fix non rendering lines
This commit is contained in:
Ruben van de Ven
parent
a3d981f42c
commit
4c126876b1
4 changed files with 70 additions and 49 deletions
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@ -36,21 +36,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/cuda/__init__.py:83: UserWarning: CUDA initialization: CUDA unknown error - this may be due to an incorrectly set up environment, e.g. changing env variable CUDA_VISIBLE_DEVICES after program start. Setting the available devices to be zero. (Triggered internally at ../c10/cuda/CUDAFunctions.cpp:109.)\n",
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" return torch._C._cuda_getDeviceCount() > 0\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"\n",
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"# Training options\n",
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"from trap.base import DistortedCamera\n",
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"\n",
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"\n",
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"input_seq_length = 36\n",
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"output_seq_length = 36\n",
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"\n",
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@ -72,7 +66,7 @@
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"calibration_path = Path(\"../DATASETS/hof3/calibration.json\")\n",
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"homography_path = Path(\"../DATASETS/hof3/homography.json\")\n",
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"device = device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
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"camera = Camera.from_paths(calibration_path, homography_path, 12)\n",
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"camera = DistortedCamera.from_paths(calibration_path, homography_path, 12)\n",
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"\n",
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"snapshot_path = Path(\"../DATASETS/hof3/output.png\")\n",
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"\n",
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@ -88,7 +82,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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@ -109,7 +103,7 @@
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[14], line 17\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(coords[:,\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 16\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(dst_img, extent\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, dst_img\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m100\u001b[39m, dst_img\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m0\u001b[39m)),\n\u001b[0;32m---> 17\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mro\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
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"Cell \u001b[0;32mIn[3], line 17\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(coords[:,\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 16\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(dst_img, extent\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, dst_img\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m100\u001b[39m, dst_img\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m100\u001b[39m,\u001b[38;5;241m0\u001b[39m)),\n\u001b[0;32m---> 17\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mro\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/pyplot.py:2767\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2765\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 2766\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mplot\u001b[39m(\u001b[38;5;241m*\u001b[39margs, scalex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, scaley\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2767\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2768\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2769\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/axes/_axes.py:1635\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1393\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1394\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1395\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1632\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1633\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1634\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1635\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1636\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1637\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
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"File \u001b[0;32m~/suspicion/trap/.venv/lib/python3.10/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 311\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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@ -157,7 +151,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -167,7 +161,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -193,7 +187,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -202,21 +196,34 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"reader = TrackReader(path, camera.fps, exclude_whitelisted = False, include_blacklisted=False)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<trap.base.Camera at 0x7f42d9b66ad0>"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"actual 26 projected 1\n"
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]
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}
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],
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"source": []
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"source": [
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"\n",
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"track = reader._tracks[\"48\"]\n",
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"\n",
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"track = track.get_with_interpolated_history()\n",
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"history = track.get_projected_history(H=None, camera=camera)\n",
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"print(\"actual\", len(track.history), \"projected\", len(history))"
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]
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},
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{
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"cell_type": "code",
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52
trap/base.py
52
trap/base.py
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@ -145,15 +145,36 @@ class DistortedCamera(ABC):
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H = self.H
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coords = cv2.perspectiveTransform(np.array([points]),H)
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if coords.shape[1:] == (1,2):
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coords = np.reshape(coords, (coords.shape[0], 2))
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# if coords.shape[1:] == (1,2):
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coords = np.reshape(coords, (len(points), 2))
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return coords
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@classmethod
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def from_calibfile(cls, calibration_path, H, fps):
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with calibration_path.open('r') as fp:
