Fixed notebook typos (#78)

Author: giswqs

docs/examples/text_prompts.ipynb

@@ -30,7 +30,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %pip install segment-geospatial leafmap localtileserver"
+    "# %pip install segment-geospatial groundingdino-py leafmap localtileserver"
    ]
   },
   {
@@ -56,15 +56,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No such comm: 96754273fbc94aabbf75b0f2bebca8cd\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "m = leafmap.Map(center=[-22.17615, -51.253043], zoom=18, height=\"800px\")\n",
     "m.add_basemap(\"SATELLITE\")\n",
@@ -98,7 +90,7 @@
    "outputs": [],
    "source": [
     "image = \"Image.tif\"\n",
-    "tms_to_geotiff(output=image, bbox=bbox, zoom=18, source=\"Satellite\", overwrite=True)"
+    "tms_to_geotiff(output=image, bbox=bbox, zoom=19, source=\"Satellite\", overwrite=True)"
    ]
   },
   {
@@ -139,7 +131,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Initialize LangSAM class"
+    "## Initialize LangSAM class\n",
+    "\n",
+    "The initialization of the LangSAM class might take a few minutes. The initialization downloads the model weights and sets up the model for inference."
    ]
   },
   {
@@ -173,7 +167,7 @@
    "source": [
     "## Segment the image\n",
     "\n",
-    "Part of the model prediction includes setting appropriate thresholds for object detection and text association with the detected objects. These threshold values range between 0 to 1 and are set while calling the predict method of the LangSAM class.\n",
+    "Part of the model prediction includes setting appropriate thresholds for object detection and text association with the detected objects. These threshold values range from 0 to 1 and are set while calling the predict method of the LangSAM class.\n",
     "\n",
     "`box_threshold`: This value is used for object detection in the image. A higher value makes the model more selective, identifying only the most confident object instances, leading to fewer overall detections. A lower value, conversely, makes the model more tolerant, leading to increased detections, including potentially less confident ones.\n",
     "\n",
@@ -188,7 +182,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sam.predict('trees.tif', text_prompt, box_threshold=0.24, text_threshold=0.24)"
+    "sam.predict(image, text_prompt, box_threshold=0.24, text_threshold=0.24)"
    ]
   },
   {
@@ -208,6 +202,7 @@
     "    cmap='Greens', \n",
     "    box_color='red', \n",
     "    title='Automatic Segmentation of Trees', \n",
+    "    blend=False,\n",
     "    output='trees.tif'\n",
     ")"
    ]
@@ -216,7 +211,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![](https://i.imgur.com/3BAsQj3.png)"
+    "![](https://i.imgur.com/ytKMTlA.png)"
    ]
   }
  ],