蚕茧视频识别AI程序关键代码（不包含资源、模型、转换库）

2025-11-18 16:36:05 +08:00
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+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2019-2025 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.
+################################################################################
+
+Prerequisites:
+- DeepStreamSDK 8.0
+- NVIDIA Triton Inference Server (optional)
+- Python 3.12
+- Gst-python
+
+To set up Triton Inference Server: (optional)
+For x86_64 and Jetson Docker:
+  1. Use the provided docker container and follow directions for
+     Triton Inference Server in the SDK README --
+     be sure to prepare the detector models.
+  2. Run the docker with this Python Bindings directory mapped
+  3. Install required Python packages inside the container:
+     $ apt update
+     $ apt install python3-gi python3-dev python3-gst-1.0 -y
+     $ pip3 install pathlib
+  4. Build and install pyds bindings:
+     Follow the instructions in bindings README in this repo to build and install
+     pyds wheel for Ubuntu 24.04
+  5. For Triton gRPC setup, please follow the instructions at below location:
+     /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app-triton-grpc/README
+
+For Jetson without Docker:
+  1. Follow instructions in the DeepStream SDK README to set up
+     Triton Inference Server:
+     2.1 Compile and install the nvdsinfer_customparser
+     2.2 Prepare at least the Triton detector models
+  2. Build and install pyds bindings:
+     Follow the instructions in bindings README in this repo to build and install
+     pyds wheel for Ubuntu 24.04
+  3. Clear the GStreamer cache if pipeline creation fails:
+     rm ~/.cache/gstreamer-1.0/*
+  4. For Triton gRPC setup, please follow the instructions at below location:
+     /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app-triton-grpc/README
+
+To setup peoplenet model and configs (optional):
+Download Peoplenet model:
+  $ mkdir -p /opt/nvidia/deepstream/deepstream/samples/models/peoplenet
+  $ cd /opt/nvidia/deepstream/deepstream/samples/models/peoplenet
+  $ wget --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/peoplenet/pruned_quantized_decrypted_v2.3.4/files?redirect=true&path=resnet34_peoplenet_int8.onnx' -O resnet34_peoplenet_int8.onnx
+  $ wget --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/peoplenet/pruned_quantized_decrypted_v2.3.4/files?redirect=true&path=resnet34_peoplenet_int8.txt' -O resnet34_peoplenet_int8.txt
+  $ wget --content-disposition 'https://api.ngc.nvidia.com/v2/models/org/nvidia/team/tao/peoplenet/pruned_quantized_decrypted_v2.3.4/files?redirect=true&path=labels.txt' -O labels.txt
+
+Additionally, for Triton and Triton gRPC
+   $ cp config.pbtxt /opt/nvidia/deepstream/deepstream/samples/models/peoplenet
+   $ mkdir -p /opt/nvidia/deepstream/deepstream/samples/models/peoplenet/1
+   $ /usr/src/tensorrt/bin/trtexec --onnx=/opt/nvidia/deepstream/deepstream/samples/models/peoplenet/resnet34_peoplenet_int8.onnx --fp16 \
+   --saveEngine=/opt/nvidia/deepstream/deepstream/samples/models/peoplenet/1/resnet34_peoplenet_int8.onnx_b2_gpu0_fp16.engine \
+   --minShapes="input_1:0":1x3x544x960 \
+   --optShapes="input_1:0":2x3x544x960 \
+   --maxShapes="input_1:0":2x3x544x960
+
+To run:
+  $ python3 deepstream_test_3.py -i <uri1> [uri2] ... [uriN] [--no-display] [--silent]
+e.g.
+  $ python3 deepstream_test_3.py -i file:///home/ubuntu/video1.mp4 file:///home/ubuntu/video2.mp4
+  $ python3 deepstream_test_3.py -i rtsp://127.0.0.1/video1 rtsp://127.0.0.1/video2 -s
+
+To run peoplenet, test3 now supports 3 modes:
+
+  1. nvinfer + peoplenet: this mode still uses TRT for inferencing.
+
+     $ python3 deepstream_test_3.py -i <uri1> [uri2] ... [uriN] --pgie nvinfer -c config_infer_primary_peoplenet.txt [--no-display] [--silent]
+
+  2. nvinferserver + peoplenet : this mode uses Triton for inferencing.
+
+     $ python3 deepstream_test_3.py -i <uri1> [uri2] ... [uriN] --pgie nvinferserver -c config_triton_infer_primary_peoplenet.txt [--no-display] [-s]
+
+  3. nvinferserver (gRPC) + peoplenet : this mode uses Triton gRPC for inferencing.
+
+     $ mkdir -p /opt/nvidia/deepstream/deepstream/samples/models/peoplenet-grpc/peoplenet
+     $ cp /opt/nvidia/deepstream/deepstream/samples/models/peoplenet/config.pbtxt /opt/nvidia/deepstream/deepstream/samples/models/peoplenet-grpc/peoplenet/config.pbtxt
+     $ cp -a /opt/nvidia/deepstream/deepstream/samples/models/peoplenet/1 /opt/nvidia/deepstream/deepstream/samples/models/peoplenet-grpc/peoplenet/1
+     $ tritonserver --model-repository=/opt/nvidia/deepstream/deepstream/samples/models/peoplenet-grpc
+     $ python3 deepstream_test_3.py -i <uri1> [uri2] ... [uriN] --pgie nvinferserver-grpc -c config_triton_grpc_infer_primary_peoplenet.txt [--no-display] [--silent]
+
+e.g.
+  $ python3 deepstream_test_3.py -i file:///home/ubuntu/video1.mp4 file:///home/ubuntu/video2.mp4 --pgie nvinfer -c config_infer_primary_peoplenet.txt --no-display --silent
+  $ python3 deepstream_test_3.py -i rtsp://127.0.0.1/video1 rtsp://127.0.0.1/video2 --pgie nvinferserver -c config_triton_infer_primary_peoplenet.txt -s
+  $ python3 deepstream_test_3.py -i rtsp://127.0.0.1/video1 rtsp://127.0.0.1/video2 --pgie nvinferserver-grpc -c config_triton_grpc_infer_primary_peoplenet.txt --no-display --silent
+
+Note:
+1) if --pgie is not specified, test3 uses nvinfer and default model, not peoplenet.
+2) Both --pgie and -c need to be provided for custom models.
+3) Configs other than peoplenet can also be provided using the above approach.
+4) --no-display option disables on-screen video display.
+5) -s/--silent option can be used to suppress verbose output.
+6) --file-loop option can be used to loop input files after EOS.
+7) --disable-probe option can be used to disable the probe function and to use nvdslogger for perf measurements.
+8) To enable Pipeline Latency Measurement, set environment variable : NVDS_ENABLE_LATENCY_MEASUREMENT=1
+9) To enable Component Level Latency Measurement, set environment variable : NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 in addition to NVDS_ENABLE_LATENCY_MEASUREMENT=1
+
+This document describes the sample deepstream-test3 application.
+
+
+ * Use multiple sources in the pipeline.
+ * Use a uridecodebin so that any type of input (e.g. RTSP/File), any GStreamer
+   supported container format, and any codec can be used as input.
+ * Configure the stream-muxer to generate a batch of frames and infer on the
+   batch for better resource utilization.
+ * Extract the stream metadata, which contains useful information about the
+   frames in the batched buffer.
+ * Showcases how to enable latency measurement using probe function
+
+Refer to the deepstream-test1 sample documentation for an example of simple
+single-stream inference, bounding-box overlay, and rendering.
+
+This sample accepts one or more H.264/H.265 video streams as input. It creates
+a source bin for each input and connects the bins to an instance of the
+"nvstreammux" element, which forms the batch of frames. The batch of
+frames is fed to "nvinfer" for batched inferencing. The batched buffer is
+composited into a 2D tile array using "nvmultistreamtiler." The rest of the
+pipeline is similar to the deepstream-test1 sample.
+
+The "width" and "height" properties must be set on the stream-muxer to set the
+output resolution. If the input frame resolution is different from
+stream-muxer's "width" and "height", the input frame will be scaled to muxer's
+output resolution.
+
+The stream-muxer waits for a user-defined timeout before forming the batch. The
+timeout is set using the "batched-push-timeout" property. If the complete batch
+is formed before the timeout is reached, the batch is pushed to the downstream
+element. If the timeout is reached before the complete batch can be formed
+(which can happen in case of rtsp sources), the batch is formed from the
+available input buffers and pushed. Ideally, the timeout of the stream-muxer
+should be set based on the framerate of the fastest source. It can also be set
+to -1 to make the stream-muxer wait infinitely.
+
+The "nvmultistreamtiler" composite streams based on their stream-ids in
+row-major order (starting from stream 0, left to right across the top row, then
+across the next row, etc.).
