From 644135714c8de278301c812ba5cfa93b45840849 Mon Sep 17 00:00:00 2001 From: Ruben van de Ven Date: Tue, 9 May 2023 14:30:10 +0200 Subject: [PATCH] Rename to different notebooks, make GPU detector --- predict_path.ipynb => 01_detect_objects.ipynb | 189 +- 02_track_objects.ipynb | 469 +++++ poetry.lock | 1775 +++++++++++++++-- pyproject.toml | 7 +- sort_cfotache.py | 9 +- utils/timer.py | 45 + 6 files changed, 2107 insertions(+), 387 deletions(-) rename predict_path.ipynb => 01_detect_objects.ipynb (96%) create mode 100644 02_track_objects.ipynb create mode 100644 utils/timer.py diff --git a/predict_path.ipynb b/01_detect_objects.ipynb similarity index 96% rename from predict_path.ipynb rename to 01_detect_objects.ipynb index 5184462..6c15892 100644 --- a/predict_path.ipynb +++ b/01_detect_objects.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -80,7 +80,7 @@ "PosixPath('../DATASETS/VIRAT_subset_0102x/VIRAT_S_010200_00_000060_000218.mp4')" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -330,175 +330,6 @@ "prediction['boxes'][prediction['labels'] == 1]\n", "prediction['scores'][prediction['labels'] == 1]" ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[340.9556, 270.1501, 361.5573, 318.5745],\n", - " [ 83.2414, 584.1043, 175.0199, 717.4326]])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Now with SORT tracking\n", - "\n", - "Using a sort implementation originally by Alex Bewley, but adapted by [Chris Fotache](https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/README.md). For an example implementation, see [his notebook](https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/PyTorch_Object_Tracking.ipynb).\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'numba'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[39], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msort_cfotache\u001b[39;00m \u001b[39mimport\u001b[39;00m Sort\n\u001b[1;32m 3\u001b[0m mot_tracker \u001b[39m=\u001b[39m Sort()\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/sort_cfotache.py:22\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[39m from: https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/sort.py\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[39m \u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[39m along with this program. If not, see .\u001b[39;00m\n\u001b[1;32m 19\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 20\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m__future__\u001b[39;00m \u001b[39mimport\u001b[39;00m print_function\n\u001b[0;32m---> 22\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnumba\u001b[39;00m \u001b[39mimport\u001b[39;00m jit\n\u001b[1;32m 23\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mos\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpath\u001b[39;00m\n\u001b[1;32m 24\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'numba'" - ] - } - ], - "source": [ - "from sort_cfotache import Sort\n", - "\n", - "mot_tracker = Sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[38], line 26\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[39m# no_grad can be used on inference, should be slightly faster\u001b[39;00m\n\u001b[1;32m 25\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m---> 26\u001b[0m predictions \u001b[39m=\u001b[39m model(batch)\n\u001b[1;32m 27\u001b[0m prediction \u001b[39m=\u001b[39m predictions[\u001b[39m0\u001b[39m] \u001b[39m# we feed only one frame at the once\u001b[39;00m\n\u001b[1;32m 29\u001b[0m mask \u001b[39m=\u001b[39m prediction[\u001b[39m'\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39m# if we want more than one: np.isin(prediction['labels'], [1,86])\u001b[39;00m\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torchvision/models/detection/retinanet.py:625\u001b[0m, in \u001b[0;36mRetinaNet.forward\u001b[0;34m(self, images, targets)\u001b[0m\n\u001b[1;32m 618\u001b[0m torch\u001b[39m.\u001b[39m_assert(\n\u001b[1;32m 619\u001b[0m \u001b[39mFalse\u001b[39;00m,\n\u001b[1;32m 620\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mAll bounding boxes should have positive height and width.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 621\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m Found invalid box \u001b[39m\u001b[39m{\u001b[39;00mdegen_bb\u001b[39m}\u001b[39;00m\u001b[39m for target at index \u001b[39m\u001b[39m{\u001b[39;00mtarget_idx\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m 622\u001b[0m )\n\u001b[1;32m 624\u001b[0m \u001b[39m# get the features from the backbone\u001b[39;00m\n\u001b[0;32m--> 625\u001b[0m features \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbackbone(images\u001b[39m.\u001b[39;49mtensors)\n\u001b[1;32m 626\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(features, torch\u001b[39m.\u001b[39mTensor):\n\u001b[1;32m 627\u001b[0m features \u001b[39m=\u001b[39m OrderedDict([(\u001b[39m\"\u001b[39m\u001b[39m0\u001b[39m\u001b[39m\"\u001b[39m, features)])\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torchvision/models/detection/backbone_utils.py:57\u001b[0m, in \u001b[0;36mBackboneWithFPN.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Dict[\u001b[39mstr\u001b[39m, Tensor]:\n\u001b[0;32m---> 57\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbody(x)\n\u001b[1;32m 58\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfpn(x)\n\u001b[1;32m 59\u001b[0m \u001b[39mreturn\u001b[39;00m x\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torchvision/models/_utils.py:69\u001b[0m, in \u001b[0;36mIntermediateLayerGetter.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 67\u001b[0m out \u001b[39m=\u001b[39m OrderedDict()\n\u001b[1;32m 68\u001b[0m \u001b[39mfor\u001b[39;00m name, module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mitems():\n\u001b[0;32m---> 69\u001b[0m x \u001b[39m=\u001b[39m module(x)\n\u001b[1;32m 70\u001b[0m \u001b[39mif\u001b[39;00m name \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturn_layers:\n\u001b[1;32m 71\u001b[0m out_name \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturn_layers[name]\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 215\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m):\n\u001b[1;32m 216\u001b[0m \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m:\n\u001b[0;32m--> 217\u001b[0m \u001b[39minput\u001b[39m \u001b[39m=\u001b[39m module(\u001b[39minput\u001b[39;49m)\n\u001b[1;32m 218\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39minput\u001b[39m\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torchvision/models/resnet.py:150\u001b[0m, in \u001b[0;36mBottleneck.