Upload app.py

app.py

@@ -1,3 +1,4 @@
+
 import cv2 as cv
 import numpy as np
 import gradio as gr
@@ -48,18 +49,15 @@ EN_TO_NL = {
 }
 
 def to_dutch_lower(label: str) -> str:
-    """Zet emotielabel om naar NL en lowercase (fallback: originele lowercase)."""
     if not label:
         return "onbekend"
     key = label.strip().lower()
     return EN_TO_NL.get(key, key)
 
-# In-memory statistieken
 emotion_stats = defaultdict(int)
 
-# ---------- Confidence helpers ----------
+# Confidence helpers
 def _format_pct(conf):
-    """Format confidence naar '82%' (int). Conf kan in [0,1] of [0,100] of None."""
     if conf is None:
         return None
     try:
@@ -72,14 +70,6 @@ def _format_pct(conf):
     return f"{int(round(c))}%"
 
 def _parse_infer_output(result):
-    """
-    Probeer robuust (label_idx, confidence) uit infer-output te halen.
-    Ondersteunt:
-      - (label, score) tuple/list
-      - [probs...] ndarray (neemt argmax + max)
-      - [label] of scalar -> (label, None)
-    """
-    # numpy array?
     if isinstance(result, np.ndarray):
         arr = result
         if arr.ndim == 1 and arr.size > 1:
@@ -89,14 +79,12 @@ def _parse_infer_output(result):
         elif arr.size == 1:
             return int(arr.flat[0]), None
         else:
-            # onbekende vorm
             try:
                 idx = int(arr[0])
                 return idx, None
             except Exception:
                 return 0, None
 
-    # list/tuple?
     if isinstance(result, (list, tuple)):
         if len(result) >= 2 and isinstance(result[1], (float, np.floating, int, np.integer)):
             try:
@@ -109,15 +97,12 @@ def _parse_infer_output(result):
             except Exception:
                 return 0, None
 
-    # scalar label
     try:
         return int(result), None
     except Exception:
         return 0, None
-# ---------------------------------------
 
 def visualize(image, det_res, labels, confs):
-    """Tekent bbox + NL-lowercase emotielabel + confidence op de output."""
     output = image.copy()
     landmark_color = [(255, 0, 0), (0, 0, 255), (0, 255, 0), (255, 0, 255), (0, 255, 255)]
     for i, (det, lab) in enumerate(zip(det_res, labels)):
@@ -128,16 +113,7 @@ def visualize(image, det_res, labels, confs):
         txt = f"{fer_type_str_nl}" + (f" {pct}" if pct else "")
 
         cv.rectangle(output, (bbox[0], bbox[1]), (bbox[0]+bbox[2], bbox[1]+bbox[3]), (0, 255, 0), 2)
-        cv.putText(
-            output,
-            txt,
-            (bbox[0], max(0, bbox[1] - 10)),
-            cv.FONT_HERSHEY_SIMPLEX,
-            0.7,
-            (0, 0, 255),
-            2,
-            cv.LINE_AA
-        )
+        cv.putText(output, txt, (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):
@@ -145,27 +121,18 @@ def visualize(image, det_res, labels, confs):
     return output
 
 def summarize_emotions(labels, confs):
-    """Maakt de grote groene NL-lowercase samenvatting met gemiddelden per emotie."""
     if not labels:
         return "## **geen gezicht gedetecteerd**"
 
-    names_nl = []
-    for lab in labels:
-        names_nl.append(to_dutch_lower(FacialExpressionRecog.getDesc(lab)))
-
-    # tel per emotie + verzamel confidences
+    names_nl = [to_dutch_lower(FacialExpressionRecog.getDesc(lab)) for lab in labels]
     counts = Counter(names_nl)
     conf_bucket = defaultdict(list)
     for i, name in enumerate(names_nl):
         if i < len(confs) and confs[i] is not None:
             conf_bucket[name].append(float(confs[i]))
 
