test-app.zip (61.9 KB)
Attatched is a modified version of deepstream-imagedata-multistream with analytics and a tracker.
def process_tracker_meta_data(batch_meta):
user_meta_list = batch_meta.batch_user_meta_list
# print('======================================================================================')
batch_tracker_decoded = {}
while user_meta_list is not None:
user_meta = pyds.NvDsUserMeta.cast(user_meta_list.data)
if user_meta.base_meta.meta_type != pyds.NvDsMetaType.NVDS_TRACKER_PAST_FRAME_META:
continue
past_frame_object_batch = pyds_tracker_meta.NvDsPastFrameObjBatch_cast(user_meta.user_meta_data)
for past_frame_object_stream in pyds_tracker_meta.NvDsPastFrameObjBatch_list(past_frame_object_batch):
streamId = past_frame_object_stream.streamID
# print(' past_frame_object_stream:', past_frame_object_stream)
# print(' streamID:', past_frame_object_stream.streamID)
# print(' surfaceStreamID:', past_frame_object_stream.surfaceStreamID)
tracked_vehicles = []
tracked_people = []
for past_frame_object_list in pyds_tracker_meta.NvDsPastFrameObjStream_list(past_frame_object_stream):
# print(' past_frame_object_list:', past_frame_object_list)
# print(' numObj:', past_frame_object_list.numObj)
# print(' uniqueId:', past_frame_object_list.uniqueId)
# print(' classId:', past_frame_object_list.classId)
# print(' objLabel:', past_frame_object_list.objLabel)
oldest_age = 0
classId = past_frame_object_list.classId
uniqueId = past_frame_object_list.uniqueId
bbox = []
conf = 0.0
counter = 0
for past_frame_object in pyds_tracker_meta.NvDsPastFrameObjList_list(past_frame_object_list):
counter += 1
# print(' past_frame_object:', past_frame_object)
# print(' frameNum:', past_frame_object.frameNum)
# print(' tBbox.left:', past_frame_object.tBbox.left)
# print(' tBbox.width:', past_frame_object.tBbox.width)
# print(' tBbox.top:', past_frame_object.tBbox.top)
# print(' tBbox.right:', past_frame_object.tBbox.height)
# print(' confidence:', past_frame_object.confidence)
# print(' age:', past_frame_object.age)
print('Unique ID',past_frame_object_list.uniqueId,' past frames: ',counter)
try:
user_meta_list = user_meta_list.next
except StopIteration:
break
def process_nvdsanalytics_meta_data(batch_meta):
# Iterate over list of FrameMeta
l_frame = batch_meta.frame_meta_list
# print('======================================================')
while l_frame is not None:
try:
# Casting l_frame.data to ipyds.NvDsFrameMeta
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
l_user = frame_meta.frame_user_meta_list
while l_user is not None:
try:
# Cast to NvDsUserMeta and check it either NvDsAnalyticsFrameMeta or not
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
if user_meta.base_meta.meta_type != pyds.nvds_get_user_meta_type(
"NVIDIA.DSANALYTICSFRAME.USER_META"):
continue
user_meta_analytics = pyds_analytics_meta.NvDsAnalyticsFrameMeta.cast(user_meta.user_meta_data)
except Exception as ex:
print('Exception', ex)
try:
l_user = l_user.next
except StopIteration:
break
except StopIteration:
break
l_obj = frame_meta.obj_meta_list
while l_obj is not None:
try:
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_number = frame_meta.frame_num
num_rects = frame_meta.num_obj_meta
remove_arr = []
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
user_meta_list = obj_meta.obj_user_meta_list
remove = False
while user_meta_list is not None:
try:
user_meta = pyds.NvDsUserMeta.cast(user_meta_list.data)
display_meta = pyds.NvDsUserMeta.cast(user_meta_list.data)
rect_params = obj_meta.rect_params # NvOSD_RectParams *
text_params = obj_meta.text_params # NvOSD_TextParams *
unique_id = obj_meta.object_id
# print('ID: ',unique_id,' text_params', pyds.get_string(text_params.display_text))
user_meta_data = user_meta.user_meta_data
if user_meta.base_meta.meta_type != pyds.nvds_get_user_meta_type(
"NVIDIA.DSANALYTICSOBJ.USER_META"):
continue
user_meta_analytics = pyds_analytics_meta.NvDsAnalyticsObjInfo.cast(
user_meta.user_meta_data)