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 147\u001b[0m out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbn1(out)\n\u001b[1;32m 148\u001b[0m out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrelu(out)\n\u001b[0;32m--> 150\u001b[0m out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv2(out)\n\u001b[1;32m 151\u001b[0m out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbn2(out)\n\u001b[1;32m 152\u001b[0m out \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrelu(out)\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/conv.py:463\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m--> 463\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_conv_forward(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n", - "File \u001b[0;32m~/spul/Projecten/suspicion/trajpred/.venv/lib/python3.11/site-packages/torch/nn/modules/conv.py:459\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m 455\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mzeros\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m 456\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mconv2d(F\u001b[39m.\u001b[39mpad(\u001b[39minput\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode),\n\u001b[1;32m 457\u001b[0m weight, bias, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstride,\n\u001b[1;32m 458\u001b[0m _pair(\u001b[39m0\u001b[39m), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdilation, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgroups)\n\u001b[0;32m--> 459\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mconv2d(\u001b[39minput\u001b[39;49m, weight, bias, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstride,\n\u001b[1;32m 460\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdilation, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgroups)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# TODO make into loop\n", - "%matplotlib inline\n", - "\n", - "\n", - "import pylab as pl\n", - "from IPython import display\n", - "\n", - "i=0\n", - "while True:\n", - " ret, frame = video.read()\n", - " i+=1\n", - " \n", - " if not ret:\n", - " print(\"Can't receive frame (stream end?). Exiting ...\")\n", - " break\n", - "\n", - " t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n", - " t.shape\n", - " # image = image[np.newaxis, :] \n", - " t = t.permute(2, 0, 1)\n", - " t.shape\n", - "\n", - " batch = [preprocess(t)]\n", - " # no_grad can be used on inference, should be slightly faster\n", - " with torch.no_grad():\n", - " predictions = model(batch)\n", - " prediction = predictions[0] # we feed only one frame at the once\n", - "\n", - " mask = prediction['labels'] == 1 # if we want more than one: np.isin(prediction['labels'], [1,86])\n", - "\n", - " scores = prediction['scores'][mask]\n", - " labels = prediction['labels'][mask]\n", - " boxes = prediction['boxes'][mask]\n", - " \n", - " # TODO: introduce confidence and NMS supression: https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/PyTorch_Object_Tracking.ipynb\n", - " # (which I _think_ we better do after filtering)\n", - " # alternatively look at Soft-NMS https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c\n", - "\n", - " labels = [weights.meta[\"categories\"][i] for i in labels]\n", - " \n", - " # dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n", - " detections = [np.append(bbox, score) for bbox, score in zip(boxes, scores)]\n", - " tracks = mot_tracker.update(detections)\n", - "\n", - " # now convert back to boxes and labels\n", - " boxes = [t[:4] for t in tracks]\n", - " labels = [t[-1] for t in tracks]\n", - "\n", - " box = draw_bounding_boxes(t, boxes=boxes,\n", - " labels=labels,\n", - " colors=\"cyan\",\n", - " width=2, \n", - " font_size=30,\n", - " font='Arial')\n", - "\n", - " im = to_pil_image(box.detach())\n", - "\n", - " display.display(im, f\"frame {i}\")\n", - " print(prediction)\n", - " display.clear_output(wait=True)\n", - "\n", - " break # for now\n", - " # pl.clf()\n", - " # # pl.plot(pl.randn(100))\n", - " # pl.figure(figsize=(24,50))\n", - " # # fig.axes[0].imshow(img)\n", - " # pl.imshow(im)\n", - " # display.display(pl.gcf(), f\"frame {i}\")\n", - " # display.clear_output(wait=True)\n", - " # time.sleep(1.0)\n", - "\n", - " # fig, ax = plt.subplots(figsize=(16, 12))\n", - " # ax.imshow(im)\n", - " # plt.show()\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -517,7 +348,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.9.2" }, "orig_nbformat": 4, "vscode": { diff --git a/02_track_objects.ipynb b/02_track_objects.ipynb new file mode 100644 index 0000000..1b9fa10 --- /dev/null +++ b/02_track_objects.ipynb @@ -0,0 +1,469 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "from pathlib import Path\n", + "import numpy as np\n", + "# from PIL import Image\n", + "import torch\n", + "from torchvision.io.video import read_video\n", + "import matplotlib.pyplot as plt\n", + "from torchvision.utils import draw_bounding_boxes\n", + "from torchvision.transforms.functional import to_pil_image\n", + "from torchvision.models.detection import retinanet_resnet50_fpn_v2, RetinaNet_ResNet50_FPN_V2_Weights\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "source = Path('../DATASETS/VIRAT_subset_0102x')\n", + "videos = source.glob('*.mp4')\n", + "homography = list(source.glob('*img2world.txt'))[0]\n", + "H = np.loadtxt(homography, delimiter=',')\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The homography matrix helps to transform points from image space to a flat world plane. The `README_homography.txt` from VIRAT describes:\n", + "\n", + "> Roughly estimated 3-by-3 homographies are included for convenience. \n", + "> Each homography H provides a mapping from image coordinate to scene-dependent world coordinate.\n", + "> \n", + "> [xw,yw,zw]' = H*[xi,yi,1]'\n", + "> \n", + "> xi: horizontal axis on image with left top corner as origin, increases right.\n", + "> yi: vertical axis on image with left top corner as origin, increases downward.\n", + "> \n", + "> xw/zw: world x coordinate\n", + "> yw/zw: world y coordiante" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# H.dot(np.array([20,300, 1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "video_path = list(videos)[0]\n", + "video_path = Path(\"../DATASETS/VIRAT_subset_0102x/VIRAT_S_010200_00_000060_000218.mp4\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('../DATASETS/VIRAT_subset_0102x/VIRAT_S_010200_00_000060_000218.mp4')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "video_path" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Suggestions from: https://stackabuse.com/retinanet-object-detection-with-pytorch-and-torchvision/" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "weights = RetinaNet_ResNet50_FPN_V2_Weights.DEFAULT\n", + "model = retinanet_resnet50_fpn_v2(weights=weights, score_thresh=0.35)\n", + "model.to(device)\n", + "# Put the model in inference mode\n", + "model.eval()\n", + "# Get the transforms for the model's weights\n", + "preprocess = weights.transforms().to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# hub.set_dir()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "video = cv2.VideoCapture(str(video_path))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> The score_thresh argument defines the threshold at which an object is detected as an object of a class. Intuitively, it's the confidence threshold, and we won't classify an object to belong to a class if the model is less than 35% confident that it belongs to a class." