def analyze_meta(frame_number, obj_meta):
print(' === obj_meta Primary Detection [%d]===' % (frame_number))
print('object ={%s}' % (PGIE_CLASS[obj_meta.class_id]))
print('object_id={}'.format(obj_meta.object_id))
print('object height={}'.format(obj_meta.rect_params.height))
print('object left={}'.format(obj_meta.rect_params.left))
print('object top={}'.format(obj_meta.rect_params.top))
print('object width={}'.format(obj_meta.rect_params.width))
if obj_meta.class_id == PGIE_CLASS_ID_VEHICLE: #Only vehicle supports secondary inference
print(' === obj_meta Secondary Detection ===')
cls_meta = obj_meta.classifier_meta_list
while cls_meta is not None:
cls = pyds.NvDsClassifierMeta.cast(cls_meta.data)
#unique_component_id : Primary Detector:1, Secondary_CarColor: 2, Secondary_CarMake : 3, Secondary_VehicleTypes:4
#print('Secondary element={}'.format(SECONDARY_ELEMENT[cls.unique_component_id]))
#print(' num_labels={}'.format(cls.num_labels))
info = cls.label_info_list # type of pyds.GList
while info is not None:
label_meta = pyds.glist_get_nvds_label_info(info.data)
if cls.unique_component_id == 2:
print('\tCarColor ={}'.format(
CAR_COLOR[label_meta.result_class_id]))
elif cls.unique_component_id == 3:
print('\tCarMake ={}'.format(
CAR_MAKE[label_meta.result_class_id]))
elif cls.unique_component_id == 4:
print('\tVehicleTypes ={}'.format(
VEHICLE_TYPES[label_meta.result_class_id]))
#print('label_meta label_id ={}'.format(label_meta.label_id))
print('\tlabel_meta result_prob ={}'.format(
label_meta.result_prob))
try:
info = info.next
except StopIteration:
break
try:
cls_meta = cls_meta.next
except StopIteration:
break
def analyze_meta(img, frame_number, obj_meta, save):
filename = '/home/spypiggy/src/test_images/result/%d_%d_' % (
frame_number, obj_meta.object_id)
print(' === obj_meta Primary Detection [%d]===' % (frame_number))
print('object ={%s}' % (PGIE_CLASS[obj_meta.class_id]))
print('object_id={}'.format(obj_meta.object_id))
print('object height={}'.format(obj_meta.rect_params.height))
print('object left={}'.format(obj_meta.rect_params.left))
print('object top={}'.format(obj_meta.rect_params.top))
print('object width={}'.format(obj_meta.rect_params.width))
filename += (PGIE_CLASS[obj_meta.class_id])
if obj_meta.class_id == PGIE_CLASS_ID_VEHICLE: #Only vehicle supports secondary inference
print(' === obj_meta Secondary Detection ===')
cls_meta = obj_meta.classifier_meta_list
filename_ex = filename
while cls_meta is not None:
cls = pyds.NvDsClassifierMeta.cast(cls_meta.data)
#unique_component_id : Primary Detector:1, Secondary_CarColor: 2, Secondary_CarMake : 3, Secondary_VehicleTypes:4
#print('Secondary element={}'.format(SECONDARY_ELEMENT[cls.unique_component_id]))
#print(' num_labels={}'.format(cls.num_labels))
info = cls.label_info_list # type of pyds.GList
while info is not None:
label_meta = pyds.glist_get_nvds_label_info(info.data)
if cls.unique_component_id == 2:
print('\tCarColor ={}'.format(
CAR_COLOR[label_meta.result_class_id]))
filename_ex += ('_' +
CAR_COLOR[label_meta.result_class_id])
elif cls.unique_component_id == 3:
print('\tCarMake ={}'.format(
CAR_MAKE[label_meta.result_class_id]))
filename_ex += ('_' + CAR_MAKE[label_meta.result_class_id])
elif cls.unique_component_id == 4:
print('\tVehicleTypes ={}'.format(
VEHICLE_TYPES[label_meta.result_class_id]))
filename_ex += ('_' +
VEHICLE_TYPES[label_meta.result_class_id])
#print('label_meta label_id ={}'.format(label_meta.label_id))
print('\tlabel_meta result_prob ={}'.format(
label_meta.result_prob))
filename_ex += ('_%4.2f' % (label_meta.result_prob))
try:
info = info.next
except StopIteration:
break
try:
cls_meta = cls_meta.next
except StopIteration:
break
filename_ex += '.jpg'
if save:
crop_img = img[
int(obj_meta.rect_params.top):int(obj_meta.rect_params.top) +
int(obj_meta.rect_params.height),
int(obj_meta.rect_params.left):int(obj_meta.rect_params.left) +
int(obj_meta.rect_params.width)]
cv2.imwrite(filename_ex, crop_img)
else: # save 'BICYCLE', 'PERSON', 'ROADSIGN'
filename_ex = filename + '.jpg'
if save:
crop_img = img[
int(obj_meta.rect_params.top):int(obj_meta.rect_params.top) +
int(obj_meta.rect_params.height),
int(obj_meta.rect_params.left):int(obj_meta.rect_params.left) +
int(obj_meta.rect_params.width)]
cv2.imwrite(filename_ex, crop_img)
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 osd_sink_pad_buffer_probe(pad, info, u_data):
frame_number = 0
#Intiallizing object counter with 0.
