dataset_88kps+some-updates

Karussell/Supervisely/.ipynb_checkpoints/Bbox from Pose-checkpoint.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "07bdc0da",
    "metadata": {},
    "outputs": [],
@@ -11,17 +11,17 @@
     "import json\n",
     "import numpy as np\n",
     "from tqdm.notebook import tqdm\n",
-    "path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
+    "path_dataset = r'D:\\Karusel\\Nurburg-karussel_88'\n",
     "from SuperviselyKeypointsGUI.SuperviselyKeypointsGUI import *\n",
     "\n",
     "\n",
-    "keypoints_3d_path = r'SuperviselyKeypointsGUI\\karussel_24kps.csv'\n",
+    "keypoints_3d_path = r'C:\\Users\\Kir\\Jupiter\\Nurburg\\keypoints_88\\karussel_88kps.csv'\n",
     "keypoints_3d = pd.read_csv(keypoints_3d_path, index_col=0).astype(float)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "ca890aa0",
    "metadata": {},
    "outputs": [],
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "b8135031",
    "metadata": {},
    "outputs": [],
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "79762c08",
    "metadata": {},
    "outputs": [],
@@ -79,10 +79,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "f9440b54",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a27ecccf8054a5082d0a51178c0f2a9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/448 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "for obj in tqdm(coco['annotations']):\n",
     "    keypoints_2d = np.array(obj['keypoints']).reshape((-1, 3))[:, :2]\n",
@@ -96,13 +111,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "dfcb675b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# with open(os.path.join(path_dataset, 'karusel_COCO.json'), 'w') as file:\n",
-    "#     json.dump(coco, file)"
+    "with open(os.path.join(path_dataset, 'karusel_COCO.json'), 'w') as file:\n",
+    "    json.dump(coco, file)"
    ]
   },
   {
@@ -115,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "3aec7a2b",
    "metadata": {},
    "outputs": [],
@@ -150,17 +165,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "aafd54fb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train, test = split_coco_json(coco, 0.1, random_state=777)"
+    "train, test = split_coco_json(coco, 0.1, random_state=77)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "f6af98fc",
    "metadata": {},
    "outputs": [],
@@ -169,7 +184,7 @@
     "train_path_images = [os.path.join(path_dataset, 'images', 'img', x['file_name']) for x in train['images']]\n",
     "\n",
     "import shutil\n",
-    "path_new_dataset = r'C:\\Users\\Kir\\Jupiter\\Nurburg\\OpenPifPaf\\Training\\Karusel_dataset'\n",
+    "path_new_dataset = r'C:\\Users\\Kir\\Jupiter\\Drivecast\\place6D_Nurbsurgring\\Karussell\\Training\\Karusel_dataset_v2_88'\n",
     "\n",
     "path_train_img = os.path.join(path_new_dataset, 'images', 'train')\n",
     "path_test_img = os.path.join(path_new_dataset, 'images', 'val')\n",

Karussell/Supervisely/.ipynb_checkpoints/ToCOCO-checkpoint.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "283f6e9c",
    "metadata": {},
    "outputs": [],
@@ -14,7 +14,7 @@
     "import json\n",
     "from tqdm.notebook import tqdm\n",
     "\n",
-    "path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
+    "path_dataset = r'D:\\Karusel\\Nurburg-karussel_88'\n",
     "with open(os.path.join(path_dataset, 'meta.json'), 'r') as j:\n",
     "    meta = json.load(j)\n",
     "\n",
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "7af948d4",
    "metadata": {},
    "outputs": [],
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "7ea0771e",
    "metadata": {},
    "outputs": [],
@@ -54,13 +54,14 @@
     "        keypoints_2d.loc[i] = nodes[label2hash(meta_json, obj)[str(i)]]['loc']\n",
     "\n",
     "    keypoints_2d['v'] = 2\n",