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result from a single prediction coming from `model(batch)` looks like:\n", + "\n", + "```python\n", + "{'boxes': tensor([[5.7001e+02, 2.5786e+02, 6.3138e+02, 3.6970e+02],\n", + " [5.0109e+02, 2.4508e+02, 5.5308e+02, 3.4852e+02],\n", + " [3.4096e+02, 2.7015e+02, 3.6156e+02, 3.1857e+02],\n", + " [5.0219e-01, 3.7588e+02, 9.7911e+01, 7.2000e+02],\n", + " [3.4096e+02, 2.7015e+02, 3.6156e+02, 3.1857e+02],\n", + " [8.3241e+01, 5.8410e+02, 1.7502e+02, 7.1743e+02]]),\n", + " 'scores': tensor([0.8525, 0.6491, 0.5985, 0.4999, 0.3753, 0.3746]),\n", + " 'labels': tensor([64, 64, 1, 64, 18, 86])}\n", + "```" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Now with SORT tracking\n", + "\n", + "Using a sort implementation originally by Alex Bewley, but adapted by [Chris Fotache](https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/README.md). For an example implementation, see [his notebook](https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/PyTorch_Object_Tracking.ipynb).\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "from sort_cfotache import Sort\n", + "\n", + "mot_tracker = Sort()\n", + "\n", + "display_image = True" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "tracked_instances = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[58], line 29\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[39m# no_grad can be used on inference, should be slightly faster\u001b[39;00m\n\u001b[1;32m 28\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[0;32m---> 29\u001b[0m predictions \u001b[39m=\u001b[39m model(batch)\n\u001b[1;32m 30\u001b[0m prediction \u001b[39m=\u001b[39m predictions[\u001b[39m0\u001b[39m] \u001b[39m# we feed only one frame at the once\u001b[39;00m\n\u001b[1;32m 32\u001b[0m mask \u001b[39m=\u001b[39m prediction[\u001b[39m'\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m \u001b[39m# if we want more than one: np.isin(prediction['labels'], [1,86])\u001b[39;00m\n", + "File \u001b[0;32m~/suspicion/trajpred/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1496\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1497\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1498\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1499\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1500\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 1502\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", + "File \u001b[0;32m~/suspicion/trajpred/.venv/lib/python3.9/site-packages/torchvision/models/detection/retinanet.py:663\u001b[0m, in \u001b[0;36mRetinaNet.forward\u001b[0;34m(self, images, targets)\u001b[0m\n\u001b[1;32m 660\u001b[0m split_anchors \u001b[39m=\u001b[39m [\u001b[39mlist\u001b[39m(a\u001b[39m.\u001b[39msplit(num_anchors_per_level)) \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m anchors]\n\u001b[1;32m 662\u001b[0m \u001b[39m# compute the detections\u001b[39;00m\n\u001b[0;32m--> 663\u001b[0m detections \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpostprocess_detections(split_head_outputs, split_anchors, images\u001b[39m.\u001b[39;49mimage_sizes)\n\u001b[1;32m 664\u001b[0m detections \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform\u001b[39m.\u001b[39mpostprocess(detections, images\u001b[39m.\u001b[39mimage_sizes, original_image_sizes)\n\u001b[1;32m 666\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39mjit\u001b[39m.\u001b[39mis_scripting():\n", + "File \u001b[0;32m~/suspicion/trajpred/.venv/lib/python3.9/site-packages/torchvision/models/detection/retinanet.py:531\u001b[0m, in \u001b[0;36mRetinaNet.postprocess_detections\u001b[0;34m(self, head_outputs, anchors, image_shapes)\u001b[0m\n\u001b[1;32m 529\u001b[0m scores_per_level \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39msigmoid(logits_per_level)\u001b[39m.\u001b[39mflatten()\n\u001b[1;32m 530\u001b[0m keep_idxs \u001b[39m=\u001b[39m scores_per_level \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mscore_thresh\n\u001b[0;32m--> 531\u001b[0m scores_per_level \u001b[39m=\u001b[39m scores_per_level[keep_idxs]\n\u001b[1;32m 532\u001b[0m topk_idxs \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mwhere(keep_idxs)[\u001b[39m0\u001b[39m]\n\u001b[1;32m 534\u001b[0m \u001b[39m# keep only topk scoring predictions\u001b[39;00m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# TODO make into loop\n", + "%matplotlib inline\n", + "\n", + "\n", + "import pylab as pl\n", + "from IPython import display\n", + "from utils.timer import Timer\n", + "\n", + "i=0\n", + "timer = Timer()\n", + "while True:\n", + " timer.tic()\n", + " ret, frame = video.read()\n", + " i+=1\n", + " \n", + " if not ret:\n", + " print(\"Can't receive frame (stream end?). Exiting ...\")\n", + " break\n", + "\n", + " t = torch.from_numpy(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n", + " t.shape\n", + " # image = image[np.newaxis, :] \n", + " t = t.permute(2, 0, 1)\n", + " t.shape\n", + "\n", + " batch = preprocess(t)[None, :].to(device)\n", + " # no_grad can be used on inference, should be slightly faster\n", + " with torch.no_grad():\n", + " predictions = model(batch)\n", + " prediction = predictions[0] # we feed only one frame at the once\n", + "\n", + " mask = prediction['labels'] == 1 # if we want more than one: np.isin(prediction['labels'], [1,86])\n", + "\n", + " scores = prediction['scores'][mask]\n", + " labels = prediction['labels'][mask]\n", + " boxes = prediction['boxes'][mask]\n", + " \n", + " # TODO: introduce confidence and NMS supression: https://github.com/cfotache/pytorch_objectdetecttrack/blob/master/PyTorch_Object_Tracking.ipynb\n", + " # (which I _think_ we better do after filtering)\n", + " # alternatively look at Soft-NMS https://towardsdatascience.com/non-maximum-suppression-nms-93ce178e177c\n", + "\n", + " \n", + " # dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]\n", + " detections = np.array([np.append(bbox, [score, label]) for bbox, score, label in zip(boxes.cpu(), scores.cpu(), labels.cpu())])\n", + " # print(detections)\n", + " tracks = mot_tracker.update(detections)\n", + "\n", + " # now convert back to boxes and labels\n", + " # print(tracks)\n", + " boxes = np.array([t[:4] for t in