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btsd.py
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import os
import xml.etree.ElementTree as ET
import numpy as np
import cv2
import cPickle
import copy
class btsd_reader(object):
def __init__(self, phase,data_path,batch_size=45,rebuild=False,flipped=True):
self.data_path = os.path.join(data_path, 'btsd')
self.cache_path = os.path.join(data_path, 'cache')
self.batch_size = batch_size
self.image_size = 448
self.cell_size = 7
# self.class_to_ind = dict(zip(self.classes, xrange(len(self.classes))))
self.flipped = flipped
self.phase = phase
self.rebuild = rebuild
self.cursor = 0
self.epoch = 1
self.gt_labels = None
self.prepare()
def get(self):
images = np.zeros((self.batch_size, self.image_size, self.image_size, 3))
labels = np.zeros((self.batch_size, self.cell_size, self.cell_size, 18))
count = 0
while count < self.batch_size:
imname = self.gt_labels[self.cursor]['imname']
flipped = self.gt_labels[self.cursor]['flipped']
images[count, :, :, :] = self.image_read(imname, flipped)
labels[count, :, :, :] = self.gt_labels[self.cursor]['label']
count += 1
self.cursor += 1
if self.cursor >= len(self.gt_labels):
np.random.shuffle(self.gt_labels)
self.cursor = 0
self.epoch += 1
return images, labels
def image_read(self, imname, flipped=False):
image = cv2.imread(imname)
image = cv2.resize(image, (self.image_size, self.image_size))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)
image = (image / 255.0) #* 2.0 - 1.0
if flipped:
image = image[:, ::-1, :]
return image
def prepare(self):
gt_labels = self.load_labels()
if self.flipped:
print('Appending horizontally-flipped training examples ...')
gt_labels_cp = copy.deepcopy(gt_labels)
for idx in range(len(gt_labels_cp)):
gt_labels_cp[idx]['flipped'] = True
gt_labels_cp[idx]['label'] = gt_labels_cp[idx]['label'][:, ::-1, :]
for i in xrange(self.cell_size):
for j in xrange(self.cell_size):
if gt_labels_cp[idx]['label'][i, j, 0] == 1:
gt_labels_cp[idx]['label'][i, j, 1] = self.image_size - 1 - gt_labels_cp[idx]['label'][i, j, 1]
gt_labels += gt_labels_cp
np.random.shuffle(gt_labels)
self.gt_labels = gt_labels
return gt_labels
def load_labels(self):
cache_file = os.path.join(self.cache_path, 'btsd_' + self.phase + '_gt_labels.pkl')
if os.path.isfile(cache_file) and not self.rebuild:
print('Loading gt_labels from: ' + cache_file)
with open(cache_file, 'rb') as f:
gt_labels = cPickle.load(f)
return gt_labels
print('Processing gt_labels from: ' + self.data_path)
if not os.path.exists(self.cache_path):
os.makedirs(self.cache_path)
if self.phase == 'train':
txtname = os.path.join(self.data_path, 'annotation',
'training.txt')
else:
txtname = os.path.join(self.data_path, 'annotation',
'testing.txt')
with open(txtname, 'r') as f:
self.image_index = list(set(x.split(';')[0] for x in f.readlines()))
# print self.image_index
# print len(self.image_index)
# print len(self.create_dict(txtname))
# exit(0)
self.mapping = self.create_dict(txtname)
gt_labels = []
for i,index in enumerate(self.image_index):
if(i%100==0):
print i,index
label, num = self.load_btsd_annotation(i,index)
if num == 0:
continue
imname = os.path.join(self.data_path,index)
gt_labels.append({'imname': imname, 'label': label, 'flipped': False})
print('Saving gt_labels to: ' + cache_file)
with open(cache_file, 'wb') as f:
cPickle.dump(gt_labels, f)
return gt_labels
def create_dict(self,txtfile):
f = open(txtfile)
mapping = {}
for line in f.readlines():
a = line.split(';')
if a[0] in mapping:
mapping[a[0]].append((a[1],a[2],a[3],a[4],a[5],a[6]))
else:
mapping[a[0]] = [(a[1],a[2],a[3],a[4],a[5],a[6])]
f.close()
return mapping
def load_btsd_annotation(self,i,index):
imname = os.path.join(self.data_path,index)
# print imname
im = cv2.imread(imname)
h_ratio = 1.0 * self.image_size / im.shape[0]
w_ratio = 1.0 * self.image_size / im.shape[1]
# im = cv2.resize(im, [self.image_size, self.image_size])
label = np.zeros((self.cell_size, self.cell_size, 18))
objs = self.mapping[index]
for obj in objs:
# Make pixel indexes 0-based
x1 = max(min((float(obj[0]) - 1) * w_ratio, self.image_size - 1), 0)
y1 = max(min((float(obj[1]) - 1) * h_ratio, self.image_size - 1), 0)
x2 = max(min((float(obj[2]) - 1) * w_ratio, self.image_size - 1), 0)
y2 = max(min((float(obj[3]) - 1) * h_ratio, self.image_size - 1), 0)
cls_ind = int(obj[5])
boxes = [(x2 + x1) / 2.0, (y2 + y1) / 2.0, x2 - x1, y2 - y1]
# print 'b ->',boxes
x_ind = int(boxes[0] * self.cell_size / self.image_size)
y_ind = int(boxes[1] * self.cell_size / self.image_size)
if label[y_ind, x_ind, 0] == 1:
continue
label[y_ind, x_ind, 0] = 1
label[y_ind, x_ind, 1:5] = boxes
label[y_ind, x_ind, 5 + cls_ind] = 1
# print label[y_ind,x_ind]
# print len(objs)
# exit(0)
if(i%100==0):
all_boxes = []
for obj in objs:
x1 = max(min((float(obj[0]) - 1) * w_ratio, self.image_size - 1), 0)
y1 = max(min((float(obj[1]) - 1) * h_ratio, self.image_size - 1), 0)
x2 = max(min((float(obj[2]) - 1) * w_ratio, self.image_size - 1), 0)
y2 = max(min((float(obj[3]) - 1) * h_ratio, self.image_size - 1), 0)
cls_ind = int(obj[5])
boxes = [(x2 + x1) / 2.0, (y2 + y1) / 2.0, x2 - x1, y2 - y1]
all_boxes.append(boxes)
self.draw_result(im,all_boxes,os.path.join('vis',str(i)+'.jpg'))
return label, len(objs)
def draw_result(self,img,result,path):
# cv2.imwrite('original'+path,img)
# print len(result)
img = cv2.resize(img, (self.image_size, self.image_size))
for i in range(len(result)):
x = int(result[i][0])
y = int(result[i][1])
w = int(result[i][2] / 2)
h = int(result[i][3] / 2)
cv2.rectangle(img, (x - w, y - h), (x + w, y + h), (0, 255, 0), 2)
cv2.imwrite(path,img)
if __name__ == '__main__':
btsd = btsd('train','data')