[autotest] add autorepeats

:Release Notes:
-

:Detailed Notes:
-

:Testing Performed:
-

:QA Notes:
-

:Issues Addressed:
-

.gitignore

@@ -1,2 +1,7 @@
 *.log
 container/Drivers/*
+__pycache__
+*.yuv
+*.mp4
+*.csv
+*log*txt

PyScripts/extra.py

@@ -0,0 +1,17 @@
+from functools import wraps
+
+def log_args_decorator(func):
+    """
+    A decorator that logs the arguments passed to a function.
+    """
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        arg_names = func.__code__.co_varnames[:func.__code__.co_argcount]
+        pos_args = dict(zip(arg_names, args))
+        all_args = {**pos_args, **kwargs}
+
+        print(f"Calling function '{func.__name__}' with arguments: {all_args}")
+        result = func(*args, **kwargs)
+        print(f"Function '{func.__name__}' returned: {result}")
+        return result
+    return wrapper

PyScripts/gstreamerAutotest.py

@@ -1,55 +1,139 @@
 #!/usr/bin/python
 from itertools import product
+import qa
+from latencyParse import getLatencyTable
+import os, stat, subprocess
+import pandas as pd
+from extra import log_args_decorator
 
 options = {
     "x264enc": {
-        "bitrate" : ["10000", "20000", "5000"],
+        "bitrate": ["10000", "20000", "5000"],
-        "speed-preset" : ["ultrafast", "fast", "medium"],
+        "speed-preset": ["ultrafast", "fast", "medium"],
         "tune": ["zerolatency"],
-        "slices-threads": ["true", "false"],
+        "sliced-threads": ["true", "false"],
         "b-adapt": ["true", "false"],
         "rc-lookahead": ["40", "0"],
         "ref": ["3", "0"]
-         
     },
-    "nvh264enc" : {
+    "nvh264enc": {
-        "bitrate" : ["10000", "20000", "5000"],
+        "bitrate": ["10000", "20000", "5000"],
-        "preset" : ["4", "5", "1"],
+        "preset": ["4", "5", "1"],
-        "rc-lookahead" : ["0"],
+        "rc-lookahead": ["0"],
-        "rc-mode" : ["2", "0", "5"],
+        "rc-mode": ["2", "0", "5"],
         "zerolatency": ["true", "false"],
-
     },
     "nvv4l2h264enc": {
-        "bitrate" : ["10000000", "20000000", "5000000"],
+        "bitrate": ["10000000", "20000000", "5000000"],
         "profile": ["0", "1", "2"],
         "preset-id": ["1", "2", "3"],
-        "control-id": ["1", "2"],
+        "control-rate": ["1", "2"],
-        "tuning-info-id": ["4", "2"]
+        "idrinterval": ["1", "256"],
+        "tuning-info-id": ["4", "2", "3"]
     }
 }
 
-videos = [""]
+videos = {
+    "base-daVinci": "./test.yuv"
+}
 
 testsource = "videotestsrc pattern=smpte"
-videosrc = ["filesrc location=", "! qtdemux ! h264parse ! avdec_h264"]
+
+videosrc = {
+    "raw":["filesrc location=", " ! rawvideoparse "],
+    "h264": ["filesrc location=", " ! decodebin"]
+    }
 
 psnr_check = {
     "x264enc": "-pixel_format yuv420p -color_range pc",
     "nvh264enc": "-pixel_format nv12 -color_range tv",
-    "nvv4l2h264enc": "-pixel_format yuv420p -color_range tv"
+    "nvv4l2h264enc": "-pixel_format nv12 -color_range tv"
 }
 
+with_docker = [ "nvv4l2h264enc" ]
+
+repeats = 3 
+
 formats = {
-    "x264enc" : "I420",
+    "x264enc": "I420",
-    "nvh264enc" : "NV12",
+    "nvh264enc": "NV12",
-    "nvv4l2h264enc": "I420"
+    "nvv4l2h264enc": "NV12"
 }
 
 profiles = ["baseline", "main"]
 
