update thresholds in notebook and empty actions list (#205)

ammico/faces.py

@@ -268,6 +268,8 @@ class EmotionDetector(AnalysisMethod):
         # one dictionary per face that is detected in the image
         # since we are only passing a subregion of the image
         # that contains one face, the list will only contain one dict
+        print("actions are:", self.actions)
+        if self.actions != []:
             fresult["result"] = DeepFace.analyze(
                 img_path=face,
                 actions=self.actions,

ammico/notebooks/DemoNotebook_ammico.ipynb

@@ -257,8 +257,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# set the thresholds for the emotion detection\n",
+    "emotion_threshold = 50  # this is the default value for the detection confidence\n",
+    "# the lowest possible value is 0\n",
+    "# the highest possible value is 100\n",
+    "race_threshold = 50\n",
+    "gender_threshold = 50\n",
     "for num, key in tqdm(enumerate(image_dict.keys()), total=len(image_dict)):    # loop through all images\n",
-    "    image_dict[key] = ammico.EmotionDetector(image_dict[key]).analyse_image() # analyse image with EmotionDetector and update dict\n",
+    "    image_dict[key] = ammico.EmotionDetector(image_dict[key],\n",
+    "                                             emotion_threshold=emotion_threshold,\n",
+    "                                             race_threshold=race_threshold,\n",
+    "                                             gender_threshold=gender_threshold).analyse_image() # analyse image with EmotionDetector and update dict\n",
     "    if num % dump_every == 0 or num == len(image_dict) - 1:      # save results every dump_every to dump_file\n",
     "        image_df = ammico.get_dataframe(image_dict)\n",
     "        image_df.to_csv(dump_file)"
@@ -1411,7 +1420,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,