tracks])\n", + " # initialize empty with the necesserary dimensions for drawing_bounding_boxes glitch\n", + " t_boxes = torch.from_numpy(boxes) if len(boxes) else torch.Tensor().new_empty([0, 6])\n", + " labels = [str(int(t[4])) for t in tracks]\n", + " # print(t_boxes, boxes, labels)\n", + "\n", + "\n", + " for track in tracks:\n", + " # TODO add to tracked_instances\n", + " track_id = str(int(track[4]))\n", + " if track_id not in tracked_instances:\n", + " tracked_instances[track_id] = []\n", + " tracked_instances[track_id].append(track)\n", + "\n", + " \n", + " # labels = [weights.meta[\"categories\"][i] for i in labels]\n", + "\n", + " if display_image:\n", + " box = draw_bounding_boxes(t, boxes=t_boxes,\n", + " labels=labels,\n", + " colors=\"cyan\",\n", + " width=2, \n", + " font_size=30,\n", + " # font='Arial'\n", + " )\n", + "\n", + " im = to_pil_image(box.detach())\n", + "\n", + " display.display(im, f\"frame {i}\")\n", + " print(prediction)\n", + " print(\"time for frame: \", timer.toc(), \", avg:\", 1/timer.average_time, \"fps\")\n", + "\n", + " display.clear_output(wait=True)\n", + "\n", + " # break # for now\n", + " # pl.clf()\n", + " # # pl.plot(pl.randn(100))\n", + " # pl.figure(figsize=(24,50))\n", + " # # fig.axes[0].imshow(img)\n", + " # pl.imshow(im)\n", + " # display.display(pl.gcf(), f\"frame {i}\")\n", + " # display.clear_output(wait=True)\n", + " # time.sleep(1.0)\n", + "\n", + " # fig, ax = plt.subplots(figsize=(16, 12))\n", + " # ax.imshow(im)\n", + " # plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['22', '24', '26', '27', '30', '31', '32', '33', '37'])" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[5.30405334e+02, 5.34641296e+02, 6.03237061e+02, 7.18612122e+02,\n", + " 9.42070127e-01, 1.00000000e+00],\n", + " [4.61479340e+02, 5.49811340e+02, 5.34607056e+02, 7.17237122e+02,\n", + " 9.26090062e-01, 1.00000000e+00],\n", + " [3.38673218e+02, 2.55078461e+02, 3.57062561e+02, 2.95217896e+02,\n", + " 6.61470771e-01, 1.00000000e+00]]),)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'17': [array([573.00909697, 551.76122438, 657.56378982, 720.05069192,\n", + " 17. , 1. ]),\n", + " array([570.16715738, 550.85464258, 652.59986304, 719.88004284,\n", + " 17. , 1. ]),\n", + " array([568.02909891, 550.10706805, 649.96206622, 720.03113806,\n", + " 17. , 1. ]),\n", + " array([562.49451695, 549.06638446, 644.29895964, 720.04103925,\n", + " 17. , 1. ])],\n", + " '13': [array([337.63475088, 255.66774475, 355.97561492, 296.69147428,\n", + " 13. , 1. ]),\n", + " array([337.77042983, 255.72223676, 356.05113319, 296.63698388,\n", + " 13. , 1. ]),\n", + " array([338.02427059, 255.89595935, 356.25536645, 296.58306741,\n", + " 13. , 1. ]),\n", + " array([338.1632419 , 255.82719651, 356.27227032, 296.33234513,\n", + " 13. , 1. ])],\n", + " '12': [array([481.57704931, 568.79192296, 570.79284909, 718.23349465,\n", + " 12. , 1. ]),\n", + " array([479.96268827, 569.31456975, 567.89464999, 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"pygments_lexer": "ipython3", + "version": "3.9.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "1135f674f58caf91385e41dd32dc418daf761a3c5d4526b1ac3bad0b893c2eb5" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock index b67721d..9e0b9cb 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,7 +2,7 @@ name = "appnope" version = "0.1.3" description = "Disable App Nap on macOS >= 10.9" -category = "dev" +category = "main" optional = false python-versions = "*" @@ -10,7 +10,7 @@ python-versions = "*" name = "asttokens" version = "2.2.1" description = "Annotate AST trees with source code positions" -category = "dev" +category = "main" optional = false python-versions = "*" @@ -24,7 +24,7 @@ test = ["astroid", "pytest"] name = "av" version = "10.0.0" description = "Pythonic bindings for FFmpeg's libraries." -category = "dev" +category = "main" optional = false python-versions = "*" @@ -32,15 +32,15 @@ python-versions = "*" name = "backcall" version = "0.2.0" description = "Specifications for callback functions passed in to an API" -category = "dev" +category = "main" optional = false python-versions = "*" [[package]] name = "certifi" -version = "2022.12.7" +version = "2023.5.7" description = "Python package for providing Mozilla's CA Bundle." -category = "dev" +category = "main" optional = false python-versions = ">=3.6" @@ -48,7 +48,7 @@ python-versions = ">=3.6" name = "cffi" version = "1.15.1" description = "Foreign Function Interface for Python calling C code." -category = "dev" +category = "main" optional = false python-versions = "*" @@ -59,15 +59,26 @@ pycparser = "*" name = "charset-normalizer" version = "3.1.0" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." -category = "dev" +category = "main" optional = false python-versions = ">=3.7.0" +[[package]] +name = "cmake" +version = "3.26.3" +description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software" +category = "main" +optional = false +python-versions = "*" + +[package.extras] +test = ["codecov (>=2.0.5)", "coverage (>=4.2)", "flake8 (>=3.0.4)", "path.py (>=11.5.0)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)", "pytest-runner (>=2.9)", "pytest-virtualenv (>=1.7.0)", "scikit-build (>=0.10.0)", "setuptools (>=28.0.0)", "virtualenv (>=15.0.3)", "wheel"] + [[package]] name = "colorama" version = "0.4.6" description = "Cross-platform colored terminal text." -category = "dev" +category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" @@ -75,7 +86,7 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7 name = "comm" version = "0.1.3" description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc." -category = "dev" +category = "main" optional = false python-versions = ">=3.6" @@ -83,7 +94,7 @@ python-versions = ">=3.6" traitlets = ">=5.3" [package.extras] -lint = ["black (>=22.6.0)", "mdformat-gfm (>=0.3.5)", "mdformat (>0.7)", "ruff (>=0.0.156)"] +lint = ["black (>=22.6.0)", "mdformat (>0.7)", "mdformat-gfm (>=0.3.5)", "ruff (>=0.0.156)"] test = ["pytest"] typing = ["mypy (>=0.990)"] @@ -91,7 +102,7 @@ typing = ["mypy (>=0.990)"] name = "contourpy" version = "1.0.7" description = "Python library for calculating contours of 2D quadrilateral grids" -category = "dev" +category = "main" optional = false python-versions = ">=3.8" @@ -101,15 +112,15 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "chromedriver", "selenium"] docs = ["furo", "sphinx-copybutton"] -mypy = ["contourpy", "docutils-stubs", "mypy (==0.991)", "types-pillow"] -test = ["matplotlib", "pillow", "pytest"] +mypy = ["contourpy[bokeh]", "docutils-stubs", "mypy (==0.991)", "types-Pillow"] +test = ["Pillow", "matplotlib", "pytest"] test-no-images = ["pytest"] [[package]] name = "cycler" version = "0.11.0" description = "Composable style cycles" -category = "dev" +category = "main" optional = false python-versions = ">=3.6" @@ -117,7 +128,7 @@ python-versions = ">=3.6" name = "debugpy" version = "1.6.7" description = "An implementation of the Debug Adapter Protocol for Python" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -125,7 +136,7 @@ python-versions = ">=3.7" name = "decorator" version = "5.1.1" description = "Decorators for Humans" -category = "dev" +category = "main" optional = false python-versions = ">=3.5" @@ -133,37 +144,50 @@ python-versions = ">=3.5" name = "executing" version = "1.2.0" description = "Get the currently executing AST node of a frame, and other information" -category = "dev" +category = "main" optional = false python-versions = "*" [package.extras] -tests = ["asttokens", "pytest", "littleutils", "rich"] +tests = ["asttokens", "littleutils", "pytest", "rich"] [[package]] name = "filelock" version = "3.12.0" description = "A platform independent file lock." -category = "dev" +category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2023.3.27)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)", "sphinx (>=6.1.3)"] -testing = ["covdefaults (>=2.3)", "coverage (>=7.2.3)", "diff-cover (>=7.5)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)", "pytest (>=7.3.1)"] +docs = ["furo (>=2023.3.27)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"] +testing = ["covdefaults (>=2.3)", "coverage (>=7.2.3)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"] + +[[package]] +name = "filterpy" +version = "1.4.5" +description = "Kalman filtering and optimal estimation library" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +matplotlib = "*" +numpy = "*" +scipy = "*" [[package]] name = "fonttools" version = "4.39.3" description = "Tools to manipulate font files" -category = "dev" +category = "main" optional = false python-versions = ">=3.8" [package.extras] -all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=15.0.0)", "xattr"] +all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["scipy", "munkres"] +interpolatable = ["munkres", "scipy"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -172,21 +196,49 @@ symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=15.0.0)"] -woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] +woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] [[package]] name = "idna" version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" -category = "dev" +category = "main" optional = false python-versions = ">=3.5" +[[package]] +name = "imageio" +version = "2.28.1" +description = "Library for reading and writing a wide range of image, video, scientific, and volumetric data formats." +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +numpy = "*" +pillow = ">=8.3.2" + +[package.extras] +all-plugins = ["astropy", "av", "imageio-ffmpeg", "psutil", "tifffile"] +all-plugins-pypy = ["av", "imageio-ffmpeg", "psutil", "tifffile"] +build = ["wheel"] +dev = ["black", "flake8", "fsspec[github]", "pytest", "pytest-cov"] +docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"] +ffmpeg = ["imageio-ffmpeg", "psutil"] +fits = ["astropy"] +full = ["astropy", "av", "black", "flake8", "fsspec[github]", "gdal", "imageio-ffmpeg", "itk", "numpydoc", "psutil", "pydata-sphinx-theme", "pytest", "pytest-cov", "sphinx (<6)", "tifffile", "wheel"] +gdal = ["gdal"] +itk = ["itk"] +linting = ["black", "flake8"] +pyav = ["av"] +test = ["fsspec[github]", "pytest", "pytest-cov"] +tifffile = ["tifffile"] + [[package]] name = "importlib-metadata" version = "6.6.0" description = "Read metadata from Python packages" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -194,15 +246,15 @@ python-versions = ">=3.7" zipp = ">=0.5" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] perf = ["ipython"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl-flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] +testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] [[package]] name = "importlib-resources" version = "5.12.0" description = "Read resources from Python packages" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -210,14 +262,14 @@ python-versions = ">=3.7" zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] [[package]] name = "ipykernel" -version = "6.22.0" +version = "6.23.0" description = "IPython Kernel for Jupyter" -category = "dev" +category = "main" optional = false python-versions = ">=3.8" @@ -237,17 +289,17 @@ tornado = ">=6.1" traitlets = ">=5.4.0" [package.extras] -cov = ["coverage", "curio", "matplotlib", "pytest-cov", "trio"] +cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"] docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"] pyqt5 = ["pyqt5"] pyside6 = ["pyside6"] -test = ["flaky", "ipyparallel", "pre-commit", "pytest-asyncio", "pytest-cov", "pytest-timeout", "pytest (>=7.0)"] +test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"] [[package]] name = "ipython" version = "8.13.2" description = "IPython: Productive Interactive Computing" -category = "dev" +category = "main" optional = false python-versions = ">=3.9" @@ -267,9 +319,9 @@ traitlets = ">=5" typing-extensions = {version = "*", markers = "python_version < \"3.10\""} [package.extras] -all = ["black", "ipykernel", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "docrepr", "matplotlib", "stack-data", "pytest (<7)", "typing-extensions", "pytest (<7.1)", "pytest-asyncio", "testpath", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.21)", "pandas", "trio"] +all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"] black = ["black"] -doc = ["ipykernel", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "docrepr", "matplotlib", "stack-data", "pytest (<7)", "typing-extensions", "pytest (<7.1)", "pytest-asyncio", "testpath"] +doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"] kernel = ["ipykernel"] nbconvert = ["nbconvert"] nbformat = ["nbformat"] @@ -277,13 +329,31 @@ notebook = ["ipywidgets", "notebook"] parallel = ["ipyparallel"] qtconsole = ["qtconsole"] test = ["pytest (<7.1)", "pytest-asyncio", "testpath"] -test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "trio"] +test_extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"] + +[[package]] +name = "ipywidgets" +version = "8.0.6" +description = "Jupyter interactive widgets" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +ipykernel = ">=4.5.1" +ipython = ">=6.1.0" +jupyterlab-widgets = ">=3.0.7,<3.1.0" +traitlets = ">=4.3.1" +widgetsnbextension = ">=4.0.7,<4.1.0" + +[package.extras] +test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] [[package]] name = "jedi" version = "0.18.2" description = "An autocompletion tool for Python that can be used for text editors." -category = "dev" +category = "main" optional = false python-versions = ">=3.6" @@ -291,15 +361,15 @@ python-versions = ">=3.6" parso = ">=0.8.0,<0.9.0" [package.extras] -docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx-rtd-theme (==0.4.3)", "sphinx (==1.8.5)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"] +docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"] qa = ["flake8 (==3.8.3)", "mypy (==0.782)"] testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"] [[package]] -name = "jinja2" +name = "Jinja2" version = "3.1.2" description = "A very fast and expressive template engine." -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -309,11 +379,19 @@ MarkupSafe = ">=2.0" [package.extras] i18n = ["Babel (>=2.7)"] +[[package]] +name = "joblib" +version = "1.2.0" +description = "Lightweight pipelining with Python functions" +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "jupyter-client" version = "8.2.0" description = "Jupyter protocol implementation and client libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3.8" @@ -326,14 +404,14 @@ tornado = ">=6.2" traitlets = ">=5.3" [package.extras] -docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinx (>=4)", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] +docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] [[package]] name = "jupyter-core" version = "5.3.0" description = "Jupyter core package. A base package on which Jupyter projects rely." -category = "dev" +category = "main" optional = false python-versions = ">=3.8" @@ -346,14 +424,42 @@ traitlets = ">=5.3" docs = ["myst-parser", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"] test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] +[[package]] +name = "jupyterlab-widgets" +version = "3.0.7" +description = "Jupyter interactive widgets for JupyterLab" +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "kiwisolver" version = "1.4.4" description = "A fast implementation of the Cassowary constraint solver" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" +[[package]] +name = "lazy_loader" +version = "0.2" +description = "lazy_loader" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +lint = ["pre-commit (>=3.1)"] +test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"] + +[[package]] +name = "lit" +version = "16.0.3" +description = "A Software Testing Tool" +category = "main" +optional = false +python-versions = "*" + [[package]] name = "llvmlite" version = "0.40.0" @@ -363,10 +469,10 @@ optional = false python-versions = ">=3.8" [[package]] -name = "markupsafe" +name = "MarkupSafe" version = "2.1.2" description = "Safely add untrusted strings to HTML/XML markup." -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -374,7 +480,7 @@ python-versions = ">=3.7" name = "matplotlib" version = "3.7.1" description = "Python plotting package" -category = "dev" +category = "main" optional = false python-versions = ">=3.8" @@ -395,7 +501,7 @@ setuptools_scm = ">=7" name = "matplotlib-inline" version = "0.1.6" description = "Inline Matplotlib backend for Jupyter" -category = "dev" +category = "main" optional = false python-versions = ">=3.5" @@ -406,12 +512,12 @@ traitlets = "*" name = "mpmath" version = "1.3.0" description = "Python library for arbitrary-precision floating-point arithmetic" -category = "dev" +category = "main" optional = false python-versions = "*" [package.extras] -develop = ["pytest (>=4.6)", "pycodestyle", "pytest-cov", "codecov", "wheel"] +develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"] docs = ["sphinx"] gmpy = ["gmpy2 (>=2.1.0a4)"] tests = ["pytest (>=4.6)"] @@ -420,7 +526,7 @@ tests = ["pytest (>=4.6)"] name = "nest-asyncio" version = "1.5.6" description = "Patch asyncio to allow nested event loops" -category = "dev" +category = "main" optional = false python-versions = ">=3.5" @@ -428,16 +534,16 @@ python-versions = ">=3.5" name = "networkx" version = "3.1" description = "Python package for creating and manipulating graphs and networks" -category = "dev" +category = "main" optional = false python-versions = ">=3.8" [package.extras] -default = ["numpy (>=1.20)", "scipy (>=1.8)", "matplotlib (>=3.4)", "pandas (>=1.3)"] -developer = ["pre-commit (>=3.2)", "mypy (>=1.1)"] -doc = ["sphinx (>=6.1)", "pydata-sphinx-theme (>=0.13)", "sphinx-gallery (>=0.12)", "numpydoc (>=1.5)", "pillow (>=9.4)", "nb2plots (>=0.6)", "texext (>=0.6.7)"] -extra = ["lxml (>=4.6)", "pygraphviz (>=1.10)", "pydot (>=1.4.2)", "sympy (>=1.10)"] -test = ["pytest (>=7.2)", "pytest-cov (>=4.0)", "codecov (>=2.1)"] +default = ["matplotlib (>=3.4)", "numpy (>=1.20)", "pandas (>=1.3)", "scipy (>=1.8)"] +developer = ["mypy (>=1.1)", "pre-commit (>=3.2)"] +doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.13)", "sphinx (>=6.1)", "sphinx-gallery (>=0.12)", "texext (>=0.6.7)"] +extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"] +test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"] [[package]] name = "numba" @@ -463,7 +569,7 @@ python-versions = ">=3.8" name = "nvidia-cublas-cu11" version = "11.10.3.66" description = "CUBLAS native runtime libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -475,7 +581,7 @@ wheel = "*" name = "nvidia-cuda-cupti-cu11" version = "11.7.101" description = "CUDA profiling tools runtime libs." -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -487,7 +593,7 @@ wheel = "*" name = "nvidia-cuda-nvrtc-cu11" version = "11.7.99" description = "NVRTC native runtime libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -499,7 +605,7 @@ wheel = "*" name = "nvidia-cuda-runtime-cu11" version = "11.7.99" description = "CUDA Runtime native Libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -511,7 +617,7 @@ wheel = "*" name = "nvidia-cudnn-cu11" version = "8.5.0.96" description = "cuDNN runtime libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -523,7 +629,7 @@ wheel = "*" name = "nvidia-cufft-cu11" version = "10.9.0.58" description = "CUFFT native runtime libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -531,7 +637,7 @@ python-versions = ">=3" name = "nvidia-curand-cu11" version = "10.2.10.91" description = "CURAND native runtime libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -543,7 +649,7 @@ wheel = "*" name = "nvidia-cusolver-cu11" version = "11.4.0.1" description = "CUDA solver native runtime libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -555,7 +661,7 @@ wheel = "*" name = "nvidia-cusparse-cu11" version = "11.7.4.91" description = "CUSPARSE native runtime libraries" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -567,7 +673,7 @@ wheel = "*" name = "nvidia-nccl-cu11" version = "2.14.3" description = "NVIDIA Collective Communication Library (NCCL) Runtime" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -575,7 +681,7 @@ python-versions = ">=3" name = "nvidia-nvtx-cu11" version = "11.7.91" description = "NVIDIA