def __init__(self, img_path):
self.img_path = img_path
self.rlist, self.rmat = Super_Region.get_region(img_path, 100.)
features = Features(img_path, self.rlist, self.rmat)
self.comb_features = features.comb_features
self.rlists = [self.rlist]
self.rmats = [self.rmat]
self.feature93s = [features.features93]
def get_multi_segs(self, rf):
num_reg = len(self.rlist)
similarity = np.ones([num_reg, num_reg])
for i in range(num_reg):
ids = self.comb_features[i]["j_ids"]
X = self.comb_features[i]["features"]
similarity[i, ids] = 1-rf.predict(X)[:, 1]
for c in C_LIST:
rlist, rmat = Super_Region.combine_region(
similarity, c, self.rlist, self.rmat)
if len(rlist) == 1:
continue
self.rlists.append(rlist)
self.rmats.append(rmat)
features = Features(self.img_path, rlist, rmat,
need_comb_features=False)
self.feature93s.append(features.features93)
def get_multi_segs(self, rf):
num_reg = len(self.rlist)
similarity = np.ones([num_reg, num_reg])
for i in range(num_reg):
ids = self.comb_features[i]["j_ids"]
X = self.comb_features[i]["features"]
similarity[i, ids] = rf.predict(X)[:, 0]
for idx, c in enumerate(C_LIST):
rlist, rmat = Super_Region.combine_region(similarity, c,
self.rlist, self.rmat)
show_segmentation_level(idx + 1, rlist, self.img_path)
if len(rlist) == 1:
rlist = self.rlists[-1].copy()
rmat = self.rmats[-1].copy()
self.rlists.append(rlist)
self.rmats.append(rmat)
features = Features(self.img_path,
rlist,
rmat,
need_comb_features=False)
self.feature93s.append(features.features93)