181 "Reference and distorted files are the same! This may lead to unexpected results or numerical issues."
182 )
183
-184 self.data: MetricData = {
-185 "vmaf": [],
-186 "psnr": [],
-187 "ssim": [],
-188 "vif": [],
-189 # "identity": [],
-190 # "msad": [],
-191 }
-192
-193 self.available_filters: List[str] = []
-194
-195 self.global_stats: GlobalStats = {}
-196
-197 if not os.path.isdir(self.tmp_dir):
-198 logger.debug(f"Creating temporary directory: {self.tmp_dir}")
-199 os.makedirs(self.tmp_dir)
-200 self.temp_files: Dict[FilterName, str] = {}
+184 if ref.endswith(".yuv") or dist.endswith(".yuv"):
+185 raise FfmpegQualityMetricsError(
+186 "YUV files are not supported, please convert to a format that ffmpeg can read natively, such as Y4M or FFV1."
+187 )
+188
+189 self.data: MetricData = {
+190 "vmaf": [],
+191 "psnr": [],
+192 "ssim": [],
+193 "vif": [],
+194 # "identity": [],
+195 # "msad": [],
+196 }
+197
+198 self.available_filters: List[str] = []
+199
+200 self.global_stats: GlobalStats = {}
201
-202 for filter_name in self.POSSIBLE_FILTERS:
-203 suffix = "txt" if filter_name != "libvmaf" else "json"
-204
-205 self.temp_files[cast(FilterName, filter_name)] = os.path.join(
-206 self.tmp_dir,
-207 f"ffmpeg_quality_metrics_{filter_name}_{os.path.basename(self.ref)}_{os.path.basename(self.dist)}.{suffix}",
-208 )
-209 logger.debug(
-210 f"Writing temporary {filter_name.upper()} information to: {self.temp_files[cast(FilterName, filter_name)]}"
-211 )
-212
-213 if scaling_algorithm not in self.ALLOWED_SCALERS:
-214 raise FfmpegQualityMetricsError(
-215 f"Allowed scaling algorithms: {self.ALLOWED_SCALERS}"
+202 if not os.path.isdir(self.tmp_dir):
+203 logger.debug(f"Creating temporary directory: {self.tmp_dir}")
+204 os.makedirs(self.tmp_dir)
+205 self.temp_files: Dict[FilterName, str] = {}
+206
+207 for filter_name in self.POSSIBLE_FILTERS:
+208 suffix = "txt" if filter_name != "libvmaf" else "json"
+209
+210 self.temp_files[cast(FilterName, filter_name)] = os.path.join(
+211 self.tmp_dir,
+212 f"ffmpeg_quality_metrics_{filter_name}_{os.path.basename(self.ref)}_{os.path.basename(self.dist)}.{suffix}",
+213 )
+214 logger.debug(
+215 f"Writing temporary {filter_name.upper()} information to: {self.temp_files[cast(FilterName, filter_name)]}"
216 )
217
-218 self._check_available_filters()
+218 if scaling_algorithm not in self.ALLOWED_SCALERS:
+219 raise FfmpegQualityMetricsError(
+220 f"Allowed scaling algorithms: {self.ALLOWED_SCALERS}"
+221 )
+222
+223 self._check_available_filters()
@@ -1054,6 +1082,280 @@ Raises:
+
+
+
+ ALLOWED_SCALERS =
+
+ ['fast_bilinear', 'bilinear', 'bicubic', 'experimental', 'neighbor', 'area', 'bicublin', 'gauss', 'sinc', 'lanczos', 'spline']
+
+
+
+
+
+
+
+
+
+ DEFAULT_SCALER =
+'bicubic'
+
+
+
+
+
+
+
+
+
+ DEFAULT_THREADS =
+0
+
+
+
+
+
+
+
+
+
+ DEFAULT_VMAF_THREADS =
+0
+
+
+
+
+
+
+
+
+
+ DEFAULT_VMAF_SUBSAMPLE =
+1
+
+
+
+
+
+
+
+
+
+ DEFAULT_VMAF_MODEL_DIRECTORY =
+'/Users/werner/Documents/Projects/slhck/ffmpeg-quality-metrics/ffmpeg_quality_metrics/vmaf_models'
+
+
+
+
+
+
+
+
+
+
DEFAULT_VMAF_OPTIONS: VmafOptions =
+
{'model_path': None, 'model_params': [], 'n_threads': 0, 'n_subsample': 1, 'features': []}
+
+
+
+
+
+
+
+
+
+ POSSIBLE_FILTERS: List[Literal['psnr', 'ssim', 'libvmaf', 'vif']] =
+['libvmaf', 'psnr', 'ssim', 'vif']
+
+
+
+
+
+
+
+
+
+ METRIC_TO_FILTER_MAP: Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], Literal['psnr', 'ssim', 'libvmaf', 'vif']] =
+{'vmaf': 'libvmaf', 'psnr': 'psnr', 'ssim': 'ssim', 'vif': 'vif'}
+
+
+
+
+
+
+
+
+
+ scaling_algorithm
+
+
+
+
+
+
+
+
+
+ framerate
+
+
+
+
+
+
+
+
+
+ dist_delay
+
+
+
+
+
+
+
+
+
+ dry_run
+
+
+
+
+
+
+
+
+
+ verbose
+
+
+
+
+
+
+
+
+
+ threads
+
+
+
+
+
+
+
+
+
+ progress
+
+
+
+
+
+
+
+
+
+ keep_tmp_files
+
+
+
+
+
+
+
+
+
+ tmp_dir
+
+
+
+
+
+
+
+
+
+ data: Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], List[Dict[str, float]]]
+
+
+
+
+
+
+
+
+
+ available_filters: List[str]
+
+
+
+
+
+
+
+
+
+ global_stats: Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], Dict[str, Dict[str, float]]]
+
+
+
+
+
+
+
+
+
+ temp_files: Dict[Literal['psnr', 'ssim', 'libvmaf', 'vif'], str]
+
+
+
+
+
+
+
@@ -1067,31 +1369,31 @@
Raises:
- 242 @staticmethod
-243 def get_framerate(input_file: str) -> float:
-244 """Parse the FPS from the input file.
