from ...utils.pascal_voc_clean_xml import pascal_voc_clean_xml
from numpy.random import permutation as perm
from .predict import preprocess
# from .misc import show
from copy import deepcopy
import pickle
import numpy as np
import os
def parse(self, exclusive = False):
meta = self.meta
ext = '.parsed'
ann = self.FLAGS.annotation
if not os.path.isdir(ann):
msg = 'Annotation directory not found {} .'
exit('Error: {}'.format(msg.format(ann)))
print('\n{} parsing {}'.format(meta['model'], ann))
dumps = pascal_voc_clean_xml(ann, meta['labels'], exclusive)
return dumps
def _batch(self, chunk):
"""
Takes a chunk of parsed annotations
returns value for placeholders of net's
input & loss layer correspond to this chunk
"""
meta = self.meta
S, B = meta['side'], meta['num']
C, labels = meta['classes'], meta['labels']
# preprocess
jpg = chunk[0]; w, h, allobj_ = chunk[1]
allobj = deepcopy(allobj_)
path = os.path.join(self.FLAGS.dataset, jpg)
img = self.preprocess(path, allobj)
# Calculate regression target
cellx = 1. * w / S
celly = 1. * h / S
for obj in allobj:
centerx = .5*(obj[1]+obj[3]) #xmin, xmax
centery = .5*(obj[2]+obj[4]) #ymin, ymax
cx = centerx / cellx
cy = centery / celly
if cx >= S or cy >= S: return None, None
obj[3] = float(obj[3]-obj[1]) / w
obj[4] = float(obj[4]-obj[2]) / h
obj[3] = np.sqrt(obj[3])
obj[4] = np.sqrt(obj[4])
obj[1] = cx - np.floor(cx) # centerx
obj[2] = cy - np.floor(cy) # centery
obj += [int(np.floor(cy) * S + np.floor(cx))]
# show(im, allobj, S, w, h, cellx, celly) # unit test
# Calculate placeholders' values
probs = np.zeros([S*S,C])
confs = np.zeros([S*S,B])
coord = np.zeros([S*S,B,4])
proid = np.zeros([S*S,C])
prear = np.zeros([S*S,4])
for obj in allobj:
probs[obj[5], :] = [0.] * C
probs[obj[5], labels.index(obj[0])] = 1.
proid[obj[5], :] = [1] * C
coord[obj[5], :, :] = [obj[1:5]] * B
prear[obj[5],0] = obj[1] - obj[3]**2 * .5 * S # xleft
prear[obj[5],1] = obj[2] - obj[4]**2 * .5 * S # yup
prear[obj[5],2] = obj[1] + obj[3]**2 * .5 * S # xright
prear[obj[5],3] = obj[2] + obj[4]**2 * .5 * S # ybot
confs[obj[5], :] = [1.] * B
# Finalise the placeholders' values
upleft = np.expand_dims(prear[:,0:2], 1)
botright = np.expand_dims(prear[:,2:4], 1)
wh = botright - upleft;
area = wh[:,:,0] * wh[:,:,1]
upleft = np.concatenate([upleft] * B, 1)
botright = np.concatenate([botright] * B, 1)
areas = np.concatenate([area] * B, 1)
# value for placeholder at input layer
inp_feed_val = img
# value for placeholder at loss layer
loss_feed_val = {
'probs': probs, 'confs': confs,
'coord': coord, 'proid': proid,
'areas': areas, 'upleft': upleft,
'botright': botright
}
return inp_feed_val, loss_feed_val
def shuffle(self):
batch = self.FLAGS.batch
data = self.parse()
size = len(data)
print('Dataset of {} instance(s)'.format(size))
if batch > size: self.FLAGS.batch = batch = size
batch_per_epoch = int(size / batch)
for i in range(self.FLAGS.epoch):
shuffle_idx = perm(np.arange(size))
for b in range(batch_per_epoch):
# yield these
x_batch = list()
feed_batch = dict()
for j in range(b*batch, b*batch+batch):
train_instance = data[shuffle_idx[j]]
try:
inp, new_feed = self._batch(train_instance)
except ZeroDivisionError:
print("This image's width or height are zeros: ", train_instance[0])
print('train_instance:', train_instance)
print('Please remove or fix it then try again.')
raise
if inp is None: continue
x_batch += [np.expand_dims(inp, 0)]
for key in new_feed:
new = new_feed[key]
old_feed = feed_batch.get(key,
np.zeros((0,) + new.shape))
feed_batch[key] = np.concatenate([
old_feed, [new]
])
x_batch = np.concatenate(x_batch, 0)
yield x_batch, feed_batch
print('Finish {} epoch(es)'.format(i + 1))
