fix fastapi version

app.ipynb

@@ -23,7 +23,51 @@
     "ade_mean=[0.485, 0.456, 0.406]\n",
     "ade_std=[0.229, 0.224, 0.225]\n",
     "\n",
-    "model_id = f\"thiagohersan/maskformer-satellite-trees\""
+    "palette = [\n",
+    "    [120, 120, 120], [4, 200, 4], [4, 4, 250], [6, 230, 230],\n",
+    "    [80, 50, 50], [120, 120, 80], [140, 140, 140], [204, 5, 255]\n",
+    "]\n",
+    "\n",
+    "model_id = f\"thiagohersan/maskformer-satellite-trees\"\n",
+    "\n",
+    "vegetation_labels = [\"vegetation\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def visualize_instance_seg_mask(img_in, mask, id2label, included_labels):\n",
+    "    img_out = np.zeros((mask.shape[0], mask.shape[1], 3))\n",
+    "    image_total_pixels = mask.shape[0] * mask.shape[1]\n",
+    "    label_ids = np.unique(mask)\n",
+    "\n",
+    "    id2color = {id: palette[id] for id in label_ids}\n",
+    "    id2count = {id: 0 for id in label_ids}\n",
+    "\n",
+    "    for i in range(img_out.shape[0]):\n",
+    "      for j in range(img_out.shape[1]):\n",
+    "        img_out[i, j, :] = id2color[mask[i, j]]\n",
+    "        id2count[mask[i, j]] = id2count[mask[i, j]] + 1\n",
+    "\n",
+    "    image_res = (0.5 * img_in + 0.5 * img_out).astype(np.uint8)\n",
+    "\n",
+    "    dataframe = [[\n",
+    "        f\"{id2label[id]}\",\n",
+    "        f\"{(100 * id2count[id] / image_total_pixels):.2f} %\",\n",
+    "        f\"{np.sqrt(id2count[id] / image_total_pixels):.2f} m\"\n",
+    "        ] for id in label_ids if id2label[id] in included_labels]\n",
+    "\n",
+    "    if len(dataframe) < 1:\n",
+    "        dataframe = [[\n",
+    "            f\"\",\n",
+    "            f\"{(0):.2f} %\",\n",
+    "            f\"{(0):.2f} m\"\n",
+    "        ]]\n",
+    "\n",
+    "    return image_res, dataframe\n"
    ]
   },
   {
@@ -38,7 +82,7 @@
     "    do_normalize=False,\n",
     "    do_rescale=False,\n",
     "    ignore_index=255,\n",
-    "    reduce_labels=False\n",
+    "    do_reduce_labels=False\n",
     ")\n",
     "\n",
     "hf_token = environ.get('HFTOKEN') or True\n",
@@ -72,9 +116,7 @@
    "outputs": [],
    "source": [
     "results = preprocessor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[img_size])[0]\n",
-    "results = results.numpy()\n",
-    "\n",
-    "labels = np.unique(results)"
+    "mask_img, dataframe = visualize_instance_seg_mask(np.array(img), results.numpy(), model.config.id2label, vegetation_labels)"
    ]
   },
   {
@@ -83,14 +125,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "for label_id in labels:\n",
-    "    print(model.config.id2label[label_id])"
+    "dataframe"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.15 ('hf-gradio')",
+   "display_name": "Python 3.8.15 ('gradio2023')",
    "language": "python",
    "name": "python3"
   },
@@ -104,12 +145,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.15"
+   "version": "3.9.17"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "4888b226c77b860705e4be316b14a092026f41c3585ee0ddb38f3008c0cb495e"
+    "hash": "311e94dbd43374307e33a15d3b7324b15a4f7b1d7ecfe8226f18075b87b9fae7"
    }
   }
  },

requirements.txt

@@ -1,5 +1,6 @@
-Pillow==9.4.0
-scipy==1.9.3
-torch==1.13.1
-torchvision==0.14.1
-transformers==4.25.1
+fastapi==0.89.0
+Pillow
+scipy
+torch
+torchvision
+transformers