-245
-246 Args:
-247 input_file (str): Input file path
-248
-249 Raises:
-250 FfmpegQualityMetricsError: A generic error
-251
-252 Returns:
-253 float: The FPS parsed
-254 """
-255 cmd = ["ffmpeg", "-nostdin", "-y", "-i", input_file]
+ 247 @staticmethod
+248 def get_framerate(input_file: str) -> float:
+249 """Parse the FPS from the input file.
+250
+251 Args:
+252 input_file (str): Input file path
+253
+254 Raises:
+255 FfmpegQualityMetricsError: A generic error
256
-257 output = run_command(cmd, allow_error=True)
-258 pattern = re.compile(r"(\d+(\.\d+)?) fps")
-259 try:
-260 if pattern_ret := pattern.search(str(output)):
-261 match = pattern_ret.groups()[0]
-262 return float(match)
-263 except Exception:
-264 pass
-265
-266 raise FfmpegQualityMetricsError(f"could not parse FPS from file {input_file}!")
+257 Returns:
+258 float: The FPS parsed
+259 """
+260 cmd = ["ffmpeg", "-nostdin", "-y", "-i", input_file]
+261
+262 output = run_command(cmd, allow_error=True)
+263 pattern = re.compile(r"(\d+(\.\d+)?) fps")
+264 try:
+265 if pattern_ret := pattern.search(str(output)):
+266 match = pattern_ret.groups()[0]
+267 return float(match)
+268 except Exception:
+269 pass
+270
+271 raise FfmpegQualityMetricsError(f"could not parse FPS from file {input_file}!")
Returns:
-
-
-
- def
- calc(self, **kwargs):
-
-
-
-
-
-
300 def calc(self, **kwargs):
-301 logger.warning("The calc method is deprecated. Use calculate() instead.")
-302 return self.calculate(**kwargs)
-
-
-
-
-
def
-
calculate( self, metrics: List[Literal['psnr', 'ssim', 'vmaf', 'vif']] = ['ssim', 'psnr'], vmaf_options: Optional[ffmpeg_quality_metrics.VmafOptions] = None) -> Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], List[Dict[str, float]]]:
+
calculate( self, metrics: List[Literal['psnr', 'ssim', 'vmaf', 'vif']] = ['ssim', 'psnr'], vmaf_options: Optional[VmafOptions] = None) -> Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], List[Dict[str, float]]]:
-
304 def calculate(
-305 self,
-306 metrics: List[MetricName] = ["ssim", "psnr"],
-307 vmaf_options: Union[VmafOptions, None] = None,
-308 ) -> Dict[MetricName, SingleMetricData]:
-309 """Calculate one or more metrics.
-310
-311 Args:
-312 metrics (list, optional): A list of metrics to calculate.
-313 Possible values are ["ssim", "psnr", "vmaf"].
-314 Defaults to ["ssim", "psnr"].
-315 vmaf_options (dict, optional): VMAF-specific options. Uses defaults if not specified.
-316
-317 Raises:
-318 FfmpegQualityMetricsError: In case of an error
-319 e: A generic error
-320
-321 Returns:
-322 dict: A dictionary of per-frame info, with the key being the metric name and the value being a dict of frame numbers ('n') and metric values.
-323 """
-324 if not metrics:
-325 raise FfmpegQualityMetricsError("No metrics specified!")
-326
-327 # check available metrics
-328 for metric_name in metrics:
-329 filter_name = self.METRIC_TO_FILTER_MAP.get(metric_name, None)
-330 if filter_name not in self.POSSIBLE_FILTERS:
-331 raise FfmpegQualityMetricsError(f"No such metric '{metric_name}'")
-332 if filter_name not in self.available_filters:
-333 raise FfmpegQualityMetricsError(
-334 f"Your ffmpeg version does not have the filter '{filter_name}'"
-335 )
-336
-337 # set VMAF options specifically
-338 if "vmaf" in metrics:
-339 self._check_libvmaf_availability()
-340 self.vmaf_options = self.DEFAULT_VMAF_OPTIONS
-341 # override with user-supplied options
-342 if vmaf_options:
-343 for key, value in vmaf_options.items():
-344 if value is not None:
-345 self.vmaf_options[key] = value # type: ignore
-346 self._set_vmaf_model_path(self.vmaf_options["model_path"])
-347
-348 filter_chains = [
-349 f"[1][0]scale2ref=flags={self.scaling_algorithm}[dist][ref]",
-350 "[dist]settb=AVTB,setpts=PTS-STARTPTS[distpts]",
-351 "[ref]settb=AVTB,setpts=PTS-STARTPTS[refpts]",
-352 ]
-353
-354 # generate split filters depending on the number of models
-355 n_splits = len(metrics)
-356 if n_splits > 1:
-357 for source in ["dist", "ref"]:
-358 suffixes = "".join([f"[{source}{n}]" for n in range(1, n_splits + 1)])
-359 filter_chains.extend(
-360 [
-361 f"[{source}pts]split={n_splits}{suffixes}",
-362 ]
-363 )
-364
-365 # special case, only one metric:
-366 if n_splits == 1:
-367 metric_name = metrics[0]
-368 filter_chains.extend(
-369 [
-370 f"[distpts][refpts]{self._get_filter_opts(self.METRIC_TO_FILTER_MAP[metric_name])}"
-371 ]
-372 )
-373 # all other cases:
-374 else:
-375 for n, metric_name in zip(range(1, n_splits + 1), metrics):
-376 filter_chains.extend(
-377 [
-378 f"[dist{n}][ref{n}]{self._get_filter_opts(self.METRIC_TO_FILTER_MAP[metric_name])}"
-379 ]
-380 )
-381
-382 try:
-383 output = self._run_ffmpeg_command(filter_chains, desc=", ".join(metrics))
-384 self._read_temp_files(metrics)
-385 if output:
-386 self._read_ffmpeg_output(output, metrics)
-387 else:
-388 raise FfmpegQualityMetricsError("ffmpeg output is empty!")
