def listener_callback(self, data):
self.get_logger().info("Received an image! ")
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
except CvBridgeError as e:
print(e)
image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB)
self.timer.start()
boxes, labels, probs = self.predictor.predict(image, 10, 0.4)
interval = self.timer.end()
print('Time: {:.2f}s, Detect Objects: {:d}.'.format(
interval, labels.size(0)))
detection_array = Detection2DArray()
for i in range(boxes.size(0)):
box = boxes[i, :]
label = f"{self.class_names[labels[i]]}: {probs[i]:.2f}"
print("Object: " + str(i) + " " + label)
cv2.rectangle(cv_image, (box[0], box[1]), (box[2], box[3]),
(255, 255, 0), 4)
# Definition of 2D array message and ading all object stored in it.
object_hypothesis_with_pose = ObjectHypothesisWithPose()
object_hypothesis_with_pose.id = str(self.class_names[labels[i]])
object_hypothesis_with_pose.score = float(probs[i])
bounding_box = BoundingBox2D()
bounding_box.center.x = float((box[0] + box[2]) / 2)
bounding_box.center.y = float((box[1] + box[3]) / 2)
bounding_box.center.theta = 0.0
bounding_box.size_x = float(2 * (bounding_box.center.x - box[0]))
bounding_box.size_y = float(2 * (bounding_box.center.y - box[1]))
detection = Detection2D()
detection.header = data.header
detection.results.append(object_hypothesis_with_pose)
detection.bbox = bounding_box
detection_array.header = data.header
detection_array.detections.append(detection)
cv2.putText(
cv_image,
label,
(box[0] + 20, box[1] + 40),
cv2.FONT_HERSHEY_SIMPLEX,
1, # font scale
(255, 0, 255),
2) # line type
# Displaying the predictions
cv2.imshow('object_detection', cv_image)
# Publishing the results onto the the Detection2DArray vision_msgs format
self.detection_publisher.publish(detection_array)
ros_image = self.bridge.cv2_to_imgmsg(cv_image)
ros_image.header.frame_id = 'camera_frame'
self.result_publisher.publish(ros_image)
cv2.waitKey(1)
def _construct_detection_msg_and_update_detection_image(
self, detection_res, channel_id, stamp):
if channel_id == Side.PORT:
multiplier = -1
else:
multiplier = 1
detection_array_msg = Detection2DArray()
detection_array_msg.header.frame_id = self.pub_frame_id
detection_array_msg.header.stamp = stamp
for object_id, detection in detection_res.items():
detection_msg = Detection2D()
detection_msg.header = detection_array_msg.header
object_hypothesis = ObjectHypothesisWithPose()
object_hypothesis.id = object_id.value
object_hypothesis.score = detection['confidence']
object_hypothesis.pose.pose = self._detection_to_pose(
detection['pos'], channel_id)
# Filter out object detection outliers
if abs(object_hypothesis.pose.pose.position.y) > self.water_depth:
continue
else:
pos = self.channel_size + multiplier * detection['pos']
self.detection_image[
0,
max(pos -
10, 0):min(pos + 10, self.channel_size *
2), :] = self.detection_colors[object_id]
detection_msg.results.append(object_hypothesis)
detection_array_msg.detections.append(detection_msg)
return detection_array_msg
def object_predict(self, object_data, header, image_np, image, offset_x,
offset_y):
image_height, image_width, channels = image.shape
obj = Detection2D()
obj_hypothesis = ObjectHypothesisWithPose()
object_id = object_data[0]
object_score = object_data[1]
dimensions = object_data[2]
# TODO - Need to make it so that the size and offset of the bounding box is corrected to the original
# 320 x 240 image. Center x and center y need to be tested, while size x and size y need to be fixed
obj.header = header
obj_hypothesis.id = object_id
obj_hypothesis.score = object_score
obj.results.append(obj_hypothesis)
obj.bbox.size_y = int((dimensions[2] - dimensions[0]) * image_height)
obj.bbox.size_x = int((dimensions[3] - dimensions[1]) * image_width)
obj.bbox.center.x = int(
(dimensions[1] + dimensions[3]) * image_height /
2) + offset_x #do visualize this later
obj.bbox.center.y = int((dimensions[0] + dimensions[2]) * image_width /
2) + offset_y #do visualize this later
return obj
def object_predict(self, object_data, header, image_np, image):
image_height, image_width, channels = image.shape
obj = Detection2D()
obj_hypothesis = ObjectHypothesisWithPose()
object_id = object_data[0]
object_score = object_data[1]
dimensions = object_data[2]
obj.header = header
obj_hypothesis.id = object_id
obj_hypothesis.score = object_score
obj.results.append(obj_hypothesis)
obj.bbox.size_y = int((dimensions[2] - dimensions[0]) * image_height)
obj.bbox.size_x = int((dimensions[3] - dimensions[1]) * image_width)
obj.bbox.center.x = int(
(dimensions[1] + dimensions[3]) * image_height / 2)
obj.bbox.center.y = int(
(dimensions[0] + dimensions[2]) * image_width / 2)
# rospy.loginfo("publish bbox.size x: %d", obj.bbox.size_x)
# rospy.loginfo("publish bbox.size y: %d", obj.bbox.size_y)
# rospy.loginfo("publish bbox.center x: %d", obj.bbox.center.x)
# rospy.loginfo("publish bbox.center y: %d", obj.bbox.center.y)
return obj
def callback(self, ros_data):
'''Callback function of subscribed topic. '''
if ros_data.header.stamp < self.latest_finish_time:
return
#### direct conversion to CV2 ####
now = rospy.Time.now()
np_arr = np.fromstring(ros_data.data, np.uint8)
image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
# dim = (int(len(image_np / 20)), int(len(image_np[0]) / 20))
# cv2.resize(image_np, dim)
# image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) # OpenCV >= 3.0:
results = detect(self.net, self.meta, image_np)
detections = Detection2DArray()
for result in results:
detection = Detection2D()
detection.header = ros_data.header
res = ObjectHypothesisWithPose()
res.id = self.get_id(result[0])
res.score = result[1]
detection.results.append(res)
detection.bbox.size_x = result[2][2]
detection.bbox.size_y = result[2][3]
detection.bbox.center.x = result[2][0]
detection.bbox.center.y = result[2][1]
detections.detections.append(detection)
self.res_pub.publish(detections)
rospy.loginfo_throttle(
1, 'Took yolo %s to process image' %
((rospy.Time.now() - now).to_sec()))
self.latest_finish_time = rospy.Time.now()
def object_predict(self, object_data, header, image_np, image):
image_height, image_width, channels = image.shape
obj = Detection2D()
obj_hypothesis = ObjectHypothesisWithPose()
object_id = object_data[0]
object_score = object_data[1]
dimensions = object_data[2]
obj.header = header
obj_hypothesis.id = object_id
obj_hypothesis.score = object_score
obj.results.append(obj_hypothesis)
obj.bbox.size_y = int((dimensions[2] - dimensions[0]) * image_height)
obj.bbox.size_x = int((dimensions[3] - dimensions[1]) * image_width)
obj.bbox.center.x = int(
(dimensions[1] + dimensions[3]) * image_height / 2)
obj.bbox.center.y = int(
(dimensions[0] + dimensions[2]) * image_width / 2)
#print(str(obj.bbox))
# print('height:'+str(image_height))
# print('width: '+str(image_width))
return obj
def callback(self):
image = rospy.wait_for_message(
"/zed2/zed_node/left/image_rect_color/compressed", CompressedImage)
time1 = rospy.Time.now().to_sec()
self.timer.header = image.header
self.timer.header.frame_id = "zed2_left_camera_frame"
self.timer.time_ref = rospy.Time.now()
self.timer_pub.publish(self.timer)
cv_image = self.bridge.compressed_imgmsg_to_cv2(image, "bgr8")
_, bboxes = detect_image(self.yolo,
cv_image,
"",
input_size=YOLO_INPUT_SIZE,
show=False,
CLASSES=TRAIN_CLASSES,
score_threshold=0.3,
iou_threshold=0.1,
rectangle_colors=(255, 0, 0))
detect = Detection2DArray()
detect.header = image.header
for Object in bboxes:
detection = Detection2D()
hypo = ObjectHypothesisWithPose()
#Start x
x1 = Object[0]
#End x
x2 = Object[2]
#Start y
y1 = Object[1]
#end y
y2 = Object[3]
#Size x
Sx = x2 - x1
#Size y
Sy = y2 - y1
#Center x
Cx = x1 + Sx / 2
#Center y
Cy = y1 + Sy / 2
detection.bbox.center.x = Cx
detection.bbox.center.y = Cy
detection.bbox.size_x = Sx
detection.bbox.size_y = Sy
hypo.id = int(Object[5])
hypo.score = Object[4]
detection.results = [
hypo,
]
detection.is_tracking = False
detect.detections.append(detection)
self.boxes_pub.publish(detect)
self.callback()
def detect(self, image):
# Convert to grayscale if needed
if image.ndim == 3:
image = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
image_height, image_width = image.shape
image_area = image_height * image_width
# Apply (inverse) binary threshold to input image
mask = cv.threshold(image, THRESHOLD, THRESHOLD_MAX,
cv.THRESH_BINARY_INV)[1]
# Dilate mask to find more reliable contours
# kernel = np.ones((5, 5), np.uint8)
# mask_dilated = cv.dilate(mask, kernel, iterations=1)
# Find external approximate contours in dilated mask
contours, hierarchy = cv.findContours(mask, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
# Filter out contours that don't qualify as a detection
detections = []
for contour in contours:
# Filer out if the contour touches the image border
x, y, w, h = cv.boundingRect(contour)
if x == 0 or y == 0 or x + w == image_width or y + h == image_height:
continue
# Filter out if the contour is too small
if cv.contourArea(contour) < 1e-4 * image_area:
continue
detections.append((x, y, w, h))
# Fill detections msg
detection_array_msg = Detection2DArray()
for detection in detections:
x, y, w, h = detection
center_x = x + w / 2.