-def makeVideoSrc(idx):
+videoconvert = {
-    return videosrc[0] + videos[idx] + videosrc[1]
+    "nvv4l2h264enc": "nvvideoconvert",
+    "nvh264enc": "videoconvert",
+    "x264enc": "videoconvert"
+}
+
+video_info = {
+    "video1":"-video_size 1920x1080 -framerate 23.98",
+    "sample-surgery":"-video_size 1280x720 -framerate 29.97",
+    "base-daVinci": "-video_size 1280x720 -framerate 59.94"
+}
+gst_video_info = {
+    "video1":"format=I420,height=1080,width=1920,framerate=24000/1001",
+    "base-daVinci": "format=2 height=720 width=1280 colorimetry=bt601 framerate=60000/1001"
+}
+
+latency_filename = "latency-traces-autotest.log"
+
+# Step-by-step:
+# 1. Generate all combinations for each encoder
+# 2. For each combination, create a GStreamer pipeline string
+# 3. Start each pipeline with latency tracing enabled
+# 3.1 Monitor CPU, GPU and memory usage during each pipeline run (nah, later, maybe)
+# 4. Start latency parsing script after each pipeline and store results in a pandas dataframe:
+#   - two key columns: encoder name, parameters string
+# 5. Run PSNR check after each pipeline and add results in the dataframe
+# 6. Save dataframe to CSV file
+class Pipeline:
+    def __init__(self):
+        self.pipeline = "gst-launch-1.0 -e "
+        self.options = ""
+
+    def add_tracing(self):
+        self.pipeline = (
+            "GST_DEBUG_COLOR_MODE=off " +
+            "GST_TRACERS=\"latency(flags=pipeline+element)\" " +
+            "GST_DEBUG=GST_TRACER:7 GST_DEBUG_FILE=" + latency_filename + " " +
+            self.pipeline
+        )
+        return self
+
+    def add_source(self, source):
+        self.pipeline += source + " ! clocksync sync-to-first=true ! "
+        return self
+
+    def __add_tee(self, encoder):
+        pass
+        #self.pipeline += "tee name=t t. ! queue max-size-time=5000000000 max-size-bytes=100485760 max-size-buffers=1000 ! filesink location=\"base-autotest.yuv\" "
+
+    def add_encoder(self, encoder, params):
+        self.pipeline += videoconvert[encoder] + " ! "
+        self.pipeline += "capsfilter caps=video/x-raw,format=" + formats[encoder] + " ! "
+        #self.__add_tee(encoder)
+        self.options += " ".join(params) + " "
+        #self.pipeline += "t. ! queue max-size-time=5000000000 max-size-bytes=100485760 max-size-buffers=1000 ! "
+        self.pipeline += encoder + " "
+        self.pipeline += " ".join(params) + " "
+        return self
+    
+    def add_profile(self, profile):
+        self.pipeline += "! capsfilter caps=\"video/x-h264,profile=" + profile + "\" ! "
+        self.options += "profile=" + profile + " "
+        return self
+    
+    def to_file(self, filename):
+        self.pipeline += "h264parse ! mpegtsmux ! filesink location=\"" + filename + "\""
+        return self
+
+
+def makeVideoSrc(videoName):
+    return videosrc["raw"][0] + videos[videoName] + videosrc["raw"][1] + gst_video_info[videoName]
+
 
 def generateEncoderStrings():
     global options
@@ -58,6 +142,7 @@ def generateEncoderStrings():
         result[encoder] = generate_combinations(value)
     return result
 
+
 def generate_combinations(config_dict):
     """
     Generate all combinations of values from a configuration dictionary.
@@ -70,23 +155,131 @@ def generate_combinations(config_dict):
     """
     combinations = []
 
-    # Get the keys and values in consistent order
     keys = list(config_dict.keys())
     value_lists = [config_dict[key] for key in keys]
 
