Update app.py

app.py

@@ -429,32 +429,27 @@ def colorize_mask(mask: np.ndarray, num_colors: int = 512) -> np.ndarray:
 
 # ===== 推理 + 实例彩色可视化 =====
 def segment_with_choice(use_box_choice, annot_value, mode="Overlay"):
-    """分割推理 + 可视化，与原始逻辑完全对齐"""
+    """分割推理 + 可视化（更深颜色、更清晰区分）"""
     if annot_value is None or len(annot_value) < 1:
-        print("❌ No annotation input")
         return None, "⚠️ 请先上传图像"
 
     img_path = annot_value[0]
     bboxes = annot_value[1] if len(annot_value) > 1 else []
 
-    print(f"🖼️ Image path: {img_path}")
     box_array = None
     if use_box_choice == "Yes" and bboxes:
         box = parse_first_bbox(bboxes)
         if box:
             xmin, ymin, xmax, ymax = map(int, box)
             box_array = [[xmin, ymin, xmax, ymax]]
-            print(f"📦 Using box: {box_array}")
 
     try:
-        # 调用分割模型
         mask = run_seg(SEG_MODEL, img_path, box=box_array, device=SEG_DEVICE)
-        print("📏 Mask shape:", mask.shape, "dtype:", mask.dtype, "unique:", np.unique(mask))
+        if mask is None:
+            return None, "❌ 分割失败"
     except Exception as e:
-        print(f"❌ Error during inference: {e}")
-        return None, f"分割失败: {str(e)}"
+        return None, f"❌ 推理错误: {e}"
 
-    # 读取原图
     try:
         img = Image.open(img_path).convert("RGB")
         img_rgb = img.resize(mask.shape[::-1], resample=Image.BILINEAR)
@@ -462,50 +457,44 @@ def segment_with_choice(use_box_choice, annot_value, mode="Overlay"):
         if img_np.max() > 1.5:
             img_np = img_np / 255.0
     except Exception as e:
-        print(f"❌ Failed to open/convert image: {e}")
-        return None, f"图像读取失败: {str(e)}"
+        return None, f"❌ 图像处理失败: {e}"
 
-    # 生成实例掩码
-    mask_np = np.array(mask)
-    inst_mask = mask_np.astype(np.int32)
+    inst_mask = np.array(mask).astype(np.int32)
     unique_ids = np.unique(inst_mask)
     num_instances = len(unique_ids[unique_ids != 0])
-    print(f"✅ Instance IDs found: {unique_ids}, Total instances: {num_instances}")
 
     if num_instances == 0:
-        print("⚠️ No instance found, returning dummy red image")
-        return Image.new("RGB", mask.shape[::-1], (255, 0, 0)), "⚠️ 未检测到细胞"
+        return Image.new("RGB", mask.shape[::-1], (255, 200, 200)), "⚠️ 未检测到细胞"
 
-    # ==== 🎨 Overlay (每个实例一个颜色 + 黄色轮廓) ====
+    # ===== 🎨 彩色 mask 生成 =====
+    color_mask = colorize_mask(inst_mask, num_colors=512).astype(np.float32) / 255.0
+
+    # ===== 🌈 半透明叠加在原图上 =====
     overlay = img_np.copy()
-    alpha = 0.75  # 增强透明度，更清晰
-    cmap = cm.get_cmap("tab20", num_instances + 1)  # 亮色系更清楚
+    alpha = 0.6
+    blended = (1 - alpha) * overlay + alpha * color_mask
 
-    for inst_id in np.unique(inst_mask):
+    # ===== 🔶 黄色轮廓线描边每个实例 =====
+    for inst_id in unique_ids:
         if inst_id == 0:
             continue
         binary_mask = (inst_mask == inst_id).astype(np.uint8)
-        color = np.array(cmap(inst_id / (num_instances + 1))[:3])
-        overlay[binary_mask == 1] = (1 - alpha) * overlay[binary_mask == 1] + alpha * color
-
-        # 🟡 绘制粗黄色轮廓
         contours = measure.find_contours(binary_mask, 0.5)
         for contour in contours:
             contour = contour.astype(np.int32)
             for dy in [-1, 0, 1]:
                 for dx in [-1, 0, 1]:
-                    yy = np.clip(contour[:, 0] + dy, 0, overlay.shape[0] - 1).astype(np.int32)
-                    xx = np.clip(contour[:, 1] + dx, 0, overlay.shape[1] - 1).astype(np.int32)
-                    overlay[yy, xx] = [1.0, 1.0, 0.0]  # 粗边缘线
+                    yy = np.clip(contour[:, 0] + dy, 0, blended.shape[0] - 1).astype(np.int32)
+                    xx = np.clip(contour[:, 1] + dx, 0, blended.shape[1] - 1).astype(np.int32)
+                    blended[yy, xx] = [1.0, 1.0, 0.0]  # 黄色边
 
-    overlay = np.clip(overlay * 255.0, 0, 255).astype(np.uint8)
+    blended = np.clip(blended * 255.0, 0, 255).astype(np.uint8)
     result_text = f"✅ 检测到 {num_instances} 个细胞"
 
     if mode == "Instance Mask Only":
-        # 输出纯彩色 mask
-        return Image.fromarray(colorize_mask(inst_mask, num_colors=512)), result_text
+        return Image.fromarray((color_mask * 255).astype(np.uint8)), result_text
 
-    return Image.fromarray(overlay), result_text
+    return Image.fromarray(blended), result_text
 
 # ===== 计数功能 =====
 def count_cells_handler(image_path):