def render_and_train(self):
print(global_variable.green)
print('Render And Train')
print(global_variable.reset_color)
# -------------------- show training image # --------------------
self.batch_size = 1
ax = yolo_cv.init_matplotlib_figure()
h, w = self.size
# -------------------- background -------------------- #
self.bg_iter_valid = yolo_gluon.load_background(
'val', self.batch_size, h, w)
self.bg_iter_train = yolo_gluon.load_background(
'train', self.batch_size, h, w)
self.car_renderer = RenderCar(h,
w,
self.classes,
self.ctx[0],
pre_load=False)
# -------------------- main loop -------------------- #
while True:
if (self.backward_counter % 10 == 0 or 'bg' not in locals()):
bg = yolo_gluon.ImageIter_next_batch(self.bg_iter_train)
bg = bg.as_in_context(self.ctx[0])
# -------------------- render dataset -------------------- #
imgs, labels = self.car_renderer.render(bg,
'train',
render_rate=0.5,
pascal_rate=0.1)
batch_xs = yolo_gluon.split_render_data(imgs, self.ctx)
car_batch_ys = yolo_gluon.split_render_data(labels, self.ctx)
self._train_batch(batch_xs, car_bys=car_batch_ys)
# -------------------- show training image # --------------------
if self.use_fp16:
img = img.astype('float32')
img = yolo_gluon.batch_ndimg_2_cv2img(batch_xs[0])[0]
img = yolo_cv.cv2_add_bbox(img,
car_batch_ys[0][0, 0].asnumpy(),
4,
use_r=0)
yolo_cv.matplotlib_show_img(ax, img)
print(car_batch_ys[0][0])
raw_input()
def _train_or_valid(self, mode):
print(global_variable.cyan)
print(mode)
print(global_variable.reset_color)
if mode == 'val':
self.batch_size = 1
ax = yolo_cv.init_matplotlib_figure()
# self.net = yolo_gluon.init_executor(
# self.export_file, self.size, self.ctx[0])
# -------------------- background -------------------- #
LP_generator = licence_plate_render.LPGenerator(*self.size)
bg_iter = yolo_gluon.load_background(mode, self.batch_size, *self.size)
# -------------------- main loop -------------------- #
self.backward_counter = 0
while True:
if (self.backward_counter % 3 == 0 or 'bg' not in locals()):
bg = yolo_gluon.ImageIter_next_batch(bg_iter)
bg = bg.as_in_context(self.ctx[0]) / 255.
# -------------------- render dataset -------------------- #
imgs, labels = LP_generator.add(bg, self.LP_r_max, add_rate=0.5)
if mode == 'train':
batch_xs = yolo_gluon.split_render_data(imgs, self.ctx)
batch_ys = yolo_gluon.split_render_data(labels, self.ctx)
self._train_batch_LP(batch_xs, batch_ys)
elif mode == 'val':
batch_out = self.net(imgs)
pred = self.predict_LP(batch_out)
img = yolo_gluon.batch_ndimg_2_cv2img(imgs)[0]
labels = labels.asnumpy()
img, _ = LP_generator.project_rect_6d.add_edges(
img, labels[0, 0, 1:7])
img, clipped_LP = LP_generator.project_rect_6d.add_edges(
img, pred[1:])
yolo_cv.matplotlib_show_img(ax, img)
print(labels)
print(pred)
raw_input('--------------------------------------------------')
def valid(self):
print(global_variable.cyan)
print('Valid')
bs = 1 # batch size = 1
h, w = self.size
# init two matplotlib figures
ax1 = yolo_cv.init_matplotlib_figure()
ax2 = yolo_cv.init_matplotlib_figure()
# init radar figure for vizualizing class distribution
radar_prob = yolo_cv.RadarProb(self.num_class, self.classes)
