Hi @Morganh
I tried and refer the same way as explained but unable to get output.
Below is the modified script.
import sys
sys.path.append('../')
import gi
import math
import cv2
import pyds
import numpy as np
import os.path
from os import path
gi.require_version('Gst', '1.0')
from gi.repository import GLib, Gst
from common.is_aarch_64 import is_aarch64
from common.bus_call import bus_call
MAX_DISPLAY_LEN = 64
MUXER_OUTPUT_WIDTH = 640
MUXER_OUTPUT_HEIGHT = 640
MUXER_BATCH_TIMEOUT_USEC = 4000000
TILED_OUTPUT_WIDTH = 640
TILED_OUTPUT_HEIGHT = 640
COLORS = [[128, 128, 64], [0, 0, 128], [0, 128, 128], [128, 0, 0],
[128, 0, 128], [128, 128, 0], [0, 128, 0], [0, 0, 64],
[0, 0, 192], [0, 128, 64], [0, 128, 192], [128, 0, 64],
[128, 0, 192], [128, 128, 128]]
def map_mask_as_display_bgr(mask):
m_list = list(set(mask.flatten()))
shp = mask.shape
bgr = np.zeros((shp[0], shp[1], 3))
for idx in m_list:
bgr[mask == idx] = COLORS[idx]
return bgr
def seg_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))
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_user = frame_meta.frame_user_meta_list
while l_user is not None:
try:
seg_user_meta = pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if seg_user_meta and seg_user_meta.base_meta.meta_type == pyds.NVDSINFER_SEGMENTATION_META:
try:
segmeta = pyds.NvDsInferSegmentationMeta.cast(seg_user_meta.user_meta_data)
except StopIteration:
break
masks = pyds.get_segmentation_masks(segmeta)
masks = np.array(masks, copy=True, order='C')
frame_image = map_mask_as_display_bgr(masks)
cv2.imwrite(folder_name + "/" + str(frame_number) + ".jpg", frame_image)
try:
l_user = l_user.next
except StopIteration:
break
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()
gststruct = caps.get_structure(0)
gstname = gststruct.get_name()
source_bin = data
features = caps.get_features(0)
if (gstname.find("video") != -1):
if features.contains("memory:NVMM"):
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 not is_aarch64() and name.find("nvv4l2decoder") != -1:
Object.set_property("cudadec-memtype", 2)
def create_source_bin(index, uri):
print("Creating source bin")
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")
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 main(args):
if len(args) < 4:
sys.stderr.write("usage: %s config_file <rtsp or video file> <path to save seg images>\n" % args[0])
sys.exit(1)
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)
config_file = args[1]
num_sources = len(args) - 3
Gst.init(None)
pipeline = Gst.Pipeline()
if not pipeline:
sys.stderr.write("Unable to create Pipeline \n")
streammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
if not streammux:
sys.stderr.write("Unable to create NvStreamMux \n")
streammux.set_property('width', MUXER_OUTPUT_WIDTH)
streammux.set_property('height', MUXER_OUTPUT_HEIGHT)
streammux.set_property('batch-size', num_sources)
streammux.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC)
pipeline.add(streammux)
# Create source bins for each stream
for i in range(num_sources):
uri_name = args[i + 2]
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)
seg = Gst.ElementFactory.make("nvinfer", "primary-nvinference-engine")
if not seg:
sys.stderr.write("Unable to create primary inference engine\n")
nvsegvisual = Gst.ElementFactory.make("nvsegvisual", "nvsegvisual")
if not nvsegvisual:
sys.stderr.write("Unable to create nvsegvisual\n")
sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
if not sink:
sys.stderr.write("Unable to create egl sink \n")
seg.set_property('config-file-path', config_file)
seg.set_property("batch-size", num_sources)
nvsegvisual.set_property('batch-size', num_sources)
nvsegvisual.set_property('width', 640)
nvsegvisual.set_property('height', 640)
sink.set_property("qos", 0)
pipeline.add(seg)
pipeline.add(nvsegvisual)
pipeline.add(sink)
streammux.link(seg)
seg.link(nvsegvisual)
nvsegvisual.link(sink)
loop = GLib.MainLoop()
bus = pipeline.get_bus()
bus.add_signal_watch()
bus.connect("message", bus_call, loop)
# Start playback
pipeline.set_state(Gst.State.PLAYING)
try:
loop.run()
except:
pass
pipeline.set_state(Gst.State.NULL)
if __name__ == '__main__':
sys.exit(main(sys.argv))
Config:
################################################################################
# SPDX-FileCopyrightText: Copyright (c) 2019-2021 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################
# Following properties are mandatory when engine files are not specified:
# int8-calib-file(Only in INT8), model-file-format
# Caffemodel mandatory properties: model-file, proto-file, output-blob-names
# UFF: uff-file, input-dims, uff-input-blob-name, output-blob-names
# ONNX: onnx-file
#
# Mandatory properties for detectors:
# num-detected-classes
#
# Optional properties for detectors:
# cluster-mode(Default=Group Rectangles), interval(Primary mode only, Default=0)
# custom-lib-path,
# parse-bbox-func-name
#
# Mandatory properties for classifiers:
# classifier-threshold, is-classifier
#
# Optional properties for classifiers:
# classifier-async-mode(Secondary mode only, Default=false)
#
# Optional properties in secondary mode:
# operate-on-gie-id(Default=0), operate-on-class-ids(Defaults to all classes),
# input-object-min-width, input-object-min-height, input-object-max-width,
# input-object-max-height
#
# Following properties are always recommended:
# batch-size(Default=1)
#
# Other optional properties:
# net-scale-factor(Default=1), network-mode(Default=0 i.e FP32),
# model-color-format(Default=0 i.e. RGB) model-engine-file, labelfile-path,
# mean-file, gie-unique-id(Default=0), offsets, process-mode (Default=1 i.e. primary),
# custom-lib-path, network-mode(Default=0 i.e FP32)
#
# The values in the config file are overridden by values set through GObject
# properties.
[property]
net-scale-factor=0.017507
offsets=123.675;116.28;103.53
gpu-id=0
net-scale-factor=1.0
model-color-format=0
uff-file=/root/data/Pritam/Script/SEGMENTATION/MODELS/model.epoch-24.uff
model-engine-file=/root/data/Pritam/Script/SEGMENTATION/MODELS/model.epoch-24.uff_b1_gpu0_fp16.engine
infer-dims=3;640;640
uff-input-order=0
uff-input-blob-name=Input
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=2
interval=0
gie-unique-id=1
network-type=3
output-blob-names=generate_detections;mask_fcn_logits/BiasAdd
output-tensor-meta=0
segmentation-threshold=0.0
#parse-bbox-func-name=NvDsInferParseCustomSSD
#custom-lib-path=nvdsinfer_custom_impl_ssd/libnvdsinfer_custom_impl_ssd.so
#scaling-filter=0
#scaling-compute-hw=0
parse-bbox-instance-mask-func-name=NvDsInferParseCustomMrcnnTLTV2
custom-lib-path=/opt/nvidia/deepstream/deepstream-7.0/lib/libnvds_infercustomparser.so
cluster-mode=4
output-instance-mask=1
[class-attrs-all]
roi-top-offset=0
roi-bottom-offset=0
pre-cluster-threshold=0.2
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0
It is not giving b-box or segmentation.