-389 except Exception as e:
-390 raise e
-391 finally:
-392 self._cleanup_temp_files()
-393
-394 # return only those data entries containing values
-395 return cast(
-396 Dict[MetricName, SingleMetricData],
-397 {k: v for k, v in self.data.items() if v},
-398 )
+ 305 def calculate(
+306 self,
+307 metrics: List[MetricName] = ["ssim", "psnr"],
+308 vmaf_options: Union[VmafOptions, None] = None,
+309 ) -> Dict[MetricName, SingleMetricData]:
+310 """Calculate one or more metrics.
+311
+312 Args:
+313 metrics (list, optional): A list of metrics to calculate.
+314 Possible values are ["ssim", "psnr", "vmaf"].
+315 Defaults to ["ssim", "psnr"].
+316 vmaf_options (dict, optional): VMAF-specific options. Uses defaults if not specified.
+317
+318 Raises:
+319 FfmpegQualityMetricsError: In case of an error
+320 e: A generic error
+321
+322 Returns:
+323 dict: A dictionary of per-frame info, with the key being the metric name and the value being a dict of frame numbers ('n') and metric values.
+324 """
+325 if not metrics:
+326 raise FfmpegQualityMetricsError("No metrics specified!")
+327
+328 # check available metrics
+329 for metric_name in metrics:
+330 filter_name = self.METRIC_TO_FILTER_MAP.get(metric_name, None)
+331 if filter_name not in self.POSSIBLE_FILTERS:
+332 raise FfmpegQualityMetricsError(f"No such metric '{metric_name}'")
+333 if filter_name not in self.available_filters:
+334 raise FfmpegQualityMetricsError(
+335 f"Your ffmpeg version does not have the filter '{filter_name}'"
+336 )
+337
+338 # set VMAF options specifically
+339 if "vmaf" in metrics:
+340 self._check_libvmaf_availability()
+341 self.vmaf_options = self.DEFAULT_VMAF_OPTIONS
+342 # override with user-supplied options
+343 if vmaf_options:
+344 for key, value in vmaf_options.items():
+345 if value is not None:
+346 self.vmaf_options[key] = value # type: ignore
+347 self._set_vmaf_model_path(self.vmaf_options["model_path"])
+348
+349 filter_chains = [
+350 f"[1][0]scale2ref=flags={self.scaling_algorithm}[dist][ref]",
+351 "[dist]settb=AVTB,setpts=PTS-STARTPTS[distpts]",
+352 "[ref]settb=AVTB,setpts=PTS-STARTPTS[refpts]",
+353 ]
+354
+355 # generate split filters depending on the number of models
+356 n_splits = len(metrics)
+357 if n_splits > 1:
+358 for source in ["dist", "ref"]:
+359 suffixes = "".join([f"[{source}{n}]" for n in range(1, n_splits + 1)])
+360 filter_chains.extend(
+361 [
+362 f"[{source}pts]split={n_splits}{suffixes}",
+363 ]
+364 )
+365
+366 # special case, only one metric:
+367 if n_splits == 1:
+368 metric_name = metrics[0]
+369 filter_chains.extend(
+370 [
+371 f"[distpts][refpts]{self._get_filter_opts(self.METRIC_TO_FILTER_MAP[metric_name])}"
+372 ]
+373 )
+374 # all other cases:
+375 else:
+376 for n, metric_name in zip(range(1, n_splits + 1), metrics):
+377 filter_chains.extend(
+378 [
+379 f"[dist{n}][ref{n}]{self._get_filter_opts(self.METRIC_TO_FILTER_MAP[metric_name])}"
+380 ]
+381 )
+382
+383 try:
+384 output = self._run_ffmpeg_command(filter_chains, desc=", ".join(metrics))
+385 self._read_temp_files(metrics)
+386 if output:
+387 self._read_ffmpeg_output(output, metrics)
+388 else:
+389 raise FfmpegQualityMetricsError("ffmpeg output is empty!")
+390 except Exception as e:
+391 raise e
+392 finally:
+393 self._cleanup_temp_files()
+394
+395 # return only those data entries containing values
+396 return cast(
+397 Dict[MetricName, SingleMetricData],
+398 {k: v for k, v in self.data.items() if v},
+399 )
Returns:
-
-
-
-
-
- def
- calc_ssim_psnr( self) -> Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], List[Dict[str, float]]]:
-
-
-
-
-
-
604 def calc_ssim_psnr(self) -> Dict[MetricName, SingleMetricData]:
-605 """Calculate SSIM and PSNR
-606
-607 Raises:
-608 e: A generic error
-609
-610 Returns:
-611 dict: SSIM and PSNR results, each with their own key
-612 """
-613 logger.warning(
-614 "The calc_ssim_psnr() method is deprecated and will be removed eventually. "
-615 "Please use calc() instead!"