@@ -0,0 +1,97 @@
+import pika
+import uuid
+import time
+import os
+import urllib.parse
+from minio import Minio
+import os
+from sca import sca
+
+
+# MinIO 配置
+MINIO_HOST = "ai.ronsunny.cn:9000"
+MINIO_ACCESS_KEY = "minioadmin"
+MINIO_SECRET_KEY = "minioadmin"
+MINIO_SECURE = True
+BUCKET_NAME = "video-sca"
+TEMP_DIR = "tmp/"
+
+# RabbitMQ 配置
+RABBITMQ_HOST = "10.10.12.101"
+RABBITMQ_PORT = 5672
+RABBITMQ_VHOST = "bbit_ai"
+RABBITMQ_USER = "ai_lab_iva_sca"
+RABBITMQ_PASS = "123456"
+RABBITMQ_QUEUE = "/sca_queue"
+
+# 初始化 MinIO 客户端
+minio_client = Minio(
+    MINIO_HOST,
+    access_key=MINIO_ACCESS_KEY,
+    secret_key=MINIO_SECRET_KEY,
+    secure=MINIO_SECURE
+)
+
+# ------------------- 消息处理 -------------------
+def callback(ch, method, properties, body):
+    import json
+    data = json.loads(body)
+    
+    for record in data.get("Records", []):
+        # 解析桶名和文件 Key
+        bucket = record["s3"]["bucket"]["name"]
+        key_encoded = record["s3"]["object"]["key"]
+        key = urllib.parse.unquote(key_encoded)
+        filename = os.path.basename(key)
+        temp_file_path = os.path.join(TEMP_DIR, filename)
+        
+        print(f"[1] 下载文件 {key} 到临时目录 {temp_file_path}")
+        minio_client.fget_object(bucket, key, temp_file_path)
+        
+        # AI 分析耗时
+        print(f"[2] AI 分析...")
+        # 生成分析后文件 UUID 名
+        new_uuid = str(uuid.uuid4())
+        new_filename = f"{new_uuid}"
+        new_file_path = os.path.join(TEMP_DIR, new_filename)
+
+        # 转为绝对路径
+        temp_file_path = os.path.abspath(temp_file_path)
+        new_file_path = os.path.abspath(new_file_path)
+        
+        # 这里暂时复制同一个视频模拟分析结果
+        sca(["file://" + temp_file_path], new_file_path)
+        print(f"[3] AI 分析完成，生成文件 {new_file_path}")
+        
+        # 上传分析后视频到 video-sca/ai/
+        dest_key = f"ai/{new_filename}"
+        minio_client.fput_object(bucket, dest_key, new_file_path)
+        print(f"[4] 上传分析视频到 MinIO {dest_key}")
+        
+        # 删除临时文件
+        os.remove(temp_file_path)
+        os.remove(new_file_path)
+        print("[5] 临时文件已删除\n")
+        print("[6] 本次任务完成\n")
+    
+    # 确认消息
+    ch.basic_ack(delivery_tag=method.delivery_tag)
+# 连接 RabbitMQ
+# 设置凭证
+credentials = pika.PlainCredentials(RABBITMQ_USER, RABBITMQ_PASS)
+connection = pika.BlockingConnection(
+    pika.ConnectionParameters(
+        host=RABBITMQ_HOST,
+        port=RABBITMQ_PORT,
+        virtual_host=RABBITMQ_VHOST,
+        credentials=credentials
+    )
+)
+channel = connection.channel()
+channel.queue_declare(queue=RABBITMQ_QUEUE, durable=True)
+
+channel.basic_qos(prefetch_count=1)  # 一次只处理一个文件
+channel.basic_consume(queue=RABBITMQ_QUEUE, on_message_callback=callback)
+
+print("Waiting for messages...")
+channel.start_consuming()
@@ -0,0 +1,67 @@
+################################################################################
+# 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.
+################################################################################
+
+import time
+from threading import Lock
+start_time=time.time()
+
+fps_mutex = Lock()
+
+class GETFPS:
+    def __init__(self,stream_id):
+        global start_time
+        self.start_time=start_time
+        self.is_first=True
+        self.frame_count=0
+        self.stream_id=stream_id
+
+    def update_fps(self):
+        end_time = time.time()
+        if self.is_first:
+            self.start_time = end_time
+            self.is_first = False
+        else:
+            global fps_mutex
+            with fps_mutex:
+                self.frame_count = self.frame_count + 1
+
+    def get_fps(self):
+        end_time = time.time()
+        with fps_mutex:
+            stream_fps = float(self.frame_count/(end_time - self.start_time))
+            self.frame_count = 0
+        self.start_time = end_time
+        return round(stream_fps, 2)
+
+    def print_data(self):
+        print('frame_count=',self.frame_count)
+        print('start_time=',self.start_time)
+
+class PERF_DATA:
+    def __init__(self, num_streams=1):
+        self.perf_dict = {}
+        self.all_stream_fps = {}
+        for i in range(num_streams):
+            self.all_stream_fps["stream{0}".format(i)]=GETFPS(i)
+
+    def perf_print_callback(self):
+        self.perf_dict = {stream_index:stream.get_fps() for (stream_index, stream) in self.all_stream_fps.items()}
+        print ("\n**PERF: ", self.perf_dict, "\n")
+        return True
+    
+    def update_fps(self, stream_index):
+        self.all_stream_fps[stream_index].update_fps()
@@ -0,0 +1,34 @@
+################################################################################
+# 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.
+################################################################################
+
+import gi
+import sys
+gi.require_version('Gst', '1.0')
+from gi.repository import Gst
+def bus_call(bus, message, loop):
+    t = message.type
+    if t == Gst.MessageType.EOS:
+        sys.stdout.write("End-of-stream\n")
+        loop.quit()
+    elif t==Gst.MessageType.WARNING:
+        err, debug = message.parse_warning()
+        sys.stderr.write("Warning: %s: %s\n" % (err, debug))
+    elif t == Gst.MessageType.ERROR:
+        err, debug = message.parse_error()
+        sys.stderr.write("Error: %s: %s\n" % (err, debug))
+        loop.quit()
+    return True
@@ -0,0 +1,94 @@
+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2024 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.
+################################################################################
+
+import sys
+import platform
+from threading import Lock
+from cuda.bindings import runtime
+from cuda.bindings import driver
+
+guard_platform_info = Lock()
+
+class PlatformInfo:
+    def __init__(self):
+        self.is_wsl_system = False
+        self.wsl_verified = False
+        self.is_integrated_gpu_system = False
+        self.is_integrated_gpu_verified = False
+        self.is_aarch64_platform = False
+        self.is_aarch64_verified = False
+
+    def is_wsl(self):
+        with guard_platform_info:
+            # Check if its already verified as WSL system or not.
+            if not self.wsl_verified:
+                try:
+                    # Open /proc/version file
+                    with open("/proc/version", "r") as version_file:
+                        # Read the content
+                        version_info = version_file.readline()
+                        version_info = version_info.lower()
+                        self.wsl_verified = True
+
+                        # Check if "microsoft" is present in the version information
+                        if "microsoft" in version_info:
+                            self.is_wsl_system = True
+                except Exception as e:
+                    print(f"ERROR: Opening /proc/version failed: {e}")
+
+        return self.is_wsl_system
+    
+    def is_integrated_gpu(self):
+        #Using cuda apis to identify whether integrated/discreet
+        #This is required to distinguish Tegra and ARM_SBSA devices
+        with guard_platform_info:
+            #Cuda initialize
+            if not self.is_integrated_gpu_verified:
+                cuda_init_result, = driver.cuInit(0)
+                if  cuda_init_result == driver.CUresult.CUDA_SUCCESS:
+                    #Get cuda devices count
+                    device_count_result, num_devices = driver.cuDeviceGetCount()
+                    if device_count_result == driver.CUresult.CUDA_SUCCESS:
+                        #If atleast one device is found, we can use the property from
+                        #the first device
+                        if num_devices >= 1:
+                            #Get properties from first device
+                            property_result, properties = runtime.cudaGetDeviceProperties(0)
+                            if property_result == runtime.cudaError_t.cudaSuccess:
+                                print("Is it Integrated GPU? :", properties.integrated)
+                                self.is_integrated_gpu_system = properties.integrated
+                                self.is_integrated_gpu_verified = True
+                            else:
+                                print("ERROR: Getting cuda device property failed: {}".format(property_result))
+                        else:
+                            print("ERROR: No cuda devices found to check whether iGPU/dGPU")
+                    else:
+                        print("ERROR: Getting cuda device count failed: {}".format(device_count_result))
+                else:
+                    print("ERROR: Cuda init failed: {}".format(cuda_init_result))
+
+        return self.is_integrated_gpu_system
+
+    def is_platform_aarch64(self):
+        #Check if platform is aarch64 using uname
+        if not self.is_aarch64_verified:
+            if platform.uname()[4] == 'aarch64':
+                self.is_aarch64_platform =  True
+            self.is_aarch64_verified = True
+        return self.is_aarch64_platform
+
+sys.path.append('/opt/nvidia/deepstream/deepstream/lib')
@@ -0,0 +1,24 @@
+################################################################################
+# 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.
+################################################################################
+
+import ctypes
+import sys
+sys.path.append('/opt/nvidia/deepstream/deepstream/lib')
+
+def long_to_uint64(l):
+    value = ctypes.c_uint64(l & 0xffffffffffffffff).value
+    return value
@@ -0,0 +1,43 @@
+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: LicenseRef-NvidiaProprietary
+#
+# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from NVIDIA CORPORATION or
+# its affiliates is strictly prohibited.
