import sys sys.path.append('/opt/nvidia/deepstream/deepstream/sources/deepstream_python_apps/apps') import os import gi gi.require_version('Gst', '1.0') from gi.repository import GLib, Gst from common.platform_info import PlatformInfo from common.bus_call import bus_call import numpy as np import ctypes import pyds from functools import partial from CommonCode.settings import get_logger, LogColorize import argparse pfm = LogColorize.watch_and_fix_permissions logger = get_logger(__name__,'/var/log/ml_vision_logs/00_watch_and_fix_permissions', stdout=True, systemd=False) target_width = 1280 target_height = 720 os.environ.pop("DISPLAY",None) MUXER_BATCH_TIMEOUT_USEC = 1000000 def embedder_results_probe(pad,info,u_data, list_add, frame_num = 0): 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)) 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 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 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 = [] if True: for i in range(tensor_meta.num_output_layers): layer = pyds.get_nvds_LayerInfo(tensor_meta, i) if layer.layerName=='embedding': ptr = ctypes.cast(pyds.get_ptr(layer.buffer), ctypes.POINTER(ctypes.c_float)) num_elements = layer.inferDims.numElements v = list(np.ctypeslib.as_array(ptr, shape=(num_elements,))) v = [float(x) for x in v] list_add.append({'frame_number':frame_number, 'vector':v}) try: l_user = l_user.next except StopIteration: break try: # indicate inference is performed on the frame frame_meta.bInferDone = True l_frame = l_frame.next except StopIteration: break return Gst.PadProbeReturn.OK def detector_results_probe(pad,info,u_data, list_add, frame_num = 0): frame_number=0 num_rects=0 got_fps = False 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)) l_frame = batch_meta.frame_meta_list while l_frame is not None: try: 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 l_user = frame_meta.frame_user_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 # param_extract = ['left','top','width','height'] # strc = '' # for param in param_extract: # strc+=str(getattr(obj_meta.rect_params, param)) # strc+=' ' # target_width # target_height score = obj_meta.confidence label = obj_meta.obj_label left = obj_meta.rect_params.left top = obj_meta.rect_params.top width = obj_meta.rect_params.width height = obj_meta.rect_params.height frame_number = frame_number class_id = obj_meta.class_id d_add = {'score':score, 'label':label, 'left':left, 'top':top, 'width':width, 'height':height, 'frame_number':frame_number, 'class_id': class_id} list_add.append(d_add) print(frame_number, label, score) if frame_number % 100 == 0: str_pr = 'FRAME_PROGRESS: '+pfm(str(frame_number) + '/' + str(frame_num)) logger.info(str_pr) try: l_obj=l_obj.next except StopIteration: break # Update frame rate through this probe stream_index = "stream{0}".format(frame_meta.pad_index) try: l_frame=l_frame.next except StopIteration: break return Gst.PadProbeReturn.OK def cb_newpad(decodebin, decoder_src_pad,data): print("In cb_newpad\n") caps=decoder_src_pad.get_current_caps() if not caps: caps = decoder_src_pad.query_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. print("gstname=",gstname) 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. print("features=",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 "source" in name: source_element = child_proxy.get_by_name("source") if source_element.find_property('drop-on-latency') != None: Object.set_property("drop-on-latency", True) def create_source_bin(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-any-format" 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 run_inference(file_path): os.environ.pop("DISPLAY",None) if not file_path.startswith('file://'): file_path = 'file://'+file_path platform_info = PlatformInfo() Gst.init(None) pipeline = Gst.Pipeline() streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer") nugget_detector = Gst.ElementFactory.make("nvinfer", "primary-inference") nugget_embedder = Gst.ElementFactory.make("nvinfer", "secondary-inference") streammux.set_property('width', target_width) streammux.set_property('height', target_height) streammux.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC) streammux.set_property('enable-padding',1) streammux.set_property('batch-size', 4) # nugget_detector.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/detector.txt") nugget_detector.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/config_infer_primary_yoloV7.txt") nugget_embedder.