-    # Generate all combinations using itertools.product
     for combo in product(*value_lists):
-        # Create a list of key=value strings
         param_strings = []
         for key, value in zip(keys, combo):
             param_strings.append(f"{key}={value}")
 
-        # Join all parameter strings with space separator
         combinations.append(" ".join(param_strings))
 
     return combinations
 
-def generateRecordString(options, ):
+qualityDataframe = pd.DataFrame()
-    pass
+latencyDataframe = pd.DataFrame()
+dockerRunString = "sudo -S docker container exec deepstream-gst bash"
 
-print(len(generateEncoderStrings()[""]))
+def execPermissions(scriptFile = "to_exec.sh"):
+    current_permissions = os.stat(scriptFile).st_mode
+    new_permissions = current_permissions | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH
+    os.chmod(scriptFile, new_permissions)
+
+def writeToExecFile(contents, file):
+    with open(file, "w") as f:
+        f.write(str(contents))
+        execPermissions(file)
+
+def is_docker(func):
+    def wrapper(pipeline):
+        script_name = "to_exec.sh"
+        for encoder in with_docker:
+            if encoder in pipeline:
+                writeToExecFile(pipeline, script_name)
+                pipeline = dockerRunString + f" {script_name}"
+            
+        func(pipeline)
+    return wrapper
+
+def is_sudo(pipeline):
+    if pipeline.startswith("sudo"):
+            return True
+    return False
+
+def passwordAuth(proc):
+    password = os.getenv("UAUTH")
+    if password is not None:
+        proc.communicate(password)
+
+def printLog(file):
+    with open(file, "r") as f:
+        out = f.read()
+        print(out)
+
+@is_docker
+@log_args_decorator
+def run_pipeline(pipeline):
+    logfile = "pipeline-log.txt"
+    with open(logfile, "w") as f:
+        proc = subprocess.Popen(pipeline, shell=True, 
+                              stdin=subprocess.PIPE, stdout=f, 
+                              stderr=subprocess.STDOUT, text=True)
+        if is_sudo(pipeline):
+            passwordAuth(proc)   
+        code = proc.wait() 
+    printLog(logfile)
+    if proc.returncode != 0:
+        raise Exception("Pipeline failed, see log for details")
+
+def time_trace(func):
+    def wrapper():
+        import time
+        start_time = time.time()
+        func()
+        end_time = time.time()
+        elapsed_time = end_time - start_time
+        print(f"Total execution time: {elapsed_time} seconds")  
+    return wrapper
+
+@time_trace
+def run_autotest():
+    encoders = generateEncoderStrings()
+    for encoder, combinations in encoders.items():
+        qualityDataframe = pd.DataFrame()
+        latencyDataframe = pd.DataFrame()
+        for params in combinations:
+            for profile in profiles:
+                for videoName, videoPath in videos.items():
+                    for _ in range(repeats):
+                        filename = "autotest-" + encoder + "-" + profile + "-test-" + videoName + ".mp4"
+                        pipeline = Pipeline()
+                        pipeline = ( 
+                            pipeline.add_tracing()
+                            .add_source(makeVideoSrc(videoName))
+                            .add_encoder(encoder, params.split(" "))
+                            .add_profile(profile)
+                            .to_file(filename)
+                        )
+                        print(pipeline.pipeline)
+                        try:
+                            run_pipeline(pipeline.pipeline)
+                        except Exception as e:
+                            print(f"Error occurred: {e}")
+                            continue
+                        psnr_metrics, ssim_metrics = qa.run_quality_check(
+                            videoPath, 
+                            filename, 
+                            video_info[videoName] + " " + psnr_check[encoder]
+                        ) 
+                        dfPsnr = qa.parse_quality_report(psnr_metrics, ssim_metrics)
+                        print("-----")
+                        dfLatency = getLatencyTable(latency_filename)
+                        columnsQ = pd.MultiIndex.from_tuples(
+                            [(encoder, profile, videoName, params, col) for col in dfPsnr.columns]
+                        )
+                        columnsLatency = pd.MultiIndex.from_tuples(
+                            [(encoder, profile, videoName, params, col) for col in dfLatency.columns]
+                        )
+                        dfPsnr.columns = columnsQ
+                        dfLatency.columns = columnsLatency
+                        qualityDataframe = pd.concat([qualityDataframe, dfPsnr], axis=1)
+                        latencyDataframe = pd.concat([latencyDataframe, dfLatency], axis=1)
+                        print("=====")
+                        print("Current results:")
+                        print(dfPsnr)
+                        print(dfLatency)
+        qualityDataframe.to_csv(f"qualityResults{encoder}.csv")
+        latencyDataframe.to_csv(f"latencyDataframe{encoder}.csv")
+
+run_autotest()

