def _msg_cb(self, msg):
rects, count = self.non_max_suppression_handler(msg.rects, self.thre)
rects_msg = RectArray()
rects_msg.header = msg.header
rects_msg.rects = rects
self.pub_rects.publish(rects_msg)
count_msg = Int64()
count_msg.data = count
self.pub_count.publish(count_msg)
def _apply(self, img_msg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(img_msg, desired_encoding='bgr8')
rects = []
dlib.find_candidate_object_locations(img, rects, min_size=self.min_size)
ros_rect_array = RectArray()
ros_rect_array.header = img_msg.header
for d in rects:
if (d.right() - d.left()) * (d.bottom() - d.top()) > self.max_size:
continue
cv2.rectangle(img, (d.left(), d.top()), (d.right(), d.bottom()), (255, 0, 0), 3)
ros_rect_array.rects.append(Rect(x=d.left(), y=d.top(), width=d.right() - d.left(), height=d.bottom() - d.top()))
imgmsg = bridge.cv2_to_imgmsg(img, encoding='bgr8')
self.debug_pub_.publish(imgmsg)
self.pub_.publish(ros_rect_array)
def image_cb(self, msg):
img = self.bridge.imgmsg_to_cv2(msg, desired_encoding='rgb8')
# RGB -> BGR
rospy.logdebug('start readtext')
results = self.reader.readtext(img[:, :, ::-1])
rospy.logdebug('end readtext')
bboxes = []
scores = []
texts = []
rect_msg = RectArray(header=msg.header)
for result in results:
bb = result[0]
text = result[1]
score = result[2]
x_min = int(np.round(bb[0][0]))
y_min = int(np.round(bb[0][1]))
x_max = int(np.round(bb[2][0]))
y_max = int(np.round(bb[2][1]))
bboxes.append([y_min, x_min, y_max, x_max])
texts.append(text)
scores.append(score)
rect = Rect(
x=x_min, y=y_min,
width=x_max - x_min, height=y_max - y_min)
rect_msg.rects.append(rect)
bboxes = np.array(bboxes)
scores = np.array(scores)
cls_msg = ClassificationResult(
header=msg.header,
classifier=self.classifier_name,
target_names=texts,
labels=np.arange(len(texts)),
label_names=texts,
label_proba=scores)
self.pub_rects.publish(rect_msg)
self.pub_class.publish(cls_msg)
if self.visualize:
fig = plt.figure(
tight_layout={'pad': 0})
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.axis('off')
fig.add_axes(ax)
vis_bbox(
img.transpose((2, 0, 1)),
bboxes, np.arange(len(texts)), scores,
label_names=texts, ax=ax)
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
vis_img = np.fromstring(
fig.canvas.tostring_rgb(), dtype=np.uint8)
vis_img.shape = (h, w, 3)
fig.clf()
plt.close()
vis_msg = self.bridge.cv2_to_imgmsg(vis_img, 'rgb8')
vis_msg.header = msg.header
self.pub_image.publish(vis_msg)
def image_cb(self, msg):
img = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
H, W = img.shape[:2]
objs = self.engine.DetectWithImage(PIL.Image.fromarray(img),
threshold=self.score_thresh,
keep_aspect_ratio=True,
relative_coord=True,
top_k=self.top_k)
bboxes = []
scores = []
labels = []
rect_msg = RectArray(header=msg.header)
for obj in objs:
x_min, y_min, x_max, y_max = obj.bounding_box.flatten().tolist()
x_min = int(np.round(x_min * W))
y_min = int(np.round(y_min * H))
x_max = int(np.round(x_max * W))
y_max = int(np.round(y_max * H))
bboxes.append([y_min, x_min, y_max, x_max])
scores.append(obj.score)
labels.append(self.label_ids.index(int(obj.label_id)))
rect = Rect(x=x_min,
y=y_min,
width=x_max - x_min,
height=y_max - y_min)
rect_msg.rects.append(rect)
bboxes = np.array(bboxes)
scores = np.array(scores)
labels = np.array(labels)
cls_msg = ClassificationResult(
header=msg.header,
classifier=self.classifier_name,
target_names=self.label_names,
labels=labels,
label_names=[self.label_names[l] for l in labels],
label_proba=scores)
self.pub_rects.publish(rect_msg)
self.pub_class.publish(cls_msg)
if self.visualize:
vis_img = img[:, :, ::-1].transpose(2, 0, 1)