+################################################################################
+
+name: "peoplenet"
+platform: "tensorrt_plan"
+max_batch_size: 2
+default_model_filename: "resnet34_peoplenet_int8.onnx_b2_gpu0_fp16.engine"
+input [
+  {
+    name: "input_1:0"
+    data_type: TYPE_FP32
+    dims: [ 3, 544, 960 ]
+  }
+]
+output [
+  {
+    name: "output_bbox/BiasAdd:0"
+    data_type: TYPE_FP32
+    dims: [ 12, 34, 60 ]
+  },
+  {
+    name: "output_cov/Sigmoid:0"
+    data_type: TYPE_FP32
+    dims: [ 3, 34, 60 ]
+  }
+]
+
+instance_group [
+  {
+    kind: KIND_GPU
+    count: 1
+    gpus: 0
+  }
+]
@@ -0,0 +1,58 @@
+%YAML:1.0
+
+BaseConfig:
+  minDetectorConfidence: 0.6   # If the confidence of a detector bbox is lower than this, then it won't be considered for tracking
+
+TargetManagement:
+  enableBboxUnClipping: 1   # In case the bbox is likely to be clipped by image border, unclip bbox
+  maxTargetsPerStream: 150  # Max number of targets to track per stream. Recommended to set >10. Note: this value should account for the targets being tracked in shadow mode as well. Max value depends on the GPU memory capacity
+
+  # [Creation & Termination Policy]
+  minIouDiff4NewTarget: 0.55   # If the IOU between the newly detected object and any of the existing targets is higher than this threshold, this newly detected object will be discarded.
+  minTrackerConfidence: 0.2   # If the confidence of an object tracker is lower than this on the fly, then it will be tracked in shadow mode. Valid Range: [0.0, 1.0]
+  probationAge: 5 # If the target's age exceeds this, the target will be considered to be valid.
+  maxShadowTrackingAge: 30  # Max length of shadow tracking. If the shadowTrackingAge exceeds this limit, the tracker will be terminated.
+  earlyTerminationAge: 1   # If the shadowTrackingAge reaches this threshold while in TENTATIVE period, the target will be terminated prematurely.
+
+TrajectoryManagement:
+  useUniqueID: 0   # Use 64-bit long Unique ID when assignining tracker ID. Default is [true]
+
+DataAssociator:
+  dataAssociatorType: 0 # the type of data associator among { DEFAULT= 0 }
+  associationMatcherType: 0 # the type of matching algorithm among { GREEDY=0, GLOBAL=1 }
+  checkClassMatch: 1  # If checked, only the same-class objects are associated with each other. Default: true
+
+  # [Association Metric: Thresholds for valid candidates]
+  minMatchingScore4Overall: 0.0   # Min total score
+  minMatchingScore4SizeSimilarity: 0.6  # Min bbox size similarity score
+  minMatchingScore4Iou: 0.0       # Min IOU score
+  minMatchingScore4VisualSimilarity: 0.7  # Min visual similarity score
+
+  # [Association Metric: Weights]
+  matchingScoreWeight4VisualSimilarity: 0.6  # Weight for the visual similarity (in terms of correlation response ratio)
+  matchingScoreWeight4SizeSimilarity: 0.0    # Weight for the Size-similarity score
+  matchingScoreWeight4Iou: 0.4   # Weight for the IOU score
+
+StateEstimator:
+  stateEstimatorType: 1  # the type of state estimator among { DUMMY=0, SIMPLE=1, REGULAR=2 }
+
+  # [Dynamics Modeling]
+  processNoiseVar4Loc: 2.0    # Process noise variance for bbox center
+  processNoiseVar4Size: 1.0   # Process noise variance for bbox size
+  processNoiseVar4Vel: 0.1    # Process noise variance for velocity
+  measurementNoiseVar4Detector: 4.0    # Measurement noise variance for detector's detection
+  measurementNoiseVar4Tracker: 16.0    # Measurement noise variance for tracker's localization
+
+VisualTracker:
+  visualTrackerType: 1 # the type of visual tracker among { DUMMY=0, NvDCF=1 }
+
+  # [NvDCF: Feature Extraction]
+  useColorNames: 1     # Use ColorNames feature
+  useHog: 0            # Use Histogram-of-Oriented-Gradient (HOG) feature
+  featureImgSizeLevel: 2  # Size of a feature image. Valid range: {1, 2, 3, 4, 5}, from the smallest to the largest
+  featureFocusOffsetFactor_y: -0.2 # The offset for the center of hanning window relative to the feature height. The center of hanning window would move by (featureFocusOffsetFactor_y*featureMatSize.height) in vertical direction
+
+  # [NvDCF: Correlation Filter]
+  filterLr: 0.075 # learning rate for DCF filter in exponential moving average. Valid Range: [0.0, 1.0]
+  filterChannelWeightsLr: 0.1 # learning rate for the channel weights among feature channels. Valid Range: [0.0, 1.0]
+  gaussianSigma: 0.75 # Standard deviation for Gaussian for desired response when creating DCF filter [pixels]
@@ -0,0 +1,81 @@
+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2019-2025 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)
+#   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]
+gpu-id=0
+net-scale-factor=0.00392156862745098
+onnx-file=../../models/best.pt.onnx
+model-engine-file=../../models/best.pt.onnx_b1_gpu0_fp16.engine
+labelfile-path=../../models/labels.txt
+batch-size=1
+process-mode=1
+model-color-format=0
+## 0=FP32, 1=INT8, 2=FP16 mode
+network-mode=2
+num-detected-classes=5
+interval=0
+gie-unique-id=1
+## 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
+cluster-mode=2
+custom-lib-path=../lib/libnvdsinfer_custom_impl_Yolo.so
+parse-bbox-func-name=NvDsInferParseYolo
+engine-create-func-name=NvDsInferYoloCudaEngineGet
+network-type=0
+maintain-aspect-ratio=1
+symmetric-padding=1
+
+[class-attrs-all]
+topk=20
+nms-iou-threshold=0.45
+pre-cluster-threshold=0.2
@@ -0,0 +1,60 @@
+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2019-2025 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.
+################################################################################
+
+[property]
+gpu-id=0
+net-scale-factor=0.0039215697906911373
+onnx-file=/opt/nvidia/deepstream/deepstream/samples/models/peoplenet/resnet34_peoplenet_int8.onnx
+labelfile-path=/opt/nvidia/deepstream/deepstream/samples/models/peoplenet/labels.txt
+model-engine-file=/opt/nvidia/deepstream/deepstream/samples/models/peoplenet/resnet34_peoplenet_int8.onnx_b1_gpu0_fp16.engine
+int8-calib-file=/opt/nvidia/deepstream/deepstream/samples/models/peoplenet/resnet34_peoplenet_int8.txt
+infer-dims=3;544;960
+uff-input-blob-name=input_1
+batch-size=1
+process-mode=1
+model-color-format=0
+## 0=FP32, 1=INT8, 2=FP16 mode
+network-mode=2
+num-detected-classes=3
+cluster-mode=2
+interval=0
+gie-unique-id=1
+output-blob-names=output_bbox/BiasAdd:0;output_cov/Sigmoid:0
+
+#Use the config params below for dbscan clustering mode
+#[class-attrs-all]
+#detected-min-w=4
+#detected-min-h=4
+#minBoxes=3
+#eps=0.7
+
+#Use the config params below for NMS clustering mode
+[class-attrs-all]
+topk=20
+nms-iou-threshold=0.5
+pre-cluster-threshold=0.2
+
+## Per class configurations
+[class-attrs-0]
+topk=20
+nms-iou-threshold=0.5
+pre-cluster-threshold=0.4
+
+#[class-attrs-1]
+#pre-cluster-threshold=0.05
+#eps=0.7
+#dbscan-min-score=0.5
@@ -0,0 +1,79 @@
+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2019-2023 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.
+################################################################################
+
+infer_config {
+  unique_id: 1
+  gpu_ids: [0]
+  max_batch_size: 1
+  backend {
+    inputs: [ {
+      name: "input_1:0"
+    }]
+    outputs: [
+      {name: "output_bbox/BiasAdd:0"},
+      {name: "output_cov/Sigmoid:0"}
+    ]
+    triton {
+      model_name: "peoplenet"
+      version: -1
+      grpc {
+        url: "0.0.0.0:8001"
+        enable_cuda_buffer_sharing: true
+      }
+    }
+  }
+
+  preprocess {
+    network_format: IMAGE_FORMAT_RGB
+    tensor_order: TENSOR_ORDER_LINEAR
+    tensor_name: "input_1:0"
+    maintain_aspect_ratio: 0
+    frame_scaling_hw: FRAME_SCALING_HW_DEFAULT
+    frame_scaling_filter: 1
+    normalize {
+      scale_factor: 0.0039215697906911373
+      channel_offsets: [0, 0, 0]
+    }
+  }
+
+  postprocess {
+    labelfile_path: "/opt/nvidia/deepstream/deepstream/samples/models/peoplenet/labels.txt"
+    detection {
+      num_detected_classes: 4
+      per_class_params {
+        key: 0
+        value { pre_threshold: 0.4 }
+      }
+      nms {
+        confidence_threshold:0.2
+        topk:20
+        iou_threshold:0.5
+      }
+    }
+  }
+
+  extra {
+    copy_input_to_host_buffers: false
+    output_buffer_pool_size: 2
+  }
+}
+input_control {
+  process_mode: PROCESS_MODE_FULL_FRAME
+  operate_on_gie_id: -1
+  interval: 0
+}
+
@@ -0,0 +1,79 @@
+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2019-2023 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.