PyScripts/latencyParse.py

@@ -8,12 +8,16 @@ import numpy as np
 
 idxCache = dict()
 
+
 def findWord(words, wordToSearch):
     global idxCache
     if wordToSearch in idxCache:
         for idx in idxCache[wordToSearch]:
-            if words[idx].startswith(wordToSearch):
+            if idx < len(words) and words[idx].startswith(wordToSearch):
                 return words[idx]
+            else:
+                if idx >= len(words):
+                    print(f"ERROR: trying to access index={idx} while: {words}")
     for word in words:
         if word.startswith(wordToSearch):
             idx = words.index(word)
@@ -24,9 +28,11 @@ def findWord(words, wordToSearch):
     return ""
 
 # taken with love from GStreamerLatencyPlotter implementation
+
+
 def readAndParse(filename):
     result = dict()
-    
+    global idxCache
     with open(filename, "r") as latencyFile:
         lines = latencyFile.readlines()
         for line in lines:
@@ -49,12 +55,15 @@ def readAndParse(filename):
             src = findAndRemove("src=(string)")
             name = name[name.find(")") + 1:len(name) - 1]
             if name not in result:
-                result[name] = {"latency":[], "ts":[]}
+                result[name] = {"latency": [], "ts": []}
 
-            timeWord = findWord(words, "time=(guint64)")
+            timeWord = findAndRemove("time=(guint64)")
-            tsWord = findWord(words, "ts=(guint64)")
+            tsWord = findAndRemove("ts=(guint64)")
-            result[name]["latency"].append(int(timeWord[14:len(timeWord) - 1])/1e6) # time=(guint64)=14
+            result[name]["latency"].append(
-            result[name]["ts"].append(int(tsWord[12:len(tsWord) - 1])/1e9) # ts=(guint64)=12
+                int(timeWord)/1e6)  # time=(guint64)=14
+            result[name]["ts"].append(int(tsWord)/1e9)  # ts=(guint64)=12
+    # drop cache for future runs
+    idxCache = dict()
     return result
 
 
@@ -78,8 +87,10 @@ def getLatencyTable(filename):
         dt_max_latency[column] = dt
 
     df_dt_max = pd.Series(dt_max_latency)
-    resultDf = pd.concat([df_dt_max, max_latency, avg_latency, median_latency, std_latency], axis=1)
+    resultDf = pd.concat(
+        [df_dt_max, max_latency, avg_latency, median_latency, std_latency], axis=1)
     resultDf.columns = ['dTmax', 'max', 'avg', 'median', 'std']
     print(resultDf)
+    return resultDf
 
-getLatencyTable("latency_traces-x264enc-big-pr-main.log")
+# getLatencyTable("latency_traces-x264enc-kpop-test-10.log")