-616 )
-617
-618 if "ssim" not in self.available_filters:
-619 raise FfmpegQualityMetricsError(
-620 "Your ffmpeg build does not have support for the 'ssim' filter. "
-621 )
-622
-623 if "psnr" not in self.available_filters:
-624 raise FfmpegQualityMetricsError(
-625 "Your ffmpeg build does not have support for the 'psnr' filter. "
-626 )
-627
-628 filter_chains = [
-629 f"[1][0]scale2ref=flags={self.scaling_algorithm}[dist][ref]",
-630 "[dist]setpts=PTS-STARTPTS[distpts]",
-631 "[ref]setpts=PTS-STARTPTS[refpts]",
-632 "[distpts]split[dist1][dist2]",
-633 "[refpts]split[ref1][ref2]",
-634 f"[dist1][ref1]{self._get_filter_opts('ssim')}",
-635 f"[dist2][ref2]{self._get_filter_opts('psnr')}",
-636 ]
-637
-638 try:
-639 self._run_ffmpeg_command(filter_chains, desc="PSNR and SSIM")
-640 self._read_temp_files(["ssim", "psnr"])
-641 except Exception as e:
-642 raise e
-643 finally:
-644 self._cleanup_temp_files()
-645
-646 return {"ssim": self.data["ssim"], "psnr": self.data["psnr"]}
-
-
-
-
Calculate SSIM and PSNR
-
-
Raises:
-
-
-
-
Returns:
-
-
- dict: SSIM and PSNR results, each with their own key
-
-
-
-
@@ -1360,28 +1568,28 @@
Returns:
- 695 @staticmethod
-696 def get_brewed_vmaf_model_path() -> Union[str, None]:
-697 """
-698 Hack to get path for VMAF model from Homebrew or Linuxbrew.
-699 This works for libvmaf 2.x
-700
-701 Returns:
-702 str or None: the path or None if not found
-703 """
-704 stdout, _ = run_command(["brew", "--prefix", "libvmaf"])
-705 cellar_path = stdout.strip()
-706
-707 model_path = os.path.join(cellar_path, "share", "libvmaf", "model")
-708
-709 if not os.path.isdir(model_path):
-710 logger.warning(
-711 f"{model_path} does not exist. "
-712 "Are you sure you have installed the most recent version of libvmaf with Homebrew?"
-713 )
-714 return None
-715
-716 return model_path
+ 652 @staticmethod
+653 def get_brewed_vmaf_model_path() -> Union[str, None]:
+654 """
+655 Hack to get path for VMAF model from Homebrew or Linuxbrew.
+656 This works for libvmaf 2.x
+657
+658 Returns:
+659 str or None: the path or None if not found
+660 """
+661 stdout, _ = run_command(["brew", "--prefix", "libvmaf"])
+662 cellar_path = stdout.strip()
+663
+664 model_path = os.path.join(cellar_path, "share", "libvmaf", "model")
+665
+666 if not os.path.isdir(model_path):
+667 logger.warning(
+668 f"{model_path} does not exist. "
+669 "Are you sure you have installed the most recent version of libvmaf with Homebrew?"
+670 )
+671 return None
+672
+673 return model_path
Returns:
718 @staticmethod
-719 def get_default_vmaf_model_path() -> str:
-720 """
-721 Return the default model path depending on whether the user is running Homebrew
-722 or has a static build.
-723
-724 Returns:
-725 str: the path
-726 """
-727 if has_brew() and ffmpeg_is_from_brew():
-728 # If the user installed ffmpeg using homebrew
-729 model_path = FfmpegQualityMetrics.get_brewed_vmaf_model_path()
-730 if model_path is not None:
-731 return os.path.join(
-732 model_path,
-733 "vmaf_v0.6.1.json",
-734 )
-735
-736 share_path = os.path.join("/usr", "local", "share", "model")
-737 if os.path.isdir(share_path):
-738 return os.path.join(share_path, "vmaf_v0.6.1.json")
-739 else:
-740 # return the bundled file as a fallback
-741 return os.path.join(
-742 FfmpegQualityMetrics.DEFAULT_VMAF_MODEL_DIRECTORY, "vmaf_v0.6.1.json"
-743 )
+ 675 @staticmethod
+676 def get_default_vmaf_model_path() -> str:
+677 """
+678 Return the default model path depending on whether the user is running Homebrew
+679 or has a static build.
+680
+681 Returns:
+682 str: the path
+683 """
+684 if has_brew() and ffmpeg_is_from_brew():
+685 # If the user installed ffmpeg using homebrew
+686 model_path = FfmpegQualityMetrics.get_brewed_vmaf_model_path()
+687 if model_path is not None:
+688 return os.path.join(
+689 model_path,
+690 "vmaf_v0.6.1.json",
+691 )
+692
+693 share_path = os.path.join("/usr", "local", "share", "model")
+694 if os.path.isdir(share_path):
+695 return os.path.join(share_path, "vmaf_v0.6.1.json")
+696 else:
+697 # return the bundled file as a fallback
+698 return os.path.join(
+699 FfmpegQualityMetrics.DEFAULT_VMAF_MODEL_DIRECTORY, "vmaf_v0.6.1.json"
+700 )
Returns:
745 @staticmethod
-746 def get_supplied_vmaf_models() -> List[str]:
-747 """
-748 Return a list of VMAF models supplied with the software.