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/embedder.txt") fakesink1 = Gst.ElementFactory.make("fakesink","fakesink") fakesink1.set_property('enable-last-sample', 0) fakesink1.set_property('sync', 0) pipeline.add(fakesink1) fakesink2 = Gst.ElementFactory.make("fakesink","fakesink2") fakesink2.set_property('enable-last-sample', 0) fakesink2.set_property('sync', 0) pipeline.add(fakesink2) pipeline.add(streammux) pipeline.add(nugget_detector) pipeline.add(nugget_embedder) # uri_name = 'file:///home/thebears/railing.mp4' # uri_name = 'file:///home/thebears/railing_00_20250213094806.mp4' source_file=create_source_bin(file_path) pipeline.add(source_file) stream_pad = streammux.request_pad_simple("sink_0") source_pad = source_file.get_static_pad("src") source_pad.link(stream_pad) tee=Gst.ElementFactory.make("tee", "nvsink-tee") pipeline.add(tee) queue1=Gst.ElementFactory.make("queue", "nvtee-que1") queue2=Gst.ElementFactory.make("queue", "nvtee-que2") pipeline.add(queue1) pipeline.add(queue2) streammux.link(tee) tee.link(queue1) tee.link(queue2) # preprocess_detector = Gst.ElementFactory.make("nvdspreprocess","preprocess_detector") # preprocess_detector.set_property('config-file', "/home/thebears/DeepStream-Yolo/detector_preprocess.txt") # preprocess_detector.set_property('config-file',pre_file) # preprocess_embedder = Gst.ElementFactory.make("nvdspreprocess","preprocess_embedder") # preprocess_embedder.set_property('config-file', "/home/thebears/DeepStream-Yolo/embedder_preprocess.txt") # preprocess_embedder.set_property('config-file',pre_file) # pipeline.add(preprocess_detector) # pipeline.add(preprocess_embedder) # queue1.link(preprocess_detector) # preprocess_detector.link(nugget_detector) # queue2.link(preprocess_embedder) # preprocess_embedder.link(nugget_embedder) queue1.link(nugget_detector) queue2.link(nugget_embedder) cmd = f'/usr/bin/ffprobe -v error -select_streams v:0 -count_packets -show_entries stream=nb_read_packets -of csv=p=0 {file_path}'#/srv/ftp/railing/2025/02/28/railing_00_20250228115800.mp4 try: frames = int(os.popen(cmd).read().strip()) except: frames = 0 logger.info(f"TOTAL_FRAMES: {frames}") embedder_list = list() embedder_results = partial(embedder_results_probe, list_add=embedder_list, frame_num = frames) nugget_embedder.get_static_pad("src").add_probe(Gst.PadProbeType.BUFFER, embedder_results, 0) detector_list = list() detector_results = partial(detector_results_probe, list_add = detector_list, frame_num = frames) nugget_detector.get_static_pad("src").add_probe(Gst.PadProbeType.BUFFER, detector_results, 0) def get_pipeline_string(pipeline): if not isinstance(pipeline, Gst.Pipeline): return None elements = [] iterator = pipeline.iterate_elements() while True: result, element = iterator.next() if result != Gst.IteratorResult.OK: break elements.append(element.get_name()) return " ! ".join(elements) nugget_detector.link(fakesink1) nugget_embedder.link(fakesink2) print(get_pipeline_string(pipeline)) # create an event loop and feed gstreamer bus mesages to it loop = GLib.MainLoop() bus = pipeline.get_bus() bus.add_signal_watch() bus.connect ("message", bus_call, loop) # start play back and listen to events print("Starting pipeline \n") pipeline.set_state(Gst.State.PLAYING) try: loop.run() except: pass # cleanup pipeline.set_state(Gst.State.NULL) return detector_list, embedder_list def get_detailed_pipeline_string(pipeline): """Generate a more detailed pipeline string with properties""" if not isinstance(pipeline, Gst.Pipeline): return None def get_element_string(element): # Get element factory name factory = element.get_factory() if factory: element_str = factory.get_name() else: element_str = element.get_name() # Add properties props = [] for prop in element.list_properties(): # Skip some properties that are typically not set in command line if prop.name in ('name', 'parent'): continue try: val = element.get_property(prop.name) if val is not None and val != prop.default_value: # Format value appropriately based on type if isinstance(val, str): props.append(f"{prop.name}=\"{val}\"") elif isinstance(val, bool): props.append(f"{prop.name}={str(val).lower()}") else: props.append(f"{prop.name}={val}") except: # Skip properties that can't be read pass if props: element_str += " " + " ".join(props) return element_str result = [] # Simple approach - just gets top-level elements iterator = pipeline.iterate_elements() while True: ret, element = iterator.next() if ret != Gst.IteratorResult.OK: break result.append(get_element_string(element)) return " ! ".join(result) if __name__ == '__main__': cpath = sys.argv[1] if cpath.endswith('-i'): cpath = '/home/thebears/local/source/short.mp4' if not cpath.startswith('file'): cpath = os.path.abspath(cpath) out = run_inference(cpath) import json with open('dump.json','w') as ff: json.dump([out[0],out[1]],ff) sys.exit()