PyScripts/qa.py

@@ -0,0 +1,118 @@
+#!/usr/bin/python3
+import subprocess
+import pandas as pd
+
+def run_psnr_check(original, encoded, video_info):
+    out = ""
+    # bad practice, but idgaf
+    # -f rawvideo {video_info} 
+    options = f"-f rawvideo {video_info} -i {original} -i {encoded} -filter_complex psnr -f null /dev/null"
+    with open("ffmpeg-log.txt", "w") as f:
+        proc = subprocess.run(["ffmpeg", *options.split()], stdout=f, stderr=subprocess.STDOUT, text=True)
+        print(f"Return code: {proc.returncode}")
+    with open("ffmpeg-log.txt", "r") as f:
+        out = f.read()
+    return out
+
+def run_ssim_check(original, encoded, video_info):
+    # bad practice, but idgaf
+    # -f rawvideo {video_info}
+    # we don't  need additional information with h264 encoded files
+    options = f"-f rawvideo {video_info} -i {original} -i {encoded} -filter_complex ssim -f null /dev/null"
+    with open("ffmpeg-log.txt", "w") as f:
+        proc = subprocess.run(["ffmpeg", *options.split()], stdout=f, stderr=subprocess.STDOUT, text=True)
+        print(f"Return code: {proc.returncode}")
+    with open("ffmpeg-log.txt", "r") as f:
+        out = f.read()
+    return out
+
+def parse_psnr_output(output):
+    for line in output.splitlines():
+        if "[Parsed_psnr" in line and "PSNR" in line:
+            parts = line.split()
+            y = parts[4].split(":")[1]
+            u = parts[5].split(":")[1]
+            v = parts[6].split(":")[1]
+            avg = parts[7].split(":")[1]
+            minYUV = parts[8].split(":")[1]
+            maxYUV = parts[9].split(":")[1]
+            return {
+                "Y": y,
+                "U": u,
+                "V": v,
+                "Average": avg,
+                "MinYUV": minYUV,
+                "MaxYUV": maxYUV
+            }
+    return {}
+
+def parse_ssim_output(output):
+    for line in output.splitlines():
+        if "[Parsed_ssim" in line and "SSIM" in line:
+            parts = line.split()
+            all_value = parts[10].split(":")[1]
+            y = parts[4].split(":")[1]
+            u = parts[6].split(":")[1]
+            v = parts[8].split(":")[1]
+            return {
+                "Y": y,
+                "U": u,
+                "V": v,
+                "Average": all_value
+            }
+    return {}
+
+def run_quality_check(original, encoded, option):
+    psnr_result = run_psnr_check(original, encoded, option)
+    ssim_result = run_ssim_check(original, encoded, option)
+    psnr_metrics = parse_psnr_output(psnr_result)
+    ssim_metrics = parse_ssim_output(ssim_result)
+    print ("PSNR Metrics:", psnr_metrics)
+    print ("SSIM Metrics:", ssim_metrics)
+    return psnr_metrics, ssim_metrics
+
+def parse_quality_report(psnr_metrics, ssim_metrics):
+    psnrSeries = pd.Series(psnr_metrics)
+    ssimSeries = pd.Series(ssim_metrics)
+    combined = pd.concat([psnrSeries, ssimSeries], axis=1)
+    combined.columns = ["PSNR", "SSIM"]
+    combined = combined.fillna(0)
+    return combined
+
+# psnr, ssim = run_quality_check(
+#         "base-x264enc-kpop-test-10.yuv", 
+#         "encoded-x264enc-kpop-test-10.mp4", 
+#         "-pixel_format yuv420p -color_range tv -video_size 1920x1080 -framerate 23.98 "
+#     )
+
+# combined = parse_quality_report(
+#     psnr,
+#     ssim
+# )
+
+# encoder = "x264enc"
+# profile = "main"
+# params = "bitrate=5000"
+
+# columns = pd.MultiIndex.from_tuples(
+#         [(encoder, profile, params, col) for col in combined.columns]
+#     )
+
+# combined.columns = columns
+
+# main_df = combined
+# profile = "baseline"
+
+# combined2 = parse_quality_report(
+#     psnr,
+#     ssim
+# )
+# columns = pd.MultiIndex.from_tuples(
+#     [(encoder, profile, params, col) for col in combined2.columns]
+# )
+# combined2.columns = columns
+# main_df = pd.concat([main_df, combined2], axis=1)
+# print(main_df)
+
+# main_df.to_csv("quality_report.csv")
+