+    "    keypoints_2d[(keypoints_2d.x==0)&(keypoints_2d.y==0)] = 0\n",
     "    keypoints_2d = keypoints_2d.astype(float).round().astype(int)\n",
-    "    return keypoints_2d[:24]"
+    "    return keypoints_2d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "26b6abf4",
    "metadata": {},
    "outputs": [],
@@ -93,7 +94,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
+   "id": "7c4d21a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def skeleton_supervisely(meta):\n",
+    "    meta_nodes = meta['classes'][0]['geometry_config']['nodes']\n",
+    "    label2hash = {}\n",
+    "    for name in meta_nodes:\n",
+    "        label2hash[meta_nodes[name]['label']] = name\n",
+    "\n",
+    "    skeleton_supervisely = []\n",
+    "    for edge in meta['classes'][0]['geometry_config']['edges']:\n",
+    "        skeleton_supervisely.append([meta_nodes[edge['src']]['label'], meta_nodes[edge['dst']]['label']])\n",
+    "    return skeleton_supervisely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "155be42d",
    "metadata": {},
    "outputs": [],
@@ -113,21 +133,22 @@
     "        for obj in ann['objects']:\n",
     "            keypoints = annotations(meta, obj)\n",
     "            annotations_list.append(ann_json(keypoints, i, obj))\n",
-    "    return image_list, annotations_list"
+    "    return image_list, annotations_list, meta"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "6593f677",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def COCO(image_list, annotations_list):\n",
+    "def COCO(image_list, annotations_list, meta):\n",
+    "    num_kpts = len(meta['classes'][0]['geometry_config']['nodes'])\n",
     "    coco = {\n",
     "\n",
     "        \"info\": {\n",
-    "            \"description\": \"karusel Dataset\", \"version\": \"2.0\"\n",
+    "            \"description\": \"karusel Dataset\", \"version\": \"2.0\", \"keypoints\":num_kpts\n",
     "        },\n",
     "\n",
     "        \"categories\": [\n",
@@ -135,13 +156,8 @@
     "                \"supercategory\": \"NurburgRing\",\n",
     "                \"id\": 1,\n",
     "                \"name\": \"karusel\",\n",
-    "                \"keypoints\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
-    "                              12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24],\n",
-    "                \"skeleton\": [\n",
-    "                    [1, 2],[2, 3],[3, 4],[4, 5],[5, 6],[6, 7],[7, 8],[8, 9],[9, 10],[10, 11],\n",
-    "                    [11, 12],[12, 13],[13, 14],[14, 15],[15, 16],[16, 17],[17, 18],[18, 19],[19, 20],\n",
-    "                    [20, 21],[21, 22],[22, 23],[23, 24],[24, 1],[24, 3],[1, 5]\n",
-    "                ]\n",
+    "                \"keypoints\": list(range(num_kpts)),\n",
+    "                \"skeleton\": skeleton_supervisely(meta)\n",
     "            }\n",
     "        ]\n",
     "    }\n",
@@ -153,14 +169,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "38880d13",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96021bfc91ce425dbef57ae348321005",
+       "model_id": "219ba034d89346f188cf37dc9a722263",
        "version_major": 2,
        "version_minor": 0
       },
@@ -178,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "314565c2",
    "metadata": {},
    "outputs": [],

Karussell/Supervisely/Bbox from Pose.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "07bdc0da",
    "metadata": {},
    "outputs": [],
@@ -11,17 +11,17 @@
     "import json\n",
     "import numpy as np\n",
     "from tqdm.notebook import tqdm\n",
-    "path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
+    "path_dataset = r'D:\\Karusel\\Nurburg-karussel_88'\n",
     "from SuperviselyKeypointsGUI.SuperviselyKeypointsGUI import *\n",
     "\n",
     "\n",
-    "keypoints_3d_path = r'SuperviselyKeypointsGUI\\karussel_24kps.csv'\n",
