update fiftyone utils (#146)

* update fiftyone utils * add fiftyone conversion into demo utils
obss · Jun 23, 2021 · 559f4bd · 559f4bd
1 parent fa4cfe6
commit 559f4bd
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diff --git a/demo/inference_for_mmdetection.ipynb b/demo/inference_for_mmdetection.ipynb
@@ -27,8 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -U sahi\n",
-    "!pip install -U mmdet mmcv-full"
+    "!pip install -U torch sahi mmdet mmcv-full"
    ]
   },
   {
@@ -77,9 +76,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mmdet config file has been downloaded to /home/fatihakyon/dev/obss/sahi/demo/mmdet_configs/v2.11.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py\n"
+     ]
+    }
+   ],
    "source": [
     "# download cascade mask rcnn model&config\n",
     "model_path = 'models/cascade_mask_rcnn.pth'\n",
@@ -107,9 +114,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Use load_from_local loader\n"
+     ]
+    }
+   ],
    "source": [
     "detection_model = MmdetDetectionModel(\n",
     "    model_path=model_path,\n",
@@ -145,9 +160,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/fatihakyon/miniconda3/envs/sahi/lib/python3.8/site-packages/mmdet/datasets/utils.py:64: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "result = get_prediction(image, detection_model)"
    ]
@@ -272,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -403,20 +427,50 @@
    "cell_type": "code",
    "execution_count": 16,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "object_prediction_list[0].to_imantics_annotation()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- ObjectPrediction's can be converted to [fiftyone](https://github.com/voxel51/fiftyone) detection format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<imantics.annotation.Annotation at 0x7f96ca34b280>"
+       "<Detection: {\n",
+       "    'id': '60d30f040a342ce5d7cd9380',\n",
+       "    'attributes': BaseDict({}),\n",
+       "    'tags': BaseList([]),\n",
+       "    'label': 'car',\n",
+       "    'bounding_box': BaseList([\n",
+       "        0.6151685393258427,\n",
+       "        0.35172413793103446,\n",
+       "        0.012172284644194757,\n",
+       "        0.017241379310344827,\n",
+       "    ]),\n",
+       "    'mask': None,\n",
+       "    'confidence': 0.9727994203567505,\n",
+       "    'index': None,\n",
+       "}>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "object_prediction_list[0].to_imantics_annotation()"
+    "object_prediction_list[0].to_fiftyone_detection(image_height=image.shape[0], image_width=image.shape[1])"
    ]
   },
   {

diff --git a/demo/inference_for_yolov5.ipynb b/demo/inference_for_yolov5.ipynb
@@ -27,8 +27,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -U sahi\n",
-    "!pip install -U yolov5"
+    "!pip install -U torch sahi yolov5"
    ]
   },
   {
@@ -50,7 +49,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -198,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -269,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,23 +342,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'image_id': None,\n",
-       " 'bbox': [749, 191, 12, 9],\n",
-       " 'score': 0.66015625,\n",
+       " 'bbox': [447, 308, 49, 33],\n",
+       " 'score': 1,\n",
        " 'category_id': 2,\n",
        " 'category_name': 'car',\n",
        " 'segmentation': [],\n",
        " 'iscrowd': 0,\n",
-       " 'area': 108}"
+       " 'area': 1617}"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,20 +378,50 @@
    "cell_type": "code",
    "execution_count": 35,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "object_prediction_list[0].to_imantics_annotation()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- ObjectPrediction's can be converted to [fiftyone](https://github.com/voxel51/fiftyone) detection format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<imantics.annotation.Annotation at 0x7fe7471a3220>"
+       "<Detection: {\n",
+       "    'id': '60d30eaed7d2c7a9c3980b8e',\n",
+       "    'attributes': BaseDict({}),\n",
+       "    'tags': BaseList([]),\n",
+       "    'label': 'car',\n",
+       "    'bounding_box': BaseList([\n",
+       "        0.41853932584269665,\n",
+       "        0.5310344827586206,\n",
+       "        0.04588014981273408,\n",
+       "        0.056896551724137934,\n",
+       "    ]),\n",
+       "    'mask': None,\n",
+       "    'confidence': 0.9154346585273743,\n",
+       "    'index': None,\n",
+       "}>"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "object_prediction_list[0].to_imantics_annotation()"
+    "object_prediction_list[0].to_fiftyone_detection(image_height=image.shape[0], image_width=image.shape[1])"
    ]
   },
   {

diff --git a/sahi/utils/fiftyon.py → sahi/utils/fiftyone.py b/sahi/utils/fiftyon.py → sahi/utils/fiftyone.py
@@ -1,8 +1,11 @@
 import os
 
-import fiftyone as fo
-from fiftyone.utils.coco import COCODetectionDatasetImporter as BaseCOCODetectionDatasetImporter
-from fiftyone.utils.coco import load_coco_detection_annotations
+try:
+    import fiftyone as fo
+    from fiftyone.utils.coco import COCODetectionDatasetImporter as BaseCOCODetectionDatasetImporter
+    from fiftyone.utils.coco import load_coco_detection_annotations
+except ImportError:
+    raise ImportError('Please run "pip install -U fiftyone" to install fiftyone first for fiftyone utilities.')
 
 
 class COCODetectionDatasetImporter(BaseCOCODetectionDatasetImporter):
@@ -74,9 +77,14 @@ def setup(self):
         self._filenames = self._preprocess_list(filenames)
 
 
-def launch_fiftyone_app(coco_image_dir: str, coco_json_path: str):
+def create_fiftyone_dataset_from_coco_file(coco_image_dir: str, coco_json_path: str):
     coco_importer = COCODetectionDatasetImporter(image_dir=coco_image_dir, json_path=coco_json_path)
     dataset = fo.Dataset.from_importer(coco_importer)
+    return dataset
+
+
+def launch_fiftyone_app(coco_image_dir: str, coco_json_path: str):
+    dataset = create_fiftyone_dataset_from_coco_file(coco_image_dir, coco_json_path)
     session = fo.launch_app()
     session.dataset = dataset
     return session