def next(self): # one img per call
self.checkAndSet()
self.genIdx()
self.parseLine()
# havnt ended yet! longway to go
# assign gdt for each anchor, but 1st, u need generate anchors
# an_gdtbox IS NOT gdtbox FOR NOW !
anchor = tool.genAnchor(self.im_info,self.angleD, self.HoW, self.sideLength,\
self.feat_shape,self.stride) # anchor : N x 5
# logging.debug('anchor size:'+str(anchor.shape))
# logging.debug('anchor data:'+str(anchor))
if config.it.debug.debug:
self.anchor=anchor
# IoU ?
start=time.time() if config.debug.debug else None
ioufile = os.path.join(self.ioutdir,self.imgName[:-4])+'_'+str(self.imgHWC[:2])+'.npy' # encode the additional id
# if self.hasiou_flag and not os.path.isfile(ioufile): # need to notify the owner
# logging.info('ioufile does not exsists: '+ioufile)
if not self.hasiou_flag or not os.path.isfile(ioufile): # calculate and save it
iou_matrix = it_IoU(anchor, self.gdt[:,1:], \
self.x_num) # BE CAREFUL ! [:,0] IS FOR LABEL !
np.save(ioufile,iou_maxtrix)
else: # just load it
iou_matrix = np.load(ioufile)
if config.it.debug.debug:
logging.debug('[IoU] time elapse:'+str(time.time()-start))
logging.debug('[IoU] maximum:%f'%np.max(iou_matrix))
# logging.debug('iou matrix:'+str(iou_matrix))
# logging.debug('begin to calculate IoU...')
# logging.debug('anchor:'+str(anchor))
# logging.debug('gdt:'+str(self.gdt))
# display the img with anchors and gdt onit !
img=self.img.asnumpy()
img=tool.draw_angleBox(img,anchor,(0,250,0))
img=tool.draw_angleBox(img,self.gdt[:,1:],(0,0,250))
plt.imshow(img)
plt.show()
self.target_label,self.target_bbx, self.target_weight =post_iou(iou_matrix,anchor,self.gdt)
self.img=nd.transpose(self.img,axes=(2,0,1))
self.img=nd.expand_dims(self.img,axis=0)
return mx.io.DataBatch( [self.img,self.gdt,self.im_info],[self.target_label,self.target_bbx, self.target_weight])
def view(self,filter_th,iou_th,block=False, onlyneed_box=False,\
quiet_model=False):
#assert 0 < filter_th < 1
#print self.raw_predict_bbox.shape, self.anchor.shape
# print self.mod.get_outputs()[0].context
# assert 0
self.indx = mx_nms(self.predict_bbox, None, self.predict_score,\
self.predict_transfered_bbox, iou_th,score_thresh=filter_th,ctx=cfg.predict.nms_ctx)#, min_area=0, max_area=np.inf)
# predict_score.shape : 1 x num x 2
#self.indx = self.predict_score[:,1] > filter_th
print('num of indexed bbox: %d'%len(self.indx))
self.filter_transfered_bbox = self.predict_transfered_bbox[self.indx,:]
if onlyneed_box:
score = self.predict_score
score = score.asnumpy()[0] if not isinstance(score,np.ndarray) else score
score = score[self.indx,1:2]
return np.concatenate( [self.filter_transfered_bbox, score], axis=1)
img = self.img
# nms...
self.view_img = tool.draw_angleBox(img, self.filter_transfered_bbox.astype(np.float),(253,0, 253))
if quiet_model:
return
# plt.figure()
plt.imshow(self.view_img)
plt.title(self.imgname)
plt.show(block=block)
s=raw_input('Press S to save raw featureMap\n')
if s=='S':
v.save_rawfeat('feature-%s'%imgname)#v.save_rawfeature('feature-%s'%imgname)
#s=raw_input('press any key to exit')
"""
##################################
# draw anchor
##################################
current_dir = os.getcwd()
os.chdir('..')
from tool import genAnchor, draw_angleBox
os.chdir(current_dir)
imgsize = 224
feat_size = 14 * 4
stride = imgsize / feat_size
stride = [imgsize, imgsize]
feat_shape = [feat_size, feat_size]
angleD = [0, 60, -60]
HoW = [2, 0.5, 1]
sideLength = [90, 60, 30]
img = cv2.imread(os.path.join(imgdir, imgname))[:, :, [2, 1, 0]]
anchor = genAnchor(None, angleD, HoW, sideLength, feat_shape, stride)
img_anchor = draw_angleBox(img, anchor, [0, 0, 255])
plt.imshow(img_anchor)
plt.show()
HOME='/disk1/c20160802009T/'
boxtxt_dir = HOME+'angleNet/metric/R2CNN_FPN_Tensorflow/tools/angleNet_res/896ckpt80000/boxes'
img_dir = HOME+'angleNet/local/data/test/896'
img_name = '34.png'
boxtxt_name = 'result-boxes%s.txt'%(img_name.split('.')[0])
img_path = os.path.join(img_dir, img_name)
assert os.path.isfile(img_path), img_path
img = cv2.imread( img_path )[:,:,[2,1,0]]
txt_path = os.path.join( boxtxt_dir, boxtxt_name )
assert os.path.join(txt_path), txt_path
boxes = np.loadtxt(txt_path)
img = draw_angleBox(img, boxes[:,:-1], (255,0,0))
plt.imshow(img)
plt.show()
"""
!TEST OK
"""
# go for 'label', 'gdt'
for i, obj in enumerate(lg):
"""
now obj: label x,y alpha(deg) rh rw
"""
label, xy, alphaD, rh, rw = obj.split('\t')
label, alphaD, rh, rw = [np.float(_) for _ in (label, alphaD, rh, rw)]
x, y = [np.float(_) for _ in xy.rsplit(',')]
alpha = np.deg2rad(alphaD) # deg 2 rad
gdt[i][:] = np.array([label, x, y, alpha, rh, rw])
return gdt
d = gen_label_dict(labeltxt)
imglist = os.listdir(imgdir)
for imgname in imglist:
s = d[imgname]
if ';' not in s:
print('invalid image[%s], skip...' % imgname)
continue
img = cv2.imread(os.path.join(imgdir, imgname))
gdts = parseLine(s)
# print (gdts)
# assert 0
img = draw_angleBox(img,
gdts[:, 1:].astype(np.float), [255, 0, 0],
line_width=2)
cv2.imwrite(os.path.join(outputdir, imgname), img)
print('Save Ground Truth into %s' % outputdir)
def next(self): # one img per call
self.checkAndSet()
self.genIdx()
self.parseLine()