center_y = y + h / 2.
bbox = BoundingBox2D()
bbox.center = Pose2D(x=center_x, y=center_y, theta=0)
bbox.size_x = w
bbox.size_y = h
object_hypothesis = ObjectHypothesisWithPose()
object_hypothesis.id = 0
object_hypothesis.score = 1.0
detection_msg = Detection2D()
detection_msg.bbox = bbox
detection_msg.results.append(object_hypothesis)
detection_array_msg.detections.append(detection_msg)
return detection_array_msg
def _handle_yolo_detect(self, req):
cv_image = None
detection_array = Detection2DArray()
detections = []
boxes = None
try:
cv_image = self.bridge.imgmsg_to_cv2(req.image, "bgr8")
except CvBridgeError as e:
rospy.logerr(e)
try:
boxes = self.yolo.predict(cv_image)
except SystemError:
pass
# rospy.loginfo('Found {} boxes'.format(len(boxes)))
for box in boxes:
detection = Detection2D()
results = []
bbox = BoundingBox2D()
center = Pose2D()
detection.header = Header()
detection.header.stamp = rospy.get_rostime()
# detection.source_img = deepcopy(req.image)
labels = box.get_all_labels()
for i in range(0,len(labels)):
object_hypothesis = ObjectHypothesisWithPose()
object_hypothesis.id = i
object_hypothesis.score = labels[i]
results.append(object_hypothesis)
detection.results = results
x, y = box.get_xy_center()
center.x = x
center.y = y
center.theta = 0.0
bbox.center = center
size_x, size_y = box.get_xy_extents()
bbox.size_x = size_x
bbox.size_y = size_y
detection.bbox = bbox
detections.append(detection)
detection_array.header = Header()
detection_array.header.stamp = rospy.get_rostime()
detection_array.detections = detections
return YoloDetectResponse(detection_array)
def _publish_marker_detection(self, marker, cos_sim):
"""Publish detected marker"""
distance = self._get_distance_to_marker(marker)
object_hypothesis = ObjectHypothesisWithPose()
object_hypothesis.id = 1
# the higher the cos_sim (further away from 90 degree angle between current_pose
# and the marker), the lower the score
object_hypothesis.score = (-cos_sim + (self.buoy_radius * 2)) / (
self.buoy_radius * 2)
marker_transformed = self._transform_pose(
marker, from_frame=marker.header.frame_id)
object_hypothesis.pose.pose = marker_transformed.pose
# Add random noise to pose of object
object_hypothesis.pose.pose.position.x += np.random.randn(
) * self.noise_sigma
object_hypothesis.pose.pose.position.y += np.random.randn(
) * self.noise_sigma
object_hypothesis.pose.pose.position.z += np.random.randn(
) * self.noise_sigma
object_hypothesis.pose.pose.orientation.x += np.random.randn(
) * self.noise_sigma
object_hypothesis.pose.pose.orientation.y += np.random.randn(
) * self.noise_sigma
object_hypothesis.pose.pose.orientation.z += np.random.randn(
) * self.noise_sigma
object_hypothesis.pose.pose.orientation.w += np.random.randn(
) * self.noise_sigma
# Wrap ObjectHypothesisWithPose msg into Detection2D msg
detection_msg = Detection2D()
detection_msg.header.frame_id = self.published_frame_id
detection_msg.header.stamp = rospy.Time.now()
detection_msg.results.append(object_hypothesis)
# Wrap Detection2D msg into Detection2DArray msg
detection_array_msg = Detection2DArray()
detection_array_msg.header = detection_msg.header
detection_array_msg.detections.append(detection_msg)
self.pub.publish(detection_array_msg)
# Publish detection as a Marker for visualization in rviz
detected_marker = copy.deepcopy(marker)
detected_marker.header.stamp = rospy.Time.now()
detected_marker.ns = 'detected_{}'.format(detected_marker.ns)
detected_marker.color = ColorRGBA(0, 1, 0, 1)
detected_marker.lifetime.secs = 1
self.pub_detected_markers.publish(detected_marker)
def callback_yolo(self, image):
image = self.image
cv_image = self.bridge.compressed_imgmsg_to_cv2(image, "bgr8")
_, bboxes = detect_image(self.yolo,
cv_image,
"",
input_size=YOLO_INPUT_SIZE,
show=False,
rectangle_colors=(255, 0, 0))
detect = Detection2DArray()
detect.header = image.header
for Object in bboxes:
detection = Detection2D()
hypo = ObjectHypothesisWithPose()
#Start x
x1 = Object[0]
#End x
x2 = Object[2]
#Start y
y1 = Object[1]
#end y
y2 = Object[3]
#Size x
Sx = x2 - x1
#Size y
Sy = y2 - y1
#Center x
Cx = x1 + Sx / 2
#Center y
Cy = y1 + Sy / 2
detection.bbox.center.x = Cx
detection.bbox.center.y = Cy
detection.bbox.size_x = Sx
detection.bbox.size_y = Sy
hypo.id = int(Object[5])
hypo.score = Object[4]
detection.results = [
hypo,
]
detect.detections.append(detection)
self.bbox_pub.publish(detect)
def create_detections_msg(self, image_np, output_dict):
img_height = image_np.shape[0]
img_width = image_np.shape[1]
boxes = output_dict['detection_boxes']
classes = output_dict['detection_classes']
scores = output_dict['detection_scores']
detections = Detection2DArray()
detections.header.stamp = self.get_clock().now().to_msg()
detections.detections = []
for i in range(len(boxes)):
det = Detection2D()
det.header = detections.header
det.results = []
detected_object = ObjectHypothesisWithPose()
detected_object.id = classes[i].item()
detected_object.score = scores[i].item()
det.results.append(detected_object)
# box is ymin, xmin, ymax, xmax in normalized coordinates
box = boxes[i]
det.bbox.size_y = (box[2] - box[0]) * img_height
det.bbox.size_x = (box[3] - box[1]) * img_width
det.bbox.center.x = (box[1] + box[3]) * img_height / 2
det.bbox.center.y = (box[0] + box[2]) * img_width / 2
if (self.republish_image):
box_img = image_np[int(box[0] * img_height):int(box[2] *
img_height),
int(box[1] * img_width):int(box[3] *
img_width)]
det.source_img = img_utils.image_np_to_image_msg(box_img)
detections.detections.append(det)
return detections
def detect_objects(self, data):
"""
This function detects and classifies the objects in the image provided through a Service Request by running on her the provided
detection model. Returns a vision_msgs/Detection2DArray, for which each detection is populated only with the bbox and results fields. Moreover,
for what it concerns the results field, each result is populated only with the id and score fields.
All the other fields are not significant for this application, so they have been ignored.
Args:
data (sensor_msgs/Image): image to perform object detection on.
Returns:
pepper_object_detectionResponse: response encapsulating data regarding detected objects, structured as in service definition.