+################################################################################
+
+infer_config {
+  unique_id: 1
+  gpu_ids: [0]
+  max_batch_size: 1
+  backend {
+    inputs: [ {
+      name: "input_1:0"
+    }]
+    outputs: [
+      {name: "output_bbox/BiasAdd:0"},
+      {name: "output_cov/Sigmoid:0"}
+    ]
+    triton {
+      model_name: "peoplenet"
+      version: -1
+      model_repo {
+        root: "/opt/nvidia/deepstream/deepstream/samples/models/"
+        strict_model_config: true
+      }
+    }
+  }
+
+  preprocess {
+    network_format: IMAGE_FORMAT_RGB
+    tensor_order: TENSOR_ORDER_LINEAR
+    tensor_name: "input_1:0"
+    maintain_aspect_ratio: 0
+    frame_scaling_hw: FRAME_SCALING_HW_DEFAULT
+    frame_scaling_filter: 1
+    normalize {
+      scale_factor: 0.0039215697906911373
+      channel_offsets: [0, 0, 0]
+    }
+  }
+
+  postprocess {
+    labelfile_path: "/opt/nvidia/deepstream/deepstream/samples/models/peoplenet/labels.txt"
+    detection {
+      num_detected_classes: 4
+      per_class_params {
+        key: 0
+        value { pre_threshold: 0.4 }
+      }
+      nms {
+        confidence_threshold:0.2
+        topk:20
+        iou_threshold:0.5
+      }
+    }
+  }
+
+  extra {
+    copy_input_to_host_buffers: false
+    output_buffer_pool_size: 2
+  }
+}
+input_control {
+  process_mode: PROCESS_MODE_FULL_FRAME
+  operate_on_gie_id: -1
+  interval: 0
+}
+
@@ -0,0 +1,635 @@
+#!/usr/bin/env python3
+
+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2019-2023 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.
+################################################################################
+
+import sys
+from pathlib import Path
+
+SCRIPT_DIR = Path(__file__).resolve().parent  # apps/mine
+DEEPSTREAM_PY_DIR = SCRIPT_DIR.parent  # deepstream_python_apps
+
+sys.path.insert(0, str(DEEPSTREAM_PY_DIR))     # common/ 在这里
+sys.path.insert(0, str(DEEPSTREAM_PY_DIR / "pyds"))  # pyds 模块
+
+from pathlib import Path
+from os import environ
+import gi
+import configparser
+import argparse
+gi.require_version('Gst', '1.0')
+from gi.repository import GLib, Gst
+from ctypes import *
+import time
+import sys
+import math
+import platform
+from common.platform_info import PlatformInfo
+from common.bus_call import bus_call
+from common.FPS import PERF_DATA
+
+import pyds
+import sys
+from pathlib import Path
+from sources.dev.app.utils.my_utils import *
+
+display_type = 0
+silent = False
+file_loop = False
+perf_data = None
+measure_latency = False
+
+MAX_DISPLAY_LEN=64
+PGIE_CLASS_ID_NORMAL = 0
+PGIE_CLASS_ID_DOUBLE_PUPA = 1
+PGIE_CLASS_ID_SPOT = 2
+PGIE_CLASS_ID_HAIRY = 3
+PGIE_CLASS_ID_MAGGOT_SHELL = 4
+MUXER_OUTPUT_WIDTH=1920
+MUXER_OUTPUT_HEIGHT=1080
+MUXER_BATCH_TIMEOUT_USEC = 33000
+TILED_OUTPUT_WIDTH=1280
+TILED_OUTPUT_HEIGHT=720
+YOLO_SIZE=768
+GST_CAPS_FEATURES_NVMM="memory:NVMM"
+OSD_PROCESS_MODE= 0
+OSD_DISPLAY_TEXT= 1
+pgie_classes_str= ["Vehicle", "TwoWheeler", "Person","RoadSign"]
+
+count_sc = 0
+od_labels = []
+
+with open("../models/labels.txt", "r") as f:
+    od_labels = [line.strip() for line in f.readlines()]
+
+def add_fps_display_meta(lineCount, batch_meta, frame_meta, display_text):
+        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]
+        py_nvosd_text_params.display_text = display_text
+        py_nvosd_text_params.x_offset = 12
+        py_nvosd_text_params.y_offset = 12 + lineCount * 24
+        py_nvosd_text_params.font_params.font_name = "Serif"
+        py_nvosd_text_params.font_params.font_size = 10
+        py_nvosd_text_params.font_params.font_color.set(1.0, 1.0, 1.0, 1.0)
+        py_nvosd_text_params.set_bg_clr = 1
+        py_nvosd_text_params.text_bg_clr.set(0.0, 0.0, 0.0, 1.0)
+        # print(pyds.get_string(py_nvosd_text_params.display_text))
+        pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
+
+# pgie_src_pad_buffer_probe  will extract metadata received on tiler sink pad
+# and update params for drawing rectangle, object information etc.
+def pgie_src_pad_buffer_probe(pad,info,u_data):
+    frame_number=0
+    num_rects=0
+    got_fps = False
+    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)
+
+    # Enable latency measurement via probe if environment variable NVDS_ENABLE_LATENCY_MEASUREMENT=1 is set.
+    # To enable component level latency measurement, please set environment variable
+    # NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 in addition to the above.
+    global measure_latency
+    if measure_latency:
+        num_sources_in_batch = pyds.nvds_measure_buffer_latency(hash(gst_buffer))
+        if num_sources_in_batch == 0:
+            print("Unable to get number of sources in GstBuffer for latency measurement")
+
+    batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
+    l_frame = batch_meta.frame_meta_list
+    global count_sc
+    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
+        l_obj=frame_meta.obj_meta_list
+        num_rects = frame_meta.num_obj_meta
+        obj_counter = {
+            PGIE_CLASS_ID_NORMAL:0,
+            PGIE_CLASS_ID_SPOT:0,
+            PGIE_CLASS_ID_DOUBLE_PUPA:0,
+            PGIE_CLASS_ID_HAIRY:0,
+            PGIE_CLASS_ID_MAGGOT_SHELL:0
+        }
+        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
+            obj_counter[obj_meta.class_id] += 1
+            try: 
+                l_obj=l_obj.next
+            except StopIteration:
+                break
+        # if not silent:
+            # print("帧ID=", frame_number, "总蚕茧数=",num_rects,"正茧数=",obj_counter[PGIE_CLASS_ID_NORMAL],"黄斑茧数=",obj_counter[PGIE_CLASS_ID_SPOT])
+
+        # Update frame rate through this probe
+        stream_index = "stream{0}".format(frame_meta.pad_index)
+        global perf_data
+        perf_data.update_fps(stream_index)
+
+        text = f"Object Count: {num_rects}\n"
+        for i in range(len(od_labels)):
+            if obj_counter[i] > 0:
+                text += f"\t{od_labels[i]}: {obj_counter[i]}\n"
+        add_fps_display_meta(0,batch_meta,frame_meta, text)
+        try:
+            l_frame=l_frame.next
+        except StopIteration:
+            break
+
+    # Multi Object Tracker----------------------------------------------------
+    l_obj=batch_meta.batch_user_meta_list
+    while l_obj is not None:
+        try:
+            user_meta=pyds.NvDsUserMeta.cast(l_obj.data)
+        except StopIteration:
+            break
+        # 遍历user_meta.base_meta.meta_type
+        if user_meta and user_meta.base_meta.meta_type==pyds.NvDsMetaType.NVDS_TRACKER_PAST_FRAME_META :
+            try:
+                pPastDataBatch = pyds.NvDsTargetMiscDataBatch.cast(user_meta.user_meta_data)
+            except StopIteration:
+                break
+            for miscDataStream in pyds.NvDsTargetMiscDataBatch.list(pPastDataBatch):
+                for miscDataObj in pyds.NvDsTargetMiscDataStream.list(miscDataStream):
+                    unique_id = miscDataObj.uniqueId
+                    if unique_id > count_sc:
+                        count_sc = unique_id
+        try:
+            l_obj=l_obj.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(index,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-%02d" %index
+    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.
+    if file_loop:
+        # use nvurisrcbin to enable file-loop
+        uri_decode_bin=Gst.ElementFactory.make("nvurisrcbin", "uri-decode-bin")
+        uri_decode_bin.set_property("file-loop", 1)
+        uri_decode_bin.set_property("cudadec-memtype", 0)
+    else:
+        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 main(args, requested_pgie=None, config=None, disable_probe=False):
+    global perf_data,TILED_OUTPUT_WIDTH,TILED_OUTPUT_HEIGHT,YOLO_SIZE,count_sc
+    perf_data = PERF_DATA(len(args))
+
+    number_sources=len(args)
+    if number_sources == 1:
+        print("重置输出分辨率至:{} x {}".format(TILED_OUTPUT_WIDTH, TILED_OUTPUT_HEIGHT))
+        TILED_OUTPUT_WIDTH, TILED_OUTPUT_HEIGHT = getVideoResolution(stream_paths[0])
+
+    platform_info = PlatformInfo()
+    # 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.
+    nvstreammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
+    if not nvstreammux:
+        sys.stderr.write(" Unable to create NvStreamMux \n")
+
+    pipeline.add(nvstreammux)
+    for i in range(number_sources):
+        print("Creating source_bin ",i," \n ")
+        uri_name=args[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= nvstreammux.request_pad_simple(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)
+    queue1=Gst.ElementFactory.make("queue","queue1")
+    queue2=Gst.ElementFactory.make("queue","queue2")
+    queue3=Gst.ElementFactory.make("queue","queue3")
+    queue4=Gst.ElementFactory.make("queue","queue4")
+    queue5=Gst.ElementFactory.make("queue","queue5")
+    pipeline.add(queue1)
+    pipeline.add(queue2)
+    pipeline.add(queue3)
+    pipeline.add(queue4)
+    pipeline.add(queue5)
+
+    nvdslogger = None
+
+    print("Creating Pgie \n ")
+    if requested_pgie != None and (requested_pgie == 'nvinferserver' or requested_pgie == 'nvinferserver-grpc') :
+        pgie = Gst.ElementFactory.make("nvinferserver", "primary-inference")
+    elif requested_pgie != None and requested_pgie == 'nvinfer':
+        # local tensorrt infer
+        pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
+    else:
+        pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
+
+    if not pgie:
+        sys.stderr.write(" Unable to create pgie :  %s\n" % requested_pgie)
+
+    if disable_probe:
+        # Use nvdslogger for perf measurement instead of probe function
+        print ("Creating nvdslogger \n")
+        nvdslogger = Gst.ElementFactory.make("nvdslogger", "nvdslogger")
+
+    print("Creating tiler \n ")
+    nvmultistreamtiler=Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
+    if not nvmultistreamtiler:
+        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")
+    nvosd.set_property('process-mode',OSD_PROCESS_MODE)
+    nvosd.set_property('display-text',OSD_DISPLAY_TEXT)
+
+    if file_loop:
+        if platform_info.is_integrated_gpu():
+            # Set nvbuf-memory-type=4 for integrated gpu for file-loop (nvurisrcbin case)
+            nvstreammux.set_property('nvbuf-memory-type', 4)
+        else:
+            # Set nvbuf-memory-type=2 for x86 for file-loop (nvurisrcbin case)
+            nvstreammux.set_property('nvbuf-memory-type', 2)
+
+    if display_type == 0:
+        sink = Gst.ElementFactory.make("filesink", "file-sink")
+        sink.set_property("location", "./out.mp4")
+        sink.set_property("sync", 0)
+    elif display_type == 1:
+        print("Creating Fakesink \n")
+        sink = Gst.ElementFactory.make("fakesink", "fakesink")
+        sink.set_property('enable-last-sample', 0)
+        sink.set_property('sync', 0)
+    else:
+        if platform_info.is_integrated_gpu():
+            print("Creating nv3dsink \n")
+            sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
+            if not sink:
+                sys.stderr.write(" Unable to create nv3dsink \n")
+        else:
+            if platform_info.is_platform_aarch64():
+                print("Creating nv3dsink \n")
+                sink = Gst.ElementFactory.make("nv3dsink", "nv3d-sink")
+            else:
+                print("Creating EGLSink \n")
+                sink = Gst.ElementFactory.make("nveglglessink", "nvvideo-renderer")
+            if not sink:
+                sys.stderr.write(" Unable to create egl sink \n")
+
+    if not sink:
+        sys.stderr.write(" Unable to create sink element \n")
+
+    if is_live:
+        print("At least one of the sources is live")
+        nvstreammux.set_property('live-source', 1)
+
+    
+    nvvidconv2 = Gst.ElementFactory.make("nvvideoconvert", "convertor2")
+    if not nvvidconv2:
+        sys.stderr.write(" Unable to create nvvidconv2 \n")
+
+    encoder = Gst.ElementFactory.make("nvv4l2h264enc", "encoder") # for h264
+    # encoder = Gst.ElementFactory.make("avenc_mpeg4", "encoder") # for mpeg4
+    if not encoder:
+        sys.stderr.write(" Unable to create encoder \n")
+    encoder.set_property("bitrate", 2000000)
+
+    h264parse = Gst.ElementFactory.make("h264parse", "parser")
+    # codeparser = Gst.ElementFactory.make("mpeg4videoparse", "mpeg4-parser")
+    if not h264parse:
+        sys.stderr.write(" Unable to create code parser \n")
+
+    qtmux = Gst.ElementFactory.make("qtmux", "qtmux")
+    if not qtmux:
+        sys.stderr.write(" Unable to create code parser \n")
+
+    nvstreammux.set_property('width', YOLO_SIZE)
+    nvstreammux.set_property('height', YOLO_SIZE)
+    nvstreammux.set_property('batch-size', number_sources)
+    nvstreammux.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC)
+    if requested_pgie == "nvinferserver" and config != None:
+        pgie.set_property('config-file-path', config)
+    elif requested_pgie == "nvinferserver-grpc" and config != None:
+        pgie.set_property('config-file-path', config)
+    elif requested_pgie == "nvinfer" and config != None:
+        pgie.set_property('config-file-path', config)
+    else:
+        pgie.set_property('config-file-path', "config/pgie_config.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))
+    nvmultistreamtiler.set_property("rows",tiler_rows)
+    nvmultistreamtiler.set_property("columns",tiler_columns)
+    nvmultistreamtiler.set_property("width", TILED_OUTPUT_WIDTH)
+    nvmultistreamtiler.set_property("height", TILED_OUTPUT_HEIGHT)
+    if platform_info.is_integrated_gpu():
+        nvmultistreamtiler.set_property("compute-hw", 2)
+    else:
+        nvmultistreamtiler.set_property("compute-hw", 1)
+    sink.set_property("qos",0)
+
+    tracker = Gst.ElementFactory.make("nvtracker", "tracker")
+    if not tracker:
+        sys.stderr.write(" Unable to create tracker \n")
+    tracker.set_property('tracker-width', YOLO_SIZE)
+    tracker.set_property('tracker-height', YOLO_SIZE)
+    # To fix 'gstnvtracker: Unable to acquire a user meta buffer. Try increasing user-meta-pool-size'
+    tracker.set_property('user-meta-pool-size', 128)
+    tracker.set_property('gpu_id', 0)
+    tracker.set_property('ll-lib-file', 'lib/libnvds_nvmultiobjecttracker.so')
+    tracker.set_property('ll-config-file', 'config/config_tracker_NvDCF_perf.yml')
+
+    print("Adding elements to Pipeline \n")
+    pipeline.add(pgie)
+    if nvdslogger:
+        pipeline.add(nvdslogger)
+    pipeline.add(nvmultistreamtiler)
+    pipeline.add(tracker)
+    pipeline.add(nvvidconv)
+    pipeline.add(nvosd)
+    pipeline.add(sink)
+
+    print("Linking elements in the Pipeline \n")
+    nvstreammux.link(queue1)
+    queue1.link(pgie)
+    pgie.link(tracker)
+    tracker.link(queue2)
+    if nvdslogger:
+        queue2.link(nvdslogger)
+        nvdslogger.link(nvmultistreamtiler)
+    else:
+        queue2.link(nvmultistreamtiler)
+    nvmultistreamtiler.link(queue3)
+    queue3.link(nvvidconv)
+    nvvidconv.link(queue4)
+    queue4.link(nvosd)
+    nvosd.link(queue5)
+
+    if display_type == 0:
+        pipeline.add(nvvidconv2)
+        pipeline.add(encoder)
+        pipeline.add(h264parse)
+        pipeline.add(qtmux)
+        
+        queue5.link(nvvidconv2)
+        nvvidconv2.link(encoder)
+        encoder.link(h264parse)
+        h264parse.link(qtmux)
+        qtmux.link(sink)
+    else:
+        queue5.link(sink)   
+
+    # 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)
+    pgie_src_pad=nvosd.get_static_pad("sink")
+    if not pgie_src_pad:
+        sys.stderr.write(" Unable to get src pad \n")
+    else:
+        if not disable_probe:
+            pgie_src_pad.add_probe(Gst.PadProbeType.BUFFER, pgie_src_pad_buffer_probe, 0)
+            # perf callback function to print fps every 5 sec
+            GLib.timeout_add(5000, perf_data.perf_print_callback)
+
+    # Enable latency measurement via probe if environment variable NVDS_ENABLE_LATENCY_MEASUREMENT=1 is set.
+    # To enable component level latency measurement, please set environment variable
+    # NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 in addition to the above.
+    if environ.get('NVDS_ENABLE_LATENCY_MEASUREMENT') == '1':
+        print ("Pipeline Latency Measurement enabled!\nPlease set env var NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 for Component Latency Measurement")
+        global measure_latency
+        measure_latency = True
+
+    # List the sources
+    print("Now playing...")
+    for i, source in enumerate(args):
+        print(i, ": ", source)
+
+    print("Starting pipeline \n")
+    # start play back and listed to events		
+    pipeline.set_state(Gst.State.PLAYING)
+    try:
+        loop.run()
+    except:
+        pass
+
+    print("总蚕茧数：",count_sc-1)
+    # cleanup
+    print("Exiting app\n")
+    pipeline.set_state(Gst.State.NULL)
+
+def parse_args():
+
+    parser = argparse.ArgumentParser(prog="deepstream_test_3",
+                    description="deepstream-test3 multi stream, multi model inference reference app")
+    parser.add_argument(
+        "-i",
+        "--input",
+        help="Path to input streams",
+        nargs="+",
+        metavar="URIs",
+        default=["a"],
+        required=True,
+    )
+    parser.add_argument(
+        "-c",
+        "--configfile",
+        metavar="config_location.txt",
+        default=None,
+        help="Choose the config-file to be used with specified pgie",
+    )
+    parser.add_argument(
+        "-g",
+        "--pgie",
+        default=None,
+        help="Choose Primary GPU Inference Engine",
+        choices=["nvinfer", "nvinferserver", "nvinferserver-grpc"],
+    )
+    parser.add_argument(
+        "--display_type",
+        type=int,
+        default=0,
+        help="Display type: 0=file, 1=fake"
+    )
+
+    parser.add_argument(
+        "--file-loop",
+        action="store_true",
+        default=False,
+        dest='file_loop',
+        help="Loop the input file sources after EOS",
+    )
+    parser.add_argument(
+        "--disable-probe",
+        action="store_true",
+        default=False,
+        dest='disable_probe',
+        help="Disable the probe function and use nvdslogger for FPS",
+    )
+    parser.add_argument(
+        "-s",
+        "--silent",
+        action="store_true",
+        default=False,
+        dest='silent',
+        help="Disable verbose output",
+    )
+    # Check input arguments
+    if len(sys.argv) == 1:
+        parser.print_help(sys.stderr)
+        sys.exit(1)
+    args = parser.parse_args()
+
+    stream_paths = args.input
+    pgie = args.pgie
+    config = args.configfile
+    disable_probe = args.disable_probe
+    global display_type
+    global silent
+    global file_loop
+    display_type = args.display_type
+    silent = args.silent
+    file_loop = args.file_loop
+
+    if config and not pgie or pgie and not config:
+        sys.stderr.write ("\nEither pgie or configfile is missing. Please specify both! Exiting...\n\n\n\n")
+        parser.print_help()
+        sys.exit(1)
+    if config:
+        config_path = Path(config)
+        if not config_path.is_file():
+            sys.stderr.write ("Specified config-file: %s doesn't exist. Exiting...\n\n" % config)
+            sys.exit(1)
+
+    print(vars(args))
+    return stream_paths, pgie, config, disable_probe
+
+if __name__ == '__main__':
+    # 此程序支持多个视频流输入，但只为单视频流输入开发
+
+    # 外部输入参数
+    stream_paths, pgie, config, disable_probe = parse_args()
+        
+    sys.exit(main(stream_paths, pgie, config, disable_probe))
@@ -0,0 +1,515 @@
+#!/usr/bin/env python3
+
+################################################################################
+# SPDX-FileCopyrightText: Copyright (c) 2019-2023 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.
+################################################################################
+
+import sys
+from pathlib import Path
+
+SCRIPT_DIR = Path(__file__).resolve().parent  # apps/mine
+DEEPSTREAM_PY_DIR = SCRIPT_DIR.parent  # deepstream_python_apps
+
+sys.path.insert(0, str(DEEPSTREAM_PY_DIR))     # common/ 在这里
+sys.path.insert(0, str(DEEPSTREAM_PY_DIR / "pyds"))  # pyds 模块
+
+from pathlib import Path
+from os import environ
+import gi
+gi.require_version('Gst', '1.0')
+from gi.repository import GLib, Gst
+from ctypes import *
+import time
+import sys
+import math
+from common.platform_info import PlatformInfo
+from common.bus_call import bus_call
+from common.FPS import PERF_DATA
+import pyds
+import sys
+from pathlib import Path
+from utils.my_utils import *
+
+display_type = 0
+silent = False
+file_loop = False
+perf_data = None
+measure_latency = False
+
+MAX_DISPLAY_LEN=64
+PGIE_CLASS_ID_NORMAL = 0
+PGIE_CLASS_ID_DOUBLE_PUPA = 1
+PGIE_CLASS_ID_SPOT = 2
+PGIE_CLASS_ID_HAIRY = 3
+PGIE_CLASS_ID_MAGGOT_SHELL = 4
+
+MUXER_OUTPUT_WIDTH=1920
+MUXER_OUTPUT_HEIGHT=1080
+MUXER_BATCH_TIMEOUT_USEC = 33000
+TILED_OUTPUT_WIDTH=1280
+TILED_OUTPUT_HEIGHT=720
+YOLO_SIZE=768
+GST_CAPS_FEATURES_NVMM="memory:NVMM"
+OSD_PROCESS_MODE= 0
+OSD_DISPLAY_TEXT= 1
+tracker_dict = {}
+
+obj_counter = {
+    PGIE_CLASS_ID_NORMAL:0,
+    PGIE_CLASS_ID_SPOT:0,
+    PGIE_CLASS_ID_DOUBLE_PUPA:0,
+    PGIE_CLASS_ID_HAIRY:0,
+    PGIE_CLASS_ID_MAGGOT_SHELL:0
+}
+
+od_labels = []
+statics={}
+timestamp_in_video = 0
+
+with open("../models/labels.txt", "r") as f:
+    od_labels = [line.strip() for line in f.readlines()]
+
+def stats_callback():
+    # 这里统计你需要的数据，例如：
+    global timestamp_in_video,statics
+    timestamp_in_video += 1  # 假设每秒钟调用一次
+    temp_json = {}
+
+    for class_id, count in obj_counter.items():
+        class_name = get_type_name_by_id(class_id)
+        temp_json[class_name] = count
+    statics[timestamp_in_video] = temp_json
+    return True   # 返回 True 才会继续循环调用
+
+
+def add_fps_display_meta(lineCount, batch_meta, frame_meta, display_text):
+        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]
+        py_nvosd_text_params.display_text = display_text
+        py_nvosd_text_params.x_offset = 12
+        py_nvosd_text_params.y_offset = 12 + lineCount * 24
+        py_nvosd_text_params.font_params.font_name = "Serif"
+        py_nvosd_text_params.font_params.font_size = 10
+        py_nvosd_text_params.font_params.font_color.set(1.0, 1.0, 1.0, 1.0)
+        py_nvosd_text_params.set_bg_clr = 1
+        py_nvosd_text_params.text_bg_clr.set(0.0, 0.0, 0.0, 1.0)
+        # print(pyds.get_string(py_nvosd_text_params.display_text))
+        pyds.nvds_add_display_meta_to_frame(frame_meta, display_meta)
+
+# pgie_src_pad_buffer_probe  will extract metadata received on tiler sink pad
+# and update params for drawing rectangle, object information etc.
+def pgie_src_pad_buffer_probe(pad,info,u_data):
+    global measure_latency, obj_counter
+    frame_number=0
+    num_rects=0
+    got_fps = False
+    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)
+
+    # Enable latency measurement via probe if environment variable NVDS_ENABLE_LATENCY_MEASUREMENT=1 is set.
+    # To enable component level latency measurement, please set environment variable
+    # NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 in addition to the above.
+    if measure_latency:
+        num_sources_in_batch = pyds.nvds_measure_buffer_latency(hash(gst_buffer))
+        if num_sources_in_batch == 0:
+            print("Unable to get number of sources in GstBuffer for latency measurement")
+
+    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
+        l_obj=frame_meta.obj_meta_list
+        num_rects = frame_meta.num_obj_meta
+
+        # 初始化
+        obj_counter = {
+            PGIE_CLASS_ID_NORMAL:0,
+            PGIE_CLASS_ID_SPOT:0,
+            PGIE_CLASS_ID_DOUBLE_PUPA:0,
+            PGIE_CLASS_ID_HAIRY:0,
+            PGIE_CLASS_ID_MAGGOT_SHELL:0
+        }
+        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
+            obj_counter[obj_meta.class_id] += 1
+            try: 
+                l_obj=l_obj.next
+            except StopIteration:
+                break
+        # if not silent:
+            # print("帧ID=", frame_number, "总蚕茧数=",num_rects,"正茧数=",obj_counter[PGIE_CLASS_ID_NORMAL],"黄斑茧数=",obj_counter[PGIE_CLASS_ID_SPOT])
+
+        # Update frame rate through this probe
+        stream_index = "stream{0}".format(frame_meta.pad_index)
+        global perf_data
+        perf_data.update_fps(stream_index)
+
+        text = f"Object Count: {num_rects}\n"
+        for i in range(len(od_labels)):
+            if obj_counter[i] > 0:
+                text += f"\t{od_labels[i]}: {obj_counter[i]}\n"
+        add_fps_display_meta(0,batch_meta,frame_meta, text)
+        try:
+            l_frame=l_frame.next
+        except StopIteration:
+            break
+
+    # Multi Object Tracker----------------------------------------------------
+    l_obj=batch_meta.batch_user_meta_list
+    while l_obj is not None:
+        try:
+            user_meta=pyds.NvDsUserMeta.cast(l_obj.data)
+        except StopIteration:
+            break
+        # 遍历user_meta.base_meta.meta_type
+        if user_meta and user_meta.base_meta.meta_type==pyds.NvDsMetaType.NVDS_TRACKER_PAST_FRAME_META :
+            try:
+                pPastDataBatch = pyds.NvDsTargetMiscDataBatch.cast(user_meta.user_meta_data)
+            except StopIteration:
+                break
+            for miscDataStream in pyds.NvDsTargetMiscDataBatch.list(pPastDataBatch):
+                for miscDataObj in pyds.NvDsTargetMiscDataStream.list(miscDataStream):
+                    unique_id = miscDataObj.uniqueId
+                    tracker_dict[unique_id] = miscDataObj.classId
+        try:
+            l_obj=l_obj.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(index,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-%02d" %index
+    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.
+    if file_loop:
+        # use nvurisrcbin to enable file-loop
+        uri_decode_bin=Gst.ElementFactory.make("nvurisrcbin", "uri-decode-bin")
+        uri_decode_bin.set_property("file-loop", 1)
+        uri_decode_bin.set_property("cudadec-memtype", 0)
+    else:
+        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 sca(stream_paths, out_put_file_name:str):
+    disable_probe=False
+    global perf_data,TILED_OUTPUT_WIDTH,TILED_OUTPUT_HEIGHT,YOLO_SIZE
+    perf_data = PERF_DATA(len(stream_paths))
+
+    number_sources=len(stream_paths)
+    if number_sources == 1:
+        print("重置输出分辨率至:{} x {}".format(TILED_OUTPUT_WIDTH, TILED_OUTPUT_HEIGHT))
+        TILED_OUTPUT_WIDTH, TILED_OUTPUT_HEIGHT = getVideoResolution(stream_paths[0])
+
+    platform_info = PlatformInfo()
+    Gst.init(None)
+
+    pipeline = Gst.Pipeline()
+    is_live = False
+
+    if not pipeline:
+        sys.stderr.write(" Unable to create Pipeline \n")
+
+    nvstreammux = Gst.ElementFactory.make("nvstreammux", "Stream-muxer")
+    if not nvstreammux:
+        sys.stderr.write(" Unable to create NvStreamMux \n")
+
+    pipeline.add(nvstreammux)
+    for i in range(number_sources):
+        uri_name=stream_paths[i]
+        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= nvstreammux.request_pad_simple(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)
+    queue1=Gst.ElementFactory.make("queue","queue1")
+    queue2=Gst.ElementFactory.make("queue","queue2")
+    queue3=Gst.ElementFactory.make("queue","queue3")
+    queue4=Gst.ElementFactory.make("queue","queue4")
+    queue5=Gst.ElementFactory.make("queue","queue5")
+    pipeline.add(queue1)
+    pipeline.add(queue2)
+    pipeline.add(queue3)
+    pipeline.add(queue4)
+    pipeline.add(queue5)
+
+    nvdslogger = None
+
+    pgie = Gst.ElementFactory.make("nvinfer", "primary-inference")
+
+    if not pgie:
+        sys.stderr.write(" Unable to create pgie")
+    pgie.set_property('config-file-path', "config/pgie_config.txt")
+
+    if disable_probe:
+        # Use nvdslogger for perf measurement instead of probe function
+        nvdslogger = Gst.ElementFactory.make("nvdslogger", "nvdslogger")
+
+    nvmultistreamtiler=Gst.ElementFactory.make("nvmultistreamtiler", "nvtiler")
+    if not nvmultistreamtiler:
+        sys.stderr.write(" Unable to create tiler \n")
+    nvvidconv = Gst.ElementFactory.make("nvvideoconvert", "convertor")
+    if not nvvidconv:
+        sys.stderr.write(" Unable to create nvvidconv \n")
+    nvosd = Gst.ElementFactory.make("nvdsosd", "onscreendisplay")
+    if not nvosd:
+        sys.stderr.write(" Unable to create nvosd \n")
+    nvosd.set_property('process-mode',OSD_PROCESS_MODE)
+    nvosd.set_property('display-text',OSD_DISPLAY_TEXT)
+    if file_loop:
+        if platform_info.is_integrated_gpu():
+            # Set nvbuf-memory-type=4 for integrated gpu for file-loop (nvurisrcbin case)
+            nvstreammux.set_property('nvbuf-memory-type', 4)
+        else:
+            # Set nvbuf-memory-type=2 for x86 for file-loop (nvurisrcbin case)
+            nvstreammux.set_property('nvbuf-memory-type', 2)
+
+    sink = Gst.ElementFactory.make("filesink", "file-sink")
+    sink.set_property("location", out_put_file_name)
+    sink.set_property("sync", 0)
+
+    if not sink:
+        sys.stderr.write(" Unable to create sink element \n")
+    if is_live:
+        nvstreammux.set_property('live-source', 1)
+    nvvidconv2 = Gst.ElementFactory.make("nvvideoconvert", "convertor2")
+    if not nvvidconv2:
+        sys.stderr.write(" Unable to create nvvidconv2 \n")
+    encoder = Gst.ElementFactory.make("nvv4l2h264enc", "encoder") # for h264
+    # encoder = Gst.ElementFactory.make("avenc_mpeg4", "encoder") # for mpeg4
+    if not encoder:
+        sys.stderr.write(" Unable to create encoder \n")
+    encoder.set_property("bitrate", 2000000)
+
+    h264parse = Gst.ElementFactory.make("h264parse", "parser")
+    # codeparser = Gst.ElementFactory.make("mpeg4videoparse", "mpeg4-parser")
+    if not h264parse:
+        sys.stderr.write(" Unable to create code parser \n")
+
+    qtmux = Gst.ElementFactory.make("qtmux", "qtmux")
+    if not qtmux:
+        sys.stderr.write(" Unable to create code parser \n")
+
+    nvstreammux.set_property('width', YOLO_SIZE)
+    nvstreammux.set_property('height', YOLO_SIZE)
+    nvstreammux.set_property('batch-size', number_sources)
+    nvstreammux.set_property('batched-push-timeout', MUXER_BATCH_TIMEOUT_USEC)
+    
+    pgie_batch_size=pgie.get_property("batch-size")
+    if(pgie_batch_size != number_sources):
+        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))
+    nvmultistreamtiler.set_property("rows",tiler_rows)
+    nvmultistreamtiler.set_property("columns",tiler_columns)
+    nvmultistreamtiler.set_property("width", TILED_OUTPUT_WIDTH)
+    nvmultistreamtiler.set_property("height", TILED_OUTPUT_HEIGHT)
+    if platform_info.is_integrated_gpu():
+        nvmultistreamtiler.set_property("compute-hw", 2)
+    else:
+        nvmultistreamtiler.set_property("compute-hw", 1)
+    sink.set_property("qos",0)
+
+    tracker = Gst.ElementFactory.make("nvtracker", "tracker")
+    if not tracker:
+        sys.stderr.write(" Unable to create tracker \n")
+    tracker.set_property('tracker-width', YOLO_SIZE)
+    tracker.set_property('tracker-height', YOLO_SIZE)
+    # To fix 'gstnvtracker: Unable to acquire a user meta buffer. Try increasing user-meta-pool-size'
+    tracker.set_property('user-meta-pool-size', 128)
+    tracker.set_property('gpu_id', 0)
+    tracker.set_property('ll-lib-file', 'lib/libnvds_nvmultiobjecttracker.so')
+    tracker.set_property('ll-config-file', 'config/config_tracker_NvDCF_perf.yml')
+
+    pipeline.add(pgie)
+    if nvdslogger:
+        pipeline.add(nvdslogger)
+    pipeline.add(nvmultistreamtiler)
+    pipeline.add(tracker)
+    pipeline.add(nvvidconv)
+    pipeline.add(nvosd)
+    pipeline.add(sink)
+
+    nvstreammux.link(queue1)
+    queue1.link(pgie)
+    pgie.link(tracker)
+    tracker.link(queue2)
+    if nvdslogger:
+        queue2.link(nvdslogger)
+        nvdslogger.link(nvmultistreamtiler)
+    else:
+        queue2.link(nvmultistreamtiler)
+    nvmultistreamtiler.link(queue3)
+    queue3.link(nvvidconv)
+    nvvidconv.link(queue4)
+    queue4.link(nvosd)
+    nvosd.link(queue5)
+
+    if display_type == 0:
+        pipeline.add(nvvidconv2)
+        pipeline.add(encoder)
+        pipeline.add(h264parse)
+        pipeline.add(qtmux)
+        
+        queue5.link(nvvidconv2)
+        nvvidconv2.link(encoder)
+        encoder.link(h264parse)
+        h264parse.link(qtmux)
+        qtmux.link(sink)
+    else:
+        queue5.link(sink)   
+
+    # create an event loop and feed gstreamer bus mesages to it
+    loop = GLib.MainLoop()
+    bus = pipeline.get_bus()
+    bus.add_signal_watch()
+    # 关闭日志
+    bus.connect ("message", bus_call, loop)
+    pgie_src_pad=nvosd.get_static_pad("sink")
+    if not pgie_src_pad:
+        sys.stderr.write(" Unable to get src pad \n")
+    else:
+        if not disable_probe:
+            pgie_src_pad.add_probe(Gst.PadProbeType.BUFFER, pgie_src_pad_buffer_probe, 0)
+            # perf callback function to print fps every 5 sec
+            GLib.timeout_add(5000, perf_data.perf_print_callback)
+    GLib.timeout_add(500, stats_callback)
+
+
+    # Enable latency measurement via probe if environment variable NVDS_ENABLE_LATENCY_MEASUREMENT=1 is set.
+    # To enable component level latency measurement, please set environment variable
+    # NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 in addition to the above.
+    if environ.get('NVDS_ENABLE_LATENCY_MEASUREMENT') == '1':
+        print ("Pipeline Latency Measurement enabled!\nPlease set env var NVDS_ENABLE_COMPONENT_LATENCY_MEASUREMENT=1 for Component Latency Measurement")
+        global measure_latency
+        measure_latency = True
+
+    # List the sources
+    for i, source in enumerate(stream_paths):
+        print(i, ": ", source)
+
+    print("Starting pipeline \n")
+    # start play back and listed to events		
+    pipeline.set_state(Gst.State.PLAYING)
+    # 开始时间
+    start_time = time.time()
+    try:
+        loop.run()
+    except:
+        pass
+
+    pipeline.set_state(Gst.State.NULL)
+    print("总蚕茧数：",len(tracker_dict))
+    print("Exiting app\n")
+    # 结束时间
+    end_time = time.time()
+    analysis_time = end_time - start_time
+    print(f"处理时间: {analysis_time} 秒")
+
+    global statics
+    save_sca_results(stream_paths[0], out_put_file_name,tracker_dict,analysis_time,statics)
@@ -0,0 +1 @@
+LOCAL_IP = "10.10.12.101"
@@ -0,0 +1,78 @@
+from utils.pgDb import pg_pool
+import json
+
+def insert_sca_video(
+    name,
+    raw_object_name,
+    ai_object_name,
+    duration,
+    size,
+    video_codec,
+    audio_codec,
+    overall_bit_rate,
+    resolution,
+    sc_analysis_time,
+    sc_analysis_total_count,
+    sc_analysis_max_count,
+    sc_analysis_primary_type,
+    sc_analysis_secondary_type,
+    other_info,
+):
+    with pg_pool.getConn() as conn:
+        with conn.cursor() as cursor:
+            other_info = json.dumps(other_info, ensure_ascii=False)
+            cursor.execute(
+                """
+                INSERT INTO sca_videos (
+                    name, raw_object_name, ai_object_name, duration, size, video_codec, audio_codec, 
+                    overall_bit_rate, resolution, sc_analysis_time, sc_analysis_total_count, sc_analysis_max_count, 
+                    sc_analysis_primary_type, sc_analysis_secondary_type, other_info, created_at 
+                )
+                VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, NOW())
+                RETURNING id
+                """,
+                (
+                    name,
+                    raw_object_name,
+                    ai_object_name,
+                    duration,
+                    size,
+                    video_codec,
+                    audio_codec,
+                    overall_bit_rate,
+                    resolution,
+                    sc_analysis_time,
+                    sc_analysis_total_count,
+                    sc_analysis_max_count,
+                    sc_analysis_primary_type,
+                    sc_analysis_secondary_type,
+                    other_info,
+                ),
+            )
+
+            new_id = cursor.fetchone()[0]
+            conn.commit()
+            return new_id
+
+def insert_sca_video_details(
+    v_id,
+    time_stamp,
+    other_info,
+):
+    with pg_pool.getConn() as conn:
+        with conn.cursor() as cursor:
+            other_info = json.dumps(other_info, ensure_ascii=False)
+            cursor.execute(
+                """
+                INSERT INTO sca_video_details (
+                    v_id, time_stamp, other_info 
+                )
+                VALUES (%s, %s, %s)
+                """,
+                (
+                    v_id,
+                    time_stamp,
+                    other_info,
+                ),
+            )
+            conn.commit()
@@ -0,0 +1,97 @@
+
+from pymediainfo import MediaInfo
+from datetime import datetime
+import json
+from utils.db import insert_sca_video,insert_sca_video_details
+from collections import Counter
+
+def getVideoResolution(file_path):
+    media_info = MediaInfo.parse(file_path)  # 解析视频文件
+    
+    for track in media_info.tracks:
+        if track.track_type == 'Video': 
+            return track.width,track.height
+
+def get_type_name_by_id(id):
+    type_dict = {
+        0: "正茧",
+        1: "双宫茧",
+        2: "黄斑茧",
+        3: "毛茧",
+        4: "蛆壳茧",
+    }
+    return type_dict.get(id, "未知类型")
+
+# 将视频信息保存至数据库
+def save_sca_results(file_path, out_put_file_name,tracker_dict,
+                     analysis_time,statics =[]):
+    media_info = MediaInfo.parse(file_path)  # 解析视频文件
+    video_data = {}  # 用于存储视频信息
+    
+    for track in media_info.tracks:
+        if track.track_type == 'General': 
+            video_data.update({
+                "v_file_name": track.file_name_extension,
+                "v_duration": round(track.duration / 1000),
+                "v_size": round(track.file_size/1024,2), 
+                "v_video_codec": track.video_format_list,
+                "v_audio_codec": track.audio_codecs,
+                "v_overall_bit_rate": track.overall_bit_rate,  
+            })
+        if track.track_type == 'Video': 
+            video_data.update({
+                "v_resolution":  f"{track.width}x{track.height}" 
+            })
+
+    # 分析 得出数量最多的种类 以及 出现次多的种类
+    counter = Counter(tracker_dict.values())     # 统计所有 value 的出现次数
+    most_common = counter.most_common(2)  # 取出现次数前两名（返回列表）
+    primary_type = get_type_name_by_id(most_common[0][0]) if most_common else ""
+    secondary_type = get_type_name_by_id(most_common[1][0]) if len(most_common) > 1 else ""
+
+      # 1️⃣ 统计 { value: count }
+    count_map = {}
+    for k, v in tracker_dict.items():
+        v_int = int(v)
+        count_map[v_int] = count_map.get(v_int, 0) + 1
+
+    # 2️⃣ 替换成类型名称
+    result = {}
+    for type_value, count in count_map.items():
+        type_name = get_type_name_by_id(type_value)
+        result[type_name] = count
+
+    other_info = result
+
+    # 分析结果进行统计 得出 单帧最大蚕茧数量
+    max_count_in_same_frame = 0
+    for timestamp_in_video,json in statics.items():
+        count_in_frame = sum(json.values())
+        if count_in_frame > max_count_in_same_frame:
+            max_count_in_same_frame = count_in_frame
+
+    # 保存至数据库
+    video_id = insert_sca_video(
+        # 取后八位
+        name=out_put_file_name.split('/')[-1].split('.')[0].replace('-','')[-8:],
+        raw_object_name=video_data.get("v_file_name", "").split('/')[-1],
+        ai_object_name=out_put_file_name.split('/')[-1],
+        duration=video_data.get("v_duration", 0),
+        size=video_data.get("v_size", 0),
+        video_codec=video_data.get("v_video_codec", ""),
+        audio_codec=video_data.get("v_audio_codec", ""),
+        overall_bit_rate=video_data.get("v_overall_bit_rate", 0),
+        resolution=video_data.get("v_resolution", ""),
+        sc_analysis_time= analysis_time,
+        sc_analysis_total_count=len(tracker_dict),
+        sc_analysis_max_count=max_count_in_same_frame,
+        sc_analysis_primary_type= primary_type,
+        sc_analysis_secondary_type= secondary_type,
+        other_info= other_info,
+    )
+
+    # 将细则存入数据库
+    counter = Counter()
+
+    for timestamp_in_video, json in statics.items():
+        insert_sca_video_details(video_id,timestamp_in_video, json)
@@ -0,0 +1,72 @@
+import logging
+import time
+from contextlib import contextmanager
+
+import psycopg
+from psycopg_pool import ConnectionPool
+from utils.GlobalVariable import LOCAL_IP
+
+logger = logging.getLogger("PGPool")
+logger.setLevel(logging.INFO)
+
+class PGPool:
+    """
+    PostgreSQL 连接池封装
+    """
+
+    def __init__(
+        self,
+        uri: str,
+        min_size: int = 1,
+        max_size: int = 20,
+        max_idle: int = 30,
+        max_lifetime: int = 300,
+        timeout: int = 10,
+        check: bool = False,
+    ):
+        """
+        :param uri: PostgreSQL 连接 URI
+        """
+        self.uri = uri
+        self.pool = ConnectionPool(
+            self.uri,
+            min_size=min_size,
+            max_size=max_size,
+            max_idle=max_idle,
+            max_lifetime=max_lifetime,
+            timeout=timeout,
+            check=check,
+        )
+
+    @contextmanager
+    def getConn(self, retries: int = 2, delay: float = 1.0):
+        """
+        获取数据库连接，带重试机制，自动健康检查。
+        使用方式：
+            with pg_pool.get_conn() as conn:
+                with conn.cursor() as cur:
+                    cur.execute(...)
+        """
+        attempt = 0
+        while attempt <= retries:
+            try:
+                with self.pool.connection() as conn:
+                    conn.autocommit = True
+                    yield conn
+                    return
+            except psycopg.OperationalError as e:
+                logger.warning(f"数据库连接异常: {e}. 尝试重试 ({attempt+1}/{retries})")
+                self.pool.check()  # 丢掉坏连接，重新建
+                attempt += 1
+                time.sleep(delay)
+            except Exception as e:
+                logger.error(f"SQL执行异常: {e}")
+                raise
+        raise psycopg.OperationalError("无法获取数据库连接，多次重试失败")
+
+
+pg_pool = PGPool(
+    uri="postgresql://postgres:123456@" + LOCAL_IP + "/ktor2",
+    min_size=1,
+    max_size=20,
+)
@@ -0,0 +1,7 @@
+from utils.GlobalVariable import LOCAL_IP
+
+RABBIT_HOST = LOCAL_IP
+RABBIT_VHOST = "bbit_ai"
+RABBIT_USER = "ai_lab"
+RABBIT_PASSWORD = "123456"
+QUEUE_NAME = "analysis_queue"