-749
-750 Returns:
-751 List[str]: A list of VMAF model names
-752 """
-753 return [
-754 f
-755 for f in os.listdir(FfmpegQualityMetrics.DEFAULT_VMAF_MODEL_DIRECTORY)
-756 if f.endswith(".json")
-757 ]
+ 702 @staticmethod
+703 def get_supplied_vmaf_models() -> List[str]:
+704 """
+705 Return a list of VMAF models supplied with the software.
+706
+707 Returns:
+708 List[str]: A list of VMAF model names
+709 """
+710 return [
+711 f
+712 for f in os.listdir(FfmpegQualityMetrics.DEFAULT_VMAF_MODEL_DIRECTORY)
+713 if f.endswith(".json")
+714 ]
Returns:
759 def get_global_stats(self) -> GlobalStats:
-760 """
-761 Return a dictionary for each calculated metric, with different statstics
-762
-763 Returns:
-764 dict: A dictionary with stats, each key being a metric name and each value being a dictionary with the stats for every submetric. The stats are: 'average', 'median', 'stdev', 'min', 'max'.
-765 """
-766 for metric_name in self.data:
-767 logger.debug(f"Aggregating stats for {metric_name}")
-768 metric_data = cast(SingleMetricData, self.data[metric_name]) # type: ignore
-769 if len(metric_data) == 0:
-770 continue
-771 submetric_keys = [k for k in metric_data[0].keys() if k != "n"]
-772
-773 stats: Dict[str, GlobalStatsData] = {}
-774 for submetric_key in submetric_keys:
-775 values = [float(frame[submetric_key]) for frame in metric_data]
-776 stats[submetric_key] = {
-777 "average": round(float(np.average(values)), 3),
-778 "median": round(float(np.median(values)), 3),
-779 "stdev": round(float(np.std(values)), 3),
-780 "min": round(np.min(values), 3),
-781 "max": round(np.max(values), 3),
-782 }
-783 self.global_stats[metric_name] = stats # type: ignore
-784
-785 return self.global_stats
+ 716 def get_global_stats(self) -> GlobalStats:
+717 """
+718 Return a dictionary for each calculated metric, with different statstics
+719
+720 Returns:
+721 dict: A dictionary with stats, each key being a metric name and each value being a dictionary with the stats for every submetric. The stats are: 'average', 'median', 'stdev', 'min', 'max'.
+722 """
+723 for metric_name in self.data:
+724 logger.debug(f"Aggregating stats for {metric_name}")
+725 metric_data = cast(SingleMetricData, self.data[metric_name]) # type: ignore
+726 if len(metric_data) == 0:
+727 continue
+728 submetric_keys = [k for k in metric_data[0].keys() if k != "n"]
+729
+730 stats: Dict[str, GlobalStatsData] = {}
+731 for submetric_key in submetric_keys:
+732 values = [float(frame[submetric_key]) for frame in metric_data]
+733 stats[submetric_key] = {
+734 "average": round(float(np.average(values)), 3),
+735 "median": round(float(np.median(values)), 3),
+736 "stdev": round(float(np.std(values)), 3),
+737 "min": round(np.min(values), 3),
+738 "max": round(np.max(values), 3),
+739 }
+740 self.global_stats[metric_name] = stats # type: ignore
+741
+742 return self.global_stats
Returns:
787 def get_results_csv(self) -> str:
-788 """
-789 Return a CSV string with the data
-790
-791 Returns:
-792 str: The CSV string
-793 """
-794 all_dfs = []
-795
-796 for metric_data in self.data.values():
-797 if not metric_data:
-798 continue
-799 all_dfs.append(pd.DataFrame(cast(SingleMetricData, metric_data)))
-800
-801 if not all_dfs:
-802 raise FfmpegQualityMetricsError("No data calculated!")
-803
-804 try:
-805 df = reduce(lambda x, y: pd.merge(x, y, on="n"), all_dfs)
-806
-807 df["input_file_dist"] = self.dist
-808 df["input_file_ref"] = self.ref
-809
-810 cols = df.columns.tolist()
-811 cols.insert(0, cols.pop(cols.index("n")))
-812 df = df.reindex(columns=cols)
-813 return df.to_csv(index=False)
-814 except Exception as e:
-815 raise FfmpegQualityMetricsError(f"Error merging data to CSV: {e}")
+ 744 def get_results_csv(self) -> str:
+745 """
+746 Return a CSV string with the data
+747
+748 Returns:
+749 str: The CSV string
+750 """
+751 all_dfs = []
+752
+753 for metric_data in self.data.values():
+754 if not metric_data:
+755 continue
+756 all_dfs.append(pd.DataFrame(cast(SingleMetricData, metric_data)))
+757
+758 if not all_dfs:
+759 raise FfmpegQualityMetricsError("No data calculated!")