+    "keypoints_3d_path = r'C:\\Users\\Kir\\Jupiter\\Nurburg\\keypoints_88\\karussel_88kps.csv'\n",
     "keypoints_3d = pd.read_csv(keypoints_3d_path, index_col=0).astype(float)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "ca890aa0",
    "metadata": {},
    "outputs": [],
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "b8135031",
    "metadata": {},
    "outputs": [],
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "79762c08",
    "metadata": {},
    "outputs": [],
@@ -79,14 +79,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "id": "f9440b54",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "36c51c895f4e4f6b806c2af9e7bc14b9",
+       "model_id": "1a27ecccf8054a5082d0a51178c0f2a9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -111,13 +111,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "dfcb675b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# with open(os.path.join(path_dataset, 'karusel_COCO.json'), 'w') as file:\n",
-    "#     json.dump(coco, file)"
+    "with open(os.path.join(path_dataset, 'karusel_COCO.json'), 'w') as file:\n",
+    "    json.dump(coco, file)"
    ]
   },
   {
@@ -130,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "id": "3aec7a2b",
    "metadata": {},
    "outputs": [],
@@ -165,7 +165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "id": "aafd54fb",
    "metadata": {},
    "outputs": [],
@@ -175,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "id": "f6af98fc",
    "metadata": {},
    "outputs": [],
@@ -184,7 +184,7 @@
     "train_path_images = [os.path.join(path_dataset, 'images', 'img', x['file_name']) for x in train['images']]\n",
     "\n",
     "import shutil\n",
-    "path_new_dataset = r'C:\\Users\\Kir\\Jupiter\\Drivecast\\place6D_Nurburgring\\Karussell\\Training\\Karusel_dataset_v2'\n",
+    "path_new_dataset = r'C:\\Users\\Kir\\Jupiter\\Drivecast\\place6D_Nurbsurgring\\Karussell\\Training\\Karusel_dataset_v2_88'\n",
     "\n",
     "path_train_img = os.path.join(path_new_dataset, 'images', 'train')\n",
     "path_test_img = os.path.join(path_new_dataset, 'images', 'val')\n",

Karussell/Supervisely/Supervisely_upload_markup.ipynb

@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4c948dcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import supervisely_lib as sly\n",
+    "import pandas as pd\n",
+    "import cv2 as cv\n",
+    "import os\n",
+    "import numpy as np\n",
+    "from utils import *\n",
+    "from tqdm.notebook import tqdm\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "keypoints_3d_24 = pd.read_csv('karussel_24kps.csv', index_col=0).astype(float)\n",
+    "keypoints_3d_88 = pd.read_csv('keypoints_88/karussel_88kps.csv', index_col=0).astype(float)\n",
+    "keypoints_3d_88.z = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2ff23cb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_ann(img_id):\n",
+    "    ann_info = api.annotation.download(img_id)\n",
+    "    return ann_info\n",
+    "\n",
+    "def label2hash(meta_json, last):\n",
+    "    for clss in meta_json['classes']:\n",
+    "        if clss['title'] == last['classTitle'].values[0]:\n",
+    "            meta_nodes = clss['geometry_config']['nodes']\n",
+    "            label2hash = {}\n",
+    "            for name in meta_nodes:\n",
+    "                label2hash[meta_nodes[name]['label']] = name\n",
+    "    return label2hash\n",
+    "\n",
+    "def new_annotations(ann_info, new_keypoints, flag=False):\n",
+    "    last = last_keypoints_on_img(ann_info)\n",
+    "    nodes = ann_info[2]['objects'][last.index[0]]['nodes']\n",
+    "    for i in new_keypoints.index:\n",
+    "        if (not label2hash(meta_json, last)[str(i)] in nodes.keys()) and flag:\n",
+    "            nodes[label2hash(meta_json, last)[str(i)]] = {'loc': [0, 0]}\n",
