Created using Colab

Author: carsen-stringer

notebooks/test_Cellpose-SAM.ipynb

@@ -3,8 +3,8 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "colab_type": "text",
-        "id": "view-in-github"
+        "id": "view-in-github",
+        "colab_type": "text"
       },
       "source": [
         "<a href=\"https://colab.research.google.com/github/MouseLand/cellpose/blob/main/notebooks/test_Cellpose-SAM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -38,7 +38,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "_lRDGixTm1Px"
+      },
       "source": [
         "### Install Cellpose-SAM"
       ]
@@ -57,7 +59,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "JRalUQBTm1Py"
+      },
       "source": [
         "Check GPU and instantiate model - will download weights."
       ]
@@ -78,17 +82,19 @@
         "import matplotlib.pyplot as plt\n",
         "\n",
         "io.logger_setup() # run this to get printing of progress\n",
-        "  \n",
+        "\n",
         "#Check if colab notebook instance has GPU access\n",
-        "if core.use_gpu()==False: \n",
+        "if core.use_gpu()==False:\n",
         "  raise ImportError(\"No GPU access, change your runtime\")\n",
         "\n",
         "model = models.CellposeModel(gpu=True)"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "fY6Vv5I3m1Py"
+      },
       "source": [
         "### Download example images"
       ]
@@ -132,7 +138,9 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "M-jKt9wsm1Pz"
+      },
       "source": [
         "### Run Cellpose-SAM"
       ]
@@ -146,7 +154,7 @@
       },
       "outputs": [],
       "source": [
-        "masks_pred, flows, styles, diams = model.eval(imgs, \n",
+        "masks_pred, flows, styles = model.eval(imgs,\n",
         "                                              niter=1000) # using more iterations for bacteria\n"
       ]
     },
@@ -162,7 +170,9 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "teNSdi1_m1Pz"
+      },
       "outputs": [],
       "source": [
         "from cellpose import transforms, plot\n",
@@ -207,16 +217,11 @@
         "plt.show()"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "rMyZtY6ym1P0"
+      },
       "source": [
         "# Run Cellpose-SAM in 3D\n",
         "\n",
@@ -229,21 +234,25 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "2b2hVxCvm1P0"
+      },
       "outputs": [],
       "source": [
         "img_3D = io.imread(\"rgb_3D.tif\")\n",
         "\n",
         "\n",
         "# 1. computes flows from 2D slices and combines into 3D flows to create masks\n",
-        "masks, flows, None = model.eval(img_3D, z_axis=0, channel_axis=1, \n",
+        "masks, flows, _ = model.eval(img_3D, z_axis=0, channel_axis=1,\n",
         "                                batch_size=32,\n",
         "                                do_3D=True, flow3D_smooth=1)\n"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "KeMtAuRom1P0"
+      },
       "source": [
         "Second way: computes masks in 2D slices and stitches masks in 3D based on mask overlap\n",
         "\n",
@@ -253,27 +262,33 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "WTyCgBUfm1P0"
+      },
       "outputs": [],
       "source": [
         "# 2. computes masks in 2D slices and stitches masks in 3D based on mask overlap\n",
         "print('running cellpose 2D + stitching masks')\n",
-        "masks_stitched, flows_stitched, None = model.eval(img_3D, z_axis=0, channel_axis=1,\n",
-        "                                                  batch_size=32, \n",
+        "masks_stitched, flows_stitched, _ = model.eval(img_3D, z_axis=0, channel_axis=1,\n",
+        "                                                  batch_size=32,\n",
         "                                                  do_3D=False, stitch_threshold=0.5)"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "wbu1j0h6m1P0"
+      },
       "source": [
         "Results from 3D flows => masks computation"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "id": "Vfg67u2dm1P0"
+      },
       "outputs": [],
       "source": [
         "# DISPLAY RESULTS 3D flows => masks\n",
@@ -291,14 +306,15 @@
     },
     {
       "cell_type": "markdown",
-      "metadata": {},
+      "metadata": {
+        "id": "dj18ZyzHm1P0"
+      },
       "source": [
         "Results from stitching"
       ]
     },
     {
-      "cell_type": "markdown",
-      "metadata": {},
+      "cell_type": "code",
       "source": [
         "# DISPLAY RESULTS stitching\n",
         "plt.figure(figsize=(15,3))\n",
@@ -311,14 +327,19 @@
         "  imgout[outX, outY] = np.array([255,75,75])\n",
         "  plt.imshow(imgout)\n",
         "  plt.title('iplane = %d'%iplane)"
-      ]
+      ],
+      "metadata": {
+        "id": "fd-6Hji-n9_H"
+      },
+      "execution_count": null,
+      "outputs": []
     }
   ],
   "metadata": {
     "accelerator": "GPU",
     "colab": {
-      "include_colab_link": true,
-      "provenance": []
+      "provenance": [],
+      "include_colab_link": true
     },
     "kernelspec": {
       "display_name": "cellpose",
@@ -340,4 +361,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}