def generate_sliding_detection_data_from_image(self, imgpath, scale_pairlist):
from PIL import Image
import iutils as iu
import imgproc
self.init_meta('sliding')
img = Image.open(imgpath)
ndata = 0
newdim = self.savedata_info['newdim']
steps = self.imgdata_info['steps']
# x1 is the size, x1 - 1 is the last pixel index
fx = lambda x1,x2: np.floor((x1 - newdim[1])/x2) + 1
fy = lambda x1,x2: np.floor((x1 - newdim[0])/x2) + 1
valid_idx = 0
for s in scale_pairlist:
ns = np.floor(np.asarray([img.size[0], img.size[1]]) * np.asarray([s[0], s[1]]))
if ns[0] < newdim[1] or ns[1] < newdim[0]:
break
cur_n = fx(ns[0],steps[0]) * fy(ns[1], steps[1])
valid_idx = valid_idx + 1
ndata = ndata + cur_n
ndata = int(ndata)
scale_pairlist = scale_pairlist[:valid_idx]
print( 'Need to generate %d data' % ndata)
filter_size = self.savedata_info['indmap_para']['filter_size']
stride = self.savedata_info['indmap_para']['stride']
mdim = self.get_indmapdim(newdim, filter_size, stride)
joint_filter_size = self.savedata_info['indmap_para']['joint_filter_size']
joint_stride = self.savedata_info['indmap_para']['joint_stride']
jtmdim = self.get_indmapdim(newdim, joint_filter_size, joint_stride)
res = self.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, ndata, self.meta['nparts'], self.meta['njoints'])
dicjtname = 'joints8' if self.meta['njoints'] == 8 else 'joints'
res[dicjtname][:] = 0
res['jointmasks'][:] = False
res['indmap'][:] = False
res['joint_indmap'][:] = False
res['is_mirror'][:] = False
res['is_positive'][:] = False
res['slide_location'] = np.zeros((2,ndata),dtype=np.float)
res['scale'] = np.zeros((2,ndata), dtype=np.float)
res['filenames'] = [imgpath for x in range(ndata)]
idx = 0
dimX = iu.prod(newdim)
for s in scale_pairlist:
ns = np.floor(np.asarray([img.size[0], img.size[1]]) * np.asarray([s[0], s[1]]))
ns = (int(ns[0]),int(ns[1]))
nimg = img.resize((ns[0],ns[1]))
arrimg = imgproc.ensure_rgb(np.asarray(nimg))
for x in range(0, ns[0] - newdim[1] + 1, steps[0]):
for y in range(0, ns[1] - newdim[0] + 1, steps[1]):
res['scale'][...,idx] = np.asarray([s[0],s[1]]).reshape((2))
res['slide_location'][...,idx] = np.asarray([x,y]).reshape((2))
res['data'][...,idx] = arrimg[y:y+newdim[0],x:x+newdim[1],:]
idx = idx + 1
if idx != ndata:
raise HMLPEError('Number of data is not consistent')
res['data'] = res['data'].reshape((-1,ndata),order='F')
return res
def ReadDataToCifarDic(imgdir, example_path, data_category, max_per_batch,
save_dir):
"""
read all data in 'data_category'
into cifar style dictionary
"""
import scipy.io as sio
import iutils as iu
import cifar
import iconvnet_datacvt as icvt
from iutils import imgproc as imgproc
from PIL import Image
if data_category != 'istest':
print 'I haven' 't implement joints8 part '
#raise ModecError('I haven''t implement joints8 part ')
all_examples = sio.loadmat(example_path)['examples']
examples = ExtractSubExample(all_examples, data_category)
ndata = examples.shape[-1]
iu.ensure_dir(save_dir)
s_first = min(ndata, max_per_batch)
d = cifar.PrepareData(s_first)
d['oridet'] = np.ndarray((4, s_first), dtype=np.int)
d['filepath'] = [str() for x in range(s_first)]
d['coords'] = np.ndarray((2, 29, s_first), dtype=np.float32)
tdsize = cifar.img_size[0] # make sure img_size[0] == img_size[1]
j = 0
bid = 1
for i in range(ndata):
if j == max_per_batch:
icvt.ut.pickle(iu.fullfile(save_dir, 'data_batch_' + str(bid)), \