+    "    \n",
+    "        nodes[label2hash(meta_json, last)[str(i)]]['loc'] = new_keypoints.loc[i].tolist()\n",
+    "    return ann_info\n",
+    "\n",
+    "def annotations(ann_info):\n",
+    "\n",
+    "    last = last_keypoints_on_img(ann_info)\n",
+    "    if len(last) == 0:\n",
+    "        return \n",
+    "    nodes = ann_info[2]['objects'][last.index[0]]['nodes']\n",
+    "    keypoints_2d = pd.DataFrame(columns=['x', 'y'])\n",
+    "\n",
+    "    for i in range(1, len(nodes)+1):\n",
+    "        keypoints_2d.loc[i] = nodes[label2hash(meta_json, last)[str(i)]]['loc']\n",
+    "\n",
+    "    return keypoints_2d\n",
+    "\n",
+    "def last_keypoints_on_img(ann_info):\n",
+    "    updated = []\n",
+    "    for obj in ann_info[2]['objects']:\n",
+    "        updated.append([obj['classTitle'], obj['updatedAt']])\n",
+    "    updated = pd.DataFrame(updated, columns=['classTitle', 'updatedAt'])\n",
+    "    updated.updatedAt = pd.to_datetime(updated.updatedAt)\n",
+    "    last = updated[updated.updatedAt == updated.updatedAt.max()]\n",
+    "    return last"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e49d4952",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adress = 'https://app.supervise.ly/'\n",
+    "token = '8N4spdUrCH0BxeQvv1nicgj7VDEsbpo1XUB7SKDk87woYPZPYkDo05o2jt1gpiCaiTaC1ePcWoYEi2Q2ehW5t9mghoQBShn5a6LUjfjhVRIXZ2ol2otf5HADq2EAszQP'\n",
+    "project_id = 192775\n",
+    "dataset_id = 658585\n",
+    "api = sly.Api(adress, token)\n",
+    "meta_json = api.project.get_meta(project_id)\n",
+    "meta = sly.ProjectMeta.from_json(meta_json)\n",
+    "images = pd.DataFrame(api.image.get_list(dataset_id)).sort_values('name', ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "558309c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# name = 'NLS4[00_04_07]006.png'\n",
+    "# index = images.loc[images.name==name].index[0]\n",
+    "\n",
+    "\n",
+    "# img = cv.imread(os.path.join(\n",
+    "#     r'C:\\Users\\Kir\\Jupiter\\Nurburg\\OpenPifPaf\\Training\\Karusel_dataset_v2\\images\\val', name))\n",
+    "# img = cv.cvtColor(img, cv.COLOR_BGR2RGB)\n",
+    "\n",
+    "# img_id = int(images.loc[index, 'id'])\n",
+    "# img_name = images.loc[index, 'name']\n",
+    "# ann_info = load_ann(img_id)\n",
+    "# keypoints_24 = annotations(ann_info)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "50bf81ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a['objects'][0]['classTitle'] = '88kps-bot'\n",
+    "# ann_info[2]['objects'] = a['objects']\n",
+    "# keypoints_24 = annotations(ann_info)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f510305b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rvec, tvec, camMatrx, dist = fit((1920, 1080), keypoints_24, keypoints_3d_24, focus=1)\n",
+    "imgpts_24 = cv.projectPoints(keypoints_3d_24.values, rvec, tvec, camMatrx, dist)[0][:, 0].astype(int)\n",
+    "imgpts_88 = cv.projectPoints(keypoints_3d_88.values, rvec, tvec, camMatrx, dist)[0][:, 0].astype(int)\n",
+    "\n",
+    "new_keypoints = pd.DataFrame(imgpts_88, columns=['x', 'y'], index=range(1, len(keypoints_3d_88)+1))\n",
+    "new_keypoints[(new_keypoints.x<0) | (new_keypoints.y<0) \\\n",
+    "              | (new_keypoints.x>=1920) | (new_keypoints.y>=1080)] *= 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f619f420",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fig = px.imshow(img)\n",
+    "# fig.add_trace(go.Scatter(x=imgpts_24[:,0], y=imgpts_24[:,1],\n",
+    "#                          marker=dict(color='white', size=5), mode='markers'))\n",
+    "# fig.add_trace(go.Scatter(x=new_keypoints.values[:,0], y=new_keypoints.values[:,1],\n",
+    "#                          marker=dict(color='blue', size=5), mode='markers'))\n",