+760
+761 try:
+762 df = reduce(lambda x, y: pd.merge(x, y, on="n"), all_dfs)
+763
+764 df["input_file_dist"] = self.dist
+765 df["input_file_ref"] = self.ref
+766
+767 cols = df.columns.tolist()
+768 cols.insert(0, cols.pop(cols.index("n")))
+769 df = df.reindex(columns=cols)
+770 return df.to_csv(index=False)
+771 except Exception as e:
+772 raise FfmpegQualityMetricsError(f"Error merging data to CSV: {e}")
Returns:
817 def get_results_json(self) -> str:
-818 """
-819 Return the results as JSON string
-820
-821 Returns:
-822 str: The JSON string
-823 """
-824 ret: Dict = {}
-825 for key in self.data:
-826 metric_data = cast(SingleMetricData, self.data[key]) # type: ignore
-827 if len(metric_data) == 0:
-828 continue
-829 ret[key] = metric_data
-830 ret["global"] = self.get_global_stats()
-831 ret["input_file_dist"] = self.dist
-832 ret["input_file_ref"] = self.ref
-833
-834 return json.dumps(ret, indent=4)
+ 774 def get_results_json(self) -> str:
+775 """
+776 Return the results as JSON string
+777
+778 Returns:
+779 str: The JSON string
+780 """
+781 ret: Dict = {}
+782 for key in self.data:
+783 metric_data = cast(SingleMetricData, self.data[key]) # type: ignore
+784 if len(metric_data) == 0:
+785 continue
+786 ret[key] = metric_data
+787 ret["global"] = self.get_global_stats()
+788 ret["input_file_dist"] = self.dist
+789 ret["input_file_ref"] = self.ref
+790
+791 return json.dumps(ret, indent=4)
Inherited Members
builtins.BaseException
with_traceback
add_note
+ args
@@ -1778,29 +1987,11 @@ Inherited Members
-
Inherited Members
-
- - builtins.dict
- - get
- - setdefault
- - pop
- - popitem
- - keys
- - items
- - values
- - update
- - fromkeys
- - clear
- - copy
-
-
-
-
- MetricName = typing.Literal['psnr', 'ssim', 'vmaf', 'vif']
+ MetricName =
+typing.Literal['psnr', 'ssim', 'vmaf', 'vif']
@@ -1811,7 +2002,8 @@ Inherited Members
- SingleMetricData = typing.List[typing.Dict[str, float]]
+ SingleMetricData =
+typing.List[typing.Dict[str, float]]
@@ -1822,7 +2014,8 @@ Inherited Members
- GlobalStatsData = typing.Dict[str, float]
+ GlobalStatsData =
+typing.Dict[str, float]
@@ -1833,7 +2026,9 @@ Inherited Members
- GlobalStats = typing.Dict[typing.Literal['psnr', 'ssim', 'vmaf', 'vif'], typing.Dict[str, typing.Dict[str, float]]]
+ GlobalStats =
+
+ typing.Dict[typing.Literal['psnr', 'ssim', 'vmaf', 'vif'], typing.Dict[str, typing.Dict[str, float]]]
@@ -1844,7 +2039,8 @@ Inherited Members
- MetricData = typing.Dict[typing.Literal['psnr', 'ssim', 'vmaf', 'vif'], typing.List[typing.Dict[str, float]]]
+ MetricData =
+typing.Dict[typing.Literal['psnr', 'ssim', 'vmaf', 'vif'], typing.List[typing.Dict[str, float]]]
@@ -1855,7 +2051,8 @@ Inherited Members
- __version__ = '3.3.0'
+ __version__ =
+'3.3.1'
diff --git a/docs/search.js b/docs/search.js
index 2fb6b38..9b21ec2 100644
--- a/docs/search.js
+++ b/docs/search.js
@@ -1,6 +1,6 @@
window.pdocSearch = (function(){
/** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();o\n"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics", "kind": "class", "doc": "A class to calculate quality metrics with FFmpeg
\n"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.__init__": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.__init__", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.__init__", "kind": "function", "doc": "Instantiate a new FfmpegQualityMetrics
\n\nArguments:
\n\n\n- ref (str): reference file
\n- dist (str): distorted file
\n- scaling_algorithm (str, optional): A scaling algorithm. Must be one of the following: [\"fast_bilinear\", \"bilinear\", \"bicubic\", \"experimental\", \"neighbor\", \"area\", \"bicublin\", \"gauss\", \"sinc\", \"lanczos\", \"spline\"]. Defaults to \"bicubic\"
\n- framerate (float, optional): Force a frame rate. Defaults to None.
\n- dist_delay (float): Delay the distorted file against the reference by this amount of seconds. Defaults to 0.
\n- dry_run (bool, optional): Don't run anything, just print commands. Defaults to False.
\n- verbose (bool, optional): Show more output. Defaults to False.
\n- threads (int, optional): Number of ffmpeg threads. Defaults to 0 (auto).
\n- progress (bool, optional): Show a progress bar. Defaults to False.
\n- keep_tmp_files (bool, optional): Keep temporary files for debugging purposes. Defaults to False.
\n- tmp_dir (str, optional): Directory to store temporary files. Will use system default if not specified. Defaults to None.
\n
\n\nRaises:
\n\n\n- FfmpegQualityMetricsError: A generic error
\n
\n", "signature": "(\tref: str,\tdist: str,\tscaling_algorithm: str = 'bicubic',\tframerate: Optional[float] = None,\tdist_delay: float = 0,\tdry_run: Optional[bool] = False,\tverbose: Optional[bool] = False,\tthreads: int = 0,\tprogress: Optional[bool] = False,\tkeep_tmp_files: Optional[bool] = False,\ttmp_dir: Optional[str] = None)"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_framerate": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_framerate", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_framerate", "kind": "function", "doc": "Parse the FPS from the input file.
\n\nArguments:
\n\n\n- input_file (str): Input file path
\n
\n\nRaises:
\n\n\n- FfmpegQualityMetricsError: A generic error
\n
\n\nReturns:
\n\n\n float: The FPS parsed
\n
\n", "signature": "(input_file: str) -> float:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.calc": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.calc", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.calc", "kind": "function", "doc": "\n", "signature": "(self, **kwargs):", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.calculate": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.calculate", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.calculate", "kind": "function", "doc": "Calculate one or more metrics.
\n\nArguments:
\n\n\n- metrics (list, optional): A list of metrics to calculate.\nPossible values are [\"ssim\", \"psnr\", \"vmaf\"].\nDefaults to [\"ssim\", \"psnr\"].
\n- vmaf_options (dict, optional): VMAF-specific options. Uses defaults if not specified.
