Update lavis dependencies (#204)

.github/workflows/ci.yml

@@ -16,18 +16,23 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-22.04,windows-latest,macos-latest]
-        python-version: [3.9]
+        python-version: [3.11]
     steps:
     - name: Checkout repository
       uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
+    - name: install ffmpeg on macos
+      if: matrix.os == 'macos-latest'
+      run: |
+        brew install ffmpeg
     - name: Install dependencies
       run: |
-        python -m pip install uv
-        uv pip install --system -e .
+        # python -m pip install uv
+        pip install -e .
+        # uv pip install --system -e .
     - name: Run pytest test_colors
       run: |
         cd ammico

README.md

@@ -24,7 +24,7 @@ Use pre-processed image files such as social media posts with comments and proce
     1. Question answering   
 1. Performing person and face recognition in images
     1. Face mask detection
-    1. Age, gender and race detection
+    1. Probabilistic detection of age, gender and race detection
     1. Emotion recognition
 1. Color analysis
     1. Analyse hue and percentage of color on image

ammico/display.py

@@ -101,8 +101,6 @@ def __init__(self, mydict: dict) -> None:
             State("setting_Text_revision_numbers", "value"),
             State("setting_Emotion_emotion_threshold", "value"),
             State("setting_Emotion_race_threshold", "value"),
-            State("setting_Emotion_gender_threshold", "value"),
-            State("setting_Emotion_age_threshold", "value"),
             State("setting_Emotion_env_var", "value"),
             State("setting_Color_delta_e_method", "value"),
             State("setting_Summary_analysis_type", "value"),
@@ -256,34 +254,6 @@ def _create_setting_layout(self):
                                     ],
                                     align="start",
                                 ),
-                                dbc.Col(
-                                    [
-                                        html.P("Gender threshold"),
-                                        dcc.Input(
-                                            type="number",
-                                            value=50,
-                                            max=100,
-                                            min=0,
-                                            id="setting_Emotion_gender_threshold",
-                                            style={"width": "100%"},
-                                        ),
-                                    ],
-                                    align="start",
-                                ),
-                                dbc.Col(
-                                    [
-                                        html.P("Age threshold"),
-                                        dcc.Input(
-                                            type="number",
-                                            value=50,
-                                            max=100,
-                                            min=0,
-                                            id="setting_Emotion_age_threshold",
-                                            style={"width": "100%"},
-                                        ),
-                                    ],
-                                    align="start",
-                                ),
                                 dbc.Col(
                                     [
                                         html.P(
@@ -493,8 +463,6 @@ def _right_output_analysis(
         settings_text_revision_numbers: str,
         setting_emotion_emotion_threshold: int,
         setting_emotion_race_threshold: int,
-        setting_emotion_gender_threshold: int,
-        setting_emotion_age_threshold: int,
         setting_emotion_env_var: str,
         setting_color_delta_e_method: str,
         setting_summary_analysis_type: str,
@@ -550,8 +518,6 @@ def _right_output_analysis(
                 image_copy,
                 emotion_threshold=setting_emotion_emotion_threshold,
                 race_threshold=setting_emotion_race_threshold,
-                gender_threshold=setting_emotion_gender_threshold,
-                age_threshold=setting_emotion_age_threshold,
                 accept_disclosure=(
                     setting_emotion_env_var
                     if setting_emotion_env_var

