README.md

@@ -6,13 +6,8 @@
   <img alt="Static Badge" src="https://img.shields.io/badge/Redis-6.4.0-red">
   <img alt="Static Badge" src="https://img.shields.io/badge/JSDelivr-in_use-brown">
   <img alt="Static Badge" src="https://img.shields.io/badge/Flask-3.1.2-8ecae6">
-  <br><br>
-  <img width="1920" height="1080" alt="image" src="https://github.com/user-attachments/assets/da5b891e-b4ac-484a-885c-0856f18e04fc" style="height: 70%; width: 70%"/>
 </p>
 
-
-
-
 <br>
 
 🌈 实现功能：
@@ -27,12 +22,10 @@
 
 ## 🚀一键安装
 
-在<b>[最新Release页面](https://github.com/ClovertaTheTrilobita/cellpose-web/releases/latest)</b>中下载最新的 <b>install.sh</b> 到你的Linux/macOS机器上。
+在<b>[Release页面](https://github.com/ClovertaTheTrilobita/cellpose-web/releases)</b>中下载最新的 <b>install.sh</b> 到你的Linux/macOS机器上。
 
 将它放到你希望项目存在的位置，并执行它，安装脚本会将项目自动拉取到同一目录下。
 
-**NOTE:** 安装脚本设计上支持Debian系、Arch系、RHEL系Linux,但目前仅测试过Arch Linux。其他发行版暂未经过测试，若出现意外错误，推荐手动安装。
-
 Windows暂时不支持通过脚本一键安装。
 
 <br>

backend/config.yaml

@@ -1,5 +1,5 @@
 backend:
-  ip: 192.168.193.141
+  ip: 10.10.25.240
   port: 5000
 
 model:

backend/cp_run.py

@@ -62,20 +62,6 @@ class Cprun:
                   diameter: float | None = None,
                   flow_threshold: float = 0.4,
                   cellprob_threshold: float = 0.0, ):
-        """
-        运行 cellpose 分割
-
-        Args:
-            images: [list] 图片存储路径
-            time: [str] 开始运行的时间，相当于本次运行的ID，用于存储运行结果
-            model: [str] 图像分割所使用的模型
-            diameter: [float]  diameters are used to rescale the image to 30 pix cell diameter.
-            flow_threshold: [float] flow error threshold (all cells with errors below threshold are kept) (not used for 3D). Defaults to 0.4.
-            cellprob_threshold: [float] all pixels with value above threshold kept for masks, decrease to find more and larger masks. Defaults to 0.0.
-
-        Returns:
-
-        """
 
         if time is None:
             return [False, "No time received"]
@@ -85,10 +71,9 @@ class Cprun:
 
         message = [f"Using {model} model"]
 
-        # 设定模型参数
         model = models.CellposeModel(gpu=True, model_type=model)
         files = images
-        imgs = [imread(f) for f in files] # 获取目录中的每一个文件
+        imgs = [imread(f) for f in files]
         masks, flows, styles = model.eval(
             imgs,
             flow_threshold=flow_threshold,
@@ -105,7 +90,7 @@ class Cprun:
             out = base + "_output"
             save_masks(imgs, mask, flow, out, tif=True)
 
-            # 用 plot 生成彩色叠加图
+            # 用 plot 生成彩色叠加图（不依赖 skimage）
             rgb = plot.image_to_rgb(img, channels=[0, 0])  # 原图转 RGB
             over = plot.mask_overlay(rgb, masks=mask, colors=None)  # 叠加彩色实例
             Image.fromarray(over).save(base + "_overlay.png")

backend/cp_train.py

@@ -19,19 +19,6 @@ os.environ["CELLPOSE_LOCAL_MODELS_PATH"] = MODELS_DIR
 r = redis.Redis(host="127.0.0.1", port=6379, db=0)
 
 def set_status(task_id, status, train_losses, test_losses, **extra):
-    """
-    修改redis数据库中某一任务的运行状态
-
-    Args:
-        task_id: 这一任务的时间戳
-        status: 任务状态
-        train_losses: 此次任务的训练loss
-        test_losses: 此次任务的测试loss
-        **extra:
-
-    Returns:
-
-    """
     payload = {"status": status,
                "updated_at": datetime.datetime.utcnow().isoformat(),
                "train_losses": train_losses.tolist() if hasattr(train_losses, "tolist") else train_losses,
@@ -68,32 +55,6 @@ class Cptrain:
                           scale_range=None,
                           channel_axis: int = None,
                           ):
-        """
-        开始训练
-
-        Args:
-            time: 此次任务的时间戳（即任务ID）
-            model_name: 训练结果命名
-            image_filter:
-            mask_filter:
-            base_model:
-            train_probs:
-            test_probs:
-            batch_size:
-            learning_rate:
-            n_epochs:
-            weight_decay:
-            normalize:
-            compute_flows:
-            min_train_masks:
-            nimg_per_epoch:
-            rescale:
-            scale_range:
-            channel_axis:
-
-        Returns:
-
-        """
 
         train_dir = Path(TRAIN_DIR) / time
         test_dir = Path(TEST_DIR) / time

backend/flaskApp.py

@@ -93,10 +93,15 @@ def run_upload():
             return default
 
     flow_threshold = _to_float(request.args.get("flow_threshold") or request.form.get("flow_threshold"), 0.4)
-    cellprob_threshold = _to_float(request.args.get("cellprob_threshold") or request.form.get("cellprob_threshold"),0.0)
+    cellprob_threshold = _to_float(request.args.get("cellprob_threshold") or request.form.get("cellprob_threshold"),
+                                   0.0)
     diameter_raw = request.args.get("diameter") or request.form.get("diameter")
     diameter = _to_float(diameter_raw, None) if diameter_raw not in (None, "") else None
 
