def recognize_srv_call(self, roi_image):
"""
Method that calls the Recognize.srv
:param roi_image: Selected roi_image by the user
"""
try:
result = self._srv(
image=self.bridge.cv2_to_imgmsg(roi_image, "bgr8"))
except Exception as e:
warning_dialog("Service Exception", str(e))
return
print result
for r in result.recognitions:
text_array = []
best = CategoryProbability(
label="unknown",
probability=r.categorical_distribution.unknown_probability)
for p in r.categorical_distribution.probabilities:
text_array.append("%s: %.2f" % (p.label, p.probability))
if p.probability > best.probability:
best = p
self._image_widget.add_detection(r.roi.x_offset, r.roi.y_offset,
r.roi.width, r.roi.height,
best.label)
if text_array:
option_dialog(
"Classification results (Unknown probability=%.2f)" %
r.categorical_distribution.unknown_probability,
text_array) # Show all results in a dropdown
def image_roi_callback(self, roi_image):
"""
Callback triggered when the user has drawn an ROI on the image
:param roi_image: The opencv image in the ROI
"""
if not self.labels:
warning_dialog("No labels specified!", "Please first specify some labels using the 'Edit labels' button")
return
height, width = roi_image.shape[:2]
option = option_dialog("Label", self.labels)
if option:
self._image_widget.add_detection(0, 0, width, height, option)
self._stage_recognition(self._image_widget.get_roi(), option)
def image_roi_callback(self, roi_image):
if self._srv is None:
warning_dialog(
"No service specified!",
"Please first specify a service via the options button (top-right gear wheel)"
)
return
try:
result = self._srv(
image=self.bridge.cv2_to_imgmsg(roi_image, "bgr8"))
except Exception as e:
warning_dialog("Service Exception", str(e))
return
text_array = [
"%s: %.2f" % (r.label, r.probability) for r in result.recognitions
]
if text_array:
self._image_widget.set_text(
text_array[0]) # Show first option in the image
option_dialog("Classification results",
text_array) # Show all results in a dropdown
def classify_srv_call(self, roi_image):
"""
Method that calls the Classify2D.srv
:param roi_image: Selected roi_image by the user
"""
try:
images = []
image = self.bridge.cv2_to_imgmsg(roi_image, "bgr8")
images.append(image)
result = self._srv(images)
except Exception as e:
warning_dialog("Service Exception", str(e))
return
# we send one image, so we get max. one result
if len(result.classifications) == 0:
return
c = result.classifications[0]
text_array = []
best = ObjectHypothesis(id=0, score=0.0)
for r in c.results:
if len(self.id_label_list) >= r.id:
text_array.append("%s: %.2f" %
(self.id_label_list.get(str(r.id)), r.score))
else:
text_array.append("%s: %.2f" % ("no_label", r.score))
if r.score > best.score:
best = r
self._image_widget.add_detection(0, 0, 1, 1, str(best.id))
if text_array:
option_dialog("Classification results",
text_array) # Show all results in a dropdown
def image_roi_callback(self, roi_image):
if not self.labels:
warning_dialog(
"No labels specified!",
"Please first specify some labels using the 'Edit labels' button"
)
return
self.roi_image = roi_image
option = option_dialog("Label", self.labels)
if option:
self.label = option
self._image_widget.set_text(option)
self.store_image()
def image_roi_callback(self, roi_image):
"""
Callback from the image widget when the user has selected a ROI
:param roi_image: The opencv image of the ROI
"""
if not self.labels:
warning_dialog(
"No labels specified!",
"Please first specify some labels using the 'Edit labels' button"
)
return
height, width = roi_image.shape[:2]
option = option_dialog("Label", self.labels)
if option:
self.label = option
self._image_widget.add_detection(0, 0, width, height, option)
self.annotate(roi_image)