vis_bbox(vis_img,
bboxes,
labels,
scores,
label_names=self.label_names)
fig = plt.gcf()
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
vis_img = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
fig.clf()
vis_img.shape = (h, w, 3)
plt.close()
vis_msg = self.bridge.cv2_to_imgmsg(vis_img, 'rgb8')
vis_msg.header = msg.header
self.pub_image.publish(vis_msg)
def _apply(self, img_msg):
bridge = cv_bridge.CvBridge()
img_bgr = bridge.imgmsg_to_cv2(img_msg, desired_encoding='bgr8')
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)
rects = []
dlib.find_candidate_object_locations(img_rgb, rects,
min_size=self.min_size)
ros_rect_array = RectArray()
ros_rect_array.header = img_msg.header
for d in rects:
if (d.right() - d.left()) * (d.bottom() - d.top()) > self.max_size:
continue
cv2.rectangle(img_bgr, (d.left(), d.top()), (d.right(), d.bottom()), (255, 0, 0), 3)
ros_rect_array.rects.append(Rect(x=d.left(), y=d.top(), width=d.right() - d.left(), height=d.bottom() - d.top()))
imgmsg = bridge.cv2_to_imgmsg(img_bgr, encoding='bgr8')
self.debug_pub_.publish(imgmsg)
self.pub_.publish(ros_rect_array)
def _detect(self, imgmsg, rects_msg):
bridge = cv_bridge.CvBridge()
im_orig = bridge.imgmsg_to_cv2(imgmsg, desired_encoding='bgr8')
im, im_scale = img_preprocessing(im_orig, self.pixel_means)
rects_orig = jsk_recognition_utils.rects_msg_to_ndarray(rects_msg)
if len(rects_orig) == 0:
return
rects = rects_orig * im_scale
scores, bbox_pred = self._im_detect(im, rects)
rects = RectArray(header=imgmsg.header)
labels = []
label_proba = []
for cls_id in range(1, len(self.target_names)):
_cls = scores[:, cls_id][:, np.newaxis]
_bbx = bbox_pred[:, cls_id * 4:(cls_id + 1) * 4]
dets = np.hstack((_bbx, _cls))
keep = nms(dets, self.nms_thresh)
dets = dets[keep, :]
orig_rects = cuda.cupy.asnumpy(rects_orig)[keep, :]
inds = np.where(dets[:, -1] >= self.conf_thresh)[0]
for i in inds:
_bbox = dets[i, :4]
x1, y1, x2, y2 = orig_rects[i]
width = x2 - x1
height = y2 - y1
center_x = x1 + 0.5 * width
center_y = y1 + 0.5 * height
dx, dy, dw, dh = _bbox
_center_x = dx * width + center_x
_center_y = dy * height + center_y
_width = np.exp(dw) * width
_height = np.exp(dh) * height
x1 = _center_x - 0.5 * _width
y1 = _center_y - 0.5 * _height
x2 = _center_x + 0.5 * _width
y2 = _center_y + 0.5 * _height
rect = Rect(x=x1, y=y1, width=x2 - x1, height=y2 - y1)
rects.rects.append(rect)
labels.append(cls_id)
label_proba.append(dets[:, -1][i])
# publish classification result
clss = ClassificationResult(
header=imgmsg.header,
classifier=self.classifier_name,
target_names=self.target_names,
labels=labels,
label_names=[self.target_names[l] for l in labels],
label_proba=label_proba,
)
self._pub_rects.publish(rects)
self._pub_class.publish(clss)
def image_cb(self, msg):
if not hasattr(self, 'engine'):
return
if self.transport_hint == 'compressed':
np_arr = np.fromstring(msg.data, np.uint8)
img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
img = img[:, :, ::-1]
else:
img = self.bridge.imgmsg_to_cv2(msg, desired_encoding='rgb8')
points, key_names, visibles, bboxes, labels, scores \
= self._estimate(img)
poses_msg = PeoplePoseArray(header=msg.header)
rects_msg = RectArray(header=msg.header)
for point, key_name, visible, bbox, label, score in zip(
points, key_names, visibles, bboxes, labels, scores):
pose_msg = PeoplePose()
for pt, key_nm, vs, sc in zip(point, key_name, visible, score):
if vs:
key_y, key_x = pt
pose_msg.limb_names.append(key_nm)
pose_msg.scores.append(sc)
pose_msg.poses.append(
Pose(position=Point(x=key_x, y=key_y)))
poses_msg.poses.append(pose_msg)
y_min, x_min, y_max, x_max = bbox