\n
\n\nRaises:
\n\n\n- FfmpegQualityMetricsError: In case of an error
\n- e: A generic error
\n
\n\nReturns:
\n\n\n dict: A dictionary of per-frame info, with the key being the metric name and the value being a dict of frame numbers ('n') and metric values.
\n
\n", "signature": "(\tself,\tmetrics: List[Literal['psnr', 'ssim', 'vmaf', 'vif']] = ['ssim', 'psnr'],\tvmaf_options: Optional[ffmpeg_quality_metrics.ffmpeg_quality_metrics.VmafOptions] = None) -> Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], List[Dict[str, float]]]:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.calc_ssim_psnr": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.calc_ssim_psnr", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.calc_ssim_psnr", "kind": "function", "doc": "Calculate SSIM and PSNR
\n\nRaises:
\n\n\n\nReturns:
\n\n\n dict: SSIM and PSNR results, each with their own key
\n
\n", "signature": "(\tself) -> Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], List[Dict[str, float]]]:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_brewed_vmaf_model_path": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_brewed_vmaf_model_path", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_brewed_vmaf_model_path", "kind": "function", "doc": "Hack to get path for VMAF model from Homebrew or Linuxbrew.\nThis works for libvmaf 2.x
\n\nReturns:
\n\n\n str or None: the path or None if not found
\n
\n", "signature": "() -> Optional[str]:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_default_vmaf_model_path": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_default_vmaf_model_path", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_default_vmaf_model_path", "kind": "function", "doc": "Return the default model path depending on whether the user is running Homebrew\nor has a static build.
\n\nReturns:
\n\n\n str: the path
\n
\n", "signature": "() -> str:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_supplied_vmaf_models": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_supplied_vmaf_models", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_supplied_vmaf_models", "kind": "function", "doc": "Return a list of VMAF models supplied with the software.
\n\nReturns:
\n\n\n List[str]: A list of VMAF model names
\n
\n", "signature": "() -> List[str]:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_global_stats": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_global_stats", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_global_stats", "kind": "function", "doc": "Return a dictionary for each calculated metric, with different statstics
\n\nReturns:
\n\n\n dict: A dictionary with stats, each key being a metric name and each value being a dictionary with the stats for every submetric. The stats are: 'average', 'median', 'stdev', 'min', 'max'.
\n
\n", "signature": "(\tself) -> Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], Dict[str, Dict[str, float]]]:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_results_csv": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_results_csv", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_results_csv", "kind": "function", "doc": "Return a CSV string with the data
\n\nReturns:
\n\n\n str: The CSV string
\n
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+ /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"ffmpeg_quality_metrics": {"fullname": "ffmpeg_quality_metrics", "modulename": "ffmpeg_quality_metrics", "kind": "module", "doc": "\n"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics", "kind": "class", "doc": "A class to calculate quality metrics with FFmpeg
\n"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.__init__": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.__init__", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.__init__", "kind": "function", "doc": "Instantiate a new FfmpegQualityMetrics
\n\nArguments:
\n\n\n- ref (str): reference file
\n- dist (str): distorted file
\n- scaling_algorithm (str, optional): A scaling algorithm. Must be one of the following: [\"fast_bilinear\", \"bilinear\", \"bicubic\", \"experimental\", \"neighbor\", \"area\", \"bicublin\", \"gauss\", \"sinc\", \"lanczos\", \"spline\"]. Defaults to \"bicubic\"
\n- framerate (float, optional): Force a frame rate. Defaults to None.
\n- dist_delay (float): Delay the distorted file against the reference by this amount of seconds. Defaults to 0.
\n- dry_run (bool, optional): Don't run anything, just print commands. Defaults to False.
\n- verbose (bool, optional): Show more output. Defaults to False.
\n- threads (int, optional): Number of ffmpeg threads. Defaults to 0 (auto).
\n- progress (bool, optional): Show a progress bar. Defaults to False.
\n- keep_tmp_files (bool, optional): Keep temporary files for debugging purposes. Defaults to False.
\n- tmp_dir (str, optional): Directory to store temporary files. Will use system default if not specified. Defaults to None.
\n
\n\nRaises:
\n\n\n- FfmpegQualityMetricsError: A generic error
\n
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"doc": "\n"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.dist": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.dist", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.dist", "kind": "variable", "doc": "\n"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.scaling_algorithm": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.scaling_algorithm", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.scaling_algorithm", "kind": "variable", "doc": "\n"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.framerate": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.framerate", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.framerate", "kind": "variable", "doc": "\n"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.dist_delay": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.dist_delay", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.dist_delay", "kind": "variable", 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\n\nReturns:
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\n\n\n- metrics (list, optional): A list of metrics to calculate.\nPossible values are [\"ssim\", \"psnr\", \"vmaf\"].\nDefaults to [\"ssim\", \"psnr\"].
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\n\nRaises:
\n\n\n- FfmpegQualityMetricsError: In case of an error
\n- e: A generic error
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\n\nReturns:
\n\n\n dict: A dictionary of per-frame info, with the key being the metric name and the value being a dict of frame numbers ('n') and metric values.
\n
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\n\nReturns:
\n\n\n str or None: the path or None if not found
\n
\n", "signature": "() -> Optional[str]:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_default_vmaf_model_path": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_default_vmaf_model_path", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_default_vmaf_model_path", "kind": "function", "doc": "Return the default model path depending on whether the user is running Homebrew\nor has a static build.
\n\nReturns:
\n\n\n str: the path
\n
\n", "signature": "() -> str:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_supplied_vmaf_models": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_supplied_vmaf_models", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_supplied_vmaf_models", "kind": "function", "doc": "Return a list of VMAF models supplied with the software.
