Add an example ipynb to use model

README.md

@@ -10,14 +10,9 @@ license: cc-by-nc-sa-4.0
 short_description: Assessing viability of orchid seeds in TZ tests with AI.
 ---
 
-This is a templated Space for [Shiny for Python](https://shiny.rstudio.com/py/). 
+This is an app created with [Shiny for Python](https://shiny.rstudio.com/py/). 
 
-
-To get started with a new app do the following:
-
-1) Install Shiny with `pip install shiny`
-2) Create a new app with `shiny create`
-3) Then run the app with `shiny run --reload`
+If you create a python environment with all the requirements, you can download and run this app locally using shiny.
 
 To learn more about this framework please see the [Documentation](https://shiny.rstudio.com/py/docs/overview.html).
 

app.py

@@ -349,8 +349,15 @@ def server(input, output, session: Session):
 
     @render.ui
     def download_results_ui():
+
         if analysis_results.get() and not is_analyzing.get():
-            return ui.download_button("download_results", "Download Results", class_="btn-success"), ui.download_button("download_segmented_images",
+            # results = analysis_results.get()
+            # current_nms = input.nms_threshold()
+            # print(f'Current NMS threshold: {current_nms}')
+            # if results[0].get('NMS threshold') != current_nms:
+            #     print('NMS changed')
+            # else:
+                return ui.download_button("download_results", "Download Results", class_="btn-success"), ui.download_button("download_segmented_images",
                                                                                                                         "Download Segmented Images",
                                                                                                                         class_="btn-success")
 

example_using_final_model.ipynb

@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "This file provides an example of how to use some of the underlying python methods used in this app.\n",
+    "\n",
+    "It requires a python environment with pytorch and detectron2. This can be set up on google colab using the following cell."
+   ],
+   "id": "54812cfe385be4e3"
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "## To run on google colab, run this cell to install requirements. You will then, however, need to copy some of our custom methods over (e.g. load_model etc..)\n",
+    "## In future we may package these properly, but they are just utility functions\n",
+    "try:\n",
+    "  import google.colab\n",
+    "  IN_COLAB = True\n",
+    "except:\n",
+    "  IN_COLAB = False\n",
+    "if IN_COLAB:\n",
+    "\n",
+    "    import sys, os, distutils.core\n",
+    "    # Note: This is a faster way to install detectron2 in Colab, but it does not include all functionalities (e.g. compiled operators).\n",
+    "    # See https://detectron2.readthedocs.io/tutorials/install.html for full installation instructions\n",
+    "    # Issues raised:\n",
+    "    # - pyaml install https://github.com/facebookresearch/detectron2/issues/5122 (think this is fixed)\n",
+    "    !git clone 'https://github.com/facebookresearch/detectron2'\n",
+    "    dist = distutils.core.run_setup(\"./detectron2/setup.py\")\n",
+    "    !python -m pip install {' '.join([f\"'{x}'\" for x in dist.install_requires])}\n",
+    "    sys.path.insert(0, os.path.abspath('./detectron2'))\n",
+    "\n",
+    "    # Properly install detectron2. (Please do not install twice in both ways)\n",
+    "    # !python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'\n",
+    "    import torch, detectron2\n",
+    "    !nvcc --version\n",
+    "    TORCH_VERSION = \".\".join(torch.__version__.split(\".\")[:2])\n",
+    "    CUDA_VERSION = torch.__version__.split(\"+\")[-1]\n",
+    "    print(\"torch: \", TORCH_VERSION, \"; cuda: \", CUDA_VERSION)\n",
+    "    print(\"detectron2:\", detectron2.__version__)\n",
+    "    print(f'GPU available: {torch.cuda.is_available()}')"
+   ],
+   "id": "ae06666362c0995d",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "## Import the model utilities that we will need, and load the model.\n",
+    "from python_utils import load_model, apply_nms, OPTIMAL_NMS_THRESHOLD\n",
+    "\n",
+    "predictor = load_model()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "## Import and display an image.\n",
+    "import cv2\n",
+    "\n",
+    "if IN_COLAB:\n",
+    "    from google.colab import files\n",
+    "    uploaded = files.upload()\n",
+    "    import cv2\n",
+    "    from google.colab.patches import cv2_imshow\n",
+    "\n",
+    "    im = cv2.imread(uploaded[0]) # just look at the first image\n",
+    "    cv2_imshow(im)\n",
+    "else:\n",
+    "\n",
+    "    import os\n",
+    "    from IPython.display import Image\n",
+    "\n",
+    "    img_file = os.path.join('assets', 'rbg_kew.jpg')\n",
+    "    display(Image(filename=img_file))\n",
+    "    im = cv2.imread(img_file)"
+   ],
+   "id": "12ab5c1116d6ff38",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "## Run the model on the image, then apply NMS to filter out overlapping masks.\n",
+    "raw_output = predictor(im)\n",
+    "prediction = apply_nms(raw_output, mask=True, cls_agnostic_nms=OPTIMAL_NMS_THRESHOLD)"
+   ],
+   "id": "d9305d58e42d6504",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "# Get the seed counts (0 = viable, 1 = non-viable, 2 = empty)\n",
+    "classes = prediction[\"instances\"].pred_classes.tolist()\n",
+    "counts = {\"viable\": classes.count(0),\n",
+    "          \"non-viable\": classes.count(1),\n",
+    "          \"empty\": classes.count(2),\n",
+    "          \"total\": len(classes)}\n",
+    "print(counts)"
+   ],
+   "id": "676bd6fab69c10f2",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "# Visualise the segmentation masks\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from app import get_overlayed_image_from_single_result\n",
+    "\n",
+    "prediction['image'] = im\n",
+    "visualiser = get_overlayed_image_from_single_result(prediction)\n",
+    "fig, ax = plt.subplots(figsize=(8, 6.4))\n",
+    "ax.imshow(cv2.cvtColor(visualiser.get_image()[:, :, ::-1], cv2.COLOR_BGR2RGB))\n",
+    "ax.get_xaxis().set_visible(False)\n",
+    "ax.get_yaxis().set_visible(False)\n",
+    "ax.set_title(\"Annotated\")\n",
+    "plt.tight_layout()"
+   ],
+   "id": "18934b98c3461a28",
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

python_utils/get_model.py

@@ -2,6 +2,7 @@ import urllib.request
 import tempfile
 
 ## Urls and model variables that might change.
+## If changing any of these, think about other places in repos where they might need changing (e.g. weights url inside config file).
 OPTIMAL_NMS_THRESHOLD = 0.7
 model_page = "https://huggingface.co/TZProject/final_tz_segmentor"
 _model_config_url = model_page + "/resolve/main/final_model_config.yaml"