"""
# Convert image from sensor_msgs/Image to numpy array
img_np = ros_numpy.numpify(data.img)
rospy.loginfo('Object detection server computing predictions...')
detections = self._detection_model(img_np)
rospy.loginfo('Predictions computed!')
message = Detection2DArray()
# Insert all the predictions into the message and return them
for class_id, score, box in zip(detections['detection_classes'],
detections['detection_scores'],
detections['detection_boxes']):
detection = Detection2D()
detection.bbox.size_x = box[3] - box[1]
detection.bbox.size_y = box[2] - box[0]
detection.bbox.center.x = box[1] + detection.bbox.size_x / 2
detection.bbox.center.y = box[0] + detection.bbox.size_y / 2
detected_object = ObjectHypothesisWithPose()
detected_object.score = score
detected_object.id = class_id
detection.results.append(detected_object)
message.detections.append(detection)
# Create a response object
response = pepper_object_detectionResponse()
response.detections = message
return response
def publish_markers(self, fid_data_array):
fidarray = FiducialTransformArray()
fidarray.header.stamp = rospy.Time.now()
vis = Detection2DArray()
vis.header.stamp = rospy.Time.now()
for fid in fid_data_array:
if VIS_MSGS:
obj = Detection2D()
oh = ObjectHypothesisWithPose()
oh.id = fid.id
oh.pose.pose.position.x = fid.translation.x
oh.pose.pose.position.y = fid.translation.y
oh.pose.pose.position.z = fid.translation.z
oh.pose.pose.orientation.w = fid.rotation.w
oh.pose.pose.orientation.x = fid.rotation.x
oh.pose.pose.orientation.y = fid.rotation.y
oh.pose.pose.orientation.z = fid.rotation.z
oh.score = math.exp(-2 * OBJECT_ERROR)
obj.results.append(oh)
vis.detections.append(obj)
else:
data = FiducialTransform()
data.fiducial_id = fid.id
data.transform.translation = fid.translation
data.transform.rotation = fid.rotation
data.image_error = IMAGE_ERROR
data.object_error = OBJECT_ERROR
data.fiducial_area = FIDUCIAL_AREA
fidarray.transforms.append(data)
if VIS_MSGS:
self.fid_pub.publish(vis)
else:
self.fid_pub.publish(fidarray)
def object_predict(self, object_data, header, image_np, image, ID):
image_height, image_width, channels = image.shape
obj = Detection2D()
obj_hypothesis = ObjectHypothesisWithPose()
object_id = ID
object_score = object_data[1]
dimensions = object_data[2]
obj.header = header
obj_hypothesis.id = object_id
obj_hypothesis.score = object_score
obj.results.append(obj_hypothesis)
obj.bbox.size_y = int((dimensions[2] - dimensions[0]) * image_height)
obj.bbox.size_x = int((dimensions[3] - dimensions[1]) * image_width)
#the center point of x coordinate is determined here...
obj.bbox.center.x = int(
(dimensions[1] + dimensions[3]) * image_width / 2)
#the center point of y coordinate is determined here...
obj.bbox.center.y = int(
(dimensions[0] + dimensions[2]) * image_height / 2)
#detection results are returned as type Detection2D to objArray...
return obj
def gather_pose_results(self, header, class_id, translation, rotation, score):
pose_msg = PoseStamped()
pose_msg.header = header
pose_msg.pose.position.x = translation[0]
pose_msg.pose.position.y = translation[1]
pose_msg.pose.position.z = translation[2]
pose_msg.pose.orientation.x = rotation[0]
pose_msg.pose.orientation.y = rotation[1]
pose_msg.pose.orientation.z = rotation[2]
pose_msg.pose.orientation.w = rotation[3]
# Add to detection3Darray
detections = Detection3D()
hypothesis = ObjectHypothesisWithPose()
hypothesis.id = class_id
hypothesis.pose.pose = pose_msg.pose
hypothesis.score = score
detections.results.append(hypothesis)
detections.bbox.center = pose_msg.pose
detections.bbox.size.x = self.dims[class_id][0] * 2
detections.bbox.size.y = self.dims[class_id][1] * 2
detections.bbox.size.z = self.dims[class_id][2] * 2
return pose_msg, detections
def encode_detection_msg(self,detections):
detections_msg = Detection2DArray()
if (len(detections)>0):
i=0
detstring='Detected:'
for det in detections:
detection = Detection2D()
detection.header.seq = self.detection_seq
detection.header.stamp = rospy.Time.now()
detection.header.frame_id = self.camera_frame
result = [ObjectHypothesisWithPose()]
result[0].id = det[0]
result[0].score = det[1]
detection.results = result
detection.bbox.center.x = (det[2]+det[4])/2
detection.bbox.center.y = (det[3]+det[5])/2
detection.bbox.size_x = det[4]-det[2]
detection.bbox.size_y = det[5]-det[3]
detections_msg.detections.append(detection)
detstring=detstring+' '+self.classes[int(det[0])]+', p=%.2f.'%(det[1])
i+=1
rospy.logwarn(detstring)
self.detection_seq += 1
return detections_msg
def EfficientDetNode():
rospy.init_node('efficient_det_node', anonymous=True)
rospy.Subscriber('input', String, image_callback, queue_size=1)
pub = rospy.Publisher('/image_detections', Detection2DArray, queue_size=10)
rate = rospy.Rate(1) # 10hz
path_list = os.listdir(path)
path_list.sort(key=lambda x: int(x.split('.')[0]))
stamp_file = open(stamp_path)
stamp_lines = stamp_file.readlines()
stamp_i = 0
for filename in path_list:
img_path = filename
cur_frame = img_path[:-4]
img_path = path + "/" + img_path
cur_stamp = ((float)(stamp_lines[stamp_i][-13:].strip('\n')))
# cur_stamp = rospy.Time.from_sec(
# ((float)(stamp_lines[stamp_i][-13:].strip('\n'))))
stamp_i += 1
detection_results = Detection2DArray()
# tf bilinear interpolation is different from any other's, just make do
input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536, 1536]
input_size = input_sizes[
compound_coef] if force_input_size is None else force_input_size
ori_imgs, framed_imgs, framed_metas = preprocess(img_path,
max_size=input_size)
if use_cuda:
x = torch.stack(
[torch.from_numpy(fi).cuda() for fi in framed_imgs], 0)
else:
x = torch.stack([torch.from_numpy(fi) for fi in framed_imgs], 0)
x = x.to(torch.float32 if not use_float16 else torch.float16).permute(
0, 3, 1, 2)
model = EfficientDetBackbone(compound_coef=compound_coef,
num_classes=len(obj_list),
ratios=anchor_ratios,
scales=anchor_scales)
model.load_state_dict(
torch.load(f'weights/efficientdet-d{compound_coef}.pth',
map_location='cpu'))
model.requires_grad_(False)
model.eval()
if use_cuda:
model = model.cuda()
if use_float16:
model = model.half()
with torch.no_grad():
features, regression, classification, anchors = model(x)
regressBoxes = BBoxTransform()
clipBoxes = ClipBoxes()
out = postprocess(x, anchors, regression, classification,
regressBoxes, clipBoxes, threshold,
iou_threshold)
out = invert_affine(framed_metas, out)
display(cur_frame, out, ori_imgs, imshow=False, imwrite=True)
for i in range(len(out)):
for j in range(len(out[i]['rois'])):
x1, y1, x2, y2 = out[i]['rois'][j].astype(np.int)
obj = obj_list[out[i]['class_ids'][j]]
score = float(out[i]['scores'][j])
result = ObjectHypothesisWithPose()
result.score = score
if (obj == 'car'):
result.id = 0
if (obj == 'person'):
result.id = 1
if (obj == 'cyclist'):
result.id = 2
detection_msg = Detection2D()
detection_msg.bbox.center.x = (x1 + x2) / 2
detection_msg.bbox.center.y = (y1 + y2) / 2
detection_msg.bbox.size_x = x2 - x1
detection_msg.bbox.size_y = y2 - y1
detection_msg.results.append(result)
detection_results.detections.append(detection_msg)
rospy.loginfo("%d: %lf", detection_msg.results[0].id,
detection_msg.results[0].score)
detection_results.header.seq = cur_frame
#detection_results.header.stamp = cur_stamp
rospy.loginfo(detection_results.header.stamp)
pub.publish(detection_results)
if not os.path.exists(txt_path):
os.makedirs(txt_path)
#with open(f'txt/{cur_frame}.txt', 'w') as f:
with open(f'{txt_path}/{cur_frame}.txt', 'w') as f:
#f.write(str((float)(stamp_lines[stamp_i][-13:].strip('\n'))) + "\n")
f.write(str(cur_stamp) + "\n")
for detection in detection_results.detections:
f.write(str(detection.bbox.center.x) + " ")
f.write(str(detection.bbox.center.y) + " ")
f.write(str(detection.bbox.size_x) + " ")
f.write(str(detection.bbox.size_y) + " ")
f.write(str(detection.results[0].id) + " ")
f.write(str(detection.results[0].score) + "\n")
f.close()
rate.sleep()
print('running speed test...')