# print('unique_id:', user_meta_analytics.unique_id)
# print('lcStatus:', user_meta_analytics.lcStatus)
# print('dirStatus:', user_meta_analytics.dirStatus)
# print('ocStatus:', user_meta_analytics.ocStatus)
# print('roiStatus:', user_meta_analytics.roiStatus)
if 'RF' in user_meta_analytics.roiStatus:
remove = True
remove_arr.append(obj_meta)
# print('Remove')
# if remove:
# pyds.nvds_remove_obj_meta_from_frame(frame_meta, obj_meta)
except StopIteration:
break
try:
user_meta_list = user_meta_list.next
except StopIteration:
break
except StopIteration:
break
try:
l_obj = l_obj.next
for obj in remove_arr:
pyds.nvds_remove_obj_meta_from_frame(frame_meta, obj)
# print('REMOVE')
except StopIteration:
break
# Get next FrameMeta in list
try:
l_frame = l_frame.next
except StopIteration:
break
# if batch_meta_decoded[0]["current"]["people_count"]:
# print('batch_meta_decoded', batch_meta_decoded)
def nvdsanalytics_src_pad_buffer_probe(pad, info, u_data):
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
process_nvdsanalytics_meta_data(batch_meta)
process_tracker_meta_data(batch_meta)
# self.parse_nvdsanalytics_meta_data3(batch_meta)
return Gst.PadProbeReturn.OK
# tiler_sink_pad_buffer_probe will extract metadata received on tiler src pad
# and update params for drawing rectangle, object information etc.
def tiler_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
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_PERSON:0,
PGIE_CLASS_ID_BICYCLE:0,
PGIE_CLASS_ID_ROADSIGN:0
}
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
# Periodically check for objects with borderline confidence value that may be false positive detections.
# If such detections are found, annoate 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
# print("Frame Number=", frame_number, "Number of Objects=",num_rects,"Vehicle_count=",obj_counter[PGIE_CLASS_ID_VEHICLE],"Person_count=",obj_counter[PGIE_CLASS_ID_PERSON])
# Get frame rate through this probe
fps_streams["stream{0}".format(frame_meta.pad_index)].get_fps()
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 draw_bounding_boxes(image,obj_meta,confidence):
confidence='{0:.2f}'.format(confidence)
rect_params=obj_meta.rect_params
top=int(rect_params.top)
left=int(rect_params.left)
width=int(rect_params.width)
height=int(rect_params.height)
obj_name=pgie_classes_str[obj_meta.class_id]
image=cv2.rectangle(image,(left,top),(left+width,top+height),(0,0,255,0),2)
# Note that on some systems cv2.putText erroneously draws horizontal lines across the image
image=cv2.putText(image,obj_name+',C='+str(confidence),(left-10,top-10),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,255,0),2)
return image
def cb_newpad(decodebin, decoder_src_pad,data):
print("In cb_newpad\n")
caps=decoder_src_pad.get_current_caps()
gststruct=caps.get_structure(0)
gstname=gststruct.get_name()
source_bin=data
features=caps.get_features(0)
# Need to check if the pad created by the decodebin is for video and not
# audio.
if(gstname.find("video")!=-1):
# Link the decodebin pad only if decodebin has picked nvidia
# decoder plugin nvdec_*. We do this by checking if the pad caps contain
# NVMM memory features.
if features.contains("memory:NVMM"):
# Get the source bin ghost pad
bin_ghost_pad=source_bin.get_static_pad("src")
if not bin_ghost_pad.set_target(decoder_src_pad):
sys.stderr.write("Failed to link decoder src pad to source bin ghost pad\n")
else:
sys.stderr.write(" Error: Decodebin did not pick nvidia decoder plugin.\n")
def decodebin_child_added(child_proxy,Object,name,user_data):
print("Decodebin child added:", name, "\n")
if(name.find("decodebin") != -1):
Object.connect("child-added",decodebin_child_added,user_data)
if(is_aarch64() and name.find("nvv4l2decoder") != -1):
print("Seting bufapi_version\n")
Object.set_property("bufapi-version",True)
def create_source_bin(index,uri):
print("Creating source bin")
# Create a source GstBin to abstract this bin's content from the rest of the
# pipeline
bin_name="source-bin-%02d" %index
print(bin_name)
nbin=Gst.Bin.new(bin_name)
if not nbin:
sys.stderr.write(" Unable to create source bin \n")