obj_counter = {
PGIE_CLASS_ID_VEHICLE: 0,
PGIE_CLASS_ID_PERSON: 0,
PGIE_CLASS_ID_BICYCLE: 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
is_first_obj = True
print("===")
print("frame_meta.frame_num={0}".format(frame_meta.frame_num))
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
print("---")
print("obj_meta.object_id={0}".format(obj_meta.object_id))
print("obj_meta.class_id={0}".format(obj_meta.class_id))
#get secondary classifier data
l_classifier = obj_meta.classifier_meta_list
if l_classifier is not None: # and class_id==XXX #apply classifier for a specific class
classifier_meta = pyds.glist_get_nvds_classifier_meta(
l_classifier.data)
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
print("sgie class={0}", classifier_class)
obj_counter[obj_meta.class_id] += 1
# Cv2 stuff
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)
#recognize license plate data
recognize_license_plate(frame_image, obj_meta, obj_meta.confidence)
try:
l_obj = l_obj.next
except StopIteration:
break
# 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={}".format(
frame_number, num_rects)
# 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
print(pyds.get_string(py_nvosd_text_params.display_text))
pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
fps_streams["stream{0}".format(frame_meta.pad_index)].get_fps()
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
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
def osd_sink_pad_buffer_probe(pad, info, u_data):
frame_number = 0
is_first_obj = True
# Intiallizing object counter with 0.
obj_counter = {
PGIE_CLASS_ID_VEHICLE: 0,
PGIE_CLASS_ID_PERSON: 0,
PGIE_CLASS_ID_BICYCLE: 0,
PGIE_CLASS_ID_ROADSIGN: 0
}
num_rects = 0
vehicles_types = []
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
center_list = []
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
## vehicle type detection ##
# Filter detections by PGIE1 network and don't include RoadSign class
if (obj_meta.unique_component_id == PGIE_UNIQUE_ID
and obj_meta.class_id !=
PGIE_CLASS_ID_ROADSIGN # Exclude RoadSign
and obj_meta.class_id !=
PGIE_CLASS_ID_BICYCLE # Exclude Bicycle
and
obj_meta.class_id != PGIE_CLASS_ID_PERSON # Exclude Person
):
#####################################
## vehicle type classification ##
#####################################
l_classifier = obj_meta.classifier_meta_list
sgie_class = -1
if l_classifier is not None: # and class_id==XXX #apply classifier for a specific class
classifier_meta = pyds.glist_get_nvds_classifier_meta(
l_classifier.data)
l_label = classifier_meta.label_info_list
label_info = pyds.glist_get_nvds_label_info(l_label.data)
sgie_class = label_info.result_class_id
rect_params = obj_meta.rect_params
w = int(rect_params.width)
h = int(rect_params.height)
center = (w // 2, h // 2)
center_list.append(center)
# vehicles_coords.append(center)
if frame_number > 0 and frame_number % DUMPINTERVAL == 0:
vehicles_types.append(SGIE_LABELS_DICT[sgie_class])
#####################################
## licence plate recognition stage ##
#####################################
# # Cv2 stuff
# 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)
# # recognize license plate data
# alrp_output = lpdetector.alpr_frame(
# frame_image, obj_meta, obj_meta.confidence, frame_number)
# print("alrp out >>> ", alrp_output)
obj_counter[obj_meta.class_id] += 1
#print("obj_meta: gie_id={0}; object_id={1}; class_id={2}; classifier_class={3}".format(obj_meta.unique_component_id,obj_meta.object_id,obj_meta.class_id,sgie_class))
try:
l_obj = l_obj.next
except StopIteration:
break
# 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])
vcoords.vec_coords.append(center_list)
if frame_number > 0 and frame_number % DUMPINTERVAL == 0:
# return degree estimate for every vehicle
# degrees = estimate_entery_exit_degrees(vehicles_coords)
py_nvosd_text_params.display_text = "Time Stamp={} Vehicle_types={} Vehicles_coords_across dump interval {} ".format(
datetime.datetime.now(), vehicles_types, vcoords.vec_coords)
# 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
# where data is dumped
if frame_number > 0 and frame_number % DUMPINTERVAL == 0:
print(pyds.get_string(py_nvosd_text_params.display_text))
vehicle_types = []
vcoords.vec_coords = []
center_list = []
pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK