diff --git a/deepstream_obj_det.py b/deepstream_obj_det.py index 6014d15..50414b5 100755 --- a/deepstream_obj_det.py +++ b/deepstream_obj_det.py @@ -16,14 +16,19 @@ 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) +Gst.debug_set_default_threshold(Gst.DebugLevel.INFO) +os.environ.pop("DISPLAY",':0') +target_width_detect = 1280 +target_height_detect = 720 -target_width = 1280 -target_height = 720 -os.environ.pop("DISPLAY",None) +target_width_embed = 512 +target_height_embed = 512 + MUXER_BATCH_TIMEOUT_USEC = 1000000 -def embedder_results_probe(pad,info,u_data, list_add, frame_num = 0): +def embed_results_probe(pad,info,u_data, list_add, frame_num = 0): gst_buffer = info.get_buffer() + print("HEY I AM PROBING EMBEDDINGS") if not gst_buffer: print("Unable to get GstBuffer ") return @@ -104,7 +109,7 @@ def detector_results_probe(pad,info,u_data, list_add, frame_num = 0): frame_number=0 num_rects=0 got_fps = False - + print("HEY I AM PROBING DETECTIONS") gst_buffer = info.get_buffer() if not gst_buffer: print("Unable to get GstBuffer ") @@ -178,7 +183,6 @@ def detector_results_probe(pad,info,u_data, list_add, frame_num = 0): 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() @@ -215,34 +219,22 @@ def decodebin_child_added(child_proxy,Object,name,user_data): 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("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: @@ -261,86 +253,116 @@ def run_inference(file_path): 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") + + + + +# DETECT + queue_detect=Gst.ElementFactory.make("queue", "nvtee-detect") + + + streammux_detect = Gst.ElementFactory.make("nvstreammux", "Stream-muxer-detector") + streammux_detect.set_property('width', target_width_detect) + streammux_detect.set_property('height', target_height_detect) + streammux_detect.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC) + streammux_detect.set_property('enable-padding',1) + streammux_detect.set_property('batch-size', 4) + + nugget_detector = Gst.ElementFactory.make("nvinfer", "primary-inference") + nugget_detector.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/config_infer_primary_yoloV7.txt") + + fakesink_detect = Gst.ElementFactory.make("fakesink","fakesink") + fakesink_detect.set_property('enable-last-sample', 0) + fakesink_detect.set_property('sync', 0) + + + +# EMBED + + queue_embed=Gst.ElementFactory.make("queue", "nvtee-que-embed") + + streammux_embed = Gst.ElementFactory.make("nvstreammux", "Stream-muxer-embed") + streammux_embed.set_property('width', target_width_embed) + streammux_embed.set_property('height', target_height_embed) + streammux_embed.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC) + streammux_embed.set_property('enable-padding',1) + streammux_embed.set_property('batch-size', 4) + + + nugget_embed = Gst.ElementFactory.make("nvinfer", "primary-inference") + nugget_embed.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/embedder.txt") +# nugget_embed.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/config_infer_primary_yoloV7.txt") + + fakesink_embed = Gst.ElementFactory.make("fakesink","fakesink2") + fakesink_embed.set_property('enable-last-sample', 0) + fakesink_embed.set_property('sync', 0) + + + + + + + + + + + +# LINKING + + # Ensure NVMM caps with a capsfilter +# capsfilter = Gst.ElementFactory.make("capsfilter", "capsfilter") +# capsfilter.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), format=NV12")) +# pipeline.add(capsfilter) + + + + pipeline.add(source_file) 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) - + nvvidconv = Gst.ElementFactory.make("nvvidconv", "nvvidconv") + pipeline.add(nvvidconv) - 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) + source_file.link(nvvidconv) + # nvvidconv.link(capsfilter) + # capsfilter.link(tee) + nvvidconv.link(tee) + + if True: + pipeline.add(queue_detect) + pipeline.add(streammux_detect) + pipeline.add(nugget_detector) + pipeline.add(fakesink_detect) + + + + tee.get_request_pad("src_%u").link(queue_detect.get_static_pad("sink")) + queue_detect.get_static_pad("src").link(streammux_detect.get_request_pad("sink_0")) + streammux_detect.link(nugget_detector) + nugget_detector.link(fakesink_detect) + + + + + if False: + pipeline.add(queue_embed) + pipeline.add(streammux_embed) + pipeline.add(nugget_embed) + pipeline.add(fakesink_embed) + + tee.get_request_pad("src_%u").link(queue_embed.get_static_pad("sink")) + queue_embed.get_static_pad("src").link(streammux_embed.get_request_pad("sink_0")) + streammux_embed.link(nugget_embed) + nugget_embed.link(fakesink_embed) + + print_pipeline_structure(pipeline) 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()) @@ -349,21 +371,24 @@ def run_inference(file_path): logger.info(f"TOTAL_FRAMES: {frames}") - embedder_list = list() + embed_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) + Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "pipeline_structure") + embed_results = partial(embed_results_probe, list_add=embed_list, frame_num = frames) + nugget_embed.get_static_pad("src").add_probe(Gst.PadProbeType.BUFFER, embed_results, 0) + + Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "/home/thebears/local/source/pipeline_structure") + 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) + - - + print("AFTER SETTING STATIC PADS") def get_pipeline_string(pipeline): if not isinstance(pipeline, Gst.Pipeline): return None @@ -378,8 +403,7 @@ def run_inference(file_path): return " ! ".join(elements) - nugget_detector.link(fakesink1) - nugget_embedder.link(fakesink2) + Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "pipeline_structure") # create an event loop and feed gstreamer bus mesages to it @@ -397,7 +421,45 @@ def run_inference(file_path): pass # cleanup pipeline.set_state(Gst.State.NULL) - return detector_list, embedder_list + return detector_list, embed_list + +def print_pipeline_structure(pipeline): + """ + Recursively prints elements in the pipeline and their properties. + """ + if not isinstance(pipeline, Gst.Pipeline): + print("Not a valid GStreamer pipeline.") + return + + def _print_element_properties(element, indent=0): + spaces = " " * indent + print(spaces + f"Element: {element.get_name()} (Type: {element.get_factory().get_name()})") + + # Print its properties + for prop in element.list_properties(): + try: + val = element.get_property(prop.name) + if val != prop.default_value: # Display only non-default properties + print(spaces + f" - {prop.name}: {val}") + except: + pass + + def _print_pipeline_structure(element, indent=0): + spaces = " " * indent + children = element.children if hasattr(element, 'children') else [] + + if len(children) > 0: + print(spaces + f"[{element.get_name()}]") + for child in children: + _print_pipeline_structure(child, indent + 2) + else: + _print_element_properties(element, indent) + + print("\nPipeline Structure:") + print("===================") + _print_pipeline_structure(pipeline) + print("===================\n") + def get_detailed_pipeline_string(pipeline): """Generate a more detailed pipeline string with properties""" diff --git a/deepstream_obj_det_pre_queue.py b/deepstream_obj_det_pre_queue.py new file mode 100755 index 0000000..50414b5 --- /dev/null +++ b/deepstream_obj_det_pre_queue.py @@ -0,0 +1,533 @@ + +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) +Gst.debug_set_default_threshold(Gst.DebugLevel.INFO) +os.environ.pop("DISPLAY",':0') +target_width_detect = 1280 +target_height_detect = 720 + + +target_width_embed = 512 +target_height_embed = 512 + +MUXER_BATCH_TIMEOUT_USEC = 1000000 +def embed_results_probe(pad,info,u_data, list_add, frame_num = 0): + gst_buffer = info.get_buffer() + print("HEY I AM PROBING EMBEDDINGS") + 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 + print("HEY I AM PROBING DETECTIONS") + 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): + 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): + + bin_name="source-bin-any-format" + + nbin=Gst.Bin.new(bin_name) + if not nbin: + sys.stderr.write(" Unable to create source bin \n") + + 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") + + uri_decode_bin.set_property("uri",uri) + + uri_decode_bin.connect("pad-added",cb_newpad, nbin) + uri_decode_bin.connect("child-added",decodebin_child_added,nbin) + + 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() + + source_file=create_source_bin(file_path) + + + + + tee=Gst.ElementFactory.make("tee", "nvsink-tee") + + + + +# DETECT + queue_detect=Gst.ElementFactory.make("queue", "nvtee-detect") + + + streammux_detect = Gst.ElementFactory.make("nvstreammux", "Stream-muxer-detector") + streammux_detect.set_property('width', target_width_detect) + streammux_detect.set_property('height', target_height_detect) + streammux_detect.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC) + streammux_detect.set_property('enable-padding',1) + streammux_detect.set_property('batch-size', 4) + + nugget_detector = Gst.ElementFactory.make("nvinfer", "primary-inference") + nugget_detector.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/config_infer_primary_yoloV7.txt") + + fakesink_detect = Gst.ElementFactory.make("fakesink","fakesink") + fakesink_detect.set_property('enable-last-sample', 0) + fakesink_detect.set_property('sync', 0) + + + +# EMBED + + queue_embed=Gst.ElementFactory.make("queue", "nvtee-que-embed") + + streammux_embed = Gst.ElementFactory.make("nvstreammux", "Stream-muxer-embed") + streammux_embed.set_property('width', target_width_embed) + streammux_embed.set_property('height', target_height_embed) + streammux_embed.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC) + streammux_embed.set_property('enable-padding',1) + streammux_embed.set_property('batch-size', 4) + + + nugget_embed = Gst.ElementFactory.make("nvinfer", "primary-inference") + nugget_embed.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/embedder.txt") +# nugget_embed.set_property('config-file-path', "/home/thebears/DeepStream-Yolo/config_infer_primary_yoloV7.txt") + + fakesink_embed = Gst.ElementFactory.make("fakesink","fakesink2") + fakesink_embed.set_property('enable-last-sample', 0) + fakesink_embed.set_property('sync', 0) + + + + + + + + + + + +# LINKING + + # Ensure NVMM caps with a capsfilter +# capsfilter = Gst.ElementFactory.make("capsfilter", "capsfilter") +# capsfilter.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), format=NV12")) +# pipeline.add(capsfilter) + + + + pipeline.add(source_file) + pipeline.add(tee) + + nvvidconv = Gst.ElementFactory.make("nvvidconv", "nvvidconv") + pipeline.add(nvvidconv) + + source_file.link(nvvidconv) + # nvvidconv.link(capsfilter) + # capsfilter.link(tee) + nvvidconv.link(tee) + + + + if True: + pipeline.add(queue_detect) + pipeline.add(streammux_detect) + pipeline.add(nugget_detector) + pipeline.add(fakesink_detect) + + + + tee.get_request_pad("src_%u").link(queue_detect.get_static_pad("sink")) + queue_detect.get_static_pad("src").link(streammux_detect.get_request_pad("sink_0")) + streammux_detect.link(nugget_detector) + nugget_detector.link(fakesink_detect) + + + + + if False: + pipeline.add(queue_embed) + pipeline.add(streammux_embed) + pipeline.add(nugget_embed) + pipeline.add(fakesink_embed) + + tee.get_request_pad("src_%u").link(queue_embed.get_static_pad("sink")) + queue_embed.get_static_pad("src").link(streammux_embed.get_request_pad("sink_0")) + streammux_embed.link(nugget_embed) + nugget_embed.link(fakesink_embed) + + print_pipeline_structure(pipeline) + 