# Source element for reading from the uri.
# We will use decodebin and let it figure out the container format of the
# stream and the codec and plug the appropriate demux and decode plugins.
uri_decode_bin=Gst.ElementFactory.make("uridecodebin", "uri-decode-bin")
if not uri_decode_bin:
sys.stderr.write(" Unable to create uri decode bin \n")
# We set the input uri to the source element
uri_decode_bin.set_property("uri",uri)
# Connect to the "pad-added" signal of the decodebin which generates a
# callback once a new pad for raw data has beed created by the decodebin
uri_decode_bin.connect("pad-added",cb_newpad,nbin)
uri_decode_bin.connect("child-added",decodebin_child_added,nbin)
# We need to create a ghost pad for the source bin which will act as a proxy
# for the video decoder src pad. The ghost pad will not have a target right
# now. Once the decode bin creates the video decoder and generates the
# cb_newpad callback, we will set the ghost pad target to the video decoder
# src pad.
Gst.Bin.add(nbin,uri_decode_bin)
bin_pad=nbin.add_pad(Gst.GhostPad.new_no_target("src",Gst.PadDirection.SRC))
if not bin_pad:
sys.stderr.write(" Failed to add ghost pad in source bin \n")
return None
return nbin
def main(args):
# Check input arguments
if len(args) < 2:
sys.stderr.write("usage: %s <uri1> [uri2] ... [uriN] <folder to save frames>\n" % args[0])
sys.exit(1)
for i in range(0,len(args)-2):
fps_streams["stream{0}".format(i)]=GETFPS(i)
number_sources=len(args)-2
global folder_name
folder_name=args[-1]
if path.exists(folder_name):
sys.stderr.write("The output folder %s already exists. Please remove it first.\n" % folder_name)
sys.exit(1)
os.mkdir(folder_name)
print("Frames will be saved in ",folder_name)
# Standard GStreamer initialization
GObject.threads_init()
Gst.init(None)
# Create gstreamer elements */
# Create Pipeline element that will form a connection of other elements
print("Creating Pipeline \n ")
pipeline = Gst.Pipeline()
is_live = False
if not pipeline:
sys.stderr.write(" Unable to create Pipeline \n")
print("Creating streamux \n ")
# Create nvstreammux instance to form batches from one or more sources.
streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
if not streammux:
sys.stderr.write(" Unable to create NvStreamMux \n")
# Set properties of tracker
config = configparser.ConfigParser()
config.read('dstest2_tracker_config.txt')
config.sections()
# Tracker
tracker = Gst.ElementFactory.make("nvtracker", "tracker")
if not tracker:
sys.stderr.write(" Unable to create tracker \n")
for key in config['tracker']:
if key == 'tracker-width':
tracker_width = config.getint('tracker', key)
tracker.set_property('tracker-width', tracker_width)
if key == 'tracker-height':
tracker_height = config.getint('tracker', key)
tracker.set_property('tracker-height', tracker_height)
if key == 'gpu-id':
tracker_gpu_id = config.getint('tracker', key)
tracker.set_property('gpu_id', tracker_gpu_id)
if key == 'll-lib-file':
tracker_ll_lib_file = config.get('tracker', key)
tracker.set_property('ll-lib-file', tracker_ll_lib_file)
if key == 'll-config-file':
tracker_ll_config_file = config.get('tracker', key)
tracker.set_property('ll-config-file', tracker_ll_config_file)
if key == 'enable-batch-process':
tracker_enable_batch_process = config.getint('tracker', key)
tracker.set_property('enable_batch_process', tracker_enable_batch_process)
tracker.set_property('enable-past-frame', 1)
# nvdsAnalytics
analytics = Gst.ElementFactory.make("nvdsanalytics", "analytics")
if not analytics:
sys.stderr.write(" Unable to create analytics \n")
# Set properties of tracker
config = configparser.ConfigParser()
config.read("config_nvdsanalytics.txt")
config.sections()
print('config', config)
# "config-file", "config_nvdsanalytics.txt",
analytics.set_property("config-file", "config_nvdsanalytics.txt")
if not tracker:
sys.stderr.write(" Unable to create tracker \n")
pipeline.add(streammux)
for i in range(number_sources):
os.mkdir(folder_name+"/stream_"+str(i))
frame_count["stream_"+str(i)]=0
saved_count["stream_"+str(i)]=0
print("Creating source_bin ",i," \n ")
uri_name=args[i+1]
if uri_name.find("rtsp://") == 0 :
is_live = True
source_bin=create_source_bin(i, uri_name)
if not source_bin:
sys.stderr.write("Unable to create source bin \n")
pipeline.add(source_bin)
padname="sink_%u" %i
sinkpad= streammux.get_request_pad(padname)
if not sinkpad:
sys.stderr.write("Unable to create sink pad bin \n")
srcpad=source_bin.get_static_pad("src")
if not srcpad:
sys.stderr.write("Unable to create src pad bin \n")
srcpad.link(sinkpad)
print("Creating Pgie \n ")
pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
if not pgie:
sys.stderr.write(" Unable to create pgie \n")
