def edge_detect_hed_ori_e(im,
net_ori,
edge_res,
ori_name='upscore-fuse-edge-occ'):
in_ = caffeTransform(im)
resedge = {}
net_ori.blobs['data'].reshape(1, *in_.shape)
net_ori.blobs['data'].data[...] = in_
# print net_ori.blobs['data'].shape
h, w = edge_res.shape
edge_res = edge_res.reshape((1, h, w))
net_ori.blobs['res_edge'].reshape(1, *edge_res.shape)
net_ori.blobs['res_edge'].data[...] = edge_res
# print net_ori.blobs['res_edge'].shape
net_ori.forward()
# add back the oritation results
resedge['occmap'] = net_ori.blobs[ori_name].data[0][0, :, :]
for iedge in range(3, 6):
name_save = 'theta' + '{}'.format(iedge)
name_type = 'upscore-dsn' + '{}'.format(iedge) + '-occ'
resedge[name_save] = net_ori.blobs[name_type].data[0][0, :, :]
return resedge
def edge_detect_hed_ori(im,
net,
net_ori,
w_edge=0,
ori_name='upscore-fuse-occ',
para={'scales': [1.0]}):
resedge = {}
for scale in para['scales']:
append = ''
img = im
if scale != 1.0:
img = scm.imresize(im, scale)
append = '_{0:.2f}'.format(scale)
append = append.replace(".", "")
print append
in_ = caffeTransform(img)
#print in_.shape
net_ori.blobs['data'].reshape(1, *in_.shape)
net_ori.blobs['data'].data[...] = in_
net_ori.forward()
if w_edge:
edgepred = edge_hed(in_, net, 0, append)
resedge['edgemap' + append] = edgepred['edgemap' + append].copy()
# add back the oritation results
res_name = 'occmap' + append
resedge[res_name] = net_ori.blobs[ori_name].data[0][0, :, :].copy()
# for iedge in range(3,6):
# name_save = 'theta'+'{}'.format(iedge)
# name_type = 'upscore-dsn'+'{}'.format(iedge)+'-occ'
# resedge[name_save] = net_ori.blobs[name_type].data[0][0,:,:]
return resedge
def edge_detect_cls_4ori_deeplab(im, net, ori_name='prob_ori'):
resedge = {}
in_ = caffeTransform(im)
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
net.forward()
res_name = 'occmap'
resedge[res_name] = net.blobs[ori_name].data[0][0, :, :]
return resedge
def edge_detect_parsenet_ori(im, net):
in_ = caffeTransform(im)
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
net.forward()
print net.blobs['score_occ'].data[0].shape
resedge = {}
resedge['occmap'] = net.blobs['score_occ'].data[0][0, :, :]
return resedge
def edge_detect_hed_wcls(im, net):
in_ = caffeTransform(im)
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
net.forward()
resedge = {}
resedge['clsmap'] = net.blobs['prob'].data[0]
resedge['edgemap_hed'] = net.blobs['sigmoid-fuse'].data[0][0, :, :]
resedge['edgemap_sem'] = net.blobs['score_prob'].data[0][0, :, :]
resedge['edgemap'] = net.blobs['sigmoid-joint'].data[0][0, :, :]
return resedge
def edge_detect_hed_fb(im, net, net_ori):
in_ = caffeTransform(im)
resedge = {}
if net:
resedge = edge_hed(in_, net)
net_ori.blobs['data'].reshape(1, *in_.shape)
net_ori.blobs['data'].data[...] = in_
net_ori.forward()
resedge['occmap'] = net_ori.blobs['softmax_fuse_occ'].data[0]
return resedge
def edge_detect_ori_deeplab(im, net, ori_name='fc_fusion_occ'):
# only predict orientation
resedge = {}
in_ = caffeTransform(im)
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
net.forward()
res_name = 'occmap'
resedge[res_name] = net.blobs[ori_name].data[0][0, :, :]
resedge['occmap4'] = net.blobs['uppool4_ms_occ'].data[0][0, :, :]
resedge['occmap8'] = net.blobs['upfc8_ms_occ'].data[0][0, :, :]
resedge['occmapmean'] = 0.5 * resedge['occmap4'] + 0.5 * resedge['occmap8']
return resedge
def edge_detect_deeplab(im, net):
# joint predict edge + orientation
in_ = caffeTransform(im)
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
net.forward()
resedge = {}
if 'fc_fusion_occ' in net.blobs.keys():
resedge['occmap'] = net.blobs['fc_fusion_occ'].data[0][0, :, :]
resedge['occmap8'] = net.blobs['upfc8_ms_occ'].data[0][0, :, :]
resedge['occmap4'] = net.blobs['uppool4_ms_occ'].data[0][0, :, :]
if 'sigmoid_fuse' in net.blobs.keys():
resedge['edgemap'] = net.blobs['sigmoid_fuse'].data[0][0, :, :]
# side output
return resedge
def edge_detect_hed(im, net):
in_ = caffeTransform(im)
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
net.forward()
resedge = {}
resedge['edgemap'] = net.blobs['sigmoid-fuse'].data[0][0, :, :]
resedge['occmap'] = net.blobs['upscore-fuse-occ'].data[0][0, :, :]
# save the response of each layer
# for edge map
for iedge in range(1, 6):
name_type = 'sigmoid-dsn' + '{}'.format(iedge)
name_save = 'edge' + '{}'.format(iedge)
resedge[name_save] = net.blobs[name_type].data[0][0, :, :]
for iedge in range(3, 6):
name_save = 'theta' + '{}'.format(iedge)
name_type = 'upscore-dsn' + '{}'.format(iedge) + '-occ'
resedge[name_save] = net.blobs[name_type].data[0][0, :, :]
return resedge