with torch.no_grad():
print('test1: model inferring and postprocessing')
print('inferring image for 10 times...')
t1 = time.time()
for _ in range(10):
_, regression, classification, anchors = model(x)
out = postprocess(x, anchors, regression, classification,
regressBoxes, clipBoxes, threshold,
iou_threshold)
out = invert_affine(framed_metas, out)
t2 = time.time()
tact_time = (t2 - t1) / 10
print(f'{tact_time} seconds, {1 / tact_time} FPS, @batch_size 1')
def detect_multi(self, images):
image_shape = images[0].shape
images_list = []
for image in images:
image = cv.resize(image,
self.image_size[::-1]) # cv uses width x height!
image = image[..., np.newaxis]
if not self.grayscale and image.shape[-1] == 1:
image = cv.cvtColor(image, cv.COLOR_GRAY2RGB)
image_torch = self.transform(image)[np.newaxis, ...]
images_list.append(image_torch)
images_torch = torch.cat(images_list, 0)
if self.half:
images_torch = images_torch.half()
if self.has_gpu and self.use_gpu:
images_torch = images_torch.cuda()
with torch.no_grad():
# Raw detection is a (N x 8190 x num_classes) tensor
start_time_inf = self.time_synchronized()
raw_detections, _ = self.model(images_torch)
elapsed_time_inf = self.time_synchronized() - start_time_inf
if self.verbose:
print('time (inf): {:.4f}s'.format(elapsed_time_inf))
if self.half:
raw_detections = raw_detections.float()
start_time_nms = self.time_synchronized()
detections_torch = self.non_max_suppression(
raw_detections.float(),
conf_thres=self.confidence_threshold,
iou_thres=self.iou_threshold)
elapsed_time_nms = self.time_synchronized() - start_time_nms
if self.verbose:
print('time (nms): {:.4f}s'.format(elapsed_time_nms))
if self.verbose:
print('time (tot): {:.4f}s\n'.format(elapsed_time_inf +
elapsed_time_nms))
detection_array_list = []
for detection_torch in detections_torch:
detection_array_msg = Detection2DArray()
if detection_torch is not None:
# Rescale bounding boxes back to original (old) image size
# shape_old: Shape of images as they come in
# shape_new: Shape of images used for inference
detection_torch = rescale_boxes(detection_torch,
shape_old=image_shape[:2],
shape_new=self.image_size)
detections = detection_torch.cpu().numpy()
for i, detection in enumerate(detections):
x1, y1, x2, y2, object_conf, class_pred = detection
class_id = int(class_pred)
# class_name = self.classes[class_id]
size_x = (x2 - x1)
size_y = (y2 - y1)
x = x1 + (size_x / 2.0)
y = y1 + (size_y / 2.0)
bbox = BoundingBox2D()
bbox.center = Pose2D(x=x, y=y, theta=0)
bbox.size_x = size_x
bbox.size_y = size_y
object_hypothesis = ObjectHypothesisWithPose()
object_hypothesis.id = class_id
object_hypothesis.score = object_conf
detection_msg = Detection2D()
detection_msg.bbox = bbox
detection_msg.results.append(object_hypothesis)
detection_array_msg.detections.append(detection_msg)
detection_array_list.append(detection_array_msg)
return detection_array_list
def run_on_image_icp(self,
image_filename, category_names_to_id, cloud_in, output_image_filepath, publish_cloud=True):
"""Image callback"""
camera_info = self.camera_info
if not self.info_manager.isCalibrated():
rospy.logwarn('Camera is not calibrated, please supply a valid camera_info_url parameter!')
img = cv2.imread(image_filename)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Update camera matrix and distortion coefficients
if self.input_is_rectified:
P = np.matrix(camera_info.P, dtype='float64')
P.resize((3, 4))
camera_matrix = P[:, :3]
dist_coeffs = np.zeros((4, 1))
else:
camera_matrix = np.matrix(camera_info.K, dtype='float64')
camera_matrix.resize((3, 3))
dist_coeffs = np.matrix(camera_info.D, dtype='float64')
dist_coeffs.resize((len(camera_info.D), 1))
# Downscale image if necessary
height, width, _ = img.shape
scaling_factor = float(self.downscale_height) / height
if scaling_factor < 1.0:
camera_matrix[:2] *= scaling_factor
img = cv2.resize(img, (int(scaling_factor * width), int(scaling_factor * height)))
for m in self.models:
self.pnp_solvers[m].set_camera_intrinsic_matrix(camera_matrix)
self.pnp_solvers[m].set_dist_coeffs(dist_coeffs)
# Copy and draw image
img_copy = img.copy()
im = Image.fromarray(img_copy)
draw = Draw(im)
detection_array = Detection3DArray()
detection_array.header = "camera"
annotations = []
start_time = time.time()
for m in self.models:
# Detect object
results = ObjectDetector.detect_object_in_image(
self.models[m].net,
self.pnp_solvers[m],
img,
self.config_detect
)
# Publish pose and overlay cube on image
for i_r, result in enumerate(results):
if result["location"] is None:
continue
print(result)
loc = result["location"]
ori = result["quaternion"]
CONVERT_SCALE_CM_TO_METERS = 100
pose_frame = "camera"
rospy.logwarn("Doing ICP for result : {}, {}".format(i_r, result["name"]))
loc_scale = np.array([loc[0] / 100, loc[1] / 100, loc[2] / 100])
R = tf.transformations.quaternion_matrix(ori)
T = tf.transformations.translation_matrix(loc_scale)
total_transform = tf.transformations.concatenate_matrices(T, R)
cloud_filtered_array = self.transform_cloud(self.mesh_clouds[result["name"]], mat=total_transform)
cloud_color = np.zeros(cloud_filtered_array.shape[0])
ros_msg = self.xyzrgb_array_to_pointcloud2(
cloud_filtered_array, cloud_color, rospy.Time.now(), "camera"
)
# self.pub_pose_cloud.publish(ros_msg)
# rospy.logwarn("Num points after downsample and filter : {}".format(cloud_filtered_array.shape[0]))
cloud_pose = pcl.PointCloud()
cloud_pose.from_array(cloud_filtered_array)
icp = cloud_pose.make_GeneralizedIterativeClosestPoint()
converged, transf, estimate, fitness = icp.gicp(cloud_pose, cloud_in, max_iter=25)
print('has converged:' + str(converged) + ' score: ' + str(fitness))
print(str(transf))
total_transform_icp = tf.transformations.concatenate_matrices(transf, total_transform)
print(str(total_transform_icp))
loc = tf.transformations.translation_from_matrix(total_transform_icp) * 100
ori = tf.transformations.quaternion_from_matrix(total_transform_icp)
pose_frame = "camera"
if publish_cloud :
cloud_filtered_array = self.transform_cloud(self.mesh_clouds[result["name"]], mat=total_transform_icp)
cloud_color = np.zeros(cloud_filtered_array.shape[0])
ros_msg = self.xyzrgb_array_to_pointcloud2(
cloud_filtered_array, cloud_color, rospy.Time.now(), "camera"
)
self.pub_pose_cloud.publish(ros_msg)
# transform orientation
transformed_ori = tf.transformations.quaternion_multiply(ori, self.model_transforms[m])
# rotate bbox dimensions if necessary
# (this only works properly if model_transform is in 90 degree angles)
dims = rotate_vector(vector=self.dimensions[m], quaternion=self.model_transforms[m])
dims = np.absolute(dims)
dims = tuple(dims)
pose_msg = PoseStamped()
# pose_msg.header = image_msg.header
pose_msg.header.stamp = rospy.Time.now()
pose_msg.header.frame_id = pose_frame
pose_msg.pose.position.x = loc[0] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.position.y = loc[1] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.position.z = loc[2] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.orientation.x = transformed_ori[0]
pose_msg.pose.orientation.y = transformed_ori[1]
pose_msg.pose.orientation.z = transformed_ori[2]
pose_msg.pose.orientation.w = transformed_ori[3]
annotations.append({
'location' : loc,
'quaternion_xyzw' : transformed_ori.tolist(),
'category_id' : category_names_to_id[m],
'id' : i_r
})
# Publish
self.pubs[m].publish(pose_msg)
self.pub_dimension[m].publish(str(dims))
# Add to Detection3DArray
detection = Detection3D()
hypothesis = ObjectHypothesisWithPose()
hypothesis.id = self.class_ids[result["name"]]
hypothesis.score = result["score"]
hypothesis.pose.pose = pose_msg.pose
detection.results.append(hypothesis)
detection.bbox.center = pose_msg.pose
detection.bbox.size.x = dims[0] / CONVERT_SCALE_CM_TO_METERS
detection.bbox.size.y = dims[1] / CONVERT_SCALE_CM_TO_METERS
detection.bbox.size.z = dims[2] / CONVERT_SCALE_CM_TO_METERS
detection_array.detections.append(detection)
# Draw the cube
if None not in result['projected_points'] and publish_cloud:
points2d = []
for pair in result['projected_points']:
points2d.append(tuple(pair))
draw.draw_cube(points2d, self.draw_colors[m])
# Publish the image with results overlaid
runtime = (time.time() - start_time)
self.pub_rgb_dope_points.publish(
CvBridge().cv2_to_imgmsg(
np.array(im)[..., ::-1],
"bgr8"
)
)
final_im = np.array(im)[..., ::-1]
cv2.imwrite(output_image_filepath, np.array(im)[..., ::-1])
self.pub_detections.publish(detection_array)
self.publish_markers(detection_array)
return annotations, runtime
def step(self):
stamp = super().step()
if not stamp:
return
# Publish camera data
if self._image_publisher.get_subscription_count(
) > 0 or self._always_publish:
self._wb_device.enable(self._timestep)
image = self._wb_device.getImage()
if image is None:
return
# Image data
msg = Image()
msg.header.stamp = stamp
msg.header.frame_id = self._frame_id
msg.height = self._wb_device.getHeight()
msg.width = self._wb_device.getWidth()
