_mask
boxes = bbox_transfor_inv(radius_map,
sin_map,
cos_map,
text_map,
wclip=(2, 8))
# nms
boxes = lanms.merge_quadrangle_n9(boxes.astype('float32'), 0.25)
boxes = boxes[:, :8].reshape((-1, 4, 2)).astype(np.int32)
if boxes.shape[0] > 1:
center = np.mean(boxes, axis=1).astype(np.int32).tolist()
paths, routes_path = minConnectPath(center)
boxes = boxes[routes_path]
top = np.mean(boxes[:, 0:2, :],
axis=1).astype(np.int32).tolist()
bot = np.mean(boxes[:, 2:4, :],
axis=1).astype(np.int32).tolist()
boundary_point = top + bot[::-1]
# for index in routes:
for ip, pp in enumerate(top):
if ip == 0:
def __call__(self, tcl_mask, radius_map, sin_map, cos_map):
imgsize = cos_map.shape
# ## 1. Reverse generation of box
proposals = bbox_transfor_inv(radius_map,
sin_map,
cos_map,
tcl_mask[:, :, 1],
wclip=self.clip)
# ## 2. remove predicted boxes with either height or width < threshold
proposals = filter_bbox(proposals, minsize=16)
# ## 3. local nms
proposals = lanms.merge_quadrangle_n9(proposals.astype('float32'),
self.nms_threshold)
# ## 4. clip bbox
if proposals.shape[0] > 0:
proposals = clip_box(proposals, imgsize)
if proposals.shape[0] > 0:
# ## 5. generate cluster label
_, label_mask = cv2.connectedComponents(tcl_mask[:, :, 0].astype(
np.uint8),
connectivity=8)
cxy = np.mean(proposals[:, :8].reshape((-1, 4, 2)),
axis=1).astype(np.int32)
# ## 6. Geometric features
x_map = cxy[:, 0]
y_map = cxy[:, 1]
gh = (radius_map[:, :, 0] + radius_map[:, :, 1])
h_map = gh[cxy[:, 1], cxy[:, 0]]
w_map = np.clip(h_map // 4, self.clip[0], self.clip[1]) * 2
c_map = cos_map[cxy[:, 1], cxy[:, 0]]
s_map = sin_map[cxy[:, 1], cxy[:, 0]]
label_map = label_mask[cxy[:, 1], cxy[:, 0]]
geo_map = np.stack(
[x_map, y_map, h_map, w_map, c_map, s_map, label_map], axis=1)
else:
geo_map = None
# ## 7. add bbox, sure proposals >self.k_at_hop[0]
gps = self.add_proposal(proposals.shape[0], 1 - tcl_mask[:, :, 0])
if gps is not None:
if geo_map is not None:
geo_map = np.concatenate([geo_map, gps], axis=0)
else:
geo_map = gps
# ## 8. adding Random Disturbance
geo_map = jitter_gt_map(geo_map, jitter=0.20)
# ## 9. [roi_num, (xc ,yc ,h, w, cos, sin, class), img_size]-->Bx9
roi_num = np.ones(
(geo_map.shape[0], 1), dtype=np.float16) * geo_map.shape[0]
img_size = np.ones(
(geo_map.shape[0], 1), dtype=np.float16) * imgsize[0]
gt_roi = np.hstack([roi_num, geo_map, img_size])
return gt_roi
def proposals_layer(self, tr_map, tcl_map, radii_map, sin_map, cos_map):
tr_pred_mask = tr_map > self.tr_thresh
tcl_pred_mask = tcl_map > self.tcl_thresh
# multiply TR and TCL
tcl_mask = tcl_pred_mask * tr_pred_mask
# regularize
sin_map, cos_map = regularize_sin_cos(sin_map, cos_map)
# find disjoint regions
tcl_mask = fill_hole(tcl_mask)
tcl_contours, _ = cv2.findContours(tcl_mask.astype(np.uint8),
cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
mask = np.zeros_like(tcl_mask)
proposals = None
for cont in tcl_contours:
deal_map = mask.copy()
cv2.drawContours(deal_map, [cont], -1, 1, -1)
if deal_map.sum() <= 100:
continue
text_map = tr_map * deal_map
# ## 1. Reverse generation of box
bboxs = bbox_transfor_inv(radii_map,
sin_map,