\n\nReturns:
\n\n\n List[str]: A list of VMAF model names
\n
\n", "signature": "() -> List[str]:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_global_stats": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_global_stats", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_global_stats", "kind": "function", "doc": "Return a dictionary for each calculated metric, with different statstics
\n\nReturns:
\n\n\n dict: A dictionary with stats, each key being a metric name and each value being a dictionary with the stats for every submetric. The stats are: 'average', 'median', 'stdev', 'min', 'max'.
\n
\n", "signature": "(\tself) -> Dict[Literal['psnr', 'ssim', 'vmaf', 'vif'], Dict[str, Dict[str, float]]]:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_results_csv": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_results_csv", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_results_csv", "kind": "function", "doc": "Return a CSV string with the data
\n\nReturns:
\n\n\n str: The CSV string
\n
\n", "signature": "(self) -> str:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_results_json": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetrics.get_results_json", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetrics.get_results_json", "kind": "function", "doc": "Return the results as JSON string
\n\nReturns:
\n\n\n str: The JSON string
\n
\n", "signature": "(self) -> str:", "funcdef": "def"}, "ffmpeg_quality_metrics.FfmpegQualityMetricsError": {"fullname": "ffmpeg_quality_metrics.FfmpegQualityMetricsError", "modulename": "ffmpeg_quality_metrics", "qualname": "FfmpegQualityMetricsError", "kind": "class", "doc": "Common base class for all non-exit exceptions.
\n", "bases": "builtins.Exception"}, "ffmpeg_quality_metrics.VmafOptions": {"fullname": "ffmpeg_quality_metrics.VmafOptions", "modulename": "ffmpeg_quality_metrics", "qualname": "VmafOptions", "kind": "class", "doc": "VMAF-specific options.
\n", "bases": "typing.TypedDict"}, "ffmpeg_quality_metrics.VmafOptions.model_path": {"fullname": "ffmpeg_quality_metrics.VmafOptions.model_path", "modulename": "ffmpeg_quality_metrics", "qualname": "VmafOptions.model_path", "kind": "variable", "doc": "Use a specific VMAF model file. If none is chosen, picks a default model.
\n", "annotation": ": Optional[str]"}, "ffmpeg_quality_metrics.VmafOptions.model_params": {"fullname": "ffmpeg_quality_metrics.VmafOptions.model_params", "modulename": "ffmpeg_quality_metrics", "qualname": "VmafOptions.model_params", "kind": "variable", "doc": "A list of params to pass to the VMAF model, specified as key=value.
\n", "annotation": ": List[str]"}, "ffmpeg_quality_metrics.VmafOptions.n_threads": {"fullname": "ffmpeg_quality_metrics.VmafOptions.n_threads", "modulename": "ffmpeg_quality_metrics", "qualname": "VmafOptions.n_threads", "kind": "variable", "doc": "Number of threads to use. Defaults to 0 (auto).
\n", "annotation": ": Optional[int]"}, "ffmpeg_quality_metrics.VmafOptions.n_subsample": {"fullname": "ffmpeg_quality_metrics.VmafOptions.n_subsample", "modulename": "ffmpeg_quality_metrics", "qualname": "VmafOptions.n_subsample", "kind": "variable", "doc": "Subsampling interval. Defaults to 1.
\n", "annotation": ": Optional[int]"}, "ffmpeg_quality_metrics.VmafOptions.features": {"fullname": "ffmpeg_quality_metrics.VmafOptions.features", "modulename": "ffmpeg_quality_metrics", "qualname": "VmafOptions.features", "kind": "variable", "doc": "List of features to enable in addition to the default features.\nEach entry must be a string beginning with name=feature_name, and additional parameters can be specified as\nkey=value, separated by colons.
\n", "annotation": ": List[str]"}, "ffmpeg_quality_metrics.MetricName": {"fullname": "ffmpeg_quality_metrics.MetricName", "modulename": "ffmpeg_quality_metrics", "qualname": "MetricName", "kind": "variable", "doc": "\n", "default_value": "typing.Literal['psnr', 'ssim', 'vmaf', 'vif']"}, "ffmpeg_quality_metrics.SingleMetricData": {"fullname": "ffmpeg_quality_metrics.SingleMetricData", "modulename": "ffmpeg_quality_metrics", "qualname": "SingleMetricData", "kind": "variable", "doc": "\n", "default_value": "typing.List[typing.Dict[str, float]]"}, "ffmpeg_quality_metrics.GlobalStatsData": {"fullname": "ffmpeg_quality_metrics.GlobalStatsData", "modulename": "ffmpeg_quality_metrics", "qualname": "GlobalStatsData", "kind": "variable", "doc": "\n", "default_value": "typing.Dict[str, float]"}, "ffmpeg_quality_metrics.GlobalStats": {"fullname": "ffmpeg_quality_metrics.GlobalStats", "modulename": "ffmpeg_quality_metrics", "qualname": "GlobalStats", "kind": "variable", "doc": "\n", 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// mirrored in build-search-index.js (part 1)
// Also split on html tags. this is a cheap heuristic, but good enough.
diff --git a/ffmpeg_quality_metrics/__init__.py b/ffmpeg_quality_metrics/__init__.py
index 87571cc..a5bd1e2 100644
--- a/ffmpeg_quality_metrics/__init__.py
+++ b/ffmpeg_quality_metrics/__init__.py
@@ -9,7 +9,7 @@
VmafOptions,
)
-__version__ = "3.3.0"
+__version__ = "3.3.1"
__all__ = [
"FfmpegQualityMetrics",
"FfmpegQualityMetricsError",