d)
bid = bid + 1
j = 0
if ndata - i < max_per_batch:
d = cifar.PrepareData(ndata - i)
fn = str(examples[i]['filepath'][0])
fp = iu.fullfile(imgdir, fn)
img = Image.open(fp)
tbox = examples[i]['torsobox'][0].reshape((4))
d['filepath'][j] = fp
d['oridet'][..., j] = tbox
d['oribbox'][..., j] = bbox = ExtendBndbox(tbox, img.size)
d['coords'][..., j] = examples[i]['coords']
orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape(
(8, 2), order='C')
d['joints8'][..., j] = TransformPoints(orijoints8, bbox,
cifar.img_size).reshape(
(16), order='C')
img = imgproc.ensure_rgb(np.asarray(img))
sub_img = Image.fromarray(img[bbox[1]:bbox[3], bbox[0]:bbox[2], :])
data_img = np.asarray(sub_img.resize((cifar.img_size[0],\
cifar.img_size[1]))).reshape((cifar.dim_data),order='F')
d['data'][..., j] = data_img
j = j + 1
icvt.ut.pickle(iu.fullfile(save_dir, 'data_batch_' + str(bid)), d)
def ReadDataToHMLPEDic(imgdir,example_path, data_category, max_per_batch,save_dir):
"""
Read all data in 'data_category'
into HMLPE dictionary
There is no need to generating training data, since they can be generated in
hmlpe.py
"""
import scipy.io as sio
import iutils as iu
import iread.myio as mio
import iread.hmlpe as hmlpe
import imgproc
from PIL import Image
if data_category != 'istest':
print 'Warn: The correctness of data type %s is not guaranteed' % data_category
all_example = sio.loadmat(example_path)['examples']
examples = ExtractSubExample(all_example, data_category)
ndata = examples.shape[-1]
iu.ensure_dir(save_dir)
buf_size = min(ndata, max_per_batch)
dimdic = {'data':(112,112,3), 'part_indmap':(8,8), 'joint_indmap':(8,8)}
nparts = 7
njoints = 8
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
d['oridet'] = np.zeros((4,buf_size), dtype=np.int)
d['coords'] = np.ndarray((2,29, buf_size), dtype=np.float32)
tdsize = dimdic['data'][0]
dsize = dimdic['data'][0] * dimdic['data'][1] * dimdic['data'][2]
d['data'] = d['data'].reshape((dsize, -1),order='F')
d['is_positive'][:] = True
d['is_mirror'][:] = False
bid = 1
j = 0
for i in range(ndata):
if j == max_per_batch:
mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
bid = bid + 1
if ndata - i < max_per_batch:
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
fp = iu.fullfile(imgdir, str(examples[i]['filepath'][0]))
img = Image.open(fp)
tbox = examples[i]['torsobox'][0].reshape((4))
d['filenames'][j] = fp
d['coords'][...,j] = examples[i]['coords']
d['oribbox'][...,j] = bbox = ExtendBndbox(tbox, img.size)
orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape((8,2),order='C') - 1 # to python stype 0-idx
d['joints8'][...,j] = TransformPoints(orijoints8, bbox, dimdic['data']).reshape((8,2),order='C')
imgarr = imgproc.ensure_rgb(np.asarray(img))
sub_img = Image.fromarray(imgarr[bbox[1]:bbox[3], bbox[0]:bbox[2],:])
data_img = np.asarray(sub_img.resize((dimdic['data'][0], dimdic['data'][1]))).reshape((dsize),order='F')
d['data'][...,j] = data_img
d['indmap'][...,j] = hmlpe.HMLPE.create_part_indicatormap(d['joints8'][...,j], hmlpe.part_idx, dimdic['part_indmap'], 0.3, 30.0, 12.0)
d['joint_indmap'][...,j] = hmlpe.HMLPE.create_joint_indicatormap(d['joints8'][...,j], dimdic['joint_indmap'], 30.0, 12.0)
d['jointmasks'][...,j] = hmlpe.HMLPE.makejointmask(dimdic['data'], d['joints8'][...,j])
j = j + 1
mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
def ReadDataToCifarDic(imgdir,example_path, data_category, max_per_batch,save_dir):
