Include a face mask detection model

.gitignore

@@ -1,3 +1,5 @@
+data
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

.gitmodules

@@ -1,3 +0,0 @@
-[submodule "Face-Mask-Detection"]
-	path = Face-Mask-Detection
-	url = https://github.com/chandrikadeb7/Face-Mask-Detection.git

Face-Mask-Detection

@@ -1 +0,0 @@
-Subproject commit 7e500749401bad4bb338790fbdb89b58e41ef2d9

MANIFEST.in

@@ -0,0 +1 @@
+recursive-include misinformation/model *.model

misinformation/faces.py

@@ -1,10 +1,20 @@
+import cv2
 import ipywidgets
+import numpy as np
+import os
 
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
 from IPython.display import display
 from deepface import DeepFace
 from retinaface import RetinaFace
 
 
+# Module-wide storage for lazily loaded mask detection model
+mask_detection_model = None
+
+
 def facial_expression_analysis(img_path):
     result = {"filename": img_path}
 
@@ -18,10 +28,19 @@ def facial_expression_analysis(img_path):
     # Find the biggest face image in the detected ones
     maxface = max(faces, key=lambda f: f.shape[0] * f.shape[1])
 
+    # Determine whether the face wears a mask
+    result["wears_mask"] = wears_mask(maxface)
+
+    # Adapt the features we are looking for depending on whether a mask is
+    # worn. White masks screw race detection, emotion detection is useless.
+    actions = ["age", "gender"]
+    if not result["wears_mask"]:
+        actions = actions + ["race", "emotion"]
+
     # Run the full DeepFace analysis
     result["deepface_results"] = DeepFace.analyze(
         img_path=maxface,
-        actions=["age", "gender", "race", "emotion"],
+        actions=actions,
         prog_bar=False,
         detector_backend="skip",
     )
@@ -33,6 +52,31 @@ def facial_expression_analysis(img_path):
     return result
 
 
+def wears_mask(face):
+    global mask_detection_model
+
+    # Preprocess the face to match the assumptions of the face mask
+    # detection model
+    face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
+    face = cv2.resize(face, (224, 224))
+    face = img_to_array(face)
+    face = preprocess_input(face)
+    face = np.expand_dims(face, axis=0)
+
+    # Lazily load the model
+    if mask_detection_model is None:
+        mask_detection_model = load_model(
+            os.path.join(os.path.split(__file__)[0], "models", "mask_detector.model")
+        )
+
+    # Run the model (ignoring output)
+    with ipywidgets.Output():
+        mask, withoutMask = mask_detection_model.predict(face)[0]
+
+    # Convert from np.bool_ to bool to later be able to serialize the result
+    return bool(mask > withoutMask)
+
+
 class JSONContainer:
     """Expose a Python dictionary as a JSON document in JupyterLab
     rich display rendering.