# Decrease FPS and see it on my display **URL:** \ **Category:** DeepStream SDK\ **Created:** [May 3, 2023, 7:45pm UTC](https://forums.developer.nvidia.com/t/decrease-fps-and-see-it-on-my-display/252122 "2023-05-03T19:45:15Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![melibmx8](https://developer.download.nvidia.com/images/forums/profile-default-devtalk-84.png) [@melibmx8](https://forums.developer.nvidia.com/u/melibmx8)\ **Post date:** [May 3, 2023, 7:45pm UTC](https://forums.developer.nvidia.com/t/decrease-fps-and-see-it-on-my-display/252122/1 "2023-05-03T19:45:16Z")
Please provide complete information as applicable to your setup. \*\*• JETSON NANO2GB \*\*• DeepStream Version = 6.0.1 Hi, my problem is: I want decrease fps in my inference but when i try it with caps.set\_property(“caps”, Gst.Caps.from\_string(“video/x-raw(memory:NVMM), format=I420,framerate=10/1”)) I cant see the results in my screen and i get this issue: 0:01:29.754772650 11648 0x3722a540 WARN nvinfer gstnvinfer.cpp:2288:gst\_nvinfer\_output\_loop: error: streaming stopped, reason not-negotiated (-4) Error: gst-stream-error-quark: Internal data stream error. (1): /dvs/git/dirty/git-master\_linux/deepstream/sdk/src/gst-plugins/gst-nvinfer/gstnvinfer.cpp(2288): gst\_nvinfer\_output\_loop (): /GstPipeline:pipeline0/GstNvInfer:primary-inference: streaming stopped, reason not-negotiated (-4) Exiting app. when i run it without framerate=10/1 i dont have problem, but i need reduce fps and see the inference in my screen my code is: def main(args): ``` number_sources = len(uri_inputs) global perf_data perf_data = PERF_DATA(number_sources) global folder_name folder_name = "out_crops" 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 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") 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 = uri_inputs[i] 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") 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") nvvidconv_postosd = Gst.ElementFactory.make("nvvideoconvert", "convertor_postosd") if not nvvidconv_postosd: sys.stderr.write(" Unable to create nvvidconv_postosd \n") # Create a caps filter caps = Gst.ElementFactory.make("capsfilter", "filter") caps.set_property("caps", Gst.Caps.from_string("video/x-raw(memory:NVMM), format=I420,framerate=10/1")) transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform") sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer") if not sink: sys.stderr.write(" Unable to create udpsink") print("Playing file {} ".format(uri_inputs)) 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', "config_infer_primary_facedetectir.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) 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) nvvidconv_postosd.set_property("nvbuf-memory-type", mem_type) print("Adding elements to Pipeline \n") pipeline.add(pgie) pipeline.add(tiler) pipeline.add(nvvidconv) pipeline.add(filter1) pipeline.add(nvvidconv1) pipeline.add(nvosd) pipeline.add(nvvidconv_postosd) pipeline.add(caps) pipeline.add(sink) pipeline.add(transform) print("Linking elements in the Pipeline \n") streammux.link(pgie) pgie.link(nvvidconv1) nvvidconv1.link(filter1) filter1.link(tiler) tiler.link(nvvidconv) nvvidconv.link(nvosd) nvosd.link(nvvidconv_postosd) nvvidconv_postosd.link(caps) caps.link(transform) transform.link(sink) loop = GLib.MainLoop() bus = pipeline.get_bus() bus.add_signal_watch() bus.connect("message", bus_call, loop) # Start streaming rtsp_port_num = 8554 server = GstRtspServer.RTSPServer.new() server.props.service = "%d" % rtsp_port_num server.attach(None) print("\n ***DeepStream: Launched RTSP Streaming at rtsp://localhost:%d/ds-test*** \n\n" % rtsp_port_num) 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) ``` ---
**Author:** ![fanzh](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/fanzh/32/115955_2.png) [@fanzh](https://forums.developer.nvidia.com/u/fanzh)\ **Post date:** [May 4, 2023, 8:55am UTC](https://forums.developer.nvidia.com/t/decrease-fps-and-see-it-on-my-display/252122/3 "2023-05-04T08:55:22Z")
**There is no update from you for a period, assuming this is not an issue anymore. Hence we are closing this topic. If need further support, please open a new one. Thanks** please refer to this topic [I want to infer 1 frame per second in deepstream-test5 app](https://forums.developer.nvidia.com/t/i-want-to-infer-1-frame-per-second-in-deepstream-test5-app/211474) ---
**Author:** ![system](https://sea2.discourse-cdn.com/nvidia/user_avatar/forums.developer.nvidia.com/system/32/68080_2.png) [@system](https://forums.developer.nvidia.com/u/system)\ **Post date:** [June 5, 2023, 1:56am UTC](https://forums.developer.nvidia.com/t/decrease-fps-and-see-it-on-my-display/252122/5 "2023-06-05T01:56:27Z")
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