Update app.py

app.py

@@ -18,47 +18,52 @@ 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
+# EN -> NL mapping (lowercase)
 EN_TO_NL = {
-    "neutral": "Neutraal",
-    "happy": "Blij",
-    "sad": "Verdrietig",
-    "surprise": "Verrast",
-    "angry": "Boos",
-    "anger": "Boos",
-    "disgust": "Walging",
-    "fear": "Bang",
-    "contempt": "Minachting",
-    "unknown": "Onbekend",
+    "neutral": "neutraal",
+    "happy": "blij",
+    "sad": "verdrietig",
+    "surprise": "verrast",
+    "angry": "boos",
+    "anger": "boos",
+    "disgust": "walging",
+    "fear": "bang",
+    "contempt": "minachting",
+    "unknown": "onbekend",
 }
 
-def to_dutch(label: str) -> str:
+def to_dutch_lower(label: str) -> str:
+    """Zet emotielabel om naar NL en lowercase (fallback: originele lowercase)."""
     if not label:
-        return "Onbekend"
+        return "onbekend"
     key = label.strip().lower()
-    return EN_TO_NL.get(key, label)
+    return EN_TO_NL.get(key, key)
 
 # In-memory statistieken
 emotion_stats = defaultdict(int)
 
 def visualize(image, det_res, fer_res):
+    """Tekent bbox + NL-lowercase emotielabel op de output."""
     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_nl = to_dutch(FacialExpressionRecog.getDesc(fer_type))
+        fer_type_str_nl = to_dutch_lower(FacialExpressionRecog.getDesc(fer_type))
+
         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_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):
             cv.circle(output, landmark, 2, landmark_color[idx], 2)
     return output
 
 def summarize_emotions(fer_res):
+    """Maakt de grote groene NL-lowercase samenvatting."""
     if not fer_res:
-        return "## **Geen gezicht gedetecteerd**"
-    names_nl = [to_dutch(FacialExpressionRecog.getDesc(x)) for x in fer_res]
+        return "## **geen gezicht gedetecteerd**"
+    names_nl = [to_dutch_lower(FacialExpressionRecog.getDesc(x)) for x in fer_res]
     counts = Counter(names_nl).most_common()
     top = counts[0][0]
     details = ", ".join([f"{name} ({n})" for name, n in counts])
@@ -77,8 +82,8 @@ def draw_bar_chart_cv(stats: dict, width=640, height=320):
     plot_h = height - top - bottom
     origin = (left, height - bottom)
 
-    cv.line(img, origin, (left + plot_w, height - bottom), (0, 0, 0), 2)
-    cv.line(img, origin, (left, height - bottom - plot_h), (0, 0, 0), 2)
+    cv.line(img, origin, (left + plot_w, height - bottom), (0, 0, 0), 2)            # x-as
+    cv.line(img, origin, (left, height - bottom - plot_h), (0, 0, 0), 2)            # y-as
 
     labels = list(stats.keys())
     values = [stats[k] for k in labels]
@@ -94,10 +99,10 @@ def draw_bar_chart_cv(stats: dict, width=640, height=320):
         h_px = int((val / max_val) * (plot_h - 10))
         y1 = height - bottom - h_px
         y2 = height - bottom - 1
-        cv.rectangle(img, (x1, y1), (x2, y2), (0, 170, 60), -1)
+        cv.rectangle(img, (x1, y1), (x2, y2), (0, 170, 60), -1)                      # groene balk
         cv.putText(img, str(val), (x1 + 2, y1 - 6), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 90, 30), 1, cv.LINE_AA)
 
-        show_lab = lab if len(lab) <= 10 else lab[:9] + "…"
+        show_lab = lab if len(lab) <= 12 else lab[:11] + "…"
         (tw, th), _ = cv.getTextSize(show_lab, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
         tx = x1 + (bar_w - tw) // 2
         ty = height - bottom + th + 12
@@ -106,7 +111,7 @@ def draw_bar_chart_cv(stats: dict, width=640, height=320):
     return cv.cvtColor(img, cv.COLOR_BGR2RGB)
 