# init background, LP adder, car renderer
BG_iter = yolo_gluon.load_background('val', bs, h, w)
LP_generator = licence_plate_render.LPGenerator(h, w)
car_renderer = RenderCar(h, w, self.classes, self.ctx[0], pre_load=False)
for bg in BG_iter:
# select background
bg = bg.data[0].as_in_context(self.ctx[0]) # b*RGB*w*h
# render images, type(imgs) = mxnet.ndarray
imgs, labels = car_renderer.render(bg, 'valid', pascal_rate=0.5, render_rate=0.9)
imgs, LP_labels = LP_generator.add(imgs, self.LP_r_max, add_rate=0.8)
# return all_output[::-1], [LP_output]
x1, x2, x3, LP_x = self.net.forward(is_train=False, data=imgs)
outs = self.predict([x1, x2, x3])
LP_outs = self.predict_LP([LP_x])
# convert ndarray to np.array
img = yolo_gluon.batch_ndimg_2_cv2img(imgs)[0]
# draw licence plate border
img, clipped_LP = LP_generator.project_rect_6d.add_edges(img, LP_outs[0, 1:])
yolo_cv.matplotlib_show_img(ax2, clipped_LP)
# draw car border
img = yolo_cv.cv2_add_bbox(img, labels[0, 0].asnumpy(), 4, use_r=0)
img = yolo_cv.cv2_add_bbox(img, outs[0], 5, use_r=0)
yolo_cv.matplotlib_show_img(ax1, img)
# vizualize class distribution
radar_prob.plot(outs[0, 0], outs[0, -self.num_class:])
raw_input('next')
def valid(self):
print(global_variable.cyan)
print('Valid')
bs = 1
h, w = self.size
ax1 = yolo_cv.init_matplotlib_figure()
radar_prob = yolo_cv.RadarProb(self.num_class, self.classes)
BG_iter = yolo_gluon.load_background('val', bs, h, w)
car_renderer = RenderCar(h,
w,
self.classes,
self.ctx[0],
pre_load=False)
for bg in BG_iter:
# -------------------- get image -------------------- #
bg = bg.data[0].as_in_context(self.ctx[0]) # b*RGB*w*h
imgs, labels = car_renderer.render(bg,
'valid',
pascal_rate=0.5,
render_rate=0.9)
# -------------------- predict -------------------- #
net_out = self.net.forward(is_train=False, data=imgs)
# net_out = [x1, x2, x3], which shapes are
# (1L, 640L, 3L, 30L), (1L, 160L, 3L, 30L), (1L, 40L, 3L, 30L)
outs = self.predict(net_out)
# -------------------- show -------------------- #
img = yolo_gluon.batch_ndimg_2_cv2img(imgs)[0]
img = yolo_cv.cv2_add_bbox(img, labels[0, 0].asnumpy(), 4, use_r=0)
img = yolo_cv.cv2_add_bbox(img, outs[0], 5, use_r=0)
yolo_cv.matplotlib_show_img(ax1, img)
radar_prob.plot3d(outs[0, 0], outs[0, -self.num_class:])
raw_input('next')
axs.append(fig.add_subplot(4, 4, i + 1))
for bg in bg_iter:
bg = bg.data[0].as_in_context(ctx[0])
imgs, labels = generator.render(bg)
score_x, class_x = net(imgs)
print(score_x.shape)
print(class_x.shape)
imgs = yolo_gluon.batch_ndimg_2_cv2img(imgs)
for i in range(bs):
ax = axs[i]
s = score_x[i]
s = nd.sigmoid(s.reshape(-1)).asnumpy()
p = class_x[i, 0].asnumpy()
p = np.argmax(p, axis=-1)
yolo_cv.matplotlib_show_img(ax, imgs[i])
ax.plot(range(8, 384, 16), (1 - s) * 160)
ax.axis('off')
s = np.concatenate(([0], s, [0]))
# zero-dimensional arrays cannot be concatenated
# Find peaks
text = ''
for i in range(24):
if s[i + 1] > 0.2 and s[i + 1] > s[i + 2] and s[i + 1] > s[i]:
c = int(p[i])
text = text + cls_names[c]
print(text)
raw_input('press Enter to next batch....')