Update app.py

app.py

@@ -2,12 +2,13 @@ import cv2 as cv
 import numpy as np
 import gradio as gr
 from pathlib import Path
+from collections import Counter
 from huggingface_hub import hf_hub_download
 
 from facial_fer_model import FacialExpressionRecog
 from yunet import YuNet
 
-# Download ONNX modellen van Hugging Face
+# Download ONNX-modellen van Hugging Face
 FD_MODEL_PATH = hf_hub_download(repo_id="opencv/face_detection_yunet", filename="face_detection_yunet_2023mar.onnx")
 FER_MODEL_PATH = hf_hub_download(repo_id="opencv/facial_expression_recognition", filename="facial_expression_recognition_mobilefacenet_2022july.onnx")
 
@@ -17,15 +18,40 @@ target_id = cv.dnn.DNN_TARGET_CPU
 fer_model = FacialExpressionRecog(modelPath=FER_MODEL_PATH, backendId=backend_id, targetId=target_id)
 detect_model = YuNet(modelPath=FD_MODEL_PATH)
 
+# === EN -> NL mapping van emotielabels ===
+EN_TO_NL = {
+    "neutral": "Neutraal",
+    "happy": "Blij",
+    "sad": "Verdrietig",
+    "surprise": "Verrast",
+    "angry": "Boos",
+    "anger": "Boos",        # sommige modellen geven 'anger'
+    "disgust": "Walging",
+    "fear": "Bang",
+    "contempt": "Minachting",
+    "unknown": "Onbekend",
+}
+
+def to_dutch(label: str) -> str:
+    if not label:
+        return "Onbekend"
+    key = label.strip().lower()
+    return EN_TO_NL.get(key, label)  # fallback naar originele label als onbekend
+
 def visualize(image, det_res, fer_res):
     output = image.copy()
     landmark_color = [(255, 0, 0), (0, 0, 255), (0, 255, 0), (255, 0, 255), (0, 255, 255)]
 
     for det, fer_type in zip(det_res, fer_res):
         bbox = det[0:4].astype(np.int32)
-        fer_type_str = FacialExpressionRecog.getDesc(fer_type)
+        # Modelbeschrijving -> NL label
+        fer_type_str_en = FacialExpressionRecog.getDesc(fer_type)
+        fer_type_str_nl = to_dutch(fer_type_str_en)
+
         cv.rectangle(output, (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3]), (0, 255, 0), 2)
-        cv.putText(output, fer_type_str, (bbox[0], bbox[1] - 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
+        # Iets grotere en vettere tekst
+        cv.putText(output, fer_type_str_nl, (bbox[0], max(0, bbox[1] - 10)),
+                   cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2, cv.LINE_AA)
 
         landmarks = det[4:14].astype(np.int32).reshape((5, 2))
         for idx, landmark in enumerate(landmarks):
@@ -33,6 +59,26 @@ def visualize(image, det_res, fer_res):
 
     return output
 
+def summarize_emotions(fer_res):
+    """
+    Maakt een Markdown-tekst met een grote, vetgedrukte samenvatting (NL).
+    Bij meerdere gezichten wordt de meest voorkomende emotie uitgelicht.
+    """
+    if not fer_res:
+        return "## **Geen gezicht gedetecteerd**"
+
+    # Haal Engelstalige labels uit het model en map naar NL
+    names_en = [FacialExpressionRecog.getDesc(x) for x in fer_res]
+    names_nl = [to_dutch(n) for n in names_en]
+    counts = Counter(names_nl).most_common()
+    top, top_n = counts[0]
+
+    # Compacte lijst: "Blij (2), Neutraal (1)"
+    details = ", ".join([f"{name} ({n})" for name, n in counts])
+
+    # Grote headline + toelichting
+    return f"# **{top}**\n\n_Gedetecteerde emoties: {details}_"
+
 def detect_expression(input_image):
     image = cv.cvtColor(input_image, cv.COLOR_RGB2BGR)
     h, w, _ = image.shape
@@ -40,7 +86,9 @@ def detect_expression(input_image):
 
     dets = detect_model.infer(image)
     if dets is None:
-        return cv.cvtColor(image, cv.COLOR_BGR2RGB)
+        # Geen gezichten
+        emotion_md = summarize_emotions([])
+        return cv.cvtColor(image, cv.COLOR_BGR2RGB), emotion_md
 
     fer_res = []
     for face_points in dets:
@@ -48,7 +96,8 @@ def detect_expression(input_image):
         fer_res.append(result[0])
 
     output = visualize(image, dets, fer_res)
-    return cv.cvtColor(output, cv.COLOR_BGR2RGB)
+    emotion_md = summarize_emotions(fer_res)
+    return cv.cvtColor(output, cv.COLOR_BGR2RGB), emotion_md
 
