def patches_by_entropy(self, num_patches):
'''
Finds high-entropy patches based on label, allows net to learn borders more effectively.
INPUT: int 'num_patches': defaults to batch_size, enter in quantity it using in conjunction with randomly sampled patches.
OUTPUT: list of patches (num_patches, 4, h, w) selected by highest entropy
'''
h,w = self.patch_size[0], self.patch_size[1]
patches, labels = [], []
ct = 0
while ct < num_patches:
#im_path = random.choice(training_images)
im_path = random.choice(self.train_data)
fn = os.path.basename(im_path)
label = io.imread('Labels/' + fn[:-4] + 'L.png')
# pick again if slice is only background
if len(np.unique(label)) == 0:
continue
img = io.imread(im_path).reshape(5, 240, 240)[:-1].astype('float')
l_ent = entropy(label, disk(self.h))
top_ent = np.percentile(l_ent, 90)
# restart if 80th entropy percentile = 0
if top_ent == 0:
continue
highest = np.argwhere(l_ent >= top_ent)
p_s = random.sample(highest, 1)
for p in p_s:
p_ix = (p[0]-(h/2), p[0]+((h+1)/2), p[1]-(w/2), p[1]+((w+1)/2))
patch = np.array([i[p_ix[0]:p_ix[1], p_ix[2]:p_ix[3]] for i in img])
# exclude any patches that are too small
if np.shape(patch) != (4,65,65):
continue
patches.append(patch)
labels.append(label[p[0],p[1]])
#print '**in patches_by_entropy,patches.shape:',np.array(patches).shape #(3,4,65,65)
#print '**in patches_by_entropy,labels.shape:',np.array(labels).shape
ct += 1
return np.array(patches[:num_patches]), np.array(labels[:num_patches])
评论列表
文章目录