msg.is_bigendian = False
msg.step = self._wb_device.getWidth() * 4
# We pass `data` directly to we avoid using `data` setter.
# Otherwise ROS2 converts data to `array.array` which slows down the simulation as it copies memory internally.
# Both, `bytearray` and `array.array`, implement Python buffer protocol, so we should not see unpredictable
# behavior.
# deepcode ignore W0212: Avoid conversion from `bytearray` to `array.array`.
msg._data = image
msg.encoding = 'bgra8'
self._image_publisher.publish(msg)
self.__message_info.header.stamp = Time(
seconds=self._node.robot.getTime()).to_msg()
self._camera_info_publisher.publish(self.__message_info)
if self._wb_device.hasRecognition(
) and (self._recognition_publisher.get_subscription_count() > 0 or
self._recognition_webots_publisher.get_subscription_count()
> 0):
self._wb_device.recognitionEnable(self._timestep)
objects = self._wb_device.getRecognitionObjects()
if objects is None:
return
# Recognition data
reco_msg = Detection2DArray()
reco_msg_webots = WbCameraRecognitionObjects()
reco_msg.header.stamp = stamp
reco_msg_webots.header.stamp = stamp
reco_msg.header.frame_id = self._frame_id
reco_msg_webots.header.frame_id = self._frame_id
for obj in objects:
# Getting Object Info
position = Point()
orientation = Quaternion()
position.x = obj.get_position()[0]
position.y = obj.get_position()[1]
position.z = obj.get_position()[2]
axangle = obj.get_orientation()
quat = axangle2quat(axangle[:-1], axangle[-1])
orientation.w = quat[0]
orientation.x = quat[1]
orientation.y = quat[2]
orientation.z = quat[3]
obj_model = obj.get_model().decode('UTF-8')
obj_center = [
float(i) for i in obj.get_position_on_image()
]
obj_size = [float(i) for i in obj.get_size_on_image()]
obj_id = obj.get_id()
obj_colors = obj.get_colors()
# Object Info -> Detection2D
reco_obj = Detection2D()
hyp = ObjectHypothesisWithPose()
hyp.id = obj_model
hyp.pose.pose.position = position
hyp.pose.pose.orientation = orientation
reco_obj.results.append(hyp)
reco_obj.bbox.center.x = obj_center[0]
reco_obj.bbox.center.y = obj_center[1]
reco_obj.bbox.size_x = obj_size[0]
reco_obj.bbox.size_y = obj_size[1]
reco_msg.detections.append(reco_obj)
# Object Info -> WbCameraRecognitionObject
reco_webots_obj = WbCameraRecognitionObject()
reco_webots_obj.id = obj_id
reco_webots_obj.model = obj_model
reco_webots_obj.pose.pose.position = position
reco_webots_obj.pose.pose.orientation = orientation
reco_webots_obj.bbox.center.x = obj_center[0]
reco_webots_obj.bbox.center.y = obj_center[1]
reco_webots_obj.bbox.size_x = obj_size[0]
reco_webots_obj.bbox.size_y = obj_size[1]
for i in range(0, obj.get_number_of_colors()):
color = ColorRGBA()
color.r = obj_colors[3 * i]
color.g = obj_colors[3 * i + 1]
color.b = obj_colors[3 * i + 2]
reco_webots_obj.colors.append(color)
reco_msg_webots.objects.append(reco_webots_obj)
self._recognition_webots_publisher.publish(reco_msg_webots)
self._recognition_publisher.publish(reco_msg)
else:
self._wb_device.recognitionDisable()
else:
self._wb_device.disable()
def run_on_image(self, image_filename, category_names_to_id, output_image_filepath):
camera_info = self.camera_info
if not self.info_manager.isCalibrated():
rospy.logwarn('Camera is not calibrated, please supply a valid camera_info_url parameter!')
img = cv2.imread(image_filename)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Update camera matrix and distortion coefficients
# camera_matrix = np.matrix(camera_info.K, dtype='float64')
# camera_matrix.resize((3, 3))
# camera_matrix = np.array([[768.16058349609375, 0, 480],
# [0, 768.16058349609375, 270],
# [0, 0, 1]])
# dist_coeffs = np.matrix(camera_info.D, dtype='float64')
# dist_coeffs.resize((len(camera_info.D), 1))
if self.input_is_rectified:
P = np.matrix(camera_info.P, dtype='float64')
P.resize((3, 4))
camera_matrix = P[:, :3]
dist_coeffs = np.zeros((4, 1))
else:
camera_matrix = np.matrix(camera_info.K, dtype='float64')
camera_matrix.resize((3, 3))
dist_coeffs = np.matrix(camera_info.D, dtype='float64')
dist_coeffs.resize((len(camera_info.D), 1))
# Downscale image if necessary
height, width, _ = img.shape
scaling_factor = float(self.downscale_height) / height
if scaling_factor < 1.0:
camera_matrix[:2] *= scaling_factor
img = cv2.resize(img, (int(scaling_factor * width), int(scaling_factor * height)))
for m in self.models:
self.pnp_solvers[m].set_camera_intrinsic_matrix(camera_matrix)
self.pnp_solvers[m].set_dist_coeffs(dist_coeffs)
# Copy and draw image
img_copy = img.copy()
im = Image.fromarray(img_copy)
draw = Draw(im)
detection_array = Detection3DArray()
# detection_array.header = image_msg.header
detection_array.header = "camera"
annotations = []
for m in self.models:
# Detect object
results = ObjectDetector.detect_object_in_image(
self.models[m].net,
self.pnp_solvers[m],
img,
self.config_detect
)
print("results : {}".format(results))
# Publish pose and overlay cube on image
for i_r, result in enumerate(results):
if result["location"] is None:
continue
loc = result["location"]
ori = result["quaternion"]
# transform orientation
# transformed_ori = tf.transformations.quaternion_multiply(ori, self.model_transforms[m])
transformed_ori = quaternion_multiply(ori, self.model_transforms[m])
# rotate bbox dimensions if necessary
# (this only works properly if model_transform is in 90 degree angles)
dims = rotate_vector(vector=self.dimensions[m], quaternion=self.model_transforms[m])
dims = np.absolute(dims)
dims = tuple(dims)
pose_msg = PoseStamped()
# pose_msg.header = image_msg.header
pose_msg.header.stamp = rospy.Time.now()
pose_msg.header.frame_id = "camera"
CONVERT_SCALE_CM_TO_METERS = 100
pose_msg.pose.position.x = loc[0] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.position.y = loc[1] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.position.z = loc[2] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.orientation.x = transformed_ori[0]
pose_msg.pose.orientation.y = transformed_ori[1]
pose_msg.pose.orientation.z = transformed_ori[2]
pose_msg.pose.orientation.w = transformed_ori[3]
annotations.append({
'location' : loc,
'quaternion_xyzw' : transformed_ori.tolist(),
'category_id' : category_names_to_id[m],
'id' : i_r
})
# Publish
self.pubs[m].publish(pose_msg)
# self.pub_dimension[m].publish(str(dims))
# Add to Detection3DArray
detection = Detection3D()
hypothesis = ObjectHypothesisWithPose()
hypothesis.id = self.class_ids[result["name"]]
hypothesis.score = result["score"]
hypothesis.pose.pose = pose_msg.pose
detection.results.append(hypothesis)
detection.bbox.center = pose_msg.pose
detection.bbox.size.x = dims[0] / CONVERT_SCALE_CM_TO_METERS
detection.bbox.size.y = dims[1] / CONVERT_SCALE_CM_TO_METERS
detection.bbox.size.z = dims[2] / CONVERT_SCALE_CM_TO_METERS
detection_array.detections.append(detection)
# Draw the cube
if None not in result['projected_points']:
points2d = []
for pair in result['projected_points']:
points2d.append(tuple(pair))
draw.draw_cube(points2d, self.draw_colors[m])
# Publish the image with results overlaid
final_im = np.array(im)[..., ::-1]
self.pub_rgb_dope_points.publish(
CvBridge().cv2_to_imgmsg(
final_im,
"bgr8"
)
)
cv2.imwrite(output_image_filepath, np.array(im)[..., ::-1])
self.pub_detections.publish(detection_array)
self.publish_markers(detection_array)
return annotations
def osd_sink_pad_buffer_probe(self,pad,info,u_data):
frame_number=0
#Intializing object counter with 0.
