def _init(self, args):
# -------------------- init_args -------------------- #
self.trt = args.trt
self.dev = args.dev
self.topic = args.topic
self.show = args.show
self.radar = args.radar
self.flip = args.flip
self.clip = (args.clip_h, args.clip_w)
self.ctx = yolo_gluon.get_ctx(args.gpu)
# -------------------- init_ros -------------------- #
rospy.init_node("YOLO_ros_node", anonymous=True)
self.bridge = CvBridge()
self.img_pub = rospy.Publisher(self.yolo.pub_img, Image, queue_size=1)
self.car_pub = rospy.Publisher(self.yolo.pub_box,
Float32MultiArray,
queue_size=1)
self.depth_image = None
self.image = None
self.mat_car = Float32MultiArray()
# -------------------- init_dev -------------------- #
if self.dev == 'ros':
rospy.Subscriber(DEPTH_TOPIC, Image, self._depth_callback)
rospy.Subscriber(self.topic, Image, self._image_callback)
print(global_variable.green)
print('Image Topic: %s' % self.topic)
print('Depth Topic: %s' % DEPTH_TOPIC)
print(global_variable.reset_color)
else:
#pass
threading.Thread(target=self._get_frame).start()
# -------------------- init_radar -------------------- #
if self.radar:
self.radar_prob = yolo_cv.RadarProb(self.yolo.num_class,
self.yolo.classes)
# -------------------- init_video_saver -------------------- #
if save_video_size is not None and len(save_video_size) == 2:
self.save_video = True
start_time = datetime.datetime.now().strftime("%m-%dx%H-%M")
out_file = os.path.join('video', 'car_%s.avi' % start_time)
fourcc = cv2.VideoWriter_fourcc(*'MJPG') # (*'MJPG')#(*'MPEG')
self.out = cv2.VideoWriter(out_file, fourcc, 30, (save_video_size))
else:
self.save_video = False
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')
def valid_Nima(self):
'''
compare with "Unsupervised Generation of a Viewpoint"
Annotated Car Dataset from Videos
-path: /media/nolan/SSD1/YOLO_backup/freiburg_static_cars_52_v1.1
|-annotations (d)
|-txt_path (d)
|-all car folders (d)
|-result_path (v)
|-all car folders with box (v)
|-img_path (v)
|-annotations (v)
|-save_txt_path (v)
|-plot (p)
|-all car azi compare (p)
d: download from "Unsupervised Generation of a Viewpoint"
v: generate from valid_Nima
o: generate from valid_Nima_plot
'''
import cv2
image_w = 960.
image_h = 540.
mx_resize = mxnet.image.ForceResizeAug(tuple(self.size[::-1]))
radar_prob = yolo_cv.RadarProb(self.num_class, self.classes)
x = np.arange(53)
x = np.delete(x, [0, 6, 20, 23, 31, 36])
for i in x:
result_path = os.path.join(freiburg_path,
"result_%s" % self.version)
save_img_path = os.path.join(result_path, "car_%d" % i)
if not os.path.exists(save_img_path):
os.makedirs(save_img_path)
save_txt_path = os.path.join(result_path, 'annotations')
if not os.path.exists(save_txt_path):
os.makedirs(save_txt_path)
txt_path = os.path.join(freiburg_path, "annotations",
"%d_annot.txt" % i)
with open(txt_path, 'r') as f:
all_lines = f.readlines()
for line_i, line in enumerate(all_lines):
img_name = line.split('\t')[0].split('.')[0] + '.png'
img_path = os.path.join(freiburg_path, img_name)
save_img = os.path.join(save_img_path, img_name.split('/')[-1])
img = cv2.imread(img_path)
# -------------------- Prediction -------------------- #
nd_img = yolo_gluon.cv_img_2_ndarray(img,
self.ctx[0],
mxnet_resize=mx_resize)
net_out = self.net.forward(is_train=False, data=nd_img)
pred_car = self.predict(net_out[:3])
pred_car[0, 5] = -1 # (1, 80)
Cout = pred_car[0]
left_ = (Cout[2] - 0.5 * Cout[4]) * image_w
up_ = (Cout[1] - 0.5 * Cout[3]) * image_h
right_ = (Cout[2] + 0.5 * Cout[4]) * image_w
down_ = (Cout[1] + 0.5 * Cout[3]) * image_h
vec_ang, vec_rad, prob = radar_prob.cls2ang(
Cout[0], Cout[-self.num_class:])
# -------------------- Ground Truth -------------------- #
box_para = np.fromstring(line.split('\t')[1],
dtype='float32',
sep=' ')
left, up, right, down = box_para
h = (down - up) / image_h
w = (right - left) / image_w
y = (down + up) / 2 / image_h
x = (right + left) / 2 / image_w
boxA = [0, y, x, h, w, 0]
azi_label = int(line.split('\t')[2]) - 90
azi_label = azi_label - 360 if azi_label > 180 else azi_label
inter = (min(right, right_) -
max(left, left_)) * (min(down, down_) - max(up, up_))
a1 = (right - left) * (down - up)
a2 = (right_ - left_) * (down_ - up_)
iou = (inter) / (a1 + a2 - inter)
save_txt = os.path.join(save_txt_path, '%d_annot' % i)
with open(save_txt, 'a') as f:
f.write('%s %f %f %f %f\n' %
(img_name, iou, azi_label, vec_ang * 180 / math.pi,
vec_rad))
yolo_cv.cv2_add_bbox(img, Cout, 3, use_r=False)
yolo_cv.cv2_add_bbox(img, boxA, 4, use_r=False)
cv2.imshow('img', img)
cv2.waitKey(1)
cv2.imwrite(save_img, img)