rect = Rect(x=x_min,
y=y_min,
width=x_max - x_min,
height=y_max - y_min)
rects_msg.rects.append(rect)
cls_msg = ClassificationResult(
header=msg.header,
classifier=self.classifier_name,
target_names=self.label_names,
labels=labels,
label_names=[self.label_names[lbl] for lbl in labels],
label_proba=[np.average(score) for score in scores])
self.pub_pose.publish(poses_msg)
self.pub_rects.publish(rects_msg)
self.pub_class.publish(cls_msg)
if self.enable_visualization:
with self.lock:
self.img = img
self.header = msg.header
self.points = points
self.visibles = visibles
def _draw_polygon(self, imgmsg, polygon_msg):
rect_msg = Rect()
if len(polygon_msg.polygon.points) == 2:
x1 = polygon_msg.polygon.points[0].x
y1 = polygon_msg.polygon.points[0].y
x2 = polygon_msg.polygon.points[1].x
y2 = polygon_msg.polygon.points[1].y
rect_msg.x = int(x1)
rect_msg.y = int(y1)
rect_msg.width = int(x2 - x1)
rect_msg.height = int(y2 - y1)
rects = [rect_msg]
rects_msg = RectArray(header=polygon_msg.header, rects=rects)
self._draw(imgmsg, rects_msg)
def image_cb(self, msg):
if not hasattr(self, 'engine'):
return
if self.transport_hint == 'compressed':
np_arr = np.fromstring(msg.data, np.uint8)
img = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
img = img[:, :, ::-1]
else:
img = self.bridge.imgmsg_to_cv2(msg, desired_encoding='rgb8')
bboxes, labels, scores = self._detect(img)
rect_msg = RectArray(header=msg.header)
for bbox in bboxes:
y_min, x_min, y_max, x_max = bbox
rect = Rect(x=x_min,
y=y_min,
width=x_max - x_min,
height=y_max - y_min)
rect_msg.rects.append(rect)
cls_msg = ClassificationResult(
header=msg.header,
classifier=self.classifier_name,
target_names=self.label_names,
labels=labels,
label_names=[self.label_names[lbl] for lbl in labels],
label_proba=scores)
self.pub_rects.publish(rect_msg)
self.pub_class.publish(cls_msg)
if self.enable_visualization:
with self.lock:
self.img = img
self.header = msg.header
self.bboxes = bboxes
self.labels = labels
self.scores = scores
def callback(self, imgmsg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(imgmsg, desired_encoding='rgb8')
img_chw = img.transpose(2, 0, 1) # C, H, W
bboxes, masks, labels, scores = self.model.predict([img_chw])
bboxes = bboxes[0]
masks = masks[0]
labels = labels[0]
scores = scores[0]
msg_indices = ClusterPointIndices(header=imgmsg.header)
msg_labels = LabelArray(header=imgmsg.header)
# -1: label for background
lbl_cls = -np.ones(img.shape[:2], dtype=np.int32)
lbl_ins = -np.ones(img.shape[:2], dtype=np.int32)
for ins_id, (mask, label) in enumerate(zip(masks, labels)):
indices = np.where(mask.flatten())[0]
indices_msg = PointIndices(header=imgmsg.header, indices=indices)
msg_indices.cluster_indices.append(indices_msg)
class_name = self.fg_class_names[label]
msg_labels.labels.append(Label(id=label, name=class_name))
lbl_cls[mask] = label
lbl_ins[mask] = ins_id # instance_id
self.pub_indices.publish(msg_indices)
self.pub_labels.publish(msg_labels)
msg_lbl_cls = bridge.cv2_to_imgmsg(lbl_cls)
msg_lbl_ins = bridge.cv2_to_imgmsg(lbl_ins)
msg_lbl_cls.header = msg_lbl_ins.header = imgmsg.header
self.pub_lbl_cls.publish(msg_lbl_cls)
self.pub_lbl_ins.publish(msg_lbl_ins)
cls_msg = ClassificationResult(
header=imgmsg.header,
classifier=self.classifier_name,
target_names=self.fg_class_names,
labels=labels,
label_names=[self.fg_class_names[l] for l in labels],
label_proba=scores,
)
rects_msg = RectArray(header=imgmsg.header)
for bbox in bboxes:
rect = Rect(x=bbox[1],
y=bbox[0],
width=bbox[3] - bbox[1],
height=bbox[2] - bbox[0])
rects_msg.rects.append(rect)
self.pub_rects.publish(rects_msg)