 # === Automatisch voorbeelden inladen uit de map "examples/" ===
 IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
@@ -66,35 +115,46 @@ example_list = [[p] for p in example_paths]
 CACHE_EXAMPLES = bool(example_list)  # cache alleen als er echt voorbeelden zijn
 # =============================================================
 
-# Gradio Interface
-with gr.Blocks(css='''.example * { font-style: italic; font-size: 18px !important; color: #0ea5e9 !important; }''') as demo:
+# Gradio Interface (NL)
+custom_css = """
+.example * { font-style: italic; font-size: 18px !important; color: #0ea5e9 !important; }
+#emotie-uitslag h1, #emotie-uitslag h2, #emotie-uitslag h3 { margin: 0.25rem 0; }
+"""
 
-    gr.Markdown("### Facial Expression Recognition (FER) with OpenCV DNN")
-    gr.Markdown("Detects faces and recognizes facial expressions using YuNet + MobileFaceNet ONNX models.")
+with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as demo:
+    gr.Markdown("## Herkenning van gezichtsuitdrukkingen (FER) met OpenCV DNN")
+    gr.Markdown("Detecteert gezichten en herkent gezichtsuitdrukkingen met YuNet + MobileFaceNet (ONNX).")
 
     with gr.Row():
-        input_image = gr.Image(type="numpy", label="Upload Image")
-        output_image = gr.Image(type="numpy", label="Facial Expression Result")
-
-    # Output leegmaken bij nieuwe upload
-    input_image.change(fn=lambda: (None), outputs=output_image)
-
-    with gr.Row():
-        submit_btn = gr.Button("Submit", variant="primary")
-        clear_btn = gr.Button("Clear")
-
-    submit_btn.click(fn=detect_expression, inputs=input_image, outputs=output_image)
-    clear_btn.click(fn=lambda: (None, None), outputs=[input_image, output_image])
-
-    gr.Markdown("Klik op een voorbeeld om het te proberen.", elem_classes=["example"])
+        with gr.Column(scale=1):
+            input_image = gr.Image(type="numpy", label="Afbeelding uploaden")
+            with gr.Row():
+                submit_btn = gr.Button("Verstuur", variant="primary")
+                clear_btn = gr.Button("Wissen")
+        with gr.Column(scale=1):
+            # Grote emotie-uitkomst bovenaan
+            emotion_md = gr.Markdown(value="## **Nog geen resultaat**", elem_id="emotie-uitslag")
+            output_image = gr.Image(type="numpy", label="Resultaat gezichtsuitdrukking")
+
+    # Output(s) leegmaken bij nieuwe upload
+    def _clear_output_on_new_image():
+        return None, "## **Nog geen resultaat**"
+    input_image.change(fn=_clear_output_on_new_image, outputs=[output_image, emotion_md])
+
+    # Knop-actie(s)
+    submit_btn.click(fn=detect_expression, inputs=input_image, outputs=[output_image, emotion_md])
+    clear_btn.click(fn=lambda: (None, None, "## **Nog geen resultaat**"),
+                    outputs=[input_image, output_image, emotion_md])
+
+    gr.Markdown("Klik op een voorbeeld om te testen.", elem_classes=["example"])
 
     gr.Examples(
-        examples=example_list,         # automatisch ingelezen paden
+        examples=example_list,               # automatisch ingelezen paden
         inputs=input_image,
-        outputs=output_image,          # <-- nodig voor cache
-        fn=detect_expression,          # <-- nodig voor cache
+        outputs=[output_image, emotion_md],  # nodig voor caching
+        fn=detect_expression,                # nodig voor caching
         examples_per_page=20,
-        cache_examples=CACHE_EXAMPLES  # True alleen als er voorbeelden zijn
+        cache_examples=CACHE_EXAMPLES
     )
 
 if __name__ == "__main__":