Update lavis dependencies (#204)

.github/workflows/ci.yml

@@ -16,7 +16,7 @@ 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
@@ -24,10 +24,15 @@ jobs:
       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
+        # python -m pip install uv
-        uv pip install --system -e .
+        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 @@ class AnalysisExplorer:
             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 @@ class AnalysisExplorer:
                                     ],
                                     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 @@ class AnalysisExplorer:
         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 @@ class AnalysisExplorer:
                 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 @@ class EmotionDetector(AnalysisMethod):
         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 @@ class EmotionDetector(AnalysisMethod):
             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 @@ class EmotionDetector(AnalysisMethod):
             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",
@@ -232,13 +222,13 @@ class EmotionDetector(AnalysisMethod):
             "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 \
@@ -246,16 +236,15 @@ class EmotionDetector(AnalysisMethod):
                     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 @@ class EmotionDetector(AnalysisMethod):
         fresult = {}
         # Determine whether the face wears a mask
         fresult["wears_mask"] = self.wears_mask(face)
-        actions = self._define_actions(fresult)
+        self._define_actions(fresult)
-        self._ensure_deepface_models(actions)
+        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 @@ class EmotionDetector(AnalysisMethod):
         """
         # 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(
+            wears_mask = result[person]["wears_mask"]
-                "Yes" if result[person]["wears_mask"] else "No"
+            self.subdict["wears_mask"].append("Yes" if wears_mask else "No")
-            )
+            for key in self.actions:
-            self.subdict["age"].append(result[person]["age"])
+                if key == "emotion":
-            # Gender is now reported as a list of dictionaries.
+                    classified_emotion = result[person]["dominant_emotion"]
-            # Each dict represents one face.
+                    confidence_value = result[person][key][classified_emotion]
-            # Each dict contains probability for Woman and Man.
+                    outcome = (
-            # We take only the higher probability result for each dict.
+                        classified_emotion
-            self.subdict["gender"].append(result[person]["gender"])
+                        if confidence_value > self.emotion_threshold and not wears_mask
-            # Race and emotion are only detected if a person does not wear a mask
+                        else None
-            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"])
-                    self.subdict["emotion (category)"].append(
-                        self.emotion_categories[result[person]["dominant_emotion"]]
                     )
-                else:
+                    # also set the emotion category
-                    self.subdict["emotion"].append(None)
+                    self.emotion_categories[outcome]
-                    self.subdict["emotion (category)"].append(None)
+                    self.subdict["emotion (category)"].append(
+                        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
+                    )
+                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

@@ -261,6 +261,15 @@
     "        image_df.to_csv(dump_file)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_df.head()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -268,6 +277,16 @@
     "`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
+THE ALGEBRAIC EIGENVALUE PROBLEM
-ALGEBRAIC
-EIGENVALUE
-PROBLEM
-DOM
-NVS TIO
-MINA
-Monographs
-on Numerical Analysis
-J.. H. WILKINSON

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",

ammico/test/test_faces.py

@@ -14,36 +14,28 @@ def test_init_EmotionDetector(monkeypatch):
     assert ed.subdict["emotion"] == [None]
     assert ed.subdict["age"] == [None]
     assert ed.emotion_threshold == 50
-    assert ed.age_threshold == 50
-    assert ed.gender_threshold == 50
     assert ed.race_threshold == 50
     assert ed.emotion_categories["angry"] == "Negative"
     assert ed.emotion_categories["happy"] == "Positive"
     assert ed.emotion_categories["surprise"] == "Neutral"
-    assert ed.accept_disclosure == "OTHER_VAR"
-    assert os.environ.get(ed.accept_disclosure) == "True"
     assert ed.accepted
-    monkeypatch.delenv(ed.accept_disclosure, raising=False)
+    monkeypatch.delenv("OTHER_VAR", raising=False)
     # different thresholds
     ed = fc.EmotionDetector(
         {},
         emotion_threshold=80,
         race_threshold=30,
-        gender_threshold=70,
-        age_threshold=90,
         accept_disclosure="OTHER_VAR",
     )
     assert ed.emotion_threshold == 80
     assert ed.race_threshold == 30
-    assert ed.gender_threshold == 70
+    monkeypatch.delenv("OTHER_VAR", raising=False)
-    assert ed.age_threshold == 90
-    monkeypatch.delenv(ed.accept_disclosure, raising=False)
     # do not accept disclosure
     monkeypatch.setattr("builtins.input", lambda _: "no")
     ed = fc.EmotionDetector({}, accept_disclosure="OTHER_VAR")
-    assert os.environ.get(ed.accept_disclosure) == "False"
+    assert os.environ.get("OTHER_VAR") == "False"
     assert not ed.accepted
-    monkeypatch.delenv(ed.accept_disclosure, raising=False)
+    monkeypatch.delenv("OTHER_VAR", raising=False)
     # now test the exceptions: thresholds
     monkeypatch.setattr("builtins.input", lambda _: "yes")
     with pytest.raises(ValueError):
@@ -54,14 +46,6 @@ def test_init_EmotionDetector(monkeypatch):
         fc.EmotionDetector({}, race_threshold=150)
     with pytest.raises(ValueError):
         fc.EmotionDetector({}, race_threshold=-50)
-    with pytest.raises(ValueError):
-        fc.EmotionDetector({}, gender_threshold=150)
-    with pytest.raises(ValueError):
-        fc.EmotionDetector({}, gender_threshold=-50)
-    with pytest.raises(ValueError):
-        fc.EmotionDetector({}, age_threshold=150)
-    with pytest.raises(ValueError):
-        fc.EmotionDetector({}, age_threshold=-50)
     # test pre-set variables: disclosure
     monkeypatch.delattr("builtins.input", raising=False)
     monkeypatch.setenv("OTHER_VAR", "something")
@@ -78,22 +62,23 @@ def test_init_EmotionDetector(monkeypatch):
 def test_define_actions(monkeypatch):
     monkeypatch.setenv("OTHER_VAR", "True")
     ed = fc.EmotionDetector({}, accept_disclosure="OTHER_VAR")
-    actions = ed._define_actions({"wears_mask": True})
+    ed._define_actions({"wears_mask": True})
-    assert actions == ["age", "gender"]
+    assert ed.actions == ["age", "gender"]
-    actions = ed._define_actions({"wears_mask": False})
+    ed._define_actions({"wears_mask": False})
-    assert actions == ["age", "gender", "race", "emotion"]
+    assert ed.actions == ["age", "gender", "race", "emotion"]
     monkeypatch.setenv("OTHER_VAR", "False")
     ed = fc.EmotionDetector({}, accept_disclosure="OTHER_VAR")
-    actions = ed._define_actions({"wears_mask": True})
+    ed._define_actions({"wears_mask": True})
-    assert actions == []
+    assert ed.actions == []
-    actions = ed._define_actions({"wears_mask": False})
+    ed._define_actions({"wears_mask": False})
-    assert actions == ["emotion"]
+    assert ed.actions == ["emotion"]
 