obj_counter = {
PGIE_CLASS_ID_VEHICLE:0,
PGIE_CLASS_ID_BICYCLE:0,
PGIE_CLASS_ID_PERSON:0,
PGIE_CLASS_ID_ROADSIGN:0
}
num_rects=0
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
# Retrieve batch metadata from the gst_buffer
# Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
# C address of gst_buffer as input, which is obtained with hash(gst_buffer)
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
l_frame = batch_meta.frame_meta_list
while l_frame is not None:
try:
# Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
# The casting is done by pyds.NvDsFrameMeta.cast()
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone.
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_number=frame_meta.frame_num
num_rects = frame_meta.num_obj_meta
l_obj=frame_meta.obj_meta_list
# Message for output of detection inference
msg = Detection2DArray()
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta=pyds.NvDsObjectMeta.cast(l_obj.data)
l_classifier = obj_meta.classifier_meta_list
# If object is a car (class ID 0), perform attribute classification
if obj_meta.class_id == 0 and l_classifier is not None:
# Creating and publishing message with output of classification inference
msg2 = Classification2D()
while l_classifier is not None:
result = ObjectHypothesis()
try:
classifier_meta = pyds.glist_get_nvds_classifier_meta(l_classifier.data)
except StopIteration:
print('Could not parse MetaData: ')
break
classifier_id = classifier_meta.unique_component_id
l_label = classifier_meta.label_info_list
label_info = pyds.glist_get_nvds_label_info(l_label.data)
classifier_class = label_info.result_class_id
if classifier_id == 2:
result.id = class_color[classifier_class]
elif classifier_id == 3:
result.id = class_make[classifier_class]
else:
result.id = class_type[classifier_class]
result.score = label_info.result_prob
msg2.results.append(result)
l_classifier = l_classifier.next
self.publisher_classification.publish(msg2)
except StopIteration:
break
obj_counter[obj_meta.class_id] += 1
# Creating message for output of detection inference
result = ObjectHypothesisWithPose()
result.id = str(class_obj[obj_meta.class_id])
result.score = obj_meta.confidence
left = obj_meta.rect_params.left
top = obj_meta.rect_params.top
width = obj_meta.rect_params.width
height = obj_meta.rect_params.height
bounding_box = BoundingBox2D()
bounding_box.center.x = float(left + (width/2))
bounding_box.center.y = float(top - (height/2))
bounding_box.size_x = width
bounding_box.size_y = height
detection = Detection2D()
detection.results.append(result)
detection.bbox = bounding_box
msg.detections.append(detection)
try:
l_obj=l_obj.next
except StopIteration:
break
# Publishing message with output of detection inference
self.publisher_detection.publish(msg)
# Acquiring a display meta object. The memory ownership remains in
# the C code so downstream plugins can still access it. Otherwise
# the garbage collector will claim it when this probe function exits.
display_meta=pyds.nvds_acquire_display_meta_from_pool(batch_meta)
display_meta.num_labels = 1
py_nvosd_text_params = display_meta.text_params[0]
# Setting display text to be shown on screen
# Note that the pyds module allocates a buffer for the string, and the
# memory will not be claimed by the garbage collector.
# Reading the display_text field here will return the C address of the
# allocated string. Use pyds.get_string() to get the string content.
py_nvosd_text_params.display_text = "Frame Number={} Number of Objects={} Vehicle_count={} Person_count={}".format(frame_number, num_rects, obj_counter[PGIE_CLASS_ID_VEHICLE], obj_counter[PGIE_CLASS_ID_PERSON])
# Now set the offsets where the string should appear
py_nvosd_text_params.x_offset = 10
py_nvosd_text_params.y_offset = 12
# Font , font-color and font-size
py_nvosd_text_params.font_params.font_name = "Serif"
py_nvosd_text_params.font_params.font_size = 10
# set(red, green, blue, alpha); set to White
py_nvosd_text_params.font_params.font_color.set(1.0, 1.0, 1.0, 1.0)
# Text background color
py_nvosd_text_params.set_bg_clr = 1
# set(red, green, blue, alpha); set to Black
py_nvosd_text_params.text_bg_clr.set(0.0, 0.0, 0.0, 1.0)
# Using pyds.get_string() to get display_text as string
pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
try:
l_frame=l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
def image_callback(self, image_msg, category_names_to_id):
"""Image callback"""
img = self.cv_bridge.imgmsg_to_cv2(image_msg, "rgb8")
# cv2.imwrite('img.png', cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # for debugging
# Update camera matrix and distortion coefficients
if self.input_is_rectified:
P = np.matrix(camera_info.P, dtype='float64')
P.resize((3, 4))
camera_matrix = P[:, :3]
dist_coeffs = np.zeros((4, 1))
else:
camera_matrix = np.matrix(camera_info.K, dtype='float64')
camera_matrix.resize((3, 3))
dist_coeffs = np.matrix(camera_info.D, dtype='float64')
dist_coeffs.resize((len(camera_info.D), 1))
# Downscale image if necessary
height, width, _ = img.shape
scaling_factor = float(self.downscale_height) / height
if scaling_factor < 1.0:
camera_matrix[:2] *= scaling_factor
img = cv2.resize(img, (int(scaling_factor * width), int(scaling_factor * height)))
for m in self.models:
self.pnp_solvers[m].set_camera_intrinsic_matrix(camera_matrix)
self.pnp_solvers[m].set_dist_coeffs(dist_coeffs)
# Copy and draw image
img_copy = img.copy()
im = Image.fromarray(img_copy)
draw = Draw(im)
detection_array = Detection3DArray()
detection_array.header = image_msg.header
for m in self.models:
# Detect object
results = ObjectDetector.detect_object_in_image(
self.models[m].net,
self.pnp_solvers[m],
img,
self.config_detect
)
# Publish pose and overlay cube on image
for i_r, result in enumerate(results):
if result["location"] is None:
continue
loc = result["location"]
ori = result["quaternion"]
# transform orientation
transformed_ori = tf.transformations.quaternion_multiply(ori, self.model_transforms[m])
# rotate bbox dimensions if necessary
# (this only works properly if model_transform is in 90 degree angles)
dims = rotate_vector(vector=self.dimensions[m], quaternion=self.model_transforms[m])
dims = np.absolute(dims)
dims = tuple(dims)
pose_msg = PoseStamped()
pose_msg.header = image_msg.header
CONVERT_SCALE_CM_TO_METERS = 100
pose_msg.pose.position.x = loc[0] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.position.y = loc[1] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.position.z = loc[2] / CONVERT_SCALE_CM_TO_METERS
pose_msg.pose.orientation.x = transformed_ori[0]
pose_msg.pose.orientation.y = transformed_ori[1]
pose_msg.pose.orientation.z = transformed_ori[2]
pose_msg.pose.orientation.w = transformed_ori[3]
# Publish
self.pubs[m].publish(pose_msg)
self.pub_dimension[m].publish(str(dims))
# Add to Detection3DArray
detection = Detection3D()
hypothesis = ObjectHypothesisWithPose()
hypothesis.id = self.class_ids[result["name"]]
hypothesis.score = result["score"]
hypothesis.pose.pose = pose_msg.pose
detection.results.append(hypothesis)
detection.bbox.center = pose_msg.pose
detection.bbox.size.x = dims[0] / CONVERT_SCALE_CM_TO_METERS
detection.bbox.size.y = dims[1] / CONVERT_SCALE_CM_TO_METERS
detection.bbox.size.z = dims[2] / CONVERT_SCALE_CM_TO_METERS
detection_array.detections.append(detection)
# Draw the cube
if None not in result['projected_points']:
points2d = []
for pair in result['projected_points']:
points2d.append(tuple(pair))
draw.draw_cube(points2d, self.draw_colors[m])