# havnt ended yet! longway to go
# assign gdt for each anchor, but 1st, u need generate anchors
# an_gdtbox IS NOT gdtbox FOR NOW !
self.stride = (self.im_info[0] / self.feat_shape[-2],
self.im_info[1] / self.feat_shape[-1])
self.anchor, self.iou_matrix = self.load_anchor_iou(
self.imgName, self.featName)
# checksum...
# if config.it.debug.debug:
# self.anchor=anchor
# if self.hasiou_flag and not os.path.isfile(ioufile): # need to notify the owner
# logging.info('ioufile does not exsists: '+ioufile)
img = self.img.asnumpy() + self.mean
self.raw_img = img.astype(np.uint8)
if config.it.debug.debug:
# logging.debug('[IoU] time elapsed:'+str(time.time()-start))
logging.debug('[IoU] maximum:%f' % np.max(self.iou_matrix))
# logging.debug('iou matrix:'+str(iou_matrix))
# logging.debug('begin to calculate IoU...')
# logging.debug('anchor:'+str(anchor))
# logging.debug('gdt:'+str(self.gdt))
# display the img with anchors and gdt onit !
show_anchor_indx = np.random.randint(0, self.anchor.shape[0],
cfg.debug.it.anchor_num)
img = tool.draw_angleBox(img, self.anchor[show_anchor_indx][:],
(0, 250, 0))
img = tool.draw_angleBox(img, self.gdt[:, 1:], (0, 0, 250))
plt.imshow(img)
ax = plt.gca() # ax.text(x,y,text)
iou_list = self.iou_matrix[show_anchor_indx][:].max(axis=1)
xy_list = self.anchor[show_anchor_indx, :2]
# logging.debug('iou list shape: '+str(iou_list.shape))
# logging.debug('xy list shape: '+str(xy_list.shape))
# logging.debug('anchor shape: '+str(self.anchor.shape))
# assert 0
tool.text_fig(ax, xy_list, iou_list)
plt.show()
self.target_label,self.target_bbx, self.rpn_inside_weight, self.rpn_outside_weight =\
post_iou(self.iou_matrix,self.anchor,self.gdt)
# print(self.target_label.shape)
# assert 0
self.img = nd.transpose(self.img, axes=(2, 0, 1))
self.img = nd.expand_dims(mx.nd.array(self.img, cfg.train.ctx), axis=0)
self.rpn_inside_weight = nd.expand_dims(mx.nd.array(
self.rpn_inside_weight, cfg.train.ctx),
axis=0)
self.rpn_outside_weight = nd.expand_dims(mx.nd.array(
self.rpn_outside_weight, cfg.train.ctx),
axis=0)
self.target_label = nd.expand_dims(mx.nd.array(self.target_label,
cfg.train.ctx),
axis=0)
self.target_bbx = nd.expand_dims(mx.nd.array(self.target_bbx,
cfg.train.ctx),
axis=0)
# self.provide_label = [('img', self.img.shape ), ('gdt', self.gdt.shape), ('im_info',self.im_info.shape)]
# self.provide_data = [('label',self.target_label.shape), ('bbx',self.target_bbx),('weight', self.target)weight.shape)]
# self.provide_data =[(self.dataNames[0], tuple(self.img.shape)),\
# (self.dataNames[1],tuple(self.gdt.shape) ),\
# (self.dataNames[2],tuple(self.im_info)),\
# (self.dataNames[3],tuple(self.rpn_inside_weight.shape)),\
# (self.dataNames[4],tuple(self.rpn_outside_weight.shape))]
# self.provide_label =[(self.labelNames[0],tuple(self.target_label.shape)),\
# (self.labelNames[1],tuple(self.target_bbx.shape))]
self.provide_data =[(self.dataNames[0], tuple(self.img.shape)),\
(self.dataNames[1],tuple(self.rpn_inside_weight.shape)),\
(self.dataNames[2],tuple(self.rpn_outside_weight.shape))]
self.provide_label =[(self.labelNames[0],tuple(self.target_label.shape)),\
(self.labelNames[1],tuple(self.target_bbx.shape))]
# return mx.io.DataBatch( [self.img,self.gdt,self.im_info, self.rpn_inside_weight, self.rpn_outside_weight],[self.target_label,self.target_bbx ],provide_data=self.provide_data, provide_label=self.provide_label)
return mx.io.DataBatch(
[self.img, self.rpn_inside_weight, self.rpn_outside_weight],
[self.target_label, self.target_bbx],
provide_data=self.provide_data,
provide_label=self.provide_label)