ffmpeg-psnr-check.sh

@@ -1,23 +1,24 @@
 #!/bin/bash
 
-if [ "$#" -ne 2 ]; then
-    echo "Usage: $0 <input_video> <reference_video>"
-    exit 1
-fi
-
 if ! command -v ffmpeg &> /dev/null
 then
     echo "ffmpeg could not be found, please install it."
     exit 1
 fi
 
+name=nvv4l2h264enc
+specific=eleph-pr-baseline-preset1-cbr-hqp-10
+
+#
 #this script checks the PSNR between two video streams using ffmpeg
 # one video stream is the reference and the other is the test stream both are recorded files
-ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format yuv444p -color_range pc -framerate 30 -i "$1" -i "$2" -filter_complex "psnr" -f null /dev/null
+ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format yuv420p -color_range tv -framerate 60 -i base-${name}-${specific}.yuv -i encoded-${name}-${specific}.mp4 -filter_complex "psnr" -f null /dev/null
 # example working:
-# ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format yuv444p -color_range pc -framerate 30 -i base.yuv -i test.mp4 -filter_complex "psnr" -f null /dev/null 
+# ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format yuv420p -color_range tv -framerate 60 -i base-${name}-${specific}.yuv -i encoded-${name}-${specific}.mp4 -filter_complex "psnr" -f null /dev/null
+# ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format nv12 -color_range tv -framerate 60 -i base-${name}-${specific}.yuv -i encoded-${name}-${specific}.mp4 -filter_complex "psnr" -f null /dev/null
+# ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format yuv420p -color_range pc -framerate 60 -i base.yuv -i test.mp4 -filter_complex "psnr" -f null /dev/null 
 
 #this script checks the SSIM between two video streams using ffmpeg
-ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format yuv444p -color_range pc -framerate 30 -i "$1" -i "$2" -filter_complex "[0:v][1:v]ssim=stats_file=ssim_results.txt" -f null /dev/null
+ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format yuv420p -color_range tv -framerate 60 -i base-${name}-${specific}.yuv -i encoded-${name}-${specific}.mp4 -filter_complex "[0:v]fps=60[v0];[1:v]fps=60[v1];[v0][v1]ssim=stats_file=ssim_results.txt" -f null /dev/null
 
 # ffmpeg -f rawvideo -video_size 1920x1080 -pixel_format yuv422p -color_range pc -framerate 30 -i base.yuv -i test.mp4 -filter_complex "[0:v][1:v]ssim=stats_file=ssim_results.txt" -f null /dev/null

gstreamer-record.sh

@@ -5,34 +5,58 @@
 # First stream is coming from decklinkvideosrc
 # Second stream is same decklinkvideosrc but encoded using x264enc
 # Both streams are saved to files using mp4mux and filesink
-
-if [ "$#" -lt 2 ]; then
-    echo "Usage: $0 <reference_video_file> <test_video_file> <optional: DEBUG>"
-    exit 1
-fi
-
 if ! command -v gst-launch-1.0 &> /dev/null
 then
     echo "gstreamer could not be found, please install it."
     exit 1
 fi
 
-# Record both streams in one run
+src='decklinkvideosrc device-number=1 connection=1 mode=1080p60 drop-no-signal-frames=true !'
-if [ "$3" == "DEBUG" ]; then
+
-    gst-launch-1.0 -e \
+if [ "$1" == "DEBUG" ]; then
-        videotestsrc pattern=smpte ! \
+    src='videotestsrc pattern=smpte !'
-        capsfilter caps="video/x-raw,format=UYVY,width=1920,height=1080,framerate=30/1" ! \
-        videoconvert ! queue ! tee name=t \
-        t. ! queue ! filesink location="$1" \
-        t. ! queue ! x264enc bitrate=20000 speed-preset=ultrafast tune=zerolatency ! h264parse ! mpegtsmux ! filesink location="$2"
-    exit 0
 fi
 