# Publish the image with results overlaid
self.pub_rgb_dope_points.publish(
CvBridge().cv2_to_imgmsg(
np.array(im)[..., ::-1],
"bgr8"
)
)
self.pub_detections.publish(detection_array)
self.publish_markers(detection_array)
return annotations
def detect(img, camera_frame_id, sensor_image):
time1 = time.time()
global ros_image
cudnn.benchmark = True
dataset = loadimg(img)
# print(dataset[3])
#plt.imshow(dataset[2][:, :, ::-1])
names = model.module.names if hasattr(model, 'module') else model.names
#colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
#colors=[[0,255,0]]
augment = 'store_true'
conf_thres = 0.3
iou_thres = 0.45
classes = (0, 1, 2, 3, 5, 7)
agnostic_nms = 'store_true'
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img
) if device.type != 'cpu' else None # run once
path = dataset[0]
img = dataset[1]
im0s = dataset[2]
vid_cap = dataset[3]
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
time2 = time.time()
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
pred = model(img, augment=augment)[0]
# Apply NMS
pred = non_max_suppression(pred,
conf_thres,
iou_thres,
classes=classes,
agnostic=agnostic_nms)
view_img = 1
save_txt = 1
save_conf = 'store_true'
time3 = time.time()
waste_detections = Detection2DArray()
waste_detections.header = Header()
waste_detections.header.stamp = rospy.get_rostime()
waste_detections.header.frame_id = camera_frame_id
for i, det in enumerate(pred): # detections per image
det_obj = Detection2D()
det_obj.header = waste_detections.header
# det_obj.source_img = sensor_image
result = ObjectHypothesisWithPose()
p, s, im0 = path, '', im0s
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None:
#print(det)
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /
gn).view(-1).tolist() # normalized xywh
line = (cls, conf,
*xywh) if save_conf else (cls,
*xywh) # label format
det_obj.bbox.center.x = xywh[0] + xywh[2] / 2
det_obj.bbox.center.y = xywh[1] + xywh[3] / 2
det_obj.bbox.size_x = xywh[2]
det_obj.bbox.size_y = xywh[3]
result.id = int(cls)
result.score = conf
if view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy,
im0,
label=label,
color=[0, 255, 0],
line_thickness=3)
det_obj.results.append(result)
waste_detections.detections.append(det_obj)
time4 = time.time()
print('************')
print('2-1', time2 - time1)
print('3-2', time3 - time2)
print('4-3', time4 - time3)
print('total', time4 - time1)
out_img = im0[:, :, [2, 1, 0]]
ros_image = out_img
cv2.imshow('YOLOV5', out_img)
a = cv2.waitKey(1)
#### Create CompressedIamge ####
if (waste_detections.detections.__sizeof__ != 0):
det_pub.publish(waste_detections)
publish_image(im0)
def callback(self, imageMsg):
image = self.bridge.imgmsg_to_cv2(imageMsg, "bgr8")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = image[:, :, ::-1].copy()
# copy to draw on
draw = image.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# Image formatting specific to Retinanet
image = preprocess_image(image)
image, scale = resize_image(image)
# Run the inferencer
try:
with self.session.as_default():
with self.session.graph.as_default():
boxes, scores, labels = self.model.predict_on_batch(np.expand_dims(image, axis=0))
except Exception as e:
rospy.logerr(e)
rospy.logwarn("WARNING: Has your model been converted to an inference model yet? "
"see https://github.com/fizyr/keras-retinanet#converting-a-training-model-to-inference-model")
return
# correct for image scale
boxes /= scale
# Construct the detection message
header = Header(frame_id=imageMsg.header.frame_id)
detections_message_out = Detection2DArray()
detections_message_out.header = header
detections_message_out.detections = []
# visualize detections
for box, score, label in zip(boxes[0], scores[0], labels[0]):
# scores are sorted so we can break
if score < self.confidence_cutoff:
break
# Add boxes and captions
b = np.array(box).astype(int)
cv2.rectangle(draw, (b[0], b[1]), (b[2], b[3]), self.color, self.thickness, cv2.LINE_AA)
if (label > len(self.labels_to_names)):
print("WARNING: Got unknown label, using 'detection' instead")
caption = "Detection {:.3f}".format(score)
else:
caption = "{} {:.3f}".format(self.labels_to_names[label], score)
cv2.putText(draw, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2)
cv2.putText(draw, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1)
#Construct the output detection message
bb = BoundingBox2D()
det = Detection2D(header=header)
hyp = ObjectHypothesisWithPose()
center = Pose2D()
hyp.id = label
hyp.score = score
bb.size_x = b[2] - b[0]
bb.size_y = b[3] - b[1]
center.x = float(b[2] + b[0])/2
center.y = float(b[3] + b[1])/2
bb.center = center
det.results.append(hyp)
det.bbox = bb
detections_message_out.detections.append(det)
self.detections_pub.publish(detections_message_out)
# Write out image
image_message_out = self.bridge.cv2_to_imgmsg(draw, encoding="rgb8")
self.img_pub.publish(image_message_out)
def callback(self, data):
bridge = CvBridge()
try:
cv_image = self.bridge.compressed_imgmsg_to_cv2(
data, desired_encoding="passthrough")
except CvBridgeError as e:
rospy.logerr(e)
results = self.engine.DetectWithImage(PIL.Image.fromarray(cv_image),
top_k=1,
threshold=self.threshold,
keep_aspect_ratio=True,
relative_coord=True)
detections = Detection2DArray()
now = rospy.get_rostime()
for detection in results:
top_left, bottom_right = detection.bounding_box
min_x, min_y = top_left
max_x, max_y = bottom_right
imheight, imwidth, _ = cv_image.shape
min_x *= imwidth
max_x *= imwidth
min_y *= imheight
max_y *= imheight
centre_x = (max_x + min_x) / 2.0
centre_y = (max_y + min_y) / 2.0
height = max_y - min_y
width = max_x - min_x
if height <= 0 or width <= 0:
continue
bbox = BoundingBox2D()
bbox.center.x = centre_x
bbox.center.y = centre_y
bbox.size_x = width
bbox.size_y = height
hypothesis = ObjectHypothesisWithPose()
# hypothesis.id = str(detection.label_id)
hypothesis.score = detection.score
hypothesis.pose.pose.position.x = centre_x
hypothesis.pose.pose.position.y = centre_y
# update the timestamp of the object
object = Detection2D()
object.header.stamp = now
object.header.frame_id = data.header.frame_id
object.results.append(hypothesis)
object.bbox = bbox
object.source_img.header.frame_id = data.header.frame_id
object.source_img.header.stamp = now
object.source_img.height = int(height)
object.source_img.width = int(width)
object.source_img.encoding = "rgb8"
object.source_img.step = int(width * 3)
# object.source_img.data = cv_image[int(min_y):int(max_y), int(min_x):int(max_x)].tobytes()
detections.detections.append(object)
if len(results) > 0:
self.detection_pub.publish(detections)
rospy.logdebug("%.2f ms" % self.engine.get_inference_time())
def object_predict(self, object_data, header, image_np, image):
image_height, image_width, channels = image.shape
obj = Detection2D()
obj_hypothesis = ObjectHypothesisWithPose()
object_id = object_data[0]
object_score = object_data[1]
dimensions = object_data[2]
obj.header = header
obj_hypothesis.id = object_id
obj_hypothesis.score = object_score
#obj.results.append(obj_hypothesis)
obj.bbox.size_y = int((dimensions[2] - dimensions[0]) * image_height)
obj.bbox.size_x = int((dimensions[3] - dimensions[1]) * image_width)
obj.bbox.center.x = int(
(dimensions[1] + dimensions[3]) * image_width / 2)
obj.bbox.center.y = int(
(dimensions[0] + dimensions[2]) * image_height / 2)
###################################
pixelDiametro = obj.bbox.size_x
# choose the bigest size
if (obj.bbox.size_x > obj.bbox.size_y):
pixelDiametro = obj.bbox.size_x