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}") + + embed_list = list() + + Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "pipeline_structure") + embed_results = partial(embed_results_probe, list_add=embed_list, frame_num = frames) + nugget_embed.get_static_pad("src").add_probe(Gst.PadProbeType.BUFFER, embed_results, 0) + + + + Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "/home/thebears/local/source/pipeline_structure") + + 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) + + + print("AFTER SETTING STATIC PADS") + 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) + + Gst.debug_bin_to_dot_file(pipeline, Gst.DebugGraphDetails.ALL, "pipeline_structure") + + + # 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, embed_list + +def print_pipeline_structure(pipeline): + """ + Recursively prints elements in the pipeline and their properties. + """ + if not isinstance(pipeline, Gst.Pipeline): + print("Not a valid GStreamer pipeline.") + return + + def _print_element_properties(element, indent=0): + spaces = " " * indent + print(spaces + f"Element: {element.get_name()} (Type: {element.get_factory().get_name()})") + + # Print its properties + for prop in element.list_properties(): + try: + val = element.get_property(prop.name) + if val != prop.default_value: # Display only non-default properties + print(spaces + f" - {prop.name}: {val}") + except: + pass + + def _print_pipeline_structure(element, indent=0): + spaces = " " * indent + children = element.children if hasattr(element, 'children') else [] + + if len(children) > 0: + print(spaces + f"[{element.get_name()}]") + for child in children: + _print_pipeline_structure(child, indent + 2) + else: + _print_element_properties(element, indent) + + print("\nPipeline Structure:") + print("===================") + _print_pipeline_structure(pipeline) + print("===================\n") + + +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() + diff --git a/dump.json b/dump.json index ea7f27b..2bde03c 100644 --- a/dump.json +++ b/dump.json @@ -1 +1 @@ -[[{"score": 0.7726675271987915, "label": "Passer domesticus", "left": 952.071044921875, "top": 277.29156494140625, "width": 245.0166015625, "height": 158.998779296875, "frame_number": 0, "class_id": 69}, {"score": 0.844989001750946, "label": "Agelaius phoeniceus", "left": 702.3428344726562, "top": 225.53204345703125, "width": 211.4481201171875, "height": 129.602783203125, "frame_number": 0, "class_id": 2}, {"score": 0.7775482535362244, "label": "Passer domesticus", "left": 952.0147705078125, "top": 277.20477294921875, "width": 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131.1395263671875, "frame_number": 25, "class_id": 2}], []] \ No newline at end of file diff --git a/new_temp.py b/new_temp.py new file mode 100644 index 0000000..56ab0b3 --- /dev/null +++ b/new_temp.py @@ -0,0 +1,79 @@ + + +import gi +gi.require_version('Gst', '1.0') +#gi.require_version('GstNv', '1.0') +from gi.repository import Gst, GObject + +# Initialize GStreamer +Gst.init(None) + +def main(): + # Create a pipeline + pipeline = Gst.Pipeline() + + # Source element: File or Camera + source_bin = Gst.ElementFactory.make("filesrc", "file-source") + + source_bin.set_property("location", '/opt/nvidia/deepstream/deepstream/samples/streams/sample_720p.mp4') + + decodebin = Gst.ElementFactory.make("decodebin", "decode-bin") + source_bin.link(decodebin) + + # Queue 1 for decodebin output + queue1 = Gst.ElementFactory.make("queue", "queue1") + pipeline.add(queue1) + + # Splitter: Tee Element + tee = Gst.ElementFactory.make("tee", "splitter") + pipeline.add(tee) + + # Resizer branch + queue_resizer = Gst.ElementFactory.make("queue", "resizer-queue") + + + resizer = Gst.ElementFactory.make("nvvideoconvert", "video-converter") + capsfilter = Gst.ElementFactory.make("capsfilter", "caps-filter") + capsfilter.