 def process_image(input_image):
-    """Helper: run detectie en retourneer (output_img, fer_res as list of ints)."""
+    """Helper: run detectie en retourneer (output_img, fer_res as list[int])."""
     image = cv.cvtColor(input_image, cv.COLOR_RGB2BGR)
     h, w, _ = image.shape
     detect_model.setInputSize([w, h])
@@ -121,8 +126,8 @@ def detect_expression(input_image):
     """Versie die WÉL statistieken bijwerkt (gebruik voor 'Verstuur')."""
     output_img, fer_res = process_image(input_image)
     emotion_md = summarize_emotions(fer_res)
-    # update stats alleen hier:
-    names_nl = [to_dutch(FacialExpressionRecog.getDesc(x)) for x in fer_res]
+    # update stats in NL-lowercase
+    names_nl = [to_dutch_lower(FacialExpressionRecog.getDesc(x)) for x in fer_res]
     for name in names_nl:
         emotion_stats[name] += 1
     stats_plot = draw_bar_chart_cv(emotion_stats)
@@ -132,8 +137,7 @@ def detect_expression_no_stats(input_image):
     """Versie die GEEN statistieken bijwerkt (gebruik voor gr.Examples & caching)."""
     output_img, fer_res = process_image(input_image)
     emotion_md = summarize_emotions(fer_res)
-    # géén update van emotion_stats
-    stats_plot = draw_bar_chart_cv(emotion_stats)  # toon huidige stand (kan leeg zijn)
+    stats_plot = draw_bar_chart_cv(emotion_stats)  # toon huidige stand
     return output_img, emotion_md, stats_plot
 
 # Voorbeelden automatisch laden
@@ -153,9 +157,10 @@ custom_css = """
 """
 
 with gr.Blocks(css=custom_css) as demo:
-    gr.Markdown("## Herkenning van gezichtsuitdrukkingen ")
-    gr.Markdown("Detecteert gezichten en herkent gezichtsuitdrukkingen ")
+    gr.Markdown("## Herkenning van gezichtsuitdrukkingen (FER) met OpenCV DNN")
+    gr.Markdown("Detecteert gezichten en herkent gezichtsuitdrukkingen met YuNet + MobileFaceNet (ONNX).")
 
+    # Rij 1: Links upload/knoppen, Rechts output + emotie
     with gr.Row():
         with gr.Column():
             input_image = gr.Image(type="numpy", label="Afbeelding uploaden")
@@ -165,29 +170,40 @@ with gr.Blocks(css=custom_css) as demo:
         with gr.Column():
             output_image = gr.Image(type="numpy", label="Resultaat gezichtsuitdrukking")
             emotion_md = gr.Markdown("## **Nog geen resultaat**", elem_id="emotie-uitslag")
-            stats_image = gr.Image(label="Statistieken", type="numpy")
+
+    # Rij 2: Links mugshots (Examples), Rechts statistieken
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("**Voorbeelden (klik om te testen):**")
+            examples_component = gr.Examples(
+                examples=example_list,
+                inputs=input_image,
+                outputs=[output_image, emotion_md],  # stats volgt hieronder in dezelfde row
+                fn=detect_expression_no_stats,
+                examples_per_page=20,
+                cache_examples=CACHE_EXAMPLES
+            )
+        with gr.Column():
+            stats_image = gr.Image(
+                label="Statistieken",
+                type="numpy",
+                value=draw_bar_chart_cv(emotion_stats)  # start met lege/actuele chart
+            )
 
     # Clear-helpers
     def clear_all_on_new():
-        return None, "## **Nog geen resultaat**", draw_bar_chart_cv(emotion_stats)
+        return None, "## **Nog geen resultaat**"
 
     def clear_all_button():
+        # reset inputs/outputs; statistieken blijven behouden
         return None, None, "## **Nog geen resultaat**", draw_bar_chart_cv(emotion_stats)
 
-    input_image.change(fn=clear_all_on_new, outputs=[output_image, emotion_md, stats_image])
+    # Nieuwe upload wist output + emotietekst (grafiek blijft staan)
+    input_image.change(fn=clear_all_on_new, outputs=[output_image, emotion_md])
+    # Verwerken
     submit_btn.click(fn=detect_expression, inputs=input_image, outputs=[output_image, emotion_md, stats_image])
+    # Wissen-knop: ook grafiek opnieuw tekenen (maar stats niet resetten)
     clear_btn.click(fn=clear_all_button, outputs=[input_image, output_image, emotion_md, stats_image])
 
-    gr.Markdown("Klik op een voorbeeld om te testen.")
-    # BELANGRIJK: gebruik de 'no_stats'-functie voor Examples zodat deze NIET meetellen
-    gr.Examples(
-        examples=example_list,
-        inputs=input_image,
-        outputs=[output_image, emotion_md, stats_image],
-        fn=detect_expression_no_stats,   # <- telt NIET mee
-        examples_per_page=20,
-        cache_examples=CACHE_EXAMPLES
-    )
-
 if __name__ == "__main__":
     demo.launch()