def demo(net, im_path, anchor_file, des_dir='demo'):
# Load the demo image
if not os.path.exists(im_path):
print 'Image `{:s}` not found!'.format(im_path)
return
#im = cv2.imread(im_path)
im = exif.load_exif_jpg(im_path)
#wdth = 1280
#eight = 720
#ulti = img.shape[0]*img.shape[1]*1.0/(width*height)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, anchor_file)
#print scores
#print boxes
#print len(scores)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
cls = 'obj'
dets = np.hstack((boxes, scores)).astype(np.float32, copy=False)
dets = boxes_filter(dets, NMS_CONFIG['PRE_NMS_TOPN'],
NMS_CONFIG['NMS_THRESH'],
NMS_CONFIG['POST_NMS_TOPN'],
CONF_THRESH
)
print 'All {} detections with p({} | box) >= {:.1f}'.format(cls, cls, CONF_THRESH)
#vis_detections(im, cls, dets, thresh=CONF_THRESH)
#print dets
# save result images
output_dir = os.path.join(cfg.ROOT_DIR, 'data', des_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# image_name = os.path.splitext(os.path.basename(im_path))[0]
# res_im_file = os.path.join(output_dir, 'res_'+image_name+'.jpg')
image_name = '_'.join(im_path.split('/')[-3:])
res_im_file = os.path.join(output_dir, image_name)
#save_detection_res(im, res_im_file, dets)
return (dets)
def demo(net, im_path, anchor_file, des_dir='demo'):
# Load the demo image
if not os.path.exists(im_path):
print 'Image `{:s}` not found!'.format(im_path)
return
#im = cv2.imread(im_path)
im = exif.load_exif_jpg(im_path)
#wdth = 1280
#eight = 720
#ulti = img.shape[0]*img.shape[1]*1.0/(width*height)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im, anchor_file)
#print scores
#print boxes
#print len(scores)
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
cls = 'obj'
dets = np.hstack((boxes, scores)).astype(np.float32, copy=False)
dets = boxes_filter(dets, NMS_CONFIG['PRE_NMS_TOPN'],
NMS_CONFIG['NMS_THRESH'], NMS_CONFIG['POST_NMS_TOPN'],
CONF_THRESH)
print 'All {} detections with p({} | box) >= {:.1f}'.format(
cls, cls, CONF_THRESH)
#vis_detections(im, cls, dets, thresh=CONF_THRESH)
#print dets
# save result images
output_dir = os.path.join(cfg.ROOT_DIR, 'data', des_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# image_name = os.path.splitext(os.path.basename(im_path))[0]
# res_im_file = os.path.join(output_dir, 'res_'+image_name+'.jpg')
image_name = '_'.join(im_path.split('/')[-3:])
res_im_file = os.path.join(output_dir, image_name)
#save_detection_res(im, res_im_file, dets)
return (dets)
def predict(self):
#args = parse_args()
prototxt = self.defining
caffemodel = self.net
anchors = self.anchors
#print('Called with args:')
#print(args)
cfg_from_file(self.cfg)
print('Using config:')
pprint.pprint(cfg)
while not os.path.exists(caffemodel):
print('Waiting for {} to exist...'.format(caffemodel))
time.sleep(10)
caffe.set_mode_gpu()
caffe.set_device(0)
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
net.name = os.path.splitext(os.path.basename(caffemodel))[0]
print '\n\nLoaded network {:s}'.format(caffemodel)
#img_list = ['tr.jpg']
#for img_name in img_list:
# print '~' * 20
# im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', img_name)
# print 'Demo for image: `{:s}`'.format(im_file)
# demo(net, im_file, anchors)
# plt.show()
db = MongoClient('10.76.0.143', 27017)['plate']
db.authenticate('plateReader', 'IamReader')
total_num = db.image.count()
# total_num=1407
#bad=open('bad_list.txt','r')
bad = open('id_path.txt', 'r')
#bad=open('id_path.txt','r')
#print total_num
#idlist=range(total_num)
#print idlist
error1 = 0
f = open('re_predict_result_new.txt', 'w')
for idlist in bad:
#for i in bad:
# num=int(i)-1
#print count
#print num
#print type(num)
re_num = re.split(' ', idlist)
#print re_num[0]
num = int(re_num[0])
#print num
temp = db.image.find_one({'_id': int(num)})
#temp=db.image.find_one({'_id',num+1})
if temp:
#temp=db.image.find_one({'_id':num+1})
im_file = temp['path']
img = exif.load_exif_jpg(im_file)
width = 1280
height = 720
multi = img.shape[0] * img.shape[1] * 1.0 / (width * height)
multi = math.sqrt(multi)
print multi
resized_img = cv2.resize(
img,
(int(img.shape[1] / multi), int(img.shape[0] / multi)))
resized_img_name = os.path.join('plate_buffer',
'resized_img.jpg')
cv2.imwrite(resized_img_name, resized_img)