"""
read all data in 'data_category'
into cifar style dictionary
"""
import scipy.io as sio
import iutils as iu
import cifar
import iconvnet_datacvt as icvt
from iutils import imgproc as imgproc
from PIL import Image
if data_category != 'istest':
print 'I haven''t implement joints8 part '
#raise ModecError('I haven''t implement joints8 part ')
all_examples = sio.loadmat(example_path)['examples']
examples = ExtractSubExample(all_examples, data_category)
ndata = examples.shape[-1]
iu.ensure_dir(save_dir)
s_first = min(ndata, max_per_batch)
d = cifar.PrepareData(s_first)
d['oridet'] = np.ndarray((4,s_first),dtype=np.int)
d['filepath'] = [str() for x in range(s_first)]
d['coords'] = np.ndarray((2,29,s_first),dtype=np.float32)
tdsize= cifar.img_size[0] # make sure img_size[0] == img_size[1]
j = 0
bid = 1
for i in range(ndata):
if j == max_per_batch:
icvt.ut.pickle(iu.fullfile(save_dir, 'data_batch_' + str(bid)), \
d)
bid = bid + 1
j = 0
if ndata - i < max_per_batch:
d = cifar.PrepareData(ndata-i)
fn = str(examples[i]['filepath'][0])
fp = iu.fullfile(imgdir, fn)
img = Image.open(fp)
tbox = examples[i]['torsobox'][0].reshape((4))
d['filepath'][j] = fp
d['oridet'][...,j] = tbox
d['oribbox'][...,j] = bbox = ExtendBndbox(tbox,img.size)
d['coords'][...,j] = examples[i]['coords']
orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape((8,2),order='C')
d['joints8'][...,j] = TransformPoints(orijoints8, bbox,cifar.img_size).reshape((16),order='C')
img = imgproc.ensure_rgb(np.asarray(img))
sub_img = Image.fromarray(img[bbox[1]:bbox[3], bbox[0]:bbox[2],:])
data_img = np.asarray(sub_img.resize((cifar.img_size[0],\
cifar.img_size[1]))).reshape((cifar.dim_data),order='F')
d['data'][...,j] = data_img
j = j + 1
icvt.ut.pickle(iu.fullfile(save_dir, 'data_batch_' + str(bid)),d)
def generate_sliding_detection_data_from_image(self, imgpath,
scale_pairlist):
from PIL import Image
import iutils as iu
import imgproc
self.init_meta('sliding')
img = Image.open(imgpath)
ndata = 0
newdim = self.savedata_info['newdim']
steps = self.imgdata_info['steps']
# x1 is the size, x1 - 1 is the last pixel index
fx = lambda x1, x2: np.floor((x1 - newdim[1]) / x2) + 1
fy = lambda x1, x2: np.floor((x1 - newdim[0]) / x2) + 1
valid_idx = 0
for s in scale_pairlist:
ns = np.floor(
np.asarray([img.size[0], img.size[1]]) *
np.asarray([s[0], s[1]]))
if ns[0] < newdim[1] or ns[1] < newdim[0]:
break
cur_n = fx(ns[0], steps[0]) * fy(ns[1], steps[1])
valid_idx = valid_idx + 1
ndata = ndata + cur_n
ndata = int(ndata)
scale_pairlist = scale_pairlist[:valid_idx]
print('Need to generate %d data' % ndata)
filter_size = self.savedata_info['indmap_para']['filter_size']
stride = self.savedata_info['indmap_para']['stride']
mdim = self.get_indmapdim(newdim, filter_size, stride)
joint_filter_size = self.savedata_info['indmap_para'][
'joint_filter_size']
joint_stride = self.savedata_info['indmap_para']['joint_stride']
jtmdim = self.get_indmapdim(newdim, joint_filter_size, joint_stride)
res = self.prepare_savebuffer({'data':newdim, 'part_indmap':mdim, \
'joint_indmap': jtmdim}, ndata, self.meta['nparts'], self.meta['njoints'])
dicjtname = 'joints8' if self.meta['njoints'] == 8 else 'joints'
res[dicjtname][:] = 0
res['jointmasks'][:] = False
res['indmap'][:] = False
res['joint_indmap'][:] = False
res['is_mirror'][:] = False
res['is_positive'][:] = False