self.pub_class.publish(cls_msg)
if self.pub_viz.get_num_connections() > 0:
n_fg_class = len(self.fg_class_names)
captions = [
'{:d}: {:s}'.format(l, self.fg_class_names[l]) for l in labels
]
viz = chainer_mask_rcnn.utils.draw_instance_bboxes(
img,
bboxes,
labels + 1,
n_class=n_fg_class + 1,
masks=masks,
captions=captions)
msg_viz = bridge.cv2_to_imgmsg(viz, encoding='rgb8')
msg_viz.header = imgmsg.header
self.pub_viz.publish(msg_viz)
def publish_face_rects(self, header, results):
rects = RectArray(
header=header,
rects=[r["rect"] for r in results],
)
self.pub_rect.publish(rects)
def image_cb(self, msg):
img = self.bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
H, W = img.shape[:2]
objs = self.engine.DetectWithImage(
PIL.Image.fromarray(img), threshold=self.score_thresh,
keep_aspect_ratio=True, relative_coord=True,
top_k=self.top_k)
bboxes = []
scores = []
labels = []
rect_msg = RectArray(header=msg.header)
for obj in objs:
x_min, y_min, x_max, y_max = obj.bounding_box.flatten().tolist()
x_min = int(np.round(x_min * W))
y_min = int(np.round(y_min * H))
x_max = int(np.round(x_max * W))
y_max = int(np.round(y_max * H))
bboxes.append([y_min, x_min, y_max, x_max])
scores.append(obj.score)
labels.append(self.label_ids.index(int(obj.label_id)))
rect = Rect(
x=x_min, y=y_min,
width=x_max-x_min, height=y_max-y_min)
rect_msg.rects.append(rect)
bboxes = np.array(bboxes)
scores = np.array(scores)
labels = np.array(labels)
cls_msg = ClassificationResult(
header=msg.header,
classifier=self.classifier_name,
target_names=self.label_names,
labels=labels,
label_names=[self.label_names[l] for l in labels],
label_proba=scores)
self.pub_rects.publish(rect_msg)
self.pub_class.publish(cls_msg)
if self.visualize:
fig = plt.figure(
tight_layout={'pad': 0})
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.axis('off')
fig.add_axes(ax)
vis_bbox(
img[:, :, ::-1].transpose((2, 0, 1)),
bboxes, labels, scores,
label_names=self.label_names, ax=ax)
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
vis_img = np.fromstring(
fig.canvas.tostring_rgb(), dtype=np.uint8)
vis_img.shape = (h, w, 3)
fig.clf()
plt.close()
vis_msg = self.bridge.cv2_to_imgmsg(vis_img, 'rgb8')
# BUG: https://answers.ros.org/question/316362/sensor_msgsimage-generates-float-instead-of-int-with-python3/ # NOQA
vis_msg.step = int(vis_msg.step)
vis_msg.header = msg.header
self.pub_image.publish(vis_msg)
def image_cb(self, msg):
if self.profiling:
rospy.loginfo(
"callback start: incomming msg is %s msec behind" %
((rospy.Time.now() - msg.header.stamp).to_sec() * 1000.0))
tprev = time.time()
try:
# transform image to RGB, float, CHW
img = self.cv_bridge.imgmsg_to_cv2(msg, desired_encoding="rgb8")
img = np.asarray(img, dtype=np.float32)
img = img.transpose(2, 0, 1) # (H, W, C) -> (C, H, W)
except Exception as e:
rospy.logerr("Failed to convert image: %s" % str(e))
return
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("convert",
(tcur - tprev) * 1000))
tprev = tcur
bboxes, labels, scores = self.model.predict([img])
bboxes, labels, scores = bboxes[0], labels[0], scores[0]
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("predict",
(tcur - tprev) * 1000))
tprev = tcur
rect_msg = RectArray(header=msg.header)
for bbox in bboxes:
rect = Rect(x=bbox[1],
y=bbox[0],
width=bbox[3] - bbox[1],
height=bbox[2] - bbox[0])
rect_msg.rects.append(rect)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("make rect msg",
(tcur - tprev) * 1000))
tprev = tcur
cls_msg = ClassificationResult(
header=msg.header,
classifier=self.classifier_name,
target_names=self.label_names,
labels=labels,
label_names=[self.label_names[l] for l in labels],