# Add nvvidconv1 and filter1 to convert the frames to RGBA
# which is easier to work with in Python.
print("Creating nvvidconv1 \n ")
nvvidconv1 = Gst.ElementFactory.make("nvvideoconvert", "convertor1")
if not nvvidconv1:
sys.stderr.write(" Unable to create nvvidconv1 \n")
print("Creating filter1 \n ")
caps1 = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
filter1 = Gst.ElementFactory.make("capsfilter", "filter1")
if not filter1:
sys.stderr.write(" Unable to get the caps filter1 \n")
filter1.set_property("caps", caps1)
print("Creating tiler \n ")
tiler=Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
if not tiler:
sys.stderr.write(" Unable to create tiler \n")
print("Creating nvvidconv \n ")
nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
if not nvvidconv:
sys.stderr.write(" Unable to create nvvidconv \n")
print("Creating nvosd \n ")
nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
if not nvosd:
sys.stderr.write(" Unable to create nvosd \n")
if(is_aarch64()):
print("Creating transform \n ")
transform=Gst.ElementFactory.make("nvegltransform", "nvegl-transform")
if not transform:
sys.stderr.write(" Unable to create transform \n")
print("Creating EGLSink \n")
sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
if not sink:
sys.stderr.write(" Unable to create egl sink \n")
if is_live:
print("Atleast one of the sources is live")
streammux.set_property('live-source', 1)
streammux.set_property('width', 1920)
streammux.set_property('height', 1080)
streammux.set_property('batch-size', number_sources)
streammux.set_property('batched-push-timeout', 4000000)
pgie.set_property('config-file-path', "dstest_imagedata_config.txt")
pgie_batch_size=pgie.get_property("batch-size")
if(pgie_batch_size != number_sources):
print("WARNING: Overriding infer-config batch-size",pgie_batch_size," with number of sources ", number_sources," \n")
pgie.set_property("batch-size",number_sources)
tiler_rows=int(math.sqrt(number_sources))
tiler_columns=int(math.ceil((1.0*number_sources)/tiler_rows))
tiler.set_property("rows",tiler_rows)
tiler.set_property("columns",tiler_columns)
tiler.set_property("width", TILED_OUTPUT_WIDTH)
tiler.set_property("height", TILED_OUTPUT_HEIGHT)
sink.set_property("sync", 0)
if not is_aarch64():