+    "# fig.add_trace(go.Scatter(x=keypoints_24.values[:,0], y=keypoints_24.values[:,1],\n",
+    "#                          marker=dict(color='black', size=5), mode='markers'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "88c81371",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_ann = new_annotations(ann_info, new_keypoints, flag=True)\n",
+    "new_ann = sly.Annotation.from_json(new_ann.annotation, meta)\n",
+    "api.annotation.upload_ann(img_id, new_ann)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45d35b7c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c2a287f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d0dfe8b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd8d392171e14a2281945b8575b435df",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/448 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "error = []\n",
+    "for index in tqdm(range(len(images))):\n",
+    "    img_id = int(images.loc[index, 'id'])\n",
+    "    img_name = images.loc[index, 'name']\n",
+    "    ann_info = load_ann(img_id)\n",
+    "    keypoints_24 = annotations(ann_info)\n",
+    "    \n",
+    "    if len(keypoints_24) != 24:\n",
+    "        error.append(img_name)\n",
+    "        continue\n",
+    "    \n",
+    "    rvec, tvec, camMatrx, dist = fit((1920, 1080), keypoints_24, keypoints_3d_24, focus=1)\n",
+    "    imgpts_88 = cv.projectPoints(keypoints_3d_88.values, rvec, tvec, camMatrx, dist)[0][:, 0].astype(int)\n",
+    "    new_keypoints = pd.DataFrame(imgpts_88, columns=['x', 'y'], index=range(1, len(keypoints_3d_88)+1))\n",
+    "    new_keypoints[(new_keypoints.x<0) | (new_keypoints.y<0) \\\n",
+    "              | (new_keypoints.x>=1920) | (new_keypoints.y>=1080)] *= 0\n",
+    "\n",
+    "    new_ann = new_annotations(ann_info, new_keypoints, flag=True)\n",
+    "    new_ann = sly.Annotation.from_json(new_ann.annotation, meta)\n",
+    "    api.annotation.upload_ann(img_id, new_ann)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0e32ce4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b70bdcf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d04e0a8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6713e0ed",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# no_ann = []\n",
+    "# for index in tqdm(range(len(images))):\n",
+    "#     img_id = int(images.loc[index, 'id'])\n",
+    "#     img_name = images.loc[index, 'name']\n",
+    "#     ann_info = load_ann(img_id)\n",
+    "\n",
+    "#     json_df = pd.read_json(os.path.join(folder,img_name+'.predictions.json'))\n",
+    "#     if len(json_df)>0:\n",
+    "#         keypoints_2d = json_df.sort_values('score').reset_index().loc[0, 'keypoints']\n",
+    "#         keypoints_2d = pd.DataFrame(np.array(keypoints_2d).reshape(-1, 3),\n",
+    "#                                     columns=['x','y','conf'], index=range(1, 25))\n",
+    "\n",
+    "#         conf_keypoints_2d = keypoints_2d[keypoints_2d['conf']>0.15]\n",
+    "#         if len(conf_keypoints_2d)>5:\n",
+    "#             rvec, tvec, camMatrx, dist = fit((1920, 1080), conf_keypoints_2d, keypoints_3d, focus=1)\n",
+    "\n",
+    "#             imgpts = cv.projectPoints(keypoints_3d.values, rvec, tvec, camMatrx, dist)[0][:, 0].astype(int)\n",
+    "#             new_keypoints = pd.DataFrame(imgpts, columns=['x', 'y'], index=range(1, len(keypoints_3d)+1))\n",
+    "#         else:   \n",
+    "#             new_keypoints = keypoints_2d[['x', 'y']].astype(int)\n",
+    "\n",
+    "#     else:\n",
+    "#         no_ann.append(img_name)\n",
+    "\n",
+    "\n",
+    "#     new_ann = new_annotations(ann_info, new_keypoints)\n",
+    "#     new_ann = sly.Annotation.from_json(new_ann.annotation, meta)\n",
