def predict(self, src):
stride = self.options["stride"]
sharpen = self.options["sharpen"]
shrink = self.options["shrink"]
p_size = self.options["p_size"]
g_size = self.options["g_size"]
n_cell = self.options["n_cell"]
n_tree_eval = self.options["n_tree_eval"]
nms = self.options["nms"] if "nms" in self.options else False
thrs = self.model["thrs"]
fids = self.model["fids"]
cids = self.model["cids"]
edge_bnds = self.model["edge_bnds"]
edge_pts = self.model["edge_pts"]
n_seg = self.model["n_seg"]
segs = self.model["segs"]
p_rad = p_size / 2
g_rad = g_size / 2
pad = cv2.copyMakeBorder(src,
p_rad,
p_rad,
p_rad,
p_rad,
borderType=cv2.BORDER_REFLECT)
reg_ch, ss_ch = self.get_shrunk_channels(pad)
if sharpen != 0:
pad = conv_tri(pad, 1)
dst = predict_core(pad, reg_ch, ss_ch, shrink, p_size, g_size, n_cell,
stride, sharpen, n_tree_eval, thrs, fids, cids,
n_seg, segs, edge_bnds, edge_pts)
if sharpen == 0:
alpha = 2.1 * stride**2 / g_size**2 / n_tree_eval
elif sharpen == 1:
alpha = 1.8 * stride**2 / g_size**2 / n_tree_eval
else:
alpha = 1.65 * stride**2 / g_size**2 / n_tree_eval
dst = N.minimum(dst * alpha, 1.0)
dst = conv_tri(dst, 1)[g_rad:src.shape[0] + g_rad,
g_rad:src.shape[1] + g_rad]
if nms:
dy, dx = N.gradient(conv_tri(dst, 4))
_, dxx = N.gradient(dx)
dyy, dxy = N.gradient(dy)
orientation = N.arctan2(dyy * N.sign(-dxy) + 1e-5, dxx)
orientation[orientation < 0] += N.pi
dst = non_maximum_supr(dst, orientation, 1, 5, 1.02)
return dst
def predict(self, src):
stride = self.options["stride"]
sharpen = self.options["sharpen"]
shrink = self.options["shrink"]
p_size = self.options["p_size"]
g_size = self.options["g_size"]
n_cell = self.options["n_cell"]
n_tree_eval = self.options["n_tree_eval"]
nms = self.options["nms"] if "nms" in self.options else False
thrs = self.model["thrs"]
fids = self.model["fids"]
cids = self.model["cids"]
edge_bnds = self.model["edge_bnds"]
edge_pts = self.model["edge_pts"]
n_seg = self.model["n_seg"]
segs = self.model["segs"]
p_rad = p_size / 2
g_rad = g_size / 2
pad = cv2.copyMakeBorder(src, p_rad, p_rad, p_rad, p_rad,
borderType=cv2.BORDER_REFLECT)
reg_ch, ss_ch = self.get_shrunk_channels(pad)
if sharpen != 0:
pad = conv_tri(pad, 1)
dst = predict_core(pad, reg_ch, ss_ch, shrink, p_size, g_size, n_cell,
stride, sharpen, n_tree_eval, thrs, fids, cids,
n_seg, segs, edge_bnds, edge_pts)
if sharpen == 0:
alpha = 2.1 * stride ** 2 / g_size ** 2 / n_tree_eval
elif sharpen == 1:
alpha = 1.8 * stride ** 2 / g_size ** 2 / n_tree_eval
else:
alpha = 1.65 * stride ** 2 / g_size ** 2 / n_tree_eval
dst = N.minimum(dst * alpha, 1.0)
dst = conv_tri(dst, 1)[g_rad: src.shape[0] + g_rad,
g_rad: src.shape[1] + g_rad]
if nms:
dy, dx = N.gradient(conv_tri(dst, 4))
_, dxx = N.gradient(dx)
dyy, dxy = N.gradient(dy)
orientation = N.arctan2(dyy * N.sign(-dxy) + 1e-5, dxx)
orientation[orientation < 0] += N.pi
dst = non_maximum_supr(dst, orientation, 1, 5, 1.02)
return dst