ammico/faces.py

@@ -149,8 +149,6 @@ def __init__(
         subdict: dict,
         emotion_threshold: float = 50.0,
         race_threshold: float = 50.0,
-        gender_threshold: float = 50.0,
-        age_threshold: float = 50.0,
         accept_disclosure: str = "DISCLOSURE_AMMICO",
     ) -> None:
         """
@@ -160,8 +158,6 @@ def __init__(
             subdict (dict): The dictionary to store the analysis results.
             emotion_threshold (float): The threshold for detecting emotions (default: 50.0).
             race_threshold (float): The threshold for detecting race (default: 50.0).
-            gender_threshold (float): The threshold for detecting gender (default: 50.0).
-            age_threshold (float): The threshold for detecting age (default: 50.0).
             accept_disclosure (str): The name of the disclosure variable, that is
                 set upon accepting the disclosure (default: "DISCLOSURE_AMMICO").
         """
@@ -172,14 +168,8 @@ def __init__(
             raise ValueError("Emotion threshold must be between 0 and 100.")
         if race_threshold < 0 or race_threshold > 100:
             raise ValueError("Race threshold must be between 0 and 100.")
-        if gender_threshold < 0 or gender_threshold > 100:
-            raise ValueError("Gender threshold must be between 0 and 100.")
-        if age_threshold < 0 or age_threshold > 100:
-            raise ValueError("Age threshold must be between 0 and 100.")
         self.emotion_threshold = emotion_threshold
         self.race_threshold = race_threshold
-        self.gender_threshold = gender_threshold
-        self.age_threshold = age_threshold
         self.emotion_categories = {
             "angry": "Negative",
             "disgust": "Negative",
@@ -232,30 +222,29 @@ def _define_actions(self, fresult: dict) -> list:
             "restricted_access_with_mask": [],
         }
         if fresult["wears_mask"] and self.accepted:
-            actions = conditional_actions["all_with_mask"]
+            self.actions = conditional_actions["all_with_mask"]
         elif fresult["wears_mask"] and not self.accepted:
-            actions = conditional_actions["restricted_access_with_mask"]
+            self.actions = conditional_actions["restricted_access_with_mask"]
         elif not fresult["wears_mask"] and self.accepted:
-            actions = conditional_actions["all"]
+            self.actions = conditional_actions["all"]
         elif not fresult["wears_mask"] and not self.accepted:
-            actions = conditional_actions["restricted_access"]
+            self.actions = conditional_actions["restricted_access"]
         else:
             raise ValueError(
                 "Invalid mask detection {} and disclosure \
                              acceptance {} result.".format(
                     fresult["wears_mask"], self.accepted
                 )
             )
-        return actions
 
-    def _ensure_deepface_models(self, actions: list):
+    def _ensure_deepface_models(self):
         # Ensure that all data has been fetched by pooch
         deepface_face_expression_model.get()
-        if "race" in actions:
+        if "race" in self.actions:
             deepface_race_model.get()
-        if "age" in actions:
+        if "age" in self.actions:
             deepface_age_model.get()
-        if "gender" in actions:
+        if "gender" in self.actions:
             deepface_gender_model.get()
 
     def analyze_single_face(self, face: np.ndarray) -> dict:
@@ -271,13 +260,13 @@ def analyze_single_face(self, face: np.ndarray) -> dict:
         fresult = {}
         # Determine whether the face wears a mask
         fresult["wears_mask"] = self.wears_mask(face)
-        actions = self._define_actions(fresult)
-        self._ensure_deepface_models(actions)
+        self._define_actions(fresult)
+        self._ensure_deepface_models()
         # Run the full DeepFace analysis
         fresult.update(
             DeepFace.analyze(
                 img_path=face,
-                actions=actions,
+                actions=self.actions,
                 prog_bar=False,
                 detector_backend="skip",
             )
@@ -325,49 +314,47 @@ def clean_subdict(self, result: dict) -> dict:
         """
         # Each person subdict converted into list for keys
         self.subdict["wears_mask"] = []
-        self.subdict["age"] = []
-        self.subdict["gender"] = []
-        self.subdict["race"] = []
         self.subdict["emotion"] = []
         self.subdict["emotion (category)"] = []
+        for key in self.actions:
+            self.subdict[key] = []
+        # now iterate over the number of faces
+        # and check thresholds
+        # the results for each person are returned as a nested dict
+        # race and emotion are given as dict with confidence values
+        # gender and age are given as one value with no confidence
+        # being passed
         for i in range(result["number_faces"]):
             person = "person{}".format(i + 1)
-            self.subdict["wears_mask"].append(
-                "Yes" if result[person]["wears_mask"] else "No"
-            )
-            self.subdict["age"].append(result[person]["age"])
-            # Gender is now reported as a list of dictionaries.
-            # Each dict represents one face.
-            # Each dict contains probability for Woman and Man.
-            # We take only the higher probability result for each dict.
-            self.subdict["gender"].append(result[person]["gender"])
-            # Race and emotion are only detected if a person does not wear a mask
-            if result[person]["wears_mask"]:
-                self.subdict["race"].append(None)
-                self.subdict["emotion"].append(None)
-                self.subdict["emotion (category)"].append(None)
-            elif not result[person]["wears_mask"]:
-                # Check whether the race threshold was exceeded
-                if (
-                    result[person]["race"][result[person]["dominant_race"]]
-                    > self.race_threshold
-                ):
-                    self.subdict["race"].append(result[person]["dominant_race"])
-                else:
-                    self.subdict["race"].append(None)
-
-                # Check whether the emotion threshold was exceeded
-                if (
-                    result[person]["emotion"][result[person]["dominant_emotion"]]
-                    > self.emotion_threshold
-                ):
-                    self.subdict["emotion"].append(result[person]["dominant_emotion"])
+            wears_mask = result[person]["wears_mask"]
+            self.subdict["wears_mask"].append("Yes" if wears_mask else "No")
+            for key in self.actions:
+                if key == "emotion":
+                    classified_emotion = result[person]["dominant_emotion"]
+                    confidence_value = result[person][key][classified_emotion]
+                    outcome = (
+                        classified_emotion
+                        if confidence_value > self.emotion_threshold and not wears_mask
+                        else None
+                    )
+                    # also set the emotion category
+                    self.emotion_categories[outcome]
                     self.subdict["emotion (category)"].append(
-                        self.emotion_categories[result[person]["dominant_emotion"]]
+                        self.emotion_categories[outcome] if outcome else None
+                    )
+                elif key == "race":
+                    classified_race = result[person]["dominant_race"]
+                    confidence_value = result[person][key][classified_race]
+                    outcome = (
+                        classified_race
+                        if confidence_value > self.race_threshold and not wears_mask
+                        else None
                     )
-                else:
-                    self.subdict["emotion"].append(None)
-                    self.subdict["emotion (category)"].append(None)
+                elif key == "age":
+                    outcome = result[person]["age"] if not wears_mask else None
+                elif key == "gender":
+                    outcome = result[person]["gender"] if not wears_mask else None
+                self.subdict[key].append(outcome)
         return self.subdict
 