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data = json.load(fp)
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return cls.from_calibdata(data, H, fps)
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@classmethod
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def from_paths(cls, calibration_path, h_path, fps):
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H = H_from_path(h_path)
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with calibration_path.open('r') as fp:
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calibdata = json.load(fp)
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if 'type' in calibdata and calibdata['type'] == 'fisheye':
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camera = FisheyeCamera.from_calibdata(calibdata, H, fps)
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else:
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camera = Camera.from_calibdata(calibdata, H, fps)
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return camera
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# return cls.from_calibfile(calibration_path, H, fps)
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def points_img_to_world(self, points: PointList, scale = 1.):
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# undistort & project
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coords = self.undistort_points(points)
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coords: np.ndarray[argparse.Any, np.dtype[np.integer[argparse.Any] | np.floating[argparse.Any]]] = self.project_points(coords, scale)
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coords = self.project_points(coords, scale)
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return coords
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class FisheyeCamera(DistortedCamera):
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@ -181,6 +202,7 @@ class FisheyeCamera(DistortedCamera):
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def undistort_points(self, distorted_points: PointList):
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points = cv2.fisheye.undistortPoints (np.array([distorted_points]).astype(np.float32), K=self.scaled_K, D=self.D, R=self._R, P=self.new_K)
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return points[0]
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@property
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@ -191,11 +213,9 @@ class FisheyeCamera(DistortedCamera):
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def projected_h(self):
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return self.dim3[1]
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@classmethod
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def from_calibfile(cls, calibration_path, H, fps):
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with calibration_path.open('r') as fp:
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data = json.load(fp)
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def from_calibdata(cls, data, H, fps):
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return cls(
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data['dim1'],
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data['dim2'],
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@ -207,10 +227,6 @@ class FisheyeCamera(DistortedCamera):
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data['balance'],
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H, fps)
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@classmethod
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def from_paths(cls, calibration_path, h_path, fps):
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H = H_from_path(h_path)
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return cls.from_calibfile(calibration_path, H, fps)
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class Camera(DistortedCamera):
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@ -225,10 +241,8 @@ class Camera(DistortedCamera):
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self.newcameramtx, self.roi = cv2.getOptimalNewCameraMatrix(self.mtx, self.dist, (self.w,self.h), 1, (self.w,self.h))
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@classmethod
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def from_calibfile(cls, calibration_path, H, fps):
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with calibration_path.open('r') as fp:
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data = json.load(fp)
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def from_calibdata(cls, data, H, fps):
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return cls(
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np.array(data['camera_matrix']),
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np.array(data['dist_coeff']),
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@ -244,10 +258,7 @@ class Camera(DistortedCamera):
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def projected_h(self):
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return self.h
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@classmethod
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def from_paths(cls, calibration_path, h_path, fps):
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H = H_from_path(h_path)
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return cls.from_calibfile(calibration_path, H, fps)
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def undistort_img(self, img: MatLike):
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return cv2.undistort(img, self.mtx, self.dist, None, self.newcameramtx)
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@ -255,7 +266,8 @@ class Camera(DistortedCamera):
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def undistort_points(self, distorted_points: PointList):
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points = cv2.undistortPoints(np.array([distorted_points]).astype('float32'), self.mtx, self.dist, None, self.newcameramtx)
|
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return points[0]
|
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# print(points.reshape())
|
||||
return points.reshape(points.shape[0], 2)
|
||||
|
||||
|
||||
@dataclass
|
||||
|
|
|
|||
|
|
@ -494,8 +494,9 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
|
|||
# cv2.imwrite(str(self.config.output_dir / "orig.png"), warpedFrame)
|
||||
cv2.rectangle(img, (0,0), (img.shape[1],25), (0,0,0), -1)
|
||||
|
||||
|
||||
def conversion(points):
|
||||
convert_world_points_to_img_points(points, scale)
|
||||
return convert_world_points_to_img_points(points, scale)
|
||||
|
||||
if not tracker_frame:
|
||||
cv2.putText(img, f"and track", (650,17), cv2.FONT_HERSHEY_PLAIN, 1, (255,255,0), 1)
|
||||
|
|
|
|||
|
|
@ -556,7 +556,7 @@ class Tracker:
|
|||
|
||||
|
||||
detections: List[Detection] = self.track_frame(frame)
|
||||
|
||||
|
||||
# Store detections into tracklets
|
||||
projected_coordinates = []
|
||||
# now in track_frame()
|
||||
|
|
@ -586,6 +586,7 @@ class Tracker:
|
|||
active_track_ids = [d.track_id for d in detections]
|
||||
active_tracks = {t.track_id: t.get_with_interpolated_history() for t in self.tracks.values() if t.track_id in active_track_ids}
|
||||
active_tracks = displacement_filter.apply_to_dict(active_tracks, frame.camera)# a filter to remove just detecting static objects
|
||||
# print(len(detections), len(active_tracks))
|
||||
|
||||
removable_tracks = []
|
||||
for track_id, track in self.tracks.items():
|
||||
|
|
|
|||
Loading…
Reference in a new issue