ml-model/notebooks/Review Object Detection Samples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['plane-dataset', 'planeBox']\n"
     ]
    }
   ],
   "source": [
    "#config\n",
    "import fiftyone as fo\n",
    "import os\n",
    "\n",
    "\n",
    "file_path = \"/tf/testing\"\n",
    "#print(planes)\n",
    "batch_size = 1000\n",
    "print(fo.list_datasets())\n",
    "dataset = fo.load_dataset(\"planeBox\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session launched. Run `session.show()` to open the App in a cell output.\n"
     ]
    }
   ],
   "source": [
    "session = fo.launch_app(dataset, auto=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:        plane-dataset\n",
      "Media type:     image\n",
      "Num samples:    293\n",
      "Tags:           ['plane']\n",
      "Sample fields:\n",
      "    filepath:     fiftyone.core.fields.StringField\n",
      "    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.Metadata)\n",
      "    icao24:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane_spot:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    labelbox_id:  fiftyone.core.fields.StringField\n",
      "    plane-box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    plane_box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    planebox:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    model:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    manufacturer: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "View stages:\n",
      "    1. Exists(field='planebox', bool=True)\n",
      "    2. Skip(skip=12000)\n",
      "    3. Limit(limit=1000)\n",
      "    4. Match(filter={'tags': {'$nin': [...]}})\n"
     ]
    }
   ],
   "source": [
    "view = dataset.exists(\"planebox\").skip(12000).limit(1000).match({\"tags\": {\"$nin\": [\"relabel\"]}})#.match({\"relabel\": {\"$exists\": False, \"$eq\": None}})\n",
    "print(view)\n",
    "session.view = view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:        plane-dataset\n",
      "Media type:     image\n",
      "Num samples:    7\n",
      "Tags:           ['plane']\n",
      "Sample fields:\n",
      "    filepath:     fiftyone.core.fields.StringField\n",
      "    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.Metadata)\n",
      "    icao24:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane_spot:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    labelbox_id:  fiftyone.core.fields.StringField\n",
      "    plane-box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    plane_box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    planebox:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    model:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    manufacturer: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "View stages:\n",
      "    1. Select(sample_ids=['60147b6eb30ffe7d0db86de6', '60147b6eb30ffe7d0db86c27', '60147b6eb30ffe7d0db86d29', ...])\n"
     ]
    }
   ],
   "source": [
    "# Create a view containing only the selected samples\n",
    "selected_view = dataset.select(session.selected)\n",
    "print(selected_view)\n",
    "for sample in selected_view:\n",
    "    sample.tags.append(\"relabel\")\n",
    "    sample.save() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find the number of photos targeted for relabeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:        plane-dataset\n",
      "Media type:     image\n",
      "Num samples:    686\n",
      "Tags:           ['plane', 'relabel']\n",
      "Sample fields:\n",
      "    filepath:     fiftyone.core.fields.StringField\n",
      "    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.Metadata)\n",
      "    icao24:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane_spot:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    labelbox_id:  fiftyone.core.fields.StringField\n",
      "    plane-box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    plane_box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    planebox:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    model:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    manufacturer: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "View stages:\n",
      "    1. MatchTags(tag='relabel')\n"
     ]
    }
   ],
   "source": [
    "relabel_view = dataset.match_tags(\"relabel\")\n",
    "print(relabel_view)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the number of images with good bounding boxes\n",
    "Add a tag that bounding boxes are good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:        plane-dataset\n",
      "Media type:     image\n",
      "Num samples:    11619\n",
      "Tags:           ['plane']\n",
      "Sample fields:\n",
      "    filepath:     fiftyone.core.fields.StringField\n",
      "    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.Metadata)\n",
      "    icao24:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane_spot:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    labelbox_id:  fiftyone.core.fields.StringField\n",
      "    plane-box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    plane_box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    planebox:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    model:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    manufacturer: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "View stages:\n",
      "    1. Exists(field='planebox', bool=True)\n",
      "    2. Match(filter={'tags': {'$nin': [...]}})\n"
     ]
    }
   ],
   "source": [
    "good_view = dataset.exists(\"planebox\").match({\"tags\": {\"$nin\": [\"relabel\"]}})\n",
    "print(good_view)\n",
    "#session.view = good_view\n",
    "\n",
    "for sample in good_view:\n",
    "    sample.tags.append(\"good_box\")\n",
    "    sample.save() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:        plane-dataset\n",
      "Media type:     image\n",
      "Num samples:    11619\n",
      "Tags:           ['good_box', 'plane']\n",
      "Sample fields:\n",
      "    filepath:     fiftyone.core.fields.StringField\n",
      "    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.Metadata)\n",
      "    icao24:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane_spot:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    labelbox_id:  fiftyone.core.fields.StringField\n",
      "    plane-box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    plane_box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    planebox:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    model:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    manufacturer: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    norm_model:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "View stages:\n",
      "    1. MatchTags(tag='good_box')\n"
     ]
    }
   ],
   "source": [
    "good_box_view = dataset.match_tags(\"good_box\")\n",
    "print(good_box_view)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Bounding Boxes with the Model\n",
    "For the image where the bounding boxes have been confirmed, we will now create bounding boxes with the normalized model.\n",
    "It will print out the Sample ID for samples that either do not have a normalize model or do not have a plane detected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "good_box_view = dataset.match_tags(\"good_box\")\n",
    "bad_detections=[]\n",
    "bad_models=[]\n",
    "for sample in good_box_view:\n",
    "    #print(sample)\n",
    "    planeDetections = sample[\"planebox\"].detections\n",
    "\n",
    "    if len(planeDetections)==0:\n",
    "        print(sample[\"id\"])\n",
    "        bad_detections.append(sample[\"id\"])\n",
    "    else:    \n",
    "        bbox = planeDetections[0][\"bounding_box\"]\n",
    "        if sample[\"norm_model\"] == None:\n",
    "            print(sample[\"id\"])\n",
    "            bad_models.append(sample[\"id\"])\n",
    "        else:\n",
    "            label = sample[\"norm_model\"].label\n",
    "            detections = [fo.Detection(label=label, bounding_box=bbox)]\n",
    "            sample[\"modelbox\"] = fo.Detections(detections=detections)\n",
    "            sample.save()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to find that some samples do not have a plane detected or no model. When there is no model found, remove the `good_box` tag. When there is no detection, remove `good_box` tag and add a `relabel` tag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sample in dataset.select(bad_detections):\n",
    "    sample.tags.remove(\"good_box\")\n",
    "    sample.tags.append(\"relabel\")\n",
    "    sample.save()\n",
    "    \n",
    "for sample in dataset.select(bad_models):\n",
    "    sample.tags.remove(\"good_box\")\n",
    "    sample.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset:        plane-dataset\n",
      "Media type:     image\n",
      "Num samples:    11276\n",
      "Tags:           ['good_box', 'plane']\n",
      "Sample fields:\n",
      "    filepath:     fiftyone.core.fields.StringField\n",
      "    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)\n",
      "    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.Metadata)\n",
      "    icao24:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    plane_spot:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    labelbox_id:  fiftyone.core.fields.StringField\n",
      "    plane-box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    plane_box:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    planebox:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "    model:        fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    manufacturer: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    norm_model:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)\n",
      "    modelbox:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)\n",
      "View stages:\n",
      "    1. MatchTags(tag='good_box')\n"
     ]
    }
   ],
   "source": [
    "good_box_view = dataset.match_tags(\"good_box\")\n",
    "print(good_box_view)\n",
    "session.view = good_box_view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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