re_img = cv2.imread(resized_img_name)
#e_img_size = re_img.shape
print im_file
try:
presicion = demo(net, resized_img_name, anchors)
print presicion
#print 'result'
#print presicion
#print int(presicion[0][2:3])
#print "a"
#print len(temp['points'])
#print "b"
if len(temp['points']) > 0:
f.write(str(num) + ' ')
#f.write(str(temp['points'][0][0][0:1][0])+' '+str(temp['points'][0][0][1:2][0])+' ')
#f.write(str(temp['points'][0][1][0:1][0])+' '+str(temp['points'][0][1][1:2][0])+' ')
#f.write(str(temp['points'][0][2][0:1][0])+' '+str(temp['points'][0][2][1:2][0])+' ')
#f.write(str(temp['points'][0][3][0:1][0])+' '+str(temp['points'][0][3][1:2][0])+' ')
#print 'flag1'
if len(presicion) != 0:
f.write('*' + ' ')
f.write(
str(int(presicion[0][2:3] * multi + 1)) + ' ' +
str(int(presicion[0][1:2] * multi - 1)) + ' ')
f.write(
str(int(presicion[0][2:3] * multi + 1)) + ' ' +
str(int(presicion[0][3:4] * multi + 1)) + ' ')
f.write(
str(int(presicion[0][0:1] * multi - 1)) + ' ' +
str(int(presicion[0][3:4] * multi + 1)) + ' ')
f.write(
str(int(presicion[0][0:1] * multi - 1)) + ' ' +
str(int(presicion[0][1:2] * multi - 1)))
#error1=error1+1
else:
f.write('* ' + '0 ' + '0 ' + '0 ' + '0 ' + '0 ' +
'0 ' + '0 ' + '0')
f.write('\n')
# print 'flags2'
except:
print 'we are wrong'
pass
#break
f.close()
def predict(self):
#args = parse_args()
prototxt = self.defining
caffemodel = self.net
anchors = self.anchors
#print('Called with args:')
#print(args)
cfg_from_file(self.cfg)
print('Using config:')
pprint.pprint(cfg)
while not os.path.exists(caffemodel):
print('Waiting for {} to exist...'.format(caffemodel))
time.sleep(10)
caffe.set_mode_gpu()
caffe.set_device(0)
net = caffe.Net(prototxt, caffemodel, caffe.TEST)
net.name = os.path.splitext(os.path.basename(caffemodel))[0]
print '\n\nLoaded network {:s}'.format(caffemodel)
#img_list = ['tr.jpg']
#for img_name in img_list:
# print '~' * 20
# im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', img_name)
# print 'Demo for image: `{:s}`'.format(im_file)
# demo(net, im_file, anchors)
# plt.show()
db = MongoClient('10.76.0.143', 27017)['plate']
db.authenticate('plateReader', 'IamReader')
total_num=db.image.count()
# total_num=1407
#bad=open('bad_list.txt','r')
bad=open('id_path.txt','r')
#bad=open('id_path.txt','r')
#print total_num
#idlist=range(total_num)
#print idlist
error1=0
f=open('re_predict_result_new.txt','w')
for idlist in bad:
#for i in bad:
# num=int(i)-1
#print count
#print num
#print type(num)
re_num=re.split(' ',idlist)
#print re_num[0]
num=int(re_num[0])
#print num
temp=db.image.find_one({'_id':int(num)})
#temp=db.image.find_one({'_id',num+1})
if temp:
#temp=db.image.find_one({'_id':num+1})
im_file=temp['path']
img = exif.load_exif_jpg(im_file)
width = 1280
height = 720
multi = img.shape[0]*img.shape[1]*1.0/(width*height)
multi = math.sqrt(multi)
print multi
resized_img = cv2.resize(img,(int(img.shape[1]/multi), int(img.shape[0]/multi)))
resized_img_name = os.path.join('plate_buffer', 'resized_img.jpg')
cv2.imwrite(resized_img_name, resized_img)
re_img = cv2.imread(resized_img_name)
#e_img_size = re_img.shape
print im_file
try:
presicion= demo(net,resized_img_name,anchors)
print presicion
#print 'result'
#print presicion
#print int(presicion[0][2:3])
#print "a"
#print len(temp['points'])
#print "b"
if len(temp['points'])>0:
f.write(str(num)+' ')
#f.write(str(temp['points'][0][0][0:1][0])+' '+str(temp['points'][0][0][1:2][0])+' ')
#f.write(str(temp['points'][0][1][0:1][0])+' '+str(temp['points'][0][1][1:2][0])+' ')
#f.write(str(temp['points'][0][2][0:1][0])+' '+str(temp['points'][0][2][1:2][0])+' ')
#f.write(str(temp['points'][0][3][0:1][0])+' '+str(temp['points'][0][3][1:2][0])+' ')
#print 'flag1'
if len(presicion)!=0:
f.write('*'+' ')
f.write(str(int(presicion[0][2:3]*multi+1))+' '+str(int(presicion[0][1:2]*multi-1))+' ')
f.write(str(int(presicion[0][2:3]*multi+1))+' '+str(int(presicion[0][3:4]*multi+1))+' ')
f.write(str(int(presicion[0][0:1]*multi-1))+' '+str(int(presicion[0][3:4]*multi+1))+' ')
f.write(str(int(presicion[0][0:1]*multi-1))+' '+str(int(presicion[0][1:2]*multi-1)))
#error1=error1+1
else:
f.write('* '+'0 '+'0 '+'0 '+'0 '+'0 '+'0 '+'0 '+'0')
f.write('\n')
# print 'flags2'
except:
print 'we are wrong'
pass
#break
f.close()