def pgie_src_pad_buffer_probe(pad, info, u_data):
frame_number = 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_user = frame_meta.frame_user_meta_list
print(l_user)
while l_user is not None:
try:
# Note that l_user.data needs a cast to pyds.NvDsUserMeta
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone.
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if (user_meta.base_meta.meta_type !=
pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
continue
tensor_meta = pyds.NvDsInferTensorMeta.cast(
user_meta.user_meta_data)
layers_info = []
layer = pyds.get_nvds_LayerInfo(tensor_meta, 0)
if layer.buffer:
ptr = ctypes.cast(pyds.get_ptr(layer.buffer),
ctypes.POINTER(ctypes.c_float))
v = np.ctypeslib.as_array(ptr, shape=(5, ))
prediction = int(np.argmax(np.array(v).squeeze(), axis=0))
classification = CLASSES[prediction]
classifications[frame_number] = classification
try:
l_user = l_user.next
except StopIteration:
break
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 sgie_sink_pad_buffer_probe(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
num_rects = frame_meta.num_obj_meta
l_obj = frame_meta.obj_meta_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
l_user = obj_meta.obj_user_meta_list
# if obj_meta.class_id == SGIE_CLASS_ID_FACE:
# print(f'obj_meta.obj_user_meta_list {l_user}')
while l_user is not None:
try:
# Casting l_user.data to pyds.NvDsUserMeta
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if (user_meta.base_meta.meta_type !=
pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
continue
# Converting to tensor metadata
# Casting user_meta.user_meta_data to NvDsInferTensorMeta
tensor_meta = pyds.NvDsInferTensorMeta.cast(
user_meta.user_meta_data)
# Get output layer as NvDsInferLayerInfo
layer = pyds.get_nvds_LayerInfo(tensor_meta, 0)
# Convert NvDsInferLayerInfo buffer to numpy array
ptr = ctypes.cast(pyds.get_ptr(layer.buffer),
ctypes.POINTER(ctypes.c_float))
v = np.ctypeslib.as_array(ptr, shape=(128, ))
# Pridict face neme
yhat = v.reshape((1, -1))
face_to_predict_embedding = normalize_vectors(yhat)
result = predict_using_classifier(faces_embeddings, labels,
face_to_predict_embedding)
result = (str(result).title())
# print('Predicted name: %s' % result)
# Generate classifer metadata and attach to obj_meta
# Get NvDsClassifierMeta object
classifier_meta = pyds.nvds_acquire_classifier_meta_from_pool(
batch_meta)
# Pobulate classifier_meta data with pridction result
classifier_meta.unique_component_id = tensor_meta.unique_id
label_info = pyds.nvds_acquire_label_info_meta_from_pool(
batch_meta)
label_info.result_prob = 0
label_info.result_class_id = 0
pyds.nvds_add_label_info_meta_to_classifier(
classifier_meta, label_info)
pyds.nvds_add_classifier_meta_to_object(
obj_meta, classifier_meta)
display_text = pyds.get_string(
obj_meta.text_params.display_text)
obj_meta.text_params.display_text = f'{display_text} {result}'
try:
l_user = l_user.next
except StopIteration:
break
try:
l_obj = l_obj.next
except StopIteration:
break
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_PERSON: 0,
PGIE_CLASS_ID_FACE: 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
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
obj_counter[obj_meta.class_id] += 1
l_user = obj_meta.obj_user_meta_list
# print(l_user)
while l_user is not None:
print('Inside l_user = obj_meta.obj_user_meta_list Loop')
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if (user_meta.base_meta.meta_type !=
pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
continue
tensor_meta = pyds.NvDsInferTensorMeta.cast(
user_meta.user_meta_data)
# Boxes in the tensor meta should be in network resolution which is
# found in tensor_meta.network_info. Use this info to scale boxes to
# the input frame resolution.
layers_info = []
for i in range(tensor_meta.num_output_layers):
layer = pyds.get_nvds_LayerInfo(tensor_meta, i)
layers_info.append(layer)
print(f'Layer: {i}, Layer name: {layer.layerName}')
try:
l_user = l_user.next
except StopIteration:
break
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={} Person_count={} Face_count={}".format(
frame_number, num_rects, obj_counter[PGIE_CLASS_ID_PERSON],
obj_counter[PGIE_CLASS_ID_FACE])
# 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)
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
def pgie_src_pad_buffer_probe(pad, info, u_data, label_path):
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
detection_params = DetectionParam(CLASS_NB, ACCURACY_ALL_CLASS)
box_size_param = BoxSizeParam(IMAGE_HEIGHT, IMAGE_WIDTH, MIN_BOX_WIDTH,
MIN_BOX_HEIGHT)
nms_param = NmsParam(TOP_K, IOU_THRESHOLD)
label_names = get_label_names_from_file(label_path)
while l_frame is not None:
try:
# Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
# 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
l_user = frame_meta.frame_user_meta_list
while l_user is not None:
try:
# Note that l_user.data needs a cast to pyds.NvDsUserMeta
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone.
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if (user_meta.base_meta.meta_type !=
pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
continue
tensor_meta = pyds.NvDsInferTensorMeta.cast(
user_meta.user_meta_data)
# Boxes in the tensor meta should be in network resolution which is
# found in tensor_meta.network_info. Use this info to scale boxes to
# the input frame resolution.
layers_info = []
for i in range(tensor_meta.num_output_layers):
layer = pyds.get_nvds_LayerInfo(tensor_meta, i)
layers_info.append(layer)
frame_object_list = nvds_infer_parse_custom_tf_ssd(
layers_info, detection_params, box_size_param, nms_param)
try:
l_user = l_user.next
except StopIteration:
break
for frame_object in frame_object_list:
add_obj_meta_to_frame(frame_object, batch_meta, frame_meta,
label_names)
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK