static void Main(string[] args)
{
//parse arguments
if (args.Length < 5)
{
Console.WriteLine(args.Length);
Console.WriteLine("Usage: <exe> <video url> <cfg file> <samplingFactor> <resolutionFactor> <category1> <category2> ...");
return;
}
string videoUrl = args[0];
bool isVideoStream;
if (videoUrl.Substring(0, 4) == "rtmp" || videoUrl.Substring(0, 4) == "http" || videoUrl.Substring(0, 3) == "mms" || videoUrl.Substring(0, 4) == "rtsp")
{
isVideoStream = true;
}
else
{
isVideoStream = false;
videoUrl = @"..\..\..\..\..\..\media\" + args[0];
}
string lineFile = @"..\..\..\..\..\..\cfg\" + args[1];
int SAMPLING_FACTOR = int.Parse(args[2]);
double RESOLUTION_FACTOR = double.Parse(args[3]);
Dictionary <string, int> category = new Dictionary <string, int>();
for (int i = 4; i < args.Length; i++)
{
category.Add(args[i], 0);
}
//initialize pipeline settings
int pplConfig = Convert.ToInt16(ConfigurationManager.AppSettings["PplConfig"]);
bool loop = false;
bool displayRawVideo = true;
bool displayBGSVideo = false;
Utils.Utils.cleanFolder(@OutputFolder.OutputFolderAll);
//create pipeline components (initialization based on pplConfig)
//-----Decoder-----
Decoder.Decoder decoder = new Decoder.Decoder(videoUrl, loop);
//-----Background Subtraction-based Detector-----
BGSObjectDetector.BGSObjectDetector bgs = new BGSObjectDetector.BGSObjectDetector();
//-----Line Detector-----
Detector lineDetector = new Detector(SAMPLING_FACTOR, RESOLUTION_FACTOR, lineFile, displayBGSVideo);
Dictionary <string, bool> occupancy = null;
List <Tuple <string, int[]> > lines = lineDetector.multiLaneDetector.getAllLines();
//-----LineTriggeredDNN (Darknet)-----
LineTriggeredDNNDarknet ltDNNDarknet = null;
List <Item> ltDNNItemListDarknet = null;
if (new int[] { 3, 4 }.Contains(pplConfig))
{
ltDNNDarknet = new LineTriggeredDNNDarknet(lines);
ltDNNItemListDarknet = new List <Item>();
}
//-----LineTriggeredDNN (TensorFlow)-----
LineTriggeredDNNTF ltDNNTF = null;
List <Item> ltDNNItemListTF = null;
if (new int[] { 5 }.Contains(pplConfig))
{
ltDNNTF = new LineTriggeredDNNTF(lines);
ltDNNItemListTF = new List <Item>();
}
//-----CascadedDNN (Darknet)-----
CascadedDNNDarknet ccDNNDarknet = null;
List <Item> ccDNNItemList = null;
if (new int[] { 3 }.Contains(pplConfig))
{
ccDNNDarknet = new CascadedDNNDarknet(lines);
ccDNNItemList = new List <Item>();
}
//-----DNN on every frame (Darknet)-----
FrameDNNDarknet frameDNNDarknet = null;
List <Item> frameDNNDarknetItemList = null;
if (new int[] { 1 }.Contains(pplConfig))
{
frameDNNDarknet = new FrameDNNDarknet("YoloV3TinyCoco", Wrapper.Yolo.DNNMode.Frame, lines);
frameDNNDarknetItemList = new List <Item>();
Utils.Utils.cleanFolder(@OutputFolder.OutputFolderFrameDNNDarknet);
}
//-----DNN on every frame (TensorFlow)-----
FrameDNNTF frameDNNTF = null;
List <Item> frameDNNTFItemList = null;
if (new int[] { 2 }.Contains(pplConfig))
{
frameDNNTF = new FrameDNNTF(lines);
frameDNNTFItemList = new List <Item>();
Utils.Utils.cleanFolder(@OutputFolder.OutputFolderFrameDNNTF);
}
//-----Call ML models deployed on Azure Machine Learning Workspace-----
AMLCaller amlCaller = null;
List <bool> amlConfirmed;
if (new int[] { 5 }.Contains(pplConfig))
{
amlCaller = new AMLCaller(ConfigurationManager.AppSettings["AMLHost"],
Convert.ToBoolean(ConfigurationManager.AppSettings["AMLSSL"]),
ConfigurationManager.AppSettings["AMLAuthKey"],
ConfigurationManager.AppSettings["AMLServiceID"]);
}
//-----Write to DB-----
List <Item> ItemList = null;
int frameIndex = 0;
int videoTotalFrame = 0;
if (!isVideoStream)
{
videoTotalFrame = decoder.getTotalFrameNum() - 1; //skip the last frame which could be wrongly encoded from vlc capture
}
//RUN PIPELINE
DateTime startTime = DateTime.Now;
DateTime prevTime = DateTime.Now;
while (true)
{
if (!loop)
{
if (!isVideoStream && frameIndex >= videoTotalFrame)
{
break;
}
}
//decoder
Mat frame = decoder.getNextFrame();
//frame pre-processor
frame = FramePreProcessor.PreProcessor.returnFrame(frame, frameIndex, SAMPLING_FACTOR, RESOLUTION_FACTOR, displayRawVideo);
frameIndex++;
if (frame == null)
{
continue;
}
//Console.WriteLine("Frame ID: " + frameIndex);
//background subtractor
Mat fgmask = null;
List <Box> foregroundBoxes = bgs.DetectObjects(DateTime.Now, frame, frameIndex, out fgmask);
//line detector
if (new int[] { 0, 3, 4, 5 }.Contains(pplConfig))
{
occupancy = lineDetector.updateLineOccupancy(frame, frameIndex, fgmask, foregroundBoxes);
}
//cheap DNN
if (new int[] { 3, 4 }.Contains(pplConfig))
{
ltDNNItemListDarknet = ltDNNDarknet.Run(frame, frameIndex, occupancy, lines, category);
ItemList = ltDNNItemListDarknet;
}
else if (new int[] { 5 }.Contains(pplConfig))
{
ltDNNItemListTF = ltDNNTF.Run(frame, frameIndex, occupancy, lines, category);
ItemList = ltDNNItemListTF;
}
//heavy DNN
if (new int[] { 3 }.Contains(pplConfig))
{
ccDNNItemList = ccDNNDarknet.Run(frame, frameIndex, ltDNNItemListDarknet, lines, category);
ItemList = ccDNNItemList;
}
//frameDNN with Darknet Yolo
if (new int[] { 1 }.Contains(pplConfig))
{
frameDNNDarknetItemList = frameDNNDarknet.Run(Utils.Utils.ImageToByteBmp(OpenCvSharp.Extensions.BitmapConverter.ToBitmap(frame)), frameIndex, lines, category, System.Drawing.Brushes.Pink);
ItemList = frameDNNDarknetItemList;
}
//frame DNN TF
if (new int[] { 2 }.Contains(pplConfig))
{
frameDNNTFItemList = frameDNNTF.Run(frame, frameIndex, category, System.Drawing.Brushes.Pink, 0.2);
ItemList = frameDNNTFItemList;
}
//Azure Machine Learning
if (new int[] { 5 }.Contains(pplConfig))
{
amlConfirmed = AMLCaller.Run(frameIndex, ItemList, category).Result;
}
//DB Write
if (new int[] { 4 }.Contains(pplConfig))
{
Position[] dir = { Position.Unknown, Position.Unknown }; // direction detection is not included
DataPersistence.PersistResult("test", videoUrl, 0, frameIndex, ItemList, dir, "Cheap", "Heavy", // ArangoDB database
"test"); // Azure blob
}
//display counts
if (ItemList != null)
{
Dictionary <string, string> kvpairs = new Dictionary <string, string>();
foreach (Item it in ItemList)
{
if (!kvpairs.ContainsKey(it.TriggerLine))
{
kvpairs.Add(it.TriggerLine, "1");
}
}
FramePreProcessor.FrameDisplay.updateKVPairs(kvpairs);
}
//print out stats
double fps = 1000 * (double)(1) / (DateTime.Now - prevTime).TotalMilliseconds;
double avgFps = 1000 * (long)frameIndex / (DateTime.Now - startTime).TotalMilliseconds;
Console.WriteLine("{0} {1,-5} {2} {3,-5} {4} {5,-15} {6} {7,-10:N2} {8} {9,-10:N2}",
"sFactor:", SAMPLING_FACTOR, "rFactor:", RESOLUTION_FACTOR, "FrameID:", frameIndex, "FPS:", fps, "avgFPS:", avgFps);
prevTime = DateTime.Now;
}
Console.WriteLine("Done!");
}
public static void Initialize(string[] args)
{
//Console.WriteLine("Usage: <exe> <video url> <pipeline> <cfg file> <samplingFactor> <resolutionFactor> <buffersize> <uptran> <downtran> <category1> <category2> ...");
videoUrl = args[0];
switch (ConfigurationManager.AppSettings["Runtime"])
{
case "docker":
videoUrl = @"./media/" + args[0];
break;
case "vs":
videoUrl = @"..\..\..\media\" + args[0];
break;
}
if (args[1] != null)
{
pplConfig = int.Parse(args[1]);
}
if (args[2] != null)
{
switch (ConfigurationManager.AppSettings["Runtime"])
{
case "docker":
if (args[2].Substring(0, 4) != "http")
{
lineFile = $@"./cfg/{args[2]}";
}
else
{
using (var client = new WebClient())
{
client.DownloadFile(args[2], @"./cfg/line.txt");
}
lineFile = @"./cfg/line.txt";
}
break;
case "vs":
if (args[2].Substring(0, 4) != "http")
{
lineFile = $@"..\..\..\cfg\{args[2]}";
}
else
{
using (var client = new WebClient())
{
client.DownloadFile(args[2], @"..\..\..\cfg\line.txt");
}
lineFile = @"..\..\..\cfg\line.txt";
}
break;
}
}
if (args[3] != null)
{
SAMPLING_FACTOR = int.Parse(args[3]);
}
if (args[4] != null)
{
RESOLUTION_FACTOR = double.Parse(args[4]);
}
if (args[5] != null)
{
DNNConfig.FRAME_SEARCH_RANGE = int.Parse(args[5]);
}
if (args[6] != null)
{
LineDetectorConfig.UP_STATE_TRANSITION_LENGTH = int.Parse(args[6]);
}
if (args[7] != null)
{
LineDetectorConfig.DOWN_STATE_TRANSITION_LENGTH = int.Parse(args[7]);
}
//if no categpry is specified, add all classes from coco dataset
if (args.Length > 8)
{
for (int i = 8; i < args.Length; i++)
{
category.Add(args[i], 0);
}
}
else
{
string[] cocoNames = null;
switch (ConfigurationManager.AppSettings["Runtime"])
{
case "docker":
cocoNames = File.ReadAllLines(@"./modelOnnx/coco.names");
break;
case "vs":
cocoNames = File.ReadAllLines(@"..\..\..\modelOnnx\coco.names");
break;
}
foreach (string name in cocoNames)
{
category.Add(name, 0);
}
}
//initialize outputfolder
switch (ConfigurationManager.AppSettings["Runtime"])
{
case "docker":
OutputFolder.OutputFolderRoot = "output/";
break;
}
OutputFolder.OutputFolderInit();
//----------
//initialize pipeline components
Utils.Utils.cleanFolder(@OutputFolder.OutputFolderAll);
//----------
if (new int[] { 5, 1, 2, 3, 4 }.Contains(pplConfig))
{
bgs = new BGSObjectDetector.BGSObjectDetector();
foregroundBoxes = null;
lineDetector = new Detector(SAMPLING_FACTOR, RESOLUTION_FACTOR, lineFile, displayBGSVideo);
counts = null;
occupancy = null;
lines = lineDetector.multiLaneDetector.getAllLines();
LVAPostProcessor.InitializeCountingResult(lines);
}
//----------
//LineTriggeredDNNTF ltDNN = new LineTriggeredDNNTF(lines);
//List<Item> ltDNNItemList = new List<Item>();
//----------
if (new int[] { 2 }.Contains(pplConfig))
{
ltDNN = new LineTriggeredDNNORTYolo(lines, "yolov3");
ltDNNItemList = new List <Item>();
}
//----------
if (new int[] { 1 }.Contains(pplConfig))
{
ltDNN = new LineTriggeredDNNORTYolo(lines, "yolov3tiny");
ltDNNItemList = new List <Item>();
}
//----------
if (new int[] { 1 }.Contains(pplConfig))
{
ccDNN = new CascadedDNNORTYolo(lines, "yolov3");
ccDNNItemList = new List <Item>();
}
//----------
//FrameDNNTF frameDNNTF = new FrameDNNTF(lines);
//List<Item> frameDNNTFItemList = new List<Item>();
//Utils.Utils.cleanFolder(@OutputFolder.OutputFolderFrameDNNTF);
//----------
if (new int[] { 3, 6 }.Contains(pplConfig))
{
frameDNNOnnxYolo = new FrameDNNOnnxYolo(lines, "yolov3", DNNMode.Frame);
frameDNNOnnxItemList = new List <Item>();
Utils.Utils.cleanFolder(@OutputFolder.OutputFolderFrameDNNONNX);
}
//----------
if (new int[] { 4, 7 }.Contains(pplConfig))
{
frameDNNOnnxYolo = new FrameDNNOnnxYolo(lines, "yolov3tiny", DNNMode.Frame);
frameDNNOnnxItemList = new List <Item>();
Utils.Utils.cleanFolder(@OutputFolder.OutputFolderFrameDNNONNX);
}
itemList = null;
//RUN PIPELINE
startTime = DateTime.Now;
prevTime = DateTime.Now;
prevCounts = new Dictionary <string, int>();
frameBuffer = new Queue <Mat>(DNNConfig.RAW_FRAME_BUFFER_SIZE);
}