Tools Extension" -category = "dev" +category = "main" optional = false python-versions = ">=3" @@ -587,26 +693,26 @@ wheel = "*" name = "opencv-python" version = "4.7.0.72" description = "Wrapper package for OpenCV python bindings." -category = "dev" +category = "main" optional = false python-versions = ">=3.6" [package.dependencies] numpy = [ {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""}, - {version = ">=1.21.2", markers = "python_version >= \"3.10\""}, - {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\""}, - {version = ">=1.22.0", markers = "python_version >= \"3.11\""}, {version = ">=1.19.3", markers = "python_version >= \"3.6\" and platform_system == \"Linux\" and platform_machine == \"aarch64\" or python_version >= \"3.9\""}, {version = ">=1.17.0", markers = "python_version >= \"3.7\""}, {version = ">=1.17.3", markers = "python_version >= \"3.8\""}, + {version = ">=1.21.2", markers = "python_version >= \"3.10\""}, + {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\""}, + {version = ">=1.22.0", markers = "python_version >= \"3.11\""}, ] [[package]] name = "packaging" version = "23.1" description = "Core utilities for Python packages" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -614,7 +720,7 @@ python-versions = ">=3.7" name = "parso" version = "0.8.3" description = "A Python Parser" -category = "dev" +category = "main" optional = false python-versions = ">=3.6" @@ -626,7 +732,7 @@ testing = ["docopt", "pytest (<6.0.0)"] name = "pexpect" version = "4.8.0" description = "Pexpect allows easy control of interactive console applications." -category = "dev" +category = "main" optional = false python-versions = "*" @@ -637,15 +743,15 @@ ptyprocess = ">=0.5" name = "pickleshare" version = "0.7.5" description = "Tiny 'shelve'-like database with concurrency support" -category = "dev" +category = "main" optional = false python-versions = "*" [[package]] -name = "pillow" +name = "Pillow" version = "9.5.0" description = "Python Imaging Library (Fork)" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -657,19 +763,19 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa name = "platformdirs" version = "3.5.0" description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." -category = "dev" +category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo (>=2023.3.27)", "proselint (>=0.13)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)", "sphinx (>=6.1.3)"] -test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest (>=7.3.1)"] +docs = ["furo (>=2023.3.27)", "proselint (>=0.13)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"] +test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)"] [[package]] name = "prompt-toolkit" version = "3.0.38" description = "Library for building powerful interactive command lines in Python" -category = "dev" +category = "main" optional = false python-versions = ">=3.7.0" @@ -680,18 +786,18 @@ wcwidth = "*" name = "psutil" version = "5.9.5" description = "Cross-platform lib for process and system monitoring in Python." -category = "dev" +category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [package.extras] -test = ["ipaddress", "mock", "enum34", "pywin32", "wmi"] +test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"] [[package]] name = "ptyprocess" version = "0.7.0" description = "Run a subprocess in a pseudo terminal" -category = "dev" +category = "main" optional = false python-versions = "*" @@ -699,7 +805,7 @@ python-versions = "*" name = "pure-eval" version = "0.2.2" description = "Safely evaluate AST nodes without side effects" -category = "dev" +category = "main" optional = false python-versions = "*" @@ -710,15 +816,15 @@ tests = ["pytest"] name = "pycparser" version = "2.21" description = "C parser in Python" -category = "dev" +category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] -name = "pygments" +name = "Pygments" version = "2.15.1" description = "Pygments is a syntax highlighting package written in Python." -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -729,29 +835,40 @@ plugins = ["importlib-metadata"] name = "pyparsing" version = "3.0.9" description = "pyparsing module - Classes and methods to define and execute parsing grammars" -category = "dev" +category = "main" optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["railroad-diagrams", "jinja2"] +diagrams = ["jinja2", "railroad-diagrams"] [[package]] name = "python-dateutil" version = "2.8.2" description = "Extensions to the standard Python datetime module" -category = "dev" +category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" [package.dependencies] six = ">=1.5" +[[package]] +name = "PyWavelets" +version = "1.4.1" +description = "PyWavelets, wavelet transform module" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +numpy = ">=1.17.3" + [[package]] name = "pywin32" version = "306" description = "Python for Window Extensions" -category = "dev" +category = "main" optional = false python-versions = "*" @@ -759,7 +876,7 @@ python-versions = "*" name = "pyzmq" version = "25.0.2" description = "Python bindings for 0MQ" -category = "dev" +category = "main" optional = false python-versions = ">=3.6" @@ -770,7 +887,7 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""} name = "requests" version = "2.30.0" description = "Python HTTP for Humans." -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -784,24 +901,102 @@ urllib3 = ">=1.21.1,<3" socks = ["PySocks (>=1.5.6,!=1.5.7)"] use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] +[[package]] +name = "scikit-image" +version = "0.20.0" +description = "Image processing in Python" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +imageio = ">=2.4.1" +lazy_loader = ">=0.1" +networkx = ">=2.8" +numpy = ">=1.21.1" +packaging = ">=20.0" +pillow = ">=9.0.1" +PyWavelets = ">=1.1.1" +scipy = [ + {version = ">=1.8,<1.9.2", markers = "python_version <= \"3.9\""}, + {version = ">=1.8", markers = "python_version > \"3.9\""}, +] +tifffile = ">=2019.7.26" + +[package.extras] +build = ["Cython (>=0.29.24)", "build", "meson-python (>=0.13.0rc0)", "ninja", "numpy (>=1.21.1)", "packaging (>=20)", "pythran", "setuptools (>=67)", "wheel"] +data = ["pooch (>=1.3.0)"] +default = ["PyWavelets (>=1.1.1)", "imageio (>=2.4.1)", "lazy_loader (>=0.1)", "networkx (>=2.8)", "numpy (>=1.21.1)", "packaging (>=20.0)", "pillow (>=9.0.1)", "scipy (>=1.8)", "scipy (>=1.8,<1.9.2)", "tifffile (>=2019.7.26)"] +developer = ["pre-commit", "rtoml"] +docs = ["dask[array] (>=2022.9.2)", "ipywidgets", "kaleido", "matplotlib (>=3.6)", "myst-parser", "numpydoc (>=1.5)", "pandas (>=1.5)", "plotly (>=5.10)", "pooch (>=1.6)", "pytest-runner", "scikit-learn", "seaborn (>=0.11)", "sphinx (>=5.2)", "sphinx-copybutton", "sphinx-gallery (>=0.11)", "tifffile (>=2022.8.12)"] +optional = ["SimpleITK", "astropy (>=3.1.2)", "cloudpickle (>=0.2.1)", "dask[array] (>=1.0.0,!