 
 def test_ensure_deepface_models(monkeypatch):
     monkeypatch.setenv("OTHER_VAR", "True")
     ed = fc.EmotionDetector({}, accept_disclosure="OTHER_VAR")
-    ed._ensure_deepface_models(["age", "gender", "race", "emotion"])
+    ed.actions = ["age", "gender", "race", "emotion"]
+    ed._ensure_deepface_models()
 
 
 def test_analyse_faces(get_path, monkeypatch):

ammico/test/test_text.py

@@ -127,8 +127,8 @@ def test_get_text_from_image(set_testdict, get_path, set_environ):
         test_obj.get_text_from_image()
         ref_file = get_path + "text_" + item + ".txt"
         with open(ref_file, "r", encoding="utf8") as file:
-            reference_text = file.read()
+            reference_text = file.read().replace("\n", " ")
-        assert test_obj.subdict["text"] == reference_text
+        assert test_obj.subdict["text"].replace("\n", " ") == reference_text
 
 
 def test_translate_text(set_testdict, get_path):
@@ -137,13 +137,13 @@ def test_translate_text(set_testdict, get_path):
         ref_file = get_path + "text_" + item + ".txt"
         trans_file = get_path + "text_translated_" + item + ".txt"
         with open(ref_file, "r", encoding="utf8") as file:
-            reference_text = file.read()
+            reference_text = file.read().replace("\n", " ")
         with open(trans_file, "r", encoding="utf8") as file:
-            true_translated_text = file.read()
+            true_translated_text = file.read().replace("\n", " ")
         test_obj.subdict["text"] = reference_text
         test_obj.translate_text()
         assert test_obj.subdict["text_language"] == lang
-        translated_text = test_obj.subdict["text_english"].lower()
+        translated_text = test_obj.subdict["text_english"].lower().replace("\n", " ")
         for word in true_translated_text.lower():
             assert word in translated_text
 
@@ -233,7 +233,7 @@ def test_read_csv(get_path):
 
 
 def test_PostprocessText(set_testdict, get_path):
-    reference_dict = "THE\nALGEBRAIC\nEIGENVALUE\nPROBLEM\nDOM\nNVS TIO\nMINA\nMonographs\non Numerical Analysis\nJ.. H. WILKINSON"
+    reference_dict = "THE ALGEBRAIC EIGENVALUE PROBLEM"
     reference_df = "Mathematische Formelsammlung\nfür Ingenieure und Naturwissenschaftler\nMit zahlreichen Abbildungen und Rechenbeispielen\nund einer ausführlichen Integraltafel\n3., verbesserte Auflage"
     img_numbers = ["IMG_3755", "IMG_3756", "IMG_3757"]
     for image_ref in img_numbers:

docs/source/notebooks/DemoNotebook_ammico.ipynb

@@ -855,11 +855,13 @@
     "\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",
+    "Gender and age probabilistic detection do not allow access to the confidence value, so that no threshold can be set for these.\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:"
    ]
   },
   {
@@ -870,7 +872,6 @@
    "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()"
    ]
   },

pyproject.toml

@@ -43,7 +43,7 @@ dependencies = [
     "pytest-cov",
     "Requests",
     "retina_face",
-    "ammico-lavis",
+    "ammico-lavis>=1.0.2.3",
     "setuptools",
     "spacy",
     "tensorflow>=2.13.0",