Update app.py

app.py

@@ -30,7 +30,7 @@ EN_TO_NL = {
 
     "surprise": "verrast",
     "surprised": "verrast",
-    "supprised": "verrast",   # veelvoorkomende typo
+    "supprised": "verrast",   # typo
     "surprized": "verrast",
 
     "angry": "boos",
@@ -40,7 +40,7 @@ EN_TO_NL = {
 
     "fear": "angstig",
     "fearful": "angstig",
-    "fearfull": "angstig",    # veelvoorkomende typo
+    "fearfull": "angstig",    # typo
 
     "contempt": "minachting",
 
@@ -66,4 +66,167 @@ 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, (_
+        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)
+    # géén stats update en ook géén stats_image teruggeven
+    return output_img, emotion_md
+
+# 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],   # <- 2 outputs
+                fn=detect_expression_no_stats,        # <- geeft 2 outputs terug
+                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()