else:
pixelDiametro = obj.bbox.size_y
#DIAMETER_LANDMARCK_M = 0.24 OR 0.5
metersDiametroLandmarck = self.DIAMETER_LANDMARCK_M
#DISTANCE_FOCAL = 750
distFocus_real = self.DISTANCE_FOCAL
altura = float(
(metersDiametroLandmarck * distFocus_real) / pixelDiametro)
# rospy.loginfo("--------------------------------")
# rospy.loginfo("Diametro Marcador Real: %f", metersDiametroLandmarck)
# # rospy.loginfo("Distancia Focal Real: %f", distFocus_real)
# rospy.loginfo("Diametro (pixel): %f", pixelDiametro)
# rospy.loginfo("Altura Drone (m): %f", altura)
###################################
pixel_x = int((obj.bbox.center.x - (image_width / 2)) * (-1))
pixel_y = int((obj.bbox.center.y - (image_height / 2)) * (1))
k = float(metersDiametroLandmarck / pixelDiametro)
obj_hypothesis.pose.pose.position.x = pixel_x * k
obj_hypothesis.pose.pose.position.y = pixel_y * k
obj_hypothesis.pose.pose.position.z = altura
obj.results.append(obj_hypothesis)
#rospy.loginfo("publish obj_hypothesis.score: %d", object_score)
# rospy.loginfo("publish bbox.size x: %d", obj.bbox.size_x)
# rospy.loginfo("publish bbox.size y: %d", obj.bbox.size_y)
# rospy.loginfo("publish bbox.center x: %d", obj.bbox.center.x)
# rospy.loginfo("publish bbox.center y: %d", obj.bbox.center.y)
return obj
def callback_synchronize(self, msg_img, msg_bbox, cam_id):
cv_img = self.cv_bridge.imgmsg_to_cv2(msg_img)
new_bbox = Detection2DArray()
print("aaaaa")
for detection in msg_bbox.detections:
box = Detection2D()
box.bbox = detection.bbox
pose = ObjectHypothesisWithPose()
print(detection.results[0].id)
if detection.results[0].id == 7: # id:7 Traffic light
x_min = int(detection.bbox.center.x -
(detection.bbox.size_x / 2))
y_min = int(detection.bbox.center.y -
(detection.bbox.size_y / 2))
x_max = int(detection.bbox.center.x +
(detection.bbox.size_x / 2))
y_max = int(detection.bbox.center.y +
(detection.bbox.size_y / 2))
img_cropped = cv_img[y_min:y_max, x_min:x_max]
preprocessed_img = self.preprocess_img(img=img_cropped)
predict_f = self.model_traffic_light(preprocessed_img)
predict = predict_f.data.max(1, keepdim=True)[1]
pose.id = int(1000 + int(predict))
cnn_prob = float(torch.exp(predict_f)[0][int(predict)])
pose.score = cnn_prob
box.results.append(pose)
# pose.id = int(1000 + int(predict))
# box.results.append(pose)
if self.validate_light_with_brightness_region(
img_cropped, int(predict)):
new_bbox.detections.append(box)
elif detection.results[0].id == 8: # id:8 Traffic sign
x_min = int(detection.bbox.center.x -
(detection.bbox.size_x / 2))
y_min = int(detection.bbox.center.y -
(detection.bbox.size_y / 2))
x_max = int(detection.bbox.center.x +
(detection.bbox.size_x / 2))
y_max = int(detection.bbox.center.y +
(detection.bbox.size_y / 2))
img_cropped = cv_img[y_min:y_max, x_min:x_max]
preprocessed_img = self.preprocess_img(img=img_cropped)
predict_f = self.model_traffic_sign(preprocessed_img)
predict = predict_f.data.max(1, keepdim=True)[1]
cnn_prob = float(torch.exp(predict_f)[0][int(predict)])
pose.score = cnn_prob
pose.id = int(2000 + int(predict))
box.results.append(pose)
new_bbox.detections.append(box)
else:
box = detection
new_bbox.detections.append(box)
new_bbox.header = msg_bbox.header
if cam_id == "cam_fm_01":
self.pub_fm01_new_bbox.publish(new_bbox)
elif cam_id == "cam_fr_01":
self.pub_fr01_new_bbox.publish(new_bbox)
elif cam_id == "cam_fl_01":
self.pub_fl01_new_bbox.publish(new_bbox)
def tiler_sink_pad_buffer_probe(self, pad, info, u_data):
frame_number = 0
num_rects = 0
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
# Retrieve batch metadata from the gst_buffer
# Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
# C address of gst_buffer as input, which is obtained with hash(gst_buffer)
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
l_frame = batch_meta.frame_meta_list
while l_frame is not None:
try:
# Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
# The casting is done by pyds.NvDsFrameMeta.cast()
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone.
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_number = frame_meta.frame_num
l_obj = frame_meta.obj_meta_list
num_rects = frame_meta.num_obj_meta
is_first_obj = True
save_image = False
obj_counter = {
PGIE_CLASS_ID_VEHICLE: 0,
PGIE_CLASS_ID_BICYCLE: 0,
PGIE_CLASS_ID_PERSON: 0,
PGIE_CLASS_ID_ROADSIGN: 0
}
# Message for output of detection inference
msg = Detection2DArray()
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
l_classifier = obj_meta.classifier_meta_list
# If object is a car (class ID 0), perform attribute classification
if obj_meta.class_id == 0 and l_classifier is not None:
# Creating and publishing message with output of classification inference
msg2 = Classification2D()
while l_classifier is not None:
result = ObjectHypothesis()
try:
classifier_meta = pyds.glist_get_nvds_classifier_meta(
l_classifier.data)
except StopIteration:
print('Could not parse MetaData: ')
break
classifier_id = classifier_meta.unique_component_id
l_label = classifier_meta.label_info_list
label_info = pyds.glist_get_nvds_label_info(
l_label.data)
classifier_class = label_info.result_class_id
if classifier_id == 2:
result.id = class_color[classifier_class]
elif classifier_id == 3:
result.id = class_make[classifier_class]
else:
result.id = class_type[classifier_class]
result.score = label_info.result_prob
msg2.results.append(result)
l_classifier = l_classifier.next
self.publisher_classification.publish(msg2)
except StopIteration:
break
obj_counter[obj_meta.class_id] += 1
# Creating message for output of detection inference
result = ObjectHypothesisWithPose()
result.id = str(class_obj[obj_meta.class_id])
result.score = obj_meta.confidence
left = obj_meta.rect_params.left
top = obj_meta.rect_params.top
width = obj_meta.rect_params.width
height = obj_meta.rect_params.height
bounding_box = BoundingBox2D()
bounding_box.center.x = float(left + (width / 2))
bounding_box.center.y = float(top - (height / 2))
bounding_box.size_x = width
bounding_box.size_y = height
detection = Detection2D()
detection.results.append(result)
detection.bbox = bounding_box
msg.detections.append(detection)
# Periodically check for objects with borderline confidence value that may be false positive detections.
# If such detections are found, annotate the frame with bboxes and confidence value.
# Save the annotated frame to file.
if ((saved_count["stream_" + str(frame_meta.pad_index)] % 30
== 0) and (obj_meta.confidence > 0.3
and obj_meta.confidence < 0.31)):
if is_first_obj:
is_first_obj = False
# Getting Image data using nvbufsurface
# the input should be address of buffer and batch_id
n_frame = pyds.get_nvds_buf_surface(
hash(gst_buffer), frame_meta.batch_id)
#convert python array into numy array format.
frame_image = np.array(n_frame, copy=True, order='C')
#covert the array into cv2 default color format
frame_image = cv2.cvtColor(frame_image,
cv2.COLOR_RGBA2BGRA)
save_image = True
frame_image = draw_bounding_boxes(frame_image, obj_meta,
obj_meta.confidence)
try:
l_obj = l_obj.next
except StopIteration:
break
# Get frame rate through this probe
fps_streams["stream{0}".format(frame_meta.pad_index)].get_fps()
# Publishing message with output of detection inference
self.publisher_detection.publish(msg)
if save_image:
cv2.imwrite(
folder_name + "/stream_" + str(frame_meta.pad_index) +
"/frame_" + str(frame_number) + ".jpg", frame_image)
saved_count["stream_" + str(frame_meta.pad_index)] += 1
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