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), width=300, height=300")) + + # Model inference element + nvinfer = Gst.ElementFactory.make("nvinfer", "primary-inference") + nvinfer.set_property("config-file-path", "path_to_config_file.txt") + + # Create a sink for visualizing output + fakesink_detect = Gst.ElementFactory.make("fakesink","fakesink") + fakesink_detect.set_property('enable-last-sample', 0) + fakesink_detect.set_property('sync', 0) + sink = fakesink_detect + + # Add and link all elements (Resizer Branch) + for element in [queue_resizer, resizer, capsfilter, nvinfer, sink]: + pipeline.add(element) + + decodebin.connect("pad-added", lambda decode, src_pad: src_pad.link(queue1.get_static_pad("sink"))) + queue1.link(tee) + + # Link the tee to the resizer pipeline + tee_src_pad1 = tee.get_request_pad("src_%u") + queue_resizer_sink_pad = queue_resizer.get_static_pad("sink") + tee_src_pad1.link(queue_resizer_sink_pad) + + queue_resizer.link(resizer) + resizer.link(capsfilter) + capsfilter.link(nvinfer) + nvinfer.link(sink) + + # Start the pipeline + pipeline.set_state(Gst.State.PLAYING) + + # Run a main loop to listen for EOS or error + loop = GObject.MainLoop() + try: + loop.run() + except KeyboardInterrupt: + pass + + pipeline.set_state(Gst.State.NULL) + +if __name__ == "__main__": + main() diff --git a/new_test.py b/new_test.py new file mode 100644 index 0000000..bdd4db3 --- /dev/null +++ b/new_test.py @@ -0,0 +1,228 @@ +import sys +import gi +gi.require_version('Gst', '1.0') +from gi.repository import GObject, Gst, GLib + +# Initialize GStreamer +Gst.init(None) + +def create_source_bin(uri, source_id): + # Create a source bin element + bin_name = "source-bin-%02d" % source_id + source_bin = Gst.Bin.new(bin_name) + if not source_bin: + sys.stderr.write("Unable to create source bin\n") + return None + + # Create elements + 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") + return None + + # Set properties + uri_decode_bin.set_property("uri", uri) + + # Add the URI decode bin to the source bin + source_bin.add(uri_decode_bin) + + # Add pad probe for data flow + pad = Gst.Ghost.Pad.new_no_target("src", Gst.PadDirection.SRC) + if not pad: + sys.stderr.write("Failed to add ghost pad in source bin\n") + return None + source_bin.add_pad(pad) + + # Connect to the "pad-added" signal of the decoder + uri_decode_bin.connect("pad-added", cb_newpad, source_bin) + return source_bin + +def cb_newpad(decoder, pad, source_bin): + # Check the media type of the pad + pad_caps = pad.get_current_caps() + gst_struct = pad_caps.get_structure(0) + pad_type = gst_struct.get_name() + + if pad_type.startswith("video"): + # Link the decoder to the ghost pad + sink_pad = source_bin.get_static_pad("src") + if not sink_pad.is_linked(): + pad.link(sink_pad) + +def main(): + # Create the main pipeline + pipeline = Gst.Pipeline() + + # Create the source bin + source_bin = create_source_bin("file:///path/to/video.mp4", 0) + + # Create a tee element to split the stream + tee = Gst.ElementFactory.make("tee", "tee") + + # Create two streammux elements + streammux1 = Gst.ElementFactory.make("nvstreammux", "stream-muxer1") + streammux2 = Gst.ElementFactory.make("nvstreammux", "stream-muxer2") + + # Set streammux properties + streammux1.set_property("width", 1920) + streammux1.set_property("height", 1080) + streammux1.set_property("batch-size", 1) + streammux1.set_property("batched-push-timeout", 4000000) + + streammux2.set_property("width", 1280) + streammux2.set_property("height", 720) + streammux2.set_property("batch-size", 1) + streammux2.set_property("batched-push-timeout", 4000000) + + # Create queue elements for each branch + queue1 = Gst.ElementFactory.make("queue", "queue1") + queue2 = Gst.ElementFactory.make("queue", "queue2") + + # Add all elements to the pipeline + pipeline.add(source_bin) + pipeline.add(tee) + pipeline.add(queue1) + pipeline.add(queue2) + pipeline.add(streammux1) + pipeline.add(streammux2) + + # Link the source bin to the tee + source_bin.link(tee) + + # Link tee to the first queue and then to streammux1 + tee_pad1 = tee.get_request_pad("src_%u") + queue1_pad = queue1.get_static_pad("sink") + tee_pad1.link(queue1_pad) + + # Link the queue1 to streammux1 + sinkpad1 = streammux1.get_request_pad("sink_0") + srcpad1 = queue1.get_static_pad("src") + srcpad1.link(sinkpad1) + + # Link tee to the second queue and then to streammux2 + tee_pad2 = tee.get_request_pad("src_%u") + queue2_pad = queue2.get_static_pad("sink") + tee_pad2.link(queue2_pad) + + # Link the queue2 to streammux2 + sinkpad2 = streammux2.get_request_pad("sink_0") + srcpad2 = queue2.get_static_pad("src") + srcpad2.link(sinkpad2) + + # Continue building your pipeline with the two streammux outputs + # For example, you can add nvinfer, nvtracker, nvvideoconvert, etc. + + # Start the pipeline + pipeline.set_state(Gst.State.PLAYING) + + # Run the main loop + loop = GLib.MainLoop() + loop.run() + + # Clean up + pipeline.set_state(Gst.State.NULL) + +if __name__ == "__main__": + main() + + + + + + + + + + + + + + + + + +import gi +gi.require_version('Gst', '1.0') +from gi.repository import Gst, GObject + +def main(): + # Initialize GStreamer + Gst.init(None) + + # Create the pipeline + pipeline = Gst.Pipeline() + + # Create a source bin (e.g., decodebin from a file source or cameras) + source_bin = create_source_bin("file://path/to/your/video") + pipeline.add(source_bin) + + # Create the tee element + tee = Gst.ElementFactory.make("tee", "tee") + pipeline.add(tee) + + # Create two nvstreammux elements + streammux1 = Gst.ElementFactory.make("nvstreammux", "streammux1") + streammux1.set_property("batch-size", 1) + pipeline.add(streammux1) + + streammux2 = Gst.ElementFactory.make("nvstreammux", "streammux2") + streammux2.set_property("batch-size", 1) + pipeline.add(streammux2) + + # Request pads from the tee element + tee_src_pad_1 = tee.get_request_pad("src_%u") + tee_src_pad_2 = tee.get_request_pad("src_%u") + + # Create two queues for each branch + queue1 = Gst.ElementFactory.make("queue", "queue1") + queue2 = Gst.ElementFactory.make("queue", "queue2") + pipeline.add(queue1) + pipeline.add(queue2) + + # Link tee to the queues + tee_src_pad_1.link(queue1.get_static_pad("sink")) + tee_src_pad_2.link(queue2.get_static_pad("sink")) + + # Link queue1 -> streammux1 and queue2 -> streammux2 + queue1.link(streammux1) + queue2.link(streammux2) + + # Add other elements (e.g., processing, display sinks) after each streammux as required. + # For simplicity, you can just add a fakesink at the end of each branch for now. + + fake_sink_1 = Gst.ElementFactory.make("fakesink", "fakesink1") + fake_sink_2 = Gst.ElementFactory.make("fakesink2") + pipeline.add(fake_sink_1) + pipeline.add(fake_sink_2) + streammux1.link(fake_sink_1) + streammux2.link(fake_sink_2) + + # Connect the source bin to the tee + source_bin_src_pad = source_bin.get_static_pad("src") + tee_sink_pad = tee.get_static_pad("sink") + source_bin_src_pad.link(tee_sink_pad) + + # Start the pipeline + pipeline.set_state(Gst.State.PLAYING) + + # Run the pipeline + loop = GObject.MainLoop() + try: + loop.run() + except KeyboardInterrupt: + print("Exiting...") + pipeline.set_state(Gst.State.NULL) + + +def create_source_bin(uri): + # Create the source bin (file or live source) + uridecodebin = Gst.ElementFactory.make("uridecodebin", "source-bin") + uridecodebin.set_property("uri", uri) + + bin_pad = uridecodebin.get_static_pad("src") + + return uridecodebin + + +if __name__ == "__main__": + main()