-gst-launch-1.0 -e \
+name=nvv4l2h264enc
-  decklinkvideosrc \
+specific=eleph-pr-baseline-preset1-cbr-lossless-10
-    device-number=1 \
+
-    connection=1 \
+echo $src
-    mode=auto \
+
-    drop-no-signal-frames=true ! videoconvert ! queue ! tee name=t \
+GST_DEBUG_COLOR_MODE=off \
-    t. ! queue ! mpegtsmux ! filesink location="$1" \
+    GST_TRACERS="latency(flags=pipeline+element)" \
-    t. ! queue ! x264enc bitrate=20000 speed-preset=ultrafast tune=zerolatency ! mpegtsmux ! filesink location="$2"
+    GST_DEBUG="GST_TRACER:7" \
+    GST_DEBUG_FILE=latency_traces-${name}-${specific}.log \
+    gst-launch-1.0 -e \
+    $src \
+    capsfilter caps="video/x-raw,format=UYVY,width=1920,height=1080,framerate=60/1" ! \
+    videoconvert ! capsfilter caps="video/x-raw,format=I420,width=1920,height=1080,framerate=60/1" ! queue ! tee name=t \
+    t. ! queue ! filesink location="base-${name}-${specific}.yuv" \
+    t. ! queue ! nvvideoconvert ! \
+	capsfilter caps="video/x-raw(memory:NVMM),format=I420,width=1920,height=1080" ! \
+    ${name} bitrate=10000000 profile=0 preset-id=1 control-rate=1 tuning-info-id=4 ! \
+    capsfilter caps="video/x-h264,profile=baseline" ! h264parse ! mpegtsmux ! filesink location="encoded-${name}-${specific}.mp4"
+
+
+# GST_DEBUG_COLOR_MODE=off \
+#     GST_TRACERS="latency(flags=pipeline+element)" \
+#     GST_DEBUG="GST_TRACER:7" \
+#     GST_DEBUG_FILE=latency_traces-${name}-${specific}.log \
+#     gst-launch-1.0 -e \
+#     $src \
+#     capsfilter caps="video/x-raw,format=UYVY,width=1920,height=1080,framerate=60/1" ! \
+#     videoconvert ! queue ! tee name=t \
+#     t. ! queue ! filesink location="base-${name}-${specific}.yuv" \
+#     t. ! queue ! x264enc bitrate=10000 speed-preset=ultrafast tune=zerolatency sliced-threads=true ! \
+#     capsfilter caps="video/x-h264,profile=baseline" ! h264parse ! mpegtsmux ! filesink location="encoded-${name}-${specific}.mp4"
+
+# GST_DEBUG_COLOR_MODE=off \
+#     GST_TRACERS="latency(flags=pipeline+element)" \
+#     GST_DEBUG="GST_TRACER:7" \
+#     GST_DEBUG_FILE=latency_traces-${name}-${specific}.log \
+#     gst-launch-1.0 -e \
+#     $src \
+#     capsfilter caps="video/x-raw,format=UYVY,width=1920,height=1080,framerate=60/1" ! \
+#     videoconvert ! capsfilter caps="video/x-raw,format=NV12,width=1920,height=1080,framerate=60/1" ! queue ! tee name=t \
+#     t. ! queue ! filesink location="base-${name}-${specific}.yuv" \
+#     t. ! queue ! ${name} bitrate=10000 preset=5 rc-lookahead=0 rc-mode=2 zerolatency=true ! \
+#     capsfilter caps="video/x-h264,profile=baseline" ! h264parse ! mpegtsmux ! filesink location="encoded-${name}-${specific}.mp4"