class ssdKeras():
def __init__(self):
#self.node_name = "ssd_keras"
#rospy.init_node(self.node_name)
self.class_names = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
self.num_classes = len(self.class_names)
self.input_shape = (300,300,3)
self.model = SSD(self.input_shape,num_classes=self.num_classes)
self.model.load_weights('/home/abdulrahman/catkin_ws/src/victim_localization/resources/ssd_keras/weights_SSD300.hdf5')
self.bbox_util = BBoxUtility(self.num_classes)
self.conf_thresh = 0.7
self.model._make_predict_function()
self.graph = tf.get_default_graph()
self.detection_index=DL_msgs_boxes()
# Create unique and somewhat visually distinguishable bright
# colors for the different classes.
self.class_colors = []
for i in range(0, self.num_classes):
# This can probably be written in a more elegant manner
hue = 255*i/self.num_classes
col = np.zeros((1,1,3)).astype("uint8")
col[0][0][0] = hue
col[0][0][1] = 128 # Saturation
col[0][0][2] = 255 # Value
cvcol = cv2.cvtColor(col, cv2.COLOR_HSV2BGR)
col = (int(cvcol[0][0][0]), int(cvcol[0][0][1]), int(cvcol[0][0][2]))
self.class_colors.append(col)
self.bridge = CvBridge() # Create the cv_bridge object
self.image_sub = rospy.Subscriber("/image_raw_converted2", Image, self.detect_image,queue_size=1) # the appropriate callbacks
self.box_coordinate_pub = rospy.Publisher("/ssd_detction/box", DL_msgs_boxes ,queue_size=5) # the appropriate callbacks
def detect_image(self, ros_image):
""" Runs the test on a video (or webcam)
# Arguments
conf_thresh: Threshold of confidence. Any boxes with lower confidence
are not visualized.
"""
#### Use cv_bridge() to convert the ROS image to OpenCV format ####
try:
image_orig = self.bridge.imgmsg_to_cv2(ros_image, "bgr8")
except CvBridgeError as e:
print(e)
##########
vidw = 1280.0 # change from cv2.cv.CV_CAP_PROP_FRAME_WIDTH
vidh = 720.0 # change from cv2.cv.CV_CAP_PROP_FRAME_HEIGHT
vidar = vidw/vidh
#print(type(image_orig))
im_size = (self.input_shape[0], self.input_shape[1])
resized = cv2.resize(image_orig, im_size)
rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
# Reshape to original aspect ratio for later visualization
# The resized version is used, to visualize what kind of resolution
# the network has to work with.
to_draw = cv2.resize(resized, (1280, 720))
# Use model to predict
inputs = [image.img_to_array(rgb)]
tmp_inp = np.array(inputs)
x = preprocess_input(tmp_inp)
start_time = time.time() #debuggin
with self.graph.as_default():
y = self.model.predict(x)
#print("--- %s seconds_for_one_image ---" % (time.time() - start_time))
# This line creates a new TensorFlow device every time. Is there a
# way to avoid that?
results = self.bbox_util.detection_out(y)
if len(results) > 0 and len(results[0]) > 0:
# Interpret output, only one frame is used
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin = results[0][:, 2]
det_ymin = results[0][:, 3]
det_xmax = results[0][:, 4]
det_ymax = results[0][:, 5]
top_indices = [i for i, conf in enumerate(det_conf) if conf >= self.conf_thresh]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin = det_xmin[top_indices]
top_ymin = det_ymin[top_indices]
top_xmax = det_xmax[top_indices]
top_ymax = det_ymax[top_indices]
#initiaze the detection msgs
box_msg = DL_msgs_box()
box_msg.xmin=0
box_msg.ymin=0
box_msg.xmax=0
box_msg.ymax=0
box_msg.Class="Non" # 100 reflect a non-class value
self.detection_index.boxes.append(box_msg)
print (top_xmin)
for i in range(top_conf.shape[0]):
self.detection_index.boxes[:]=[]
xmin = int(round(top_xmin[i] * to_draw.shape[1]))
ymin = int(round(top_ymin[i] * to_draw.shape[0]))
xmax = int(round(top_xmax[i] * to_draw.shape[1]))
ymax = int(round(top_ymax[i] * to_draw.shape[0]))
#include the corner to be published
box_msg = DL_msgs_box()
box_msg.xmin=xmin
box_msg.ymin=ymin
box_msg.xmax=xmax
box_msg.ymax=ymax
box_msg.Class=self.class_names[int(top_label_indices[i])]
self.detection_index.boxes.append(box_msg)
# Draw the box on top of the to_draw image
class_num = int(top_label_indices[i])
if (self.class_names[class_num]=="person"):
cv2.rectangle(to_draw, (xmin, ymin), (xmax, ymax),
self.class_colors[class_num], 2)
text = self.class_names[class_num] + " " + ('%.2f' % top_conf[i])
text_top = (xmin, ymin-10)
text_bot = (xmin + 80, ymin + 5)
text_pos = (xmin + 5, ymin)
cv2.rectangle(to_draw, text_top, text_bot, self.class_colors[class_num], -1)
cv2.putText(to_draw, text, text_pos, cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0,0,0), 1)
#cv2.circle(to_draw, (xmax, ymax),1,self.class_colors[class_num],30);
self.detection_index.header = std_msgs.msg.Header()
self.detection_index.header.stamp=rospy.Time.now()
print (self.detection_index)
self.box_coordinate_pub.publish(self.detection_index)
self.detection_index.boxes[:]=[]
#self.detection_index.boxes.clear()
cv2.imshow("SSD result", to_draw)
cv2.waitKey(1)
def main(self):
rospy.spin()
im_size = (input_shape[0], input_shape[1])
while (True):
# Capture frame-by-frame
ret, img = cap.read()
st = time.time()
resized = cv2.resize(img, im_size)
rgb = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
to_draw = cv2.resize(resized,
(int(input_shape[0] * imgar) * 3, input_shape[1] * 3))
# Use model to predict
inputs = [image.img_to_array(rgb)]
tmp_inp = np.array(inputs)
x = preprocess_input(tmp_inp)
y = model.predict(x)
results = bbox_util.detection_out(y)
if len(results) > 0 and len(results[0]) > 0:
# Interpret output, only one frame is used
det_label = results[0][:, 0]
det_conf = results[0][:, 1]
det_xmin = results[0][:, 2]
det_ymin = results[0][:, 3]
det_xmax = results[0][:, 4]
det_ymax = results[0][:, 5]
top_indices = [
i for i, conf in enumerate(det_conf) if conf >= conf_thresh
]
