deepstream_test_2.py

#!/usr/bin/env python3

################################################################################
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
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import sys
sys.path.append('../')
import configparser

import numpy as np
import gi
gi.require_version('Gst', '1.0')
from gi.repository import GObject, Gst
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
from common.FPS import GETFPS
from facenet_utils import load_dataset, normalize_vectors, predict_using_classifier

import ctypes
import pyds

fps_stream=None
face_counter= []
PGIE_CLASS_ID_VEHICLE = 0
PGIE_CLASS_ID_PERSON = 2

SGIE_CLASS_ID_LP = 1
SGIE_CLASS_ID_FACE = 0

pgie_classes = ["Vehicle", "TwoWheeler", "Person", "Roadsign"]

PRIMARY_DETECTOR_UID = 1
SECONDARY_DETECTOR_UID = 2
DATASET_PATH = 'embeddings/psu_embeddings_nano_2.npz'

faces_embeddings, labels = load_dataset(DATASET_PATH)

def osd_sink_pad_buffer_probe(pad,info,u_data):
    global fps_stream, face_counter
    frame_number=0
    #Intiallizing object counter with 0.
    vehicle_count = 0
    person_count = 0
    face_count = 0
    lp_count = 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.glist_get_nvds_frame_meta()
            # 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
            if obj_meta.unique_component_id == PRIMARY_DETECTOR_UID:
                if obj_meta.class_id == PGIE_CLASS_ID_VEHICLE:
                   vehicle_count += 1
                if obj_meta.class_id == PGIE_CLASS_ID_PERSON:
                   person_count += 1

            if obj_meta.unique_component_id == SECONDARY_DETECTOR_UID:
                if obj_meta.class_id == SGIE_CLASS_ID_FACE:
                   face_count += 1
                if obj_meta.class_id == SGIE_CLASS_ID_LP:
                   lp_count += 1
            
            try: 
                l_obj=l_obj.next
            except StopIteration:
                break
        fps_stream.get_fps()
        # 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, person_count, face_count)
        face_counter.append(face_count)

        # 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 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 main(args):
    global fps_stream
    # Check input arguments
    if len(args) != 2:
        sys.stderr.write("usage: %s <media file or uri>\n" % args[0])
        sys.exit(1)
    fps_stream = GETFPS(0)
    print(fps_stream)
    # 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()

    if not pipeline:
        sys.stderr.write(" Unable to create Pipeline \n")

    # Source element for reading from the file
    print("Creating Source \n ")
    source = Gst.ElementFactory.make("filesrc", "file-source")
    if not source:
        sys.stderr.write(" Unable to create Source \n")

    # Since the data format in the input file is elementary h264 stream,
    # we need a h264parser
    print("Creating H264Parser \n")
    h264parser = Gst.ElementFactory.make("h264parse", "h264-parser")
    if not h264parser:
        sys.stderr.write(" Unable to create h264 parser \n")

    # Use nvdec_h264 for hardware accelerated decode on GPU
    print("Creating Decoder \n")
    decoder = Gst.ElementFactory.make("nvv4l2decoder", "nvv4l2-decoder")
    if not decoder:
        sys.stderr.write(" Unable to create Nvv4l2 Decoder \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")

   
    tracker = Gst.ElementFactory.make("nvtracker", "tracker")
    if not tracker:
        sys.stderr.write(" Unable to create tracker \n")

    # Use nvinfer to run inferencing on decoder's output,
    # behaviour of inferencing is set through config file
    face_detector = Gst.ElementFactory.make("nvinfer", "primary-inference face detector")
    if not face_detector:
        sys.stderr.write(" Unable to create face_detector \n")

    face_classifier = Gst.ElementFactory.make("nvinfer", "secondary-inference face_classifier")
    if not face_classifier:
        sys.stderr.write(" Unable to create face_classifier \n")

    # Use convertor to convert from NV12 to RGBA as required by nvosd
    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
    if not nvvidconv:
        sys.stderr.write(" Unable to create nvvidconv \n")

    # Create OSD to draw on the converted RGBA buffer
    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")

    if not nvosd:
        sys.stderr.write(" Unable to create nvosd \n")

    # Finally render the osd output
    if is_aarch64():
        transform = Gst.ElementFactory.make("nvegltransform", "nvegl-transform")

    print("Creating EGLSink \n")
    sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
    if not sink:
        sys.stderr.write(" Unable to create egl sink \n")

    print("Playing file %s " %args[1])
    source.set_property('location', args[1])
    streammux.set_property('width', 1920)
    streammux.set_property('height', 1080)
    streammux.set_property('batch-size', 1)
    streammux.set_property('batched-push-timeout', 4000000)
    face_detector.set_property('config-file-path', "detector_config.txt")
    face_classifier.set_property('config-file-path', "classifier_config.txt")

    #Set properties of tracker
    config = configparser.ConfigParser()
    config.read('dstest2_tracker_config.txt')
    config.sections()

    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)


    print("Adding elements to Pipeline \n")
    pipeline.add(source)
    pipeline.add(h264parser)
    pipeline.add(decoder)
    pipeline.add(streammux)
    pipeline.add(tracker)
    pipeline.add(face_detector)
    pipeline.add(face_classifier)
    pipeline.add(nvvidconv)
    pipeline.add(nvosd)
    pipeline.add(sink)
    if is_aarch64():
        pipeline.add(transform)

    # we link the elements together
    # file-source -> h264-parser -> nvh264-decoder ->
    # nvinfer -> nvvidconv -> nvosd -> video-renderer
    print("Linking elements in the Pipeline \n")
    source.link(h264parser)
    h264parser.link(decoder)

    sinkpad = streammux.get_request_pad("sink_0")
    if not sinkpad:
        sys.stderr.write(" Unable to get the sink pad of streammux \n")
    srcpad = decoder.get_static_pad("src")
    if not srcpad:
        sys.stderr.write(" Unable to get source pad of decoder \n")

        
    srcpad.link(sinkpad)
    streammux.link(face_detector)
    face_detector.link(tracker)
    tracker.link(face_classifier)
    face_classifier.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)

    # Lets add probe to get informed of the meta data generated, we add probe to
    # the sink pad of the osd element, since by that time, the buffer would have
    # had got all the metadata.
    osdsinkpad = nvosd.get_static_pad("sink")
    if not osdsinkpad:
        sys.stderr.write(" Unable to get sink pad of nvosd \n")

    osdsinkpad.add_probe(Gst.PadProbeType.BUFFER, osd_sink_pad_buffer_probe, 0)

    vidconvsinkpad = nvvidconv.get_static_pad("sink")
    if not vidconvsinkpad:
        sys.stderr.write(" Unable to get sink pad of nvvidconv \n")

    vidconvsinkpad.add_probe(Gst.PadProbeType.BUFFER, sgie_sink_pad_buffer_probe, 0)

    # 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)

if __name__ == '__main__':
    sys.exit(main(sys.argv))