-    # top-emotie op basis van count
     top = counts.most_common(1)[0][0]
-
-    # details: "blij (2, gem. 79%)"
     parts = []
-    # sorteer op frequentie aflopend, dan alfabetisch
     for name, n in sorted(counts.items(), key=lambda kv: (-kv[1], kv[0])):
         if conf_bucket[name]:
             avg = sum(conf_bucket[name]) / len(conf_bucket[name])
@@ -177,7 +144,6 @@ def summarize_emotions(labels, confs):
     return f"# **{top}**\n\n_Gedetecteerde emoties: {details}_"
 
 def process_image(input_image):
-    """Helper: run detectie en retourneer (output_img, labels[int], confs[float|None])."""
     image = cv.cvtColor(input_image, cv.COLOR_RGB2BGR)
     h, w, _ = image.shape
     detect_model.setInputSize([w, h])
@@ -194,10 +160,8 @@ def process_image(input_image):
     return cv.cvtColor(output, cv.COLOR_BGR2RGB), labels, confs, dets
 
 def detect_expression(input_image):
-    """Versie die WÉL statistieken bijwerkt (gebruik voor 'Verstuur')."""
     output_img, labels, confs, _ = process_image(input_image)
     emotion_md = summarize_emotions(labels, confs)
-    # update stats in NL-lowercase
     for lab in labels:
         name_nl = to_dutch_lower(FacialExpressionRecog.getDesc(lab))
         emotion_stats[name_nl] += 1
@@ -205,13 +169,10 @@ def detect_expression(input_image):
     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, labels, confs, _ = process_image(input_image)
     emotion_md = summarize_emotions(labels, confs)
-    # géén stats update en ook géén stats_image teruggeven
     return output_img, emotion_md
 
-# --- 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)
@@ -224,8 +185,8 @@ 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)            # x-as
-    cv.line(img, origin, (left, height - bottom - plot_h), (0, 0, 0), 2)            # y-as
+    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)
 
     labels = list(stats.keys())
     values = [stats[k] for k in labels]
@@ -241,7 +202,7 @@ 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)                      # groene balk
+        cv.rectangle(img, (x1, y1), (x2, y2), (0, 170, 60), -1)
         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] + "…"
@@ -252,7 +213,6 @@ def draw_bar_chart_cv(stats: dict, width=640, height=320):
 
     return cv.cvtColor(img, cv.COLOR_BGR2RGB)
 
-# Voorbeelden automatisch laden
 IMAGE_EXTS = {".jpg", ".jpeg", ".png", ".bmp", ".webp"}
 EXAMPLES_DIR = Path("examples")
 if EXAMPLES_DIR.exists() and EXAMPLES_DIR.is_dir():
@@ -262,7 +222,6 @@ else:
 example_list = [[p] for p in example_paths]
 CACHE_EXAMPLES = bool(example_list)
 
-# Uitlegblok (HTML) – netjes opgemaakt
 INFO_HTML = """
 <div>
   <h3>Hoe werkt deze gezichtsuitdrukking-herkenner?</h3>
@@ -288,15 +247,12 @@ INFO_HTML = """
 </div>
 """
 
-# CSS (groene emotietekst + uitlegblok styling)
 custom_css = """
 #emotie-uitslag { color: #16a34a; }
 #emotie-uitslag h1, #emotie-uitslag h2, #emotie-uitslag h3 { margin: 0.25rem 0; }
-
-/* Uitlegblok onder de mugshots */
 #uitleg-blok {
-  background: #f3f4f6;        /* lichtgrijs */
-  border: 1px solid #e5e7eb;  /* subtiele rand */
+  background: #f3f4f6;
+  border: 1px solid #e5e7eb;
   border-radius: 10px;
   padding: 12px 14px;
 }
@@ -310,7 +266,6 @@ 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")
@@ -321,41 +276,34 @@ with gr.Blocks(css=custom_css) as demo:
             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 + uitleg), Rechts statistieken
     with gr.Row():
         with gr.Column():
             gr.Markdown("**Voorbeelden (klik om te testen):**")
             gr.Examples(
                 examples=example_list,
                 inputs=input_image,
-                outputs=[output_image, emotion_md],   # <- 2 outputs
-                fn=detect_expression_no_stats,        # <- geeft 2 outputs terug
+                outputs=[output_image, emotion_md],
+                fn=detect_expression_no_stats,
                 examples_per_page=20,
                 cache_examples=CACHE_EXAMPLES
             )
-            # Uitlegblok onder de mugshots
             gr.HTML(INFO_HTML, elem_id="uitleg-blok")
 
         with gr.Column():
             stats_image = gr.Image(
                 label="Statistieken",
                 type="numpy",
-                value=draw_bar_chart_cv(emotion_stats)  # start met lege/actuele chart
+                value=draw_bar_chart_cv(emotion_stats)
             )
 
-    # 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__":