Update app.py

app.py

@@ -18,17 +18,32 @@ 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 (lowercase)
+# EN -> NL mapping (lowercase) incl. varianten/typo's
 EN_TO_NL = {
     "neutral": "neutraal",
+
     "happy": "blij",
+    "happiness": "blij",
+
     "sad": "verdrietig",
+    "sadness": "verdrietig",
+
     "surprise": "verrast",
+    "surprised": "verrast",
+    "supprised": "verrast",   # veelvoorkomende typo
+    "surprized": "verrast",
+
     "angry": "boos",
     "anger": "boos",
+
     "disgust": "walging",
-    "fear": "bang",
+
+    "fear": "angstig",
+    "fearful": "angstig",
+    "fearfull": "angstig",    # veelvoorkomende typo
+
     "contempt": "minachting",
+
     "unknown": "onbekend",
 }
 
@@ -51,159 +66,4 @@ def visualize(image, det_res, fer_res):
         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_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])
-    return f"# **{top}**\n\n_Gedetecteerde emoties: {details}_"
-
-# --- Staafdiagram tekenen met OpenCV (geen matplotlib nodig) ---
-def draw_bar_chart_cv(stats: dict, width=640, height=320):
-    img = np.full((height, width, 3), 255, dtype=np.uint8)
-    cv.putText(img, "Live emotie-statistieken", (12, 28), cv.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2, cv.LINE_AA)
-    if not stats:
-        cv.putText(img, "Nog geen statistieken", (12, height//2), cv.FONT_HERSHEY_SIMPLEX, 0.9, (128, 128, 128), 2, cv.LINE_AA)
-        return cv.cvtColor(img, cv.COLOR_BGR2RGB)
-
-    left, right, top, bottom = 60, 20, 50, 40
-    plot_w = width - left - right
-    plot_h = height - top - bottom
-    origin = (left, height - bottom)
-
-    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]
-    max_val = max(values) if max(values) > 0 else 1
-
-    n = len(labels)
-    gap = 12
-    bar_w = max(10, int((plot_w - gap * (n + 1)) / max(1, n)))
-
-    for i, (lab, val) in enumerate(zip(labels, values)):
-        x1 = left + gap + i * (bar_w + gap)
-        x2 = x1 + bar_w
-        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)                      # 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) <= 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
-        cv.putText(img, show_lab, (tx, ty), cv.FONT_HERSHEY_SIMPLEX, 0.5, (40, 40, 40), 1, cv.LINE_AA)
-
-    return cv.cvtColor(img, cv.COLOR_BGR2RGB)
-
-def process_image(input_image):
-    """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])
-    dets = detect_model.infer(image)
-    if dets is None:
-        return cv.cvtColor(image, cv.COLOR_BGR2RGB), []
-    fer_res = [fer_model.infer(image, face_points[:-1])[0] for face_points in dets]
-    output = visualize(image, dets, fer_res)
-    return cv.cvtColor(output, cv.COLOR_BGR2RGB), fer_res
-
-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 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)
-    return output_img, emotion_md, stats_plot
-
-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)
-    stats_plot = draw_bar_chart_cv(emotion_stats)  # toon huidige stand
-    return output_img, emotion_md, stats_plot
-
-# Voorbeelden automatisch laden
-IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
-EXAMPLES_DIR = Path("examples")
-if EXAMPLES_DIR.exists() and EXAMPLES_DIR.is_dir():
-    example_paths = [str(p) for p in sorted(EXAMPLES_DIR.iterdir()) if Path(p).suffix.lower() in IMAGE_EXTS]
-else:
-    example_paths = []
-example_list = [[p] for p in example_paths]
-CACHE_EXAMPLES = bool(example_list)
-
-# CSS (groene emotietekst)
-custom_css = """
-#emotie-uitslag { color: #16a34a; }
-#emotie-uitslag h1, #emotie-uitslag h2, #emotie-uitslag h3 { margin: 0.25rem 0; }
-"""
-
-with gr.Blocks(css=custom_css) as demo:
-    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")
-            with gr.Row():
-                submit_btn = gr.Button("Verstuur", variant="primary")
-                clear_btn = gr.Button("Wissen")
-        with gr.Column():
-            output_image = gr.Image(type="numpy", label="Resultaat gezichtsuitdrukking")
-            emotion_md = gr.Markdown("## **Nog geen resultaat**", elem_id="emotie-uitslag")
-
-    # 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**"
-
-    def clear_all_button():
-        # reset inputs/outputs; statistieken blijven behouden
-        return None, None, "## **Nog geen resultaat**", draw_bar_chart_cv(emotion_stats)
-
-    # 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])
-
-if __name__ == "__main__":
-    demo.launch()
+        cv.putText(output, fer_type_str_nl, (_