# Use CUDA unified memory in the pipeline so frames
# can be easily accessed on CPU in Python.
mem_type = int(pyds.NVBUF_MEM_CUDA_UNIFIED)
streammux.set_property("nvbuf-memory-type", mem_type)
nvvidconv.set_property("nvbuf-memory-type", mem_type)
nvvidconv1.set_property("nvbuf-memory-type", mem_type)
tiler.set_property("nvbuf-memory-type", mem_type)
print("Adding elements to Pipeline \n")
pipeline.add(pgie)
pipeline.add(tracker)
pipeline.add(analytics)
pipeline.add(tiler)
pipeline.add(nvvidconv)
pipeline.add(filter1)
pipeline.add(nvvidconv1)
pipeline.add(nvosd)
if is_aarch64():
pipeline.add(transform)
if is_aarch64():
pipeline.add(transform)
pipeline.add(sink)
print("Linking elements in the Pipeline \n")
streammux.link(pgie)
pgie.link(tracker)
tracker.link(analytics)
analytics.link(nvvidconv1)
nvvidconv1.link(filter1)
filter1.link(tiler)
tiler.link(nvvidconv)
nvvidconv.link(nvosd)
if is_aarch64():
nvosd.link(transform)
transform.link(sink)
else:
nvosd.link(sink)
# create an event loop and feed gstreamer bus mesages to it
loop = GObject.MainLoop()
bus = pipeline.get_bus()
bus.add_signal_watch()
bus.connect ("message", bus_call, loop)
tiler_sink_pad=tiler.get_static_pad("sink")
if not tiler_sink_pad:
sys.stderr.write(" Unable to get src pad \n")
else:
tiler_sink_pad.add_probe(Gst.PadProbeType.BUFFER, tiler_sink_pad_buffer_probe, 0)
nvdsanalytics_src_pad = analytics.get_static_pad("src")
# nvdsanalytics_src_pad = gst_element_get_static_pad(nvdsanalytics, "src");
if not nvdsanalytics_src_pad:
sys.stderr.write(" Unable to get src pad \n")
else:
nvdsanalytics_src_pad.add_probe(Gst.PadProbeType.BUFFER, nvdsanalytics_src_pad_buffer_probe, 0)
# List the sources
print("Now playing...")
for i, source in enumerate(args[:-1]):
if (i != 0):
print(i, ": ", source)
print("Starting pipeline \n")
# start play back and listed to events
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except:
pass
# cleanup
print("Exiting app\n")
pipeline.set_state(Gst.State.NULL)
if __name__ == '__main__':
sys.exit(main(sys.argv))
The objects within the ROI-RF (which is the while screen) are correctly removed, their bboxs and text do not appear in the OSD.
but the console printout shows the tracker meta is still in the batch meta
function => process_tracker_meta_data
Decodebin child added: source
Decodebin child added: decodebin0
Decodebin child added: rtph264depay0
Decodebin child added: h264parse0
Decodebin child added: capsfilter0
Decodebin child added: nvv4l2decoder0
Seting bufapi_version
Opening in BLOCKING MODE
NvMMLiteOpen : Block : BlockType = 261
NVMEDIA: Reading vendor.tegra.display-size : status: 6
NvMMLiteBlockCreate : Block : BlockType = 261
In cb_newpad
Unique ID 0 past frames: 3
Unique ID 1 past frames: 3
Unique ID 2 past frames: 3
Unique ID 3 past frames: 3
Unique ID 4 past frames: 3
Unique ID 5 past frames: 3
Unique ID 6 past frames: 14
Unique ID 7 past frames: 9
Unique ID 8 past frames: 4
**********************FPS*****************************************
Fps of stream 0 is 26.8
Unique ID 9 past frames: 13
**********************FPS*****************************************
Fps of stream 0 is 17.8
Unique ID 11 past frames: 12
Unique ID 12 past frames: 10
Unique ID 13 past frames: 12
Unique ID 14 past frames: 3
Unique ID 15 past frames: 3
Unique ID 16 past frames: 4
Unique ID 18 past frames: 3
**********************FPS*****************************************
Fps of stream 0 is 27.0
Unique ID 20 past frames: 13
**********************FPS*****************************************
Fps of stream 0 is 27.6
Unique ID 24 past frames: 3
Unique ID 25 past frames: 3
Unique ID 26 past frames: 6
Unique ID 27 past frames: 3
**********************FPS*****************************************
Fps of stream 0 is 27.6
Unique ID 28 past frames: 3
Unique ID 29 past frames: 15
**********************FPS*****************************************
Fps of stream 0 is 25.2
**********************FPS*****************************************
Fps of stream 0 is 25.6
Unique ID 31 past frames: 3
Unique ID 33 past frames: 13
**********************FPS*****************************************
Fps of stream 0 is 25.6
Unique ID 35 past frames: 30
**********************FPS*****************************************
Fps of stream 0 is 24.4
Unique ID 36 past frames: 7
Unique ID 37 past frames: 3
**********************FPS*****************************************
Fps of stream 0 is 25.2
Unique ID 38 past frames: 4
Unique ID 40 past frames: 3
Unique ID 39 past frames: 7
