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app.py

@@ -97,7 +97,7 @@ def segment(img_bgr, sample_type, hsv_low_h, hsv_high_h, color_tol, min_area_px,
             continue
         rect = cv2.minAreaRect(cnt)
         box = cv2.boxPoints(rect)
-        box = np.int0(box)
+        box = box.astype(np.int32)
         m = cv2.moments(cnt)
         if m["m00"] == 0:
             cx, cy = 0, 0
@@ -192,27 +192,30 @@ def render_overlay(img_bgr, px_per_mm, ref, comps, df):
     return cv2.cvtColor(out, cv2.COLOR_BGR2RGB)
 
 def analyze(image, sample_type, expected_count, ref_mode, ref_size_mm, min_area_px, max_area_px, color_tol, hsv_low_h, hsv_high_h):
-    if image is None:
-        return None, pd.DataFrame(), None, []
-    img_rgb = np.array(image)
-    img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
-    img_bgr, scale = downscale_bgr(img_bgr)
-    px_per_mm, ref_center, ref_type = detect_reference(img_bgr, ref_mode, ref_size_mm)
-    comps = segment(img_bgr, sample_type, hsv_low_h, hsv_high_h, color_tol, min_area_px, max_area_px)
-    if sample_type == "leaves":
-        comps.sort(key=lambda c: c["center"][0])
-    else:
-        comps.sort(key=lambda c: c["center"][1] * 0.3 + c["center"][0] * 0.7)
-    if expected_count and expected_count > 0:
-        comps = comps[:int(expected_count)]
-    df = compute_metrics(img_bgr, comps, px_per_mm)
-    overlay = render_overlay(img_bgr.copy(), px_per_mm, (ref_center, ref_type), comps, df)
-    csv = df.to_csv(index=False)
-    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
-    tmp.write(csv.encode("utf-8"))
-    tmp.close()
-    js = df.to_dict(orient="records")
-    return overlay, df, tmp.name, js
+    try:
+        if image is None:
+            return None, pd.DataFrame(), None, []
+        img_rgb = np.array(image)
+        img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
+        img_bgr, scale = downscale_bgr(img_bgr)
+        px_per_mm, ref_center, ref_type = detect_reference(img_bgr, ref_mode, ref_size_mm)
+        comps = segment(img_bgr, sample_type, hsv_low_h, hsv_high_h, color_tol, min_area_px, max_area_px)
+        if sample_type == "leaves":
+            comps.sort(key=lambda c: c["center"][0])
+        else:
+            comps.sort(key=lambda c: c["center"][1] * 0.3 + c["center"][0] * 0.7)
+        if expected_count and expected_count > 0:
+            comps = comps[:int(expected_count)]
+        df = compute_metrics(img_bgr, comps, px_per_mm)
+        overlay = render_overlay(img_bgr.copy(), px_per_mm, (ref_center, ref_type), comps, df)
+        csv = df.to_csv(index=False)
+        tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
+        tmp.write(csv.encode("utf-8"))
+        tmp.close()
+        js = df.to_dict(orient="records")
+        return overlay, df, tmp.name, js
+    except Exception as e:
+        return None, pd.DataFrame(), None, [{"error": str(e)}]
 
 with gr.Blocks(theme=gr.themes.Default()) as demo:
     gr.Markdown("# Biological Sample Quantifier (Leaves / Seeds)")