update paths in demo notebook

ammico/notebooks/DemoNotebook_ammico.ipynb

@@ -145,9 +145,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true
-   },
+   "metadata": {},
    "source": [
     "# Step 1: Read your data into AMMICO\n",
     "The ammico package reads in one or several input files given in a folder for processing. The user can select to read in all image files in a folder, to include subfolders via the `recursive` option, and can select the file extension that should be considered (for example, only \"jpg\" files, or both \"jpg\" and \"png\" files). For reading in the files, the ammico function `find_files` is used, with optional keywords:\n",
@@ -984,6 +982,13 @@
     "| `emotion (category)` | `list[str]` | the detected emotion category (positive, negative, or neutral), up to three faces, if above the confidence threshold |"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Image Multimodal Search"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1039,7 +1044,7 @@
    "outputs": [],
    "source": [
     "image_dict = ammico.find_files(\n",
-    "    path = \"../../../test-images-politicians\",\n",
+    "    path=str(data_path),\n",
     ")"
    ]
   },
@@ -1076,7 +1081,7 @@
     "    features_image_stacked,\n",
     ") = my_obj.parsing_images(\n",
     "    model_type, \n",
-    "    path_to_save_tensors=\"../../../test-images-politicians\",\n",
+    "    path_to_save_tensors=str(data_path),\n",
     "    )"
    ]
   },
@@ -1133,8 +1138,7 @@
    "outputs": [],
    "source": [
     "import importlib_resources                                                                       # only require for image query example\n",
-    "# image_example_query = str(importlib_resources.files(\"ammico\") / \"data\" / \"test-crop-image.png\")   # creating the path to the image for the image query example\n",
-    "image_example_query = \"../../../test-images-politicians/5e4c7722-d620-42ab-915e-9b1a048e2992Original.jpg\"  \n",
+    "image_example_query = str(importlib_resources.files(\"ammico\") / \"data\" / \"test-crop-image.png\")   # creating the path to the image for the image query example\n",
     "\n",
     "search_query = [\n",
     "    {\"image\": image_example_query},      # This is how looks image query, here `image_example_path` is the path to query image like \"data/test-crop-image.png\"\n",
@@ -1184,7 +1188,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sorted_lists # rank sorted by similarity\n"
+    "sorted_lists"
    ]
   },
   {
@@ -1218,8 +1222,14 @@
    "source": [
     "my_obj.show_results(\n",
     "    search_query[0], # you can change the index to see the results for other queries\n",
-    ")\n",
-    "# possible to show the averages?"
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Formulate your search queries: Search for the best match using multiple reference images, for example, of a person"
    ]
   },
   {
@@ -1233,74 +1243,6 @@
     "# in the end, the best matches will be averaged over for each picture and a list of averaged best matches will be provided"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import importlib_resources                                                                       # only require for image query example\n",
-    "# image_example_query = str(importlib_resources.files(\"ammico\") / \"data\" / \"test-crop-image.png\")   # creating the path to the image for the image query example\n",
-    "image_example_query = \"../../../test-images-politicians/5e4c7722-d620-42ab-915e-9b1a048e2992Original.jpg\"  # creating the path to the image for the image query example\n",
-    "image_example_query2 = \"../../../test-images-politicians/service-pnp-ppbd-00600-00608_150px.jpg\"\n",
-    "\n",
-    "search_query = [\n",
-    "    {\"image\": image_example_query},      # This is how looks image query, here `image_example_path` is the path to query image like \"data/test-crop-image.png\"\n",
-    "    {\"image\": image_example_query2},\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "similarity, sorted_lists = my_obj.multimodal_search(\n",
-    "    model,\n",
-    "    vis_processors,\n",
-    "    txt_processors,\n",
-    "    model_type,\n",
-    "    image_keys,\n",
-    "    features_image_stacked,\n",
-    "    search_query,\n",
-    "    filter_number_of_images=20,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "similarity # now a 2D tensor"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# average similarities\n",
-    "print(similarity.mean(dim = 1)) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# add the similarity average to the image_dict\n",
-    "for key in image_dict.keys():\n",
-    "    # find the similarities for each image in the search query\n",
-    "    similarities = [image_dict[key][query[\"image\"]] for query in search_query]\n",
-    "    image_dict[key][\"similarity_average\"] = sum(similarities)/len(similarities)\n",
-    "    image_dict[key][\"rank similarity_average\"] = \"some_value\""
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1520,7 +1462,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ammico",
    "language": "python",
    "name": "python3"
   },
@@ -1534,7 +1476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.21"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,