+    "#     api.annotation.upload_ann(img_id, new_ann)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ab1e73a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d632830",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a97bbbc3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8598689",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0974e494",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "18a70aa4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# \n",
+    "# import glob\n",
+    "# folder = r'C:\\Users\\Kir\\Downloads\\NLS\\images'\n",
+    "# list_png = glob.glob(folder+'\\*.png')\n",
+    "# list_json = glob.glob(folder+'\\*.json')\n",
+    "\n",
+    "# df = pd.DataFrame(np.array([list_png, list_json]).T, columns=['png', 'json'])\n",
+    "\n",
+    "\n",
+    "# def funk(str):\n",
+    "#     str = os.path.basename(str)\n",
+    "#     return os.path.join(folder, str[4:8]+'['+str[27:35]+']'+str[41:44]+'.png')\n",
+    "# funk(df.png[40])\n",
+    "\n",
+    "# df['png_new'] = df['png'].apply(funk) \n",
+    "# df['json_new'] = df['json'].apply(funk) \n",
+    "\n",
+    "# for i in range(len(df)):\n",
+    "#     os.rename(df['png'][i], df['png_new'][i])\n",
+    "#     os.rename(df['json'][i], df['json_new'][i])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Karussell/Supervisely/ToCOCO.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "283f6e9c",
    "metadata": {},
    "outputs": [],
@@ -14,7 +14,7 @@
     "import json\n",
     "from tqdm.notebook import tqdm\n",
     "\n",
-    "path_dataset = r'D:\\downloads\\Nurburg-karussel'\n",
+    "path_dataset = r'D:\\Karusel\\Nurburg-karussel_88'\n",
     "with open(os.path.join(path_dataset, 'meta.json'), 'r') as j:\n",
     "    meta = json.load(j)\n",
     "\n",
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "id": "7af948d4",
    "metadata": {},
    "outputs": [],
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "7ea0771e",
    "metadata": {},
    "outputs": [],
@@ -54,13 +54,14 @@
     "        keypoints_2d.loc[i] = nodes[label2hash(meta_json, obj)[str(i)]]['loc']\n",
     "\n",
     "    keypoints_2d['v'] = 2\n",
+    "    keypoints_2d[(keypoints_2d.x==0)&(keypoints_2d.y==0)] = 0\n",
     "    keypoints_2d = keypoints_2d.astype(float).round().astype(int)\n",
-    "    return keypoints_2d[:24]"
+    "    return keypoints_2d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "26b6abf4",
    "metadata": {},
    "outputs": [],
@@ -93,7 +94,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
+   "id": "7c4d21a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def skeleton_supervisely(meta):\n",
+    "    meta_nodes = meta['classes'][0]['geometry_config']['nodes']\n",
+    "    label2hash = {}\n",
+    "    for name in meta_nodes:\n",
+    "        label2hash[meta_nodes[name]['label']] = name\n",
+    "\n",
+    "    skeleton_supervisely = []\n",
+    "    for edge in meta['classes'][0]['geometry_config']['edges']:\n",
+    "        skeleton_supervisely.append([meta_nodes[edge['src']]['label'], meta_nodes[edge['dst']]['label']])\n",
+    "    return skeleton_supervisely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "id": "155be42d",
    "metadata": {},
    "outputs": [],
@@ -113,21 +133,22 @@
     "        for obj in ann['objects']:\n",
     "            keypoints = annotations(meta, obj)\n",
     "            annotations_list.append(ann_json(keypoints, i, obj))\n",
-    "    return image_list, annotations_list"
+    "    return image_list, annotations_list, meta"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "id": "6593f677",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def COCO(image_list, annotations_list):\n",
+    "def COCO(image_list, annotations_list, meta):\n",
+    "    num_kpts = len(meta['classes'][0]['geometry_config']['nodes'])\n",
     "    coco = {\n",
     "\n",
     "        \"info\": {\n",
-    "            \"description\": \"karusel Dataset\", \"version\": \"2.0\"\n",
+    "            \"description\": \"karusel Dataset\", \"version\": \"2.0\", \"keypoints\":num_kpts\n",
     "        },\n",
     "\n",
     "        \"categories\": [\n",
@@ -135,13 +156,8 @@
     "                \"supercategory\": \"NurburgRing\",\n",
     "                \"id\": 1,\n",
     "                \"name\": \"karusel\",\n",
-    "                \"keypoints\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
-    "                              12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24],\n",
-    "                \"skeleton\": [\n",
-    "                    [1, 2],[2, 3],[3, 4],[4, 5],[5, 6],[6, 7],[7, 8],[8, 9],[9, 10],[10, 11],\n",
-    "                    [11, 12],[12, 13],[13, 14],[14, 15],[15, 16],[16, 17],[17, 18],[18, 19],[19, 20],\n",
-    "                    [20, 21],[21, 22],[22, 23],[23, 24],[24, 1],[24, 3],[1, 5]\n",
-    "                ]\n",
+    "                \"keypoints\": list(range(num_kpts)),\n",
+    "                \"skeleton\": skeleton_supervisely(meta)\n",
     "            }\n",
     "        ]\n",
     "    }\n",
@@ -153,14 +169,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "id": "38880d13",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96021bfc91ce425dbef57ae348321005",
+       "model_id": "219ba034d89346f188cf37dc9a722263",
        "version_major": 2,
        "version_minor": 0
       },
@@ -178,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "314565c2",
    "metadata": {},
    "outputs": [],

Karussell/Supervisely/karusell_create_3d_88.ipynb

@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b13894c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import cv2 as cv\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go\n",
+    "%cd ..\n",
+    "from utils import *\n",
+    "%cd Karusell_implantation\n",
+    "from __init__ import *\n",
+    "box_pict = pict_points(425)\n",
+    "logo, pts_pict = readShield(\"mercedes2.png\")\n",
+    "%cd -\n",
+    "%cd keypoints_88\n",
+    "\n",
+    "def roration(x):\n",
+    "    x = np.deg2rad(x)\n",
+    "    return np.array([[np.cos(x), -np.sin(x)],\n",
+    "                    [np.sin(x), np.cos(x)]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fc452a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(r'sputnik_88_kps.json', 'r') as file:\n",
+    "    img_json = json.load(file)\n",
+    "    \n",
+    "img = cv.imread(r'sputnik.png')\n",
+    "\n",
+    "df = pd.DataFrame([img_json['objects'][i]['points']['exterior'][0]\\\n",
+    "                   for i in range(len(img_json['objects']))], columns=['x','y'], index=range(1,89))\n",
+    "df_raw = df.copy()\n",
+    "center = (732, 758)\n",
+    "df -= center\n",
+    "# df[['x', 'y']] = df.values[:, :2]@roration(90) # поворот x и y в плоскости земли\n",
+    "df['z'] = 0\n",
+    "\n",
+    "\n",
+    "\n",
+    "df = df.astype(float)\n",
+    "\n",
+    "# img = cv.circle(img, center, 5, (255,255,255), -1)\n",
+    "# fig = px.imshow(img)\n",
+    "# fig.add_trace(go.Scatter(x=df.x+center[0], y=df.y+center[1], text=df.index,\n",
+    "#                          marker=dict(color='red', size=5), mode='markers'))\n",
+    "\n",
+    "df[['x', 'y']] = df.values[:, :2]@roration(-3)\n",
+    "\n",
+    "rvec, tvec, camMatrx, dist = fit(img.shape[:2], df_raw, df)\n",
+    "imgpts = cv.projectPoints(box_pict, rvec, tvec, camMatrx, dist)[0][:, 0].astype(int)\n",
+    "img = overlaying(logo, img, pts_pict, imgpts[1:])\n",
+    "px.imshow(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12fa21fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df.to_csv('karussel_88kps.csv')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}