label_proba=scores,
)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("make cls msg",
(tcur - tprev) * 1000))
tprev = tcur
self.pub_rects.publish(rect_msg)
self.pub_class.publish(cls_msg)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("publish msg",
(tcur - tprev) * 1000))
tprev = tcur
if self.visualize:
self.publish_bbox_image(img, bboxes, labels, scores)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("callback end",
(tcur - tprev) * 1000))
tprev = tcur
def image_cb(self, msg):
if self.profiling:
rospy.loginfo(
"callback start: incomming msg is %s msec behind" %
((rospy.Time.now() - msg.header.stamp).to_sec() * 1000.0))
tprev = time.time()
try:
# transform image to RGB, float, CHW
img = self.cv_bridge.imgmsg_to_cv2(msg, desired_encoding="rgb8")
img = np.asarray(img, dtype=np.float32)
img = img.transpose(2, 0, 1) # (H, W, C) -> (C, H, W)
except Exception as e:
rospy.logerr("Failed to convert image: %s" % str(e))
return
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("convert",
(tcur - tprev) * 1000))
tprev = tcur
if self.gpu >= 0:
chainer.cuda.get_device_from_id(self.gpu).use()
bboxes, labels, scores = self.model.predict([img])
bboxes, labels, scores = bboxes[0], labels[0], scores[0]
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("predict",
(tcur - tprev) * 1000))
tprev = tcur
labels_msg = LabelArray(header=msg.header)
for l in labels:
l_name = self.label_names[l]
labels_msg.labels.append(Label(id=l, name=l_name))
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("make labels msg",
(tcur - tprev) * 1000))
cluster_indices_msg = ClusterPointIndices(header=msg.header)
H = img.shape[1]
W = img.shape[2]
for bbox in bboxes:
ymin = max(0, int(np.floor(bbox[0])))
xmin = max(0, int(np.floor(bbox[1])))
ymax = min(H, int(np.ceil(bbox[2])))
xmax = min(W, int(np.ceil(bbox[3])))
indices = [
range(W * y + xmin, W * y + xmax) for y in range(ymin, ymax)
]
indices = np.array(indices, dtype=np.int32).flatten()
indices_msg = PointIndices(header=msg.header, indices=indices)
cluster_indices_msg.cluster_indices.append(indices_msg)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("make cluster_indices msg",
(tcur - tprev) * 1000))
tprev = tcur
rect_msg = RectArray(header=msg.header)
for bbox in bboxes:
rect = Rect(x=bbox[1],
y=bbox[0],
width=bbox[3] - bbox[1],
height=bbox[2] - bbox[0])
rect_msg.rects.append(rect)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("make rect msg",
(tcur - tprev) * 1000))
tprev = tcur
cls_msg = ClassificationResult(
header=msg.header,
classifier=self.classifier_name,
target_names=self.label_names,
labels=labels,
label_names=[self.label_names[l] for l in labels],
label_proba=scores,
)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("make cls msg",
(tcur - tprev) * 1000))
tprev = tcur
self.pub_labels.publish(labels_msg)
self.pub_indices.publish(cluster_indices_msg)
self.pub_rects.publish(rect_msg)
self.pub_class.publish(cls_msg)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("publish msg",
(tcur - tprev) * 1000))
tprev = tcur
if self.visualize:
self.publish_bbox_image(img, bboxes, labels, scores, msg.header)
if self.profiling:
tcur = time.time()
rospy.loginfo("%s: elapsed %f msec" % ("callback end",
(tcur - tprev) * 1000))
tprev = tcur
def callback(self, imgmsg):
raw_image = None
self.image_width = imgmsg.width
self.image_height = imgmsg.height
try:
raw_image = self.bridge.imgmsg_to_cv2(imgmsg,
desired_encoding='bgr8')
except:
rospy.logerr('failed transform image')
return
dataset = ImageDataset(pkg_dir,
raw_image,
self.templates_dir,
thresh=self.thresh,
image_name=str(imgmsg.header.stamp))