res['slide_location'] = np.zeros((2, ndata), dtype=np.float)
res['scale'] = np.zeros((2, ndata), dtype=np.float)
res['filenames'] = [imgpath for x in range(ndata)]
idx = 0
dimX = iu.prod(newdim)
for s in scale_pairlist:
ns = np.floor(
np.asarray([img.size[0], img.size[1]]) *
np.asarray([s[0], s[1]]))
ns = (int(ns[0]), int(ns[1]))
nimg = img.resize((ns[0], ns[1]))
arrimg = imgproc.ensure_rgb(np.asarray(nimg))
for x in range(0, ns[0] - newdim[1] + 1, steps[0]):
for y in range(0, ns[1] - newdim[0] + 1, steps[1]):
res['scale'][..., idx] = np.asarray([s[0], s[1]]).reshape(
(2))
res['slide_location'][..., idx] = np.asarray([x,
y]).reshape(
(2))
res['data'][..., idx] = arrimg[y:y + newdim[0],
x:x + newdim[1], :]
idx = idx + 1
if idx != ndata:
raise HMLPEError('Number of data is not consistent')
res['data'] = res['data'].reshape((-1, ndata), order='F')
return res
def ReadDataToHMLPEDic(imgdir, example_path, data_category, max_per_batch,
save_dir):
"""
Read all data in 'data_category'
into HMLPE dictionary
There is no need to generating training data, since they can be generated in
hmlpe.py
"""
import scipy.io as sio
import iutils as iu
import iread.myio as mio
import iread.hmlpe as hmlpe
import imgproc
from PIL import Image
if data_category != 'istest':
print 'Warn: The correctness of data type %s is not guaranteed' % data_category
all_example = sio.loadmat(example_path)['examples']
examples = ExtractSubExample(all_example, data_category)
ndata = examples.shape[-1]
iu.ensure_dir(save_dir)
buf_size = min(ndata, max_per_batch)
dimdic = {
'data': (112, 112, 3),
'part_indmap': (8, 8),
'joint_indmap': (8, 8)
}
nparts = 7
njoints = 8
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts, njoints)
d['oridet'] = np.zeros((4, buf_size), dtype=np.int)
d['coords'] = np.ndarray((2, 29, buf_size), dtype=np.float32)
tdsize = dimdic['data'][0]
dsize = dimdic['data'][0] * dimdic['data'][1] * dimdic['data'][2]
d['data'] = d['data'].reshape((dsize, -1), order='F')
d['is_positive'][:] = True
d['is_mirror'][:] = False
bid = 1
j = 0
for i in range(ndata):
if j == max_per_batch:
mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
bid = bid + 1
if ndata - i < max_per_batch:
d = hmlpe.HMLPE.prepare_savebuffer(dimdic, buf_size, nparts,
njoints)
fp = iu.fullfile(imgdir, str(examples[i]['filepath'][0]))
img = Image.open(fp)
tbox = examples[i]['torsobox'][0].reshape((4))
d['filenames'][j] = fp
d['coords'][..., j] = examples[i]['coords']
d['oribbox'][..., j] = bbox = ExtendBndbox(tbox, img.size)
orijoints8 = CvtCoordsToJoints(examples[i]['coords']).reshape(
(8, 2), order='C') - 1 # to python stype 0-idx
d['joints8'][..., j] = TransformPoints(orijoints8, bbox,
dimdic['data']).reshape(
(8, 2), order='C')
imgarr = imgproc.ensure_rgb(np.asarray(img))
sub_img = Image.fromarray(imgarr[bbox[1]:bbox[3], bbox[0]:bbox[2], :])
data_img = np.asarray(
sub_img.resize((dimdic['data'][0], dimdic['data'][1]))).reshape(
(dsize), order='F')
d['data'][..., j] = data_img
d['indmap'][..., j] = hmlpe.HMLPE.create_part_indicatormap(
d['joints8'][..., j], hmlpe.part_idx, dimdic['part_indmap'], 0.3,
30.0, 12.0)
d['joint_indmap'][..., j] = hmlpe.HMLPE.create_joint_indicatormap(
d['joints8'][..., j], dimdic['joint_indmap'], 30.0, 12.0)
d['jointmasks'][...,
j] = hmlpe.HMLPE.makejointmask(dimdic['data'],
d['joints8'][..., j])
j = j + 1
mio.pickle(iu.fullfile(save_dir, 'data_batch_%d' % bid), d)