+    print("cpt:" + str(cellprob_threshold))
+    print("flow:" + str(flow_threshold))
+    print("diameter:" + str(diameter))
+
     # 将文件保存在本地目录中
     ts = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + f"-{int(time.time()*1000)%1000:03d}"
     os.makedirs(Path(UPLOAD_DIR) / ts, exist_ok=True)
@@ -135,39 +140,19 @@ def run_upload():
 
 @app.post("/train_upload")
 def train_upload():
-    """
-    从前端获取训练数据和测试数据，并开始训练
-
-    Returns:
-
-    """
 
     def _to_float(x, default):
-        """
-        将变量转为float类型
-        Args:
-            x: 变量
-            default: 默认值
-        """
-
         try:
             return float(x)
         except (TypeError, ValueError):
             return default
 
     def _to_int(x, default):
-        """
-        将变量转为int类型
-        Args:
-            x: 变量
-            default: 默认值
-        """
         try:
             return int(x)
         except (TypeError, ValueError):
             return default
 
-    # 获取从前端传来的参数
     ts = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + f"-{int(time.time()*1000)%1000:03d}"
     model_name = request.args.get("model_name") or f"custom_model-{ts}"
     image_filter = request.args.get("image_filter") or "_img"
@@ -197,7 +182,6 @@ def train_upload():
     scale_range = _to_float(request.args.get("scale_range"), None)
     channel_axis = _to_int(request.args.get("channel_axis"), None)
 
-    # 创建工作目录
     train_files = request.files.getlist("train_files")
     test_files = request.files.getlist("test_files")
     os.makedirs(Path(TRAIN_DIR) /  ts, exist_ok=True)
@@ -219,30 +203,34 @@ def train_upload():
         saved.append(os.path.join(TEST_DIR, ts, name))
 
     def job():
-        """
-        子线程方法
-        """
         return asyncio.run(Cptrain.start_train(
-            time=ts, model_name=model_name, image_filter=image_filter, mask_filter=mask_filter, base_model=base_model,
+            time=ts,
-            batch_size=batch_size, learning_rate=learning_rate, n_epochs=n_epochs, weight_decay=weight_decay,
+            model_name=model_name,
-            normalize=normalize, compute_flows=compute_flows, min_train_masks=min_train_masks, nimg_per_epoch=nimg_per_epoch,
+            image_filter=image_filter,
-            rescale=rescale, scale_range=scale_range, channel_axis=channel_axis,
+            mask_filter=mask_filter,
+            base_model=base_model,
+            batch_size=batch_size,
+            learning_rate=learning_rate,
+            n_epochs=n_epochs,
+            weight_decay=weight_decay,
+            normalize=normalize,
+            compute_flows=compute_flows,
+            min_train_masks=min_train_masks,
+            nimg_per_epoch=nimg_per_epoch,
+            rescale=rescale,
+            scale_range=scale_range,
+            channel_axis=channel_axis,
         ))
 
-    # 创建一个子线程，防止阻塞主线程
     fut = executor.submit(job)
 
     def done_cb(f):
-        """
-        获取训练结果，并存入redis数据库
-        """
         try:
             train_losses, test_losses = f.result()
             set_train_status(ts, "success", train_losses, test_losses)
         except Exception as e:
             set_status(ts, "failed", error=str(e))
 
-    # 添加回调，在子线程执行完后更新redis中任务状态
     fut.add_done_callback(done_cb)
 
     return jsonify({"ok": True, "count": len(saved), "id": ts})
@@ -263,13 +251,6 @@ def status():
 
 @app.get("/preview")
 def preview():
-    """
-    获取本次分割结果的预览
-
-    Returns:
-
-    """
-
     task_id = request.args.get('id')
     task_dir = Path(OUTPUT_DIR) / task_id
     if not task_dir.exists():
@@ -294,24 +275,11 @@ def preview():
 
 @app.get("/models")
 def list_models():
-    """
+    models_list = os.listdir(MODELS_DIR)
-    获取现有模型列表
-
-    Returns:
-
-    """
-
-    models_list = os.listdir(MODELS_DIR) # 查询模型列表中有哪些文件
     return jsonify({"ok": True, "models": models_list})
 
 @app.get("/result")
 def list_results():
-    """
-    获取运行结果
-
-    Returns:
-
-    """
     task_id = request.args.get('id')
     st = get_status(task_id)
     if not st:

backend/main.py

@@ -4,7 +4,7 @@ from multiprocessing import Process
 
 
 if __name__ == "__main__":
-    # 启动测试服务器
+    # Cprun.run_test()
     p = Process(target=run_dev)
     p.start()
     print(f"Flask running in PID {p.pid}")

frontend/api.js

@@ -1,7 +1,7 @@
 const config = {
   server: {
     protocol: 'http',
-    host: '192.168.193.141',
+    host: '10.10.25.240',
     port: 5000
   }
 };