=2.17.0)", "matplotlib (>=3.3)", "pooch (>=1.3.0)", "pyamg"] +test = ["asv", "codecov", "matplotlib (>=3.3)", "pooch (>=1.3.0)", "pytest (>=5.2.0)", "pytest-cov (>=2.7.0)", "pytest-faulthandler", "pytest-localserver"] + +[[package]] +name = "scikit-learn" +version = "1.2.2" +description = "A set of python modules for machine learning and data mining" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +joblib = ">=1.1.1" +numpy = ">=1.17.3" +scipy = ">=1.3.2" +threadpoolctl = ">=2.0.0" + +[package.extras] +benchmark = ["matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "pandas (>=1.0.5)"] +docs = ["Pillow (>=7.1.2)", "matplotlib (>=3.1.3)", "memory-profiler (>=0.57.0)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "plotly (>=5.10.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)", "sphinx (>=4.0.1)", "sphinx-gallery (>=0.7.0)", "sphinx-prompt (>=1.3.0)", "sphinxext-opengraph (>=0.4.2)"] +examples = ["matplotlib (>=3.1.3)", "pandas (>=1.0.5)", "plotly (>=5.10.0)", "pooch (>=1.6.0)", "scikit-image (>=0.16.2)", "seaborn (>=0.9.0)"] +tests = ["black (>=22.3.0)", "flake8 (>=3.8.2)", "matplotlib (>=3.1.3)", "mypy (>=0.961)", "numpydoc (>=1.2.0)", "pandas (>=1.0.5)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pytest (>=5.3.1)", "pytest-cov (>=2.9.0)", "scikit-image (>=0.16.2)"] + +[[package]] +name = "scipy" +version = "1.9.1" +description = "SciPy: Scientific Library for Python" +category = "main" +optional = false +python-versions = ">=3.8,<3.12" + +[package.dependencies] +numpy = ">=1.18.5,<1.25.0" + +[[package]] +name = "scipy" +version = "1.9.3" +description = "Fundamental algorithms for scientific computing in Python" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +numpy = ">=1.18.5,<1.26.0" + +[package.extras] +dev = ["flake8", "mypy", "pycodestyle", "typing_extensions"] +doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-panels (>=0.5.2)", "sphinx-tabs"] +test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] + [[package]] name = "setuptools" version = "67.7.2" description = "Easily download, build, install, upgrade, and uninstall Python packages" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-timeout", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)", "pytest-flake8"] -testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] [[package]] name = "setuptools-scm" version = "7.1.0" description = "the blessed package to manage your versions by scm tags" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -819,7 +1014,7 @@ toml = ["setuptools (>=42)"] name = "six" version = "1.16.0" description = "Python 2 and 3 compatibility utilities" -category = "dev" +category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" @@ -827,7 +1022,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" name = "stack-data" version = "0.6.2" description = "Extract data from python stack frames and tracebacks for informative displays" -category = "dev" +category = "main" optional = false python-versions = "*" @@ -837,24 +1032,46 @@ executing = ">=1.2.0" pure-eval = "*" [package.extras] -tests = ["pytest", "typeguard", "pygments", "littleutils", "cython"] +tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"] [[package]] name = "sympy" version = "1.11.1" description = "Computer algebra system (CAS) in Python" -category = "dev" +category = "main" optional = false python-versions = ">=3.8" [package.dependencies] mpmath = ">=0.19" +[[package]] +name = "threadpoolctl" +version = "3.1.0" +description = "threadpoolctl" +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "tifffile" +version = "2023.4.12" +description = "Read and write TIFF files" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +numpy = "*" + +[package.extras] +all = ["defusedxml", "fsspec", "imagecodecs (>=2023.1.23)", "lxml", "matplotlib", "zarr"] + [[package]] name = "tomli" version = "2.0.1" description = "A lil' TOML parser" -category = "dev" +category = "main" optional = false python-versions = ">=3.7" @@ -862,7 +1079,7 @@ python-versions = ">=3.7" name = "torch" version = "2.0.0" description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" -category = "dev" +category = "main" optional = false python-versions = ">=3.8.0" 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-python = "^3.9" +python = "^3.9,<3.12" numpy = "^1.24.3" opencv-python = "^4.7.0.72" ipykernel = "^6.22.0" @@ -15,6 +15,11 @@ torchvision = "^0.15.1" av = "^10.0.0" matplotlib = "^3.7.1" numba = "^0.57.0" +scikit-image = "^0.20.0" +scikit-learn = "^1.2.2" +filterpy = "^1.4.5" +tqdm = "^4.65.0" +ipywidgets = "^8.0.6" [build-system] diff --git a/sort_cfotache.py b/sort_cfotache.py index 7d26dd0..1db27b7 100644 --- a/sort_cfotache.py +++ b/sort_cfotache.py @@ -25,7 +25,8 @@ import numpy as np ##import matplotlib.pyplot as plt ##import matplotlib.patches as patches from skimage import io -from sklearn.utils.linear_assignment_ import linear_assignment +# from sklearn.utils.linear_assignment_ import linear_assignment +from scipy.optimize import linear_sum_assignment as linear_assignment import glob import time import argparse @@ -102,7 +103,7 @@ class KalmanBoxTracker(object): self.hits = 0 self.hit_streak = 0 self.age = 0 - self.objclass = bbox[6] + self.objclass = bbox[5] def update(self,bbox): """ @@ -148,6 +149,10 @@ def associate_detections_to_trackers(detections,trackers,iou_threshold = 0.3): for t,trk in enumerate(trackers): iou_matrix[d,t] = iou(det,trk) matched_indices = linear_assignment(-iou_matrix) + # compatibility linear_assignment->linear_sum_assignment https://stackoverflow.com/a/57992848 + matched_indices = np.asarray(matched_indices) + matched_indices = np.transpose(matched_indices) + unmatched_detections = [] for d,det in enumerate(detections): diff --git a/utils/timer.py b/utils/timer.py new file mode 100644 index 0000000..b475fed --- /dev/null +++ b/utils/timer.py @@ -0,0 +1,45 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +import time + + +class Timer(object): + """A simple timer.""" + def __init__(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + + self.duration = 0. + + def tic(self): + # using time.time instead of time.clock because time time.clock + # does not normalize for multithreading + self.start_time = time.time() + + def toc(self, average=True): + self.diff = time.time() - self.start_time + self.total_time += self.diff + self.calls += 1 + self.average_time = self.total_time / self.calls + if average: + self.duration = self.average_time + else: + self.duration = self.diff + return self.duration + + def clear(self): + self.total_time = 0. + self.calls = 0 + self.start_time = 0. + self.diff = 0. + self.average_time = 0. + self.duration = 0. +