x, y, w, h = i
cv2.rectangle(buff_img, (x - w // 2, y - h // 2),
(x + w // 2, y + h // 2), (0, 255, 0), 2)
cv2.imshow('result', buff_img)
cv2.waitKey(1)
# set output
b0, b1, b2, c0, c1, c2 = netpart.model_out
# set and load session
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
M.loadSess('./modelveri_tiny/', sess)
import time
def get_coord_from_detection(img):
buff_out = sess.run([b0, b1, b2, c0, c1, c2],
feed_dict={netpart.inpholder: [img]})
bs, cs = buff_out[:3], buff_out[3:]
res = crop(img, bs, cs)
cropped_imgs = [k[0] for k in res]
coords = [k[1] for k in res]
# get score and output
veri_output = sess.run(net_veri.output,
feed_dict={net_veri.inputholder: cropped_imgs})
mod.fcLayer(11)
return mod.get_current_layer()
def build_graph():
img_holder = tf.placeholder(tf.float32,[None,28*28])
last_layer = build_model(img_holder)
last_layer_7seg = build_7seg_model(img_holder)
last_layer_FD = build_FD_model(img_holder)
return img_holder,last_layer,last_layer_7seg,last_layer_FD
img_holder,last_layer,last_layer_7seg,last_layer_FD = build_graph()
config = tf.ConfigProto(allow_soft_placement = True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
M.loadSess('./rune_module/model_rune/model_mnist/',sess,var_list=M.get_all_vars('mnist'))
M.loadSess('./rune_module/model_rune/model_7seg/',sess,var_list=M.get_all_vars('7seg_detection'))
M.loadSess('./rune_module/model_rune/model_flaming/',sess,var_list=M.get_all_vars('FD_detection'))
def get_pred(imgs):
scr = sess.run(last_layer,feed_dict={img_holder:imgs})
scr = np.argmax(scr,1)
return scr
def get_pred_7seg(imgs):
scr = sess.run(last_layer_7seg,feed_dict={img_holder:imgs})
scr = np.argmax(scr,1)
return scr
def get_pred_flaming(imgs):
scr = sess.run(last_layer_FD,feed_dict={img_holder:imgs})
cv2.waitKey(1)
# set output
b0,b1,b2,c0,c1,c2 = netpart.model_out
B0,B1,C0,C1 = netpart_s.model_out
# set and load session
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
v1 = M.get_all_vars('VERI') + M.get_all_vars('MSRPN_v3')
v2 = M.get_all_vars('VERI_s') + M.get_all_vars('MSRPN_v3_s')
v1 = [item for item in v1 if item not in v2]
M.loadSess('./modelveri_tiny/',sess,var_list=v1)
M.loadSess('./modelveri_tiny_s/',sess,var_list=v2)
import time
def get_coord_from_detection(img):
#t1 = time.time()
buff_out = sess.run([b0,b1,b2,c0,c1,c2],feed_dict={netpart.inpholder:[img]})
bs,cs = buff_out[:3],buff_out[3:]
#t2 = time.time()
res = crop(img,bs,cs)
#t3 = time.time()
cropped_imgs = [k[0] for k in res]
coords = [k[1] for k in res]
# get score and output