scores, w_array, h_array, label_list = calculate_scores(
self.model, dataset)
boxes, indices = nms(scores, w_array, h_array, dataset.thresh,
label_list)
output_image = plot_results(dataset.image_raw,
boxes,
label_list,
indices,
show=False,
save_name=None)
labels_msg = LabelArray()
rects_msg = RectArray()
original_size_rects_msg = RectArray()
for i in range(len(indices)):
rect_msg = Rect()
label_msg = Label()
original_size_rect_msg = Rect()
box = boxes[i][None, :, :][0]
x, y, width, height = self.check_out_of_image(box)
rect_msg.x = x
rect_msg.y = y
rect_msg.width = width
rect_msg.height = height
original_size_rect_msg.x = x * 1 / self.resize_scale
original_size_rect_msg.y = y * 1 / self.resize_scale
original_size_rect_msg.width = width * 1 / self.resize_scale
original_size_rect_msg.height = height * 1 / self.resize_scale
# rect_msg.x = box[0][0]
# rect_msg.y = box[0][1]
# rect_msg.width = box[1][0] - box[0][0]
# rect_msg.height = box[1][1] - box[0][1]
label_msg.name = label_list[indices[i]]
rects_msg.rects.append(rect_msg)
labels_msg.labels.append(label_msg)
original_size_rects_msg.rects.append(original_size_rect_msg)
rects_msg.header = imgmsg.header
labels_msg.header = imgmsg.header
original_size_rects_msg.header = imgmsg.header
self.labels_pub.publish(labels_msg)
self.rects_pub.publish(rects_msg)
self.original_size_rects_pub.publish(original_size_rects_msg)
msg_viz = cv_bridge.CvBridge().cv2_to_imgmsg(output_image,
encoding='bgr8')
msg_viz.header = imgmsg.header
self.pub_viz.publish(msg_viz)
def process_image(self, image, debug=False):
# resize
orgHeight, orgWidth = image.shape[:2]
image = cv2.resize(
image, (orgWidth / self.reduction, orgHeight / self.reduction))
# hsv filter
if (self.h_min < self.h_max):
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower_red = np.array([self.h_min, self.s_min, self.v_min])
upper_red = np.array([self.h_max, self.s_max, self.v_max])
mask = cv2.inRange(hsv, lower_red, upper_red)
else:
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower_red1 = np.array([self.h_min, self.s_min, self.v_min])
upper_red1 = np.array([180, self.s_max, self.v_max])
mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
lower_red2 = np.array([0, self.s_min, self.v_min])
upper_red2 = np.array([self.h_max, self.s_max, self.v_max])
mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
mask = mask1 + mask2
if debug:
display = cv2.bitwise_and(image, image, mask=mask)
cv2.imshow("hsv filter", display)
# morphology processing
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (13, 13))
mask = cv2.dilate(mask, kernel, iterations=1)
mask = cv2.erode(mask, kernel, iterations=1)
if debug:
display = cv2.bitwise_and(image, image, mask=mask)
cv2.imshow("morphology processing", display)
# make contour
_, contours, _ = cv2.findContours(mask, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
if debug:
display = np.zeros(mask.shape, dtype=np.uint8)
for c in contours:
for elem in c:
display[elem[0, 1], elem[0, 0]] = 255
cv2.imshow("make contours", display)
# make region
rects = []
for contour in contours:
approx = cv2.convexHull(contour)
rect = cv2.boundingRect(approx)
rects.append(rect)
rects_msg = RectArray()
for rect in rects:
cv2.rectangle(image, (rect[0], rect[1]),
(rect[0] + rect[2], rect[1] + rect[3]), (0, 255, 0),
thickness=1)
rect_msg = Rect()
rect_msg.x = rect[0] * self.reduction
rect_msg.y = rect[1] * self.reduction
rect_msg.width = rect[2] * self.reduction
rect_msg.height = rect[3] * self.reduction
rects_msg.rects.append(rect_msg)
if debug:
cv2.imshow("make region", image)
if debug:
cv2.waitKey(1)
return rects_msg