cos_map,
text_map,
wclip=self.clip,
expend=self.expend)
# ## 3. local nms
bboxs = lanms.merge_quadrangle_n9(bboxs.astype('float32'), 0.25)
reconstruct_mask = mask.copy()
boxes = bboxs[:, :8].reshape((-1, 4, 2)).astype(np.int32)
cv2.drawContours(reconstruct_mask, boxes, -1, 1, -1)
if (reconstruct_mask *
tr_pred_mask).sum() < reconstruct_mask.sum() * 0.5:
continue
if proposals is None:
proposals = bboxs
else:
proposals = np.concatenate([proposals, bboxs], axis=0)
if proposals is None or proposals.shape[0] <= 0:
return None, None
# ## 5. generate cluster label
cxy = np.mean(proposals[:, :8].reshape((-1, 4, 2)),
axis=1).astype(np.int32)
# ## 6. Geometric features
gh = (radii_map[:, :, 0] + radii_map[:, :, 1])
h_map = gh[cxy[:, 1], cxy[:, 0]]
w_map = np.clip(h_map // 2, 2 * self.clip[0], 2 * self.clip[1])
c_map = cos_map[cxy[:, 1], cxy[:, 0]]
s_map = sin_map[cxy[:, 1], cxy[:, 0]]
geo_map = np.stack([cxy[:, 0], cxy[:, 1], h_map, w_map, c_map, s_map],
axis=1)
return geo_map, proposals
def detect_contours(self, tr_pred, tcl_pred, sin_pred, cos_pred, radii_pred):
# thresholding
tr_pred_mask = tr_pred > self.tr_thresh
tcl_pred_mask = tcl_pred > self.tcl_thresh
# multiply TR and TCL
tcl_mask = tcl_pred_mask * tr_pred_mask
# regularize
sin_pred, cos_pred = regularize_sin_cos(sin_pred, cos_pred)
# find disjoint regions
tcl_mask = fill_hole(tcl_mask)
tcl_contours, _ = cv2.findContours(tcl_mask.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
mask = np.zeros_like(tcl_mask)
bbox_contours = list()
for cont in tcl_contours:
deal_map = mask.copy()
cv2.drawContours(deal_map, [cont], -1, 1, -1)
if deal_map.sum() <= 100:
continue
text_map = tr_pred * deal_map
bboxs = self.bbox_transfor_inv(radii_pred, sin_pred, cos_pred, text_map, wclip=(4, 12))
# nms
boxes = lanms.merge_quadrangle_n9(bboxs.astype('float32'), 0.25)
boxes = boxes[:, :8].reshape((-1, 4, 2)).astype(np.int32)
boundary_point = None
if boxes.shape[0] > 1:
center = np.mean(boxes, axis=1).astype(np.int32).tolist()
paths, routes_path = minConnectPath(center)
boxes = boxes[routes_path]
top = np.mean(boxes[:, 0:2, :], axis=1).astype(np.int32).tolist()
bot = np.mean(boxes[:, 2:4, :], axis=1).astype(np.int32).tolist()
edge0 = self.select_edge(top + bot[::-1], boxes[0])
edge1 = self.select_edge(top + bot[::-1], boxes[-1])
if edge0 is not None:
top.insert(0, edge0[0])
bot.insert(0, edge0[1])
if edge1 is not None:
top.append(edge1[0])
bot.append(edge1[1])
boundary_point = np.array(top + bot[::-1])
elif boxes.shape[0] == 1:
top = boxes[0, 0:2, :].astype(np.int32).tolist()
bot = boxes[0, 2:4:-1, :].astype(np.int32).tolist()
boundary_point = np.array(top + bot)
if boundary_point is None:
continue
reconstruct_mask = mask.copy()
cv2.drawContours(reconstruct_mask, [boundary_point], -1, 1, -1)
if (reconstruct_mask * tr_pred_mask).sum() < reconstruct_mask.sum() * 0.5:
continue
# if reconstruct_mask.sum() < 200:
# continue
rect = cv2.minAreaRect(boundary_point)
if min(rect[1][0], rect[1][1]) < 10 or rect[1][0] * rect[1][1] < 300:
continue
bbox_contours.append([boundary_point, np.array(np.stack([top, bot], axis=1))])
return bbox_contours