     def wears_mask(self, face: np.ndarray) -> bool:

ammico/notebooks/DemoNotebook_ammico.ipynb

@@ -261,13 +261,32 @@
     "        image_df.to_csv(dump_file)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_df.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "`TextDetector`:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ.pop(\"DISCLOSURE_AMMICO\")\n",
+    "os.environ.get(\"DISCLOSURE_AMMICO\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -876,11 +895,13 @@
     "\n",
     "From the seven facial expressions, an overall dominating emotion category is identified: negative, positive, or neutral emotion. These are defined with the facial expressions angry, disgust, fear and sad for the negative category, happy for the positive category, and surprise and neutral for the neutral category.\n",
     "\n",
-    "A similar threshold as for the emotion recognition is set for the race/ethnicity, gender and age detection, `race_threshold`, `gender_threshold`, `age_threshold`, with the default set to 50% so that a confidence for the race above 0.5 only will return a value in the analysis. \n",
+    "A similar threshold as for the emotion recognition is set for the race/ethnicity detection, `race_threshold`, with the default set to 50% so that a confidence for the race above 0.5 only will return a value in the analysis. \n",
+    "\n",
+    "For age and gender, unfortunately no confidence value is accessible so that no threshold values can be set for this type of analysis.\n",
     "\n",
     "You may also pass the name of the environment variable that determines if you accept or reject the ethical disclosure statement. By default, the variable is named `DISCLOSURE_AMMICO`.\n",
     "\n",
-    "Summarizing, the face detection is carried out using the following method call and keywords, where `emotion_threshold`, `race_threshold`, `gender_threshold`, `age_threshold` are optional:"
+    "Summarizing, the face detection is carried out using the following method call and keywords, where `emotion_threshold`, `race_threshold`, `accept_disclosure` are optional:"
    ]
   },
   {
@@ -891,7 +912,6 @@
    "source": [
     "for key in image_dict.keys():\n",
     "    image_dict[key] = ammico.EmotionDetector(image_dict[key], emotion_threshold=50, race_threshold=50,\n",
-    "                                             gender_threshold=50, age_threshold=50, \n",
     "                                             accept_disclosure=\"DISCLOSURE_AMMICO\").analyse_image()"
    ]
   },

ammico/test/data/text_IMG_3757.txt

@@ -1,10 +1 @@
-THE
-ALGEBRAIC
-EIGENVALUE
-PROBLEM
-DOM
-NVS TIO
-MINA
-Monographs
-on Numerical Analysis
-J.. H. WILKINSON
+THE ALGEBRAIC EIGENVALUE PROBLEM

ammico/test/data/text_translated_IMG_3757.txt

@@ -1 +1 @@
-algebraic eigenvalue problem monographs numerical analysis
+the algebraic eigenvalue problem

ammico/test/test_display.py

@@ -54,8 +54,6 @@ def test_right_output_analysis_summary(get_AE, get_options, monkeypatch):
         None,
         50,
         50,
-        50,
-        50,
         "OTHER_VAR",
         "CIE 1976",
         "summary_and_questions",
@@ -76,8 +74,6 @@ def test_right_output_analysis_emotions(get_AE, get_options, monkeypatch):
         None,
         50,
         50,
-        50,
-        50,
         "OTHER_VAR",
         "CIE 1976",
         "summary_and_questions",