initialize base model object for opendbm api

opendbm/api_lib/__init__.py

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+from .model import DEEEPSPEECH_URL, DEEPSPEECH_MODELS, OPENDBM_DATA, AudioModel

opendbm/api_lib/model.py

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+import os
+import platform
+import subprocess
+import tempfile
+from pathlib import Path
+
+from opendbm.api_lib.util import docker_command_dec, wsllize
+from opendbm.dbm_lib import config_derive_feature, config_raw_feature, config_reader
+
+OPENFACE_PATH_VIDEO = "pkg/open_dbm/OpenFace/build/bin/FaceLandmarkVid"
+OPENFACE_PATH = "pkg/open_dbm/OpenFace/build/bin/FeatureExtraction"
+DEEEPSPEECH_URL = "https://github.com/mozilla/DeepSpeech/releases/download/v0.9.1"
+DEEPSPEECH_MODELS = ["deepspeech-0.9.1-models.pbmm", "deepspeech-0.9.1-models.scorer"]
+MODEL_PATH = os.path.dirname(__file__)
+OPENDBM_DATA = Path.home() / ".opendbm"
+DLIB_SHAPE_MODEL = os.path.abspath(
+    os.path.join(
+        MODEL_PATH, "../pkg/shape_detector/shape_predictor_68_face_landmarks.dat"
+    )
+)
+FACIAL_ACTIVITY_ARGS = [
+    "-q",
+    "-2Dfp",
+    "-3Dfp",
+    "-pdmparams",
+    "-pose",
+    "-aus",
+    "-gaze",
+    "-f",
+]
+
+
+class Model(object):
+    def __init__(self):
+        self.s_config = config_reader.ConfigReader()
+        self.r_config = config_raw_feature.ConfigRawReader()
+        self.d_config = config_derive_feature.ConfigDeriveReader()
+        self._df = None
+        self._params = []
+
+    def to_dataframe(self):
+        """
+        Convert the result of the processed data into dataframe.
+        Returns:
+            pandas dataframe
+        """
+        if self._df is None:
+            raise Exception("Model has not been fit yet")
+        else:
+            return self._df
+
+    def mean(self):
+        """
+        get mean/average of data
+        Returns:
+            pandas.Series
+        """
+        return self._df[self._params].mean()
+
+    def std(self):
+        """
+        get std of data
+        Returns:
+            pandas.Series
+        """
+        return self._df[self._params].std()
+
+
+class VideoModel(Model):
+    """
+    A class to process the data of facial and Movement.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    @docker_command_dec
+    def _fit(self, path, dbm_group):
+        """
+        A function where the model is processing the data.
+        The model lived in the docker image,
+        where the full path of the model is stated in a variable named openface_call
+        Args:
+            path: input path of the file
+            dbm_group: self-explanatory. This function only accept dbm_group of facial and movement.
+
+        Returns:
+           output path of the processed file by the model.
+        """
+        docker_temp_dir = "/app/tmp/"
+        wsl_cmd, temp_dir = wsllize((tempfile.gettempdir()))
+        filename = os.path.basename(path)
+        bn, _ = os.path.splitext(filename)
+
+        facial_args = " ".join(FACIAL_ACTIVITY_ARGS)
+        docker_call = wsl_cmd + ["docker", "exec", "dbm_container", "/bin/bash", "-c"]
+
+        openface_call = [
+            docker_call
+            + [f"{OPENFACE_PATH} {facial_args} {path} -out_dir {docker_temp_dir}"],
+            docker_call
+            + [
+                f"{OPENFACE_PATH_VIDEO} {facial_args} {path} -out_dir {docker_temp_dir}"
+            ],
+        ]
+
+        out_dir_openface = [
+            f"{temp_dir}/{bn}/{bn}_openface/",
+            f"{temp_dir}/{bn}_landmark_output/{bn}_landmark_output_openface_lmk/",
+        ]
+        result_path = [
+            docker_temp_dir + bn + ".csv",
+            docker_temp_dir + bn + "_landmark_output.csv",
+        ]
+
+        if dbm_group == "facial":
+            openface_csv = self._processing_video(
+                dbm_group,
+                openface_call[0],
+                out_dir_openface[0],
+                result_path[0],
+                wsl_cmd,
+                temp_dir,
+                bn,
+            )
+
+            return openface_csv, bn
+        else:
+
+            openface_csv = self._processing_video(
+                "facial",
+                openface_call[0],
+                out_dir_openface[0],
+                result_path[0],
+                wsl_cmd,
+                temp_dir,
+                bn,
+            )
+            openface_lmk_csv = self._processing_video(
+                "movement",
+                openface_call[1],
+                out_dir_openface[1],
+                result_path[1],
+                wsl_cmd,
+                temp_dir,
+                bn,
+            )
+
+            return openface_csv, openface_lmk_csv, bn
+
+    def _processing_video(
+        self, dbm_group, call, out_dir, result_path, wsl_cmd, temp_dir, bn
+    ):
+        """
+        Helper function for _fit method
+        """
+
+        subprocess.Popen(
+            call,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            stdin=subprocess.PIPE,
+        ).wait()
+        mkdir_cmd = wsl_cmd + ["mkdir", "-p", out_dir]
+
+        copy_cmd = wsl_cmd + ["docker", "cp", f"dbm_container:/{result_path}", out_dir]
+        subprocess.Popen(
+            mkdir_cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            stdin=subprocess.PIPE,
+        ).wait()
+        subprocess.Popen(
+            copy_cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            stdin=subprocess.PIPE,
+        ).wait()
+
+        if platform.system() == "Windows":
+            path_in_temp = out_dir[len(temp_dir) :]
+            out_dir = (tempfile.gettempdir()) + path_in_temp
+
+        if dbm_group == "facial":
+            return out_dir + bn + ".csv"
+        else:
+            return out_dir + bn + "_landmark_output.csv"
+
+
+class AudioModel(Model):
+    """
+    A class to process the data of speech and acoustic
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def prep_func(func):
+        def wrapper(self, *args, **kwargs):
+            path = args[0]
+
+            df = func(self, path, **kwargs)
+            return df
+
+        return wrapper