From d20a3ea87e00b8304e9bef02727893aa796ebd6e Mon Sep 17 00:00:00 2001 From: wbenbihi Date: Wed, 17 Aug 2022 22:27:47 +0800 Subject: [PATCH] [ADD] scripts and notebooks --- notebooks/01 - Parsing MPII Dataset.ipynb | 800 ++++++++++++++ notebooks/__init__.py | 0 poetry.lock | 1208 ++++++++++++++++++++- pyproject.toml | 3 + scripts/A_convert_data_to_htf.py | 33 + 5 files changed, 2043 insertions(+), 1 deletion(-) create mode 100644 notebooks/01 - Parsing MPII Dataset.ipynb create mode 100644 notebooks/__init__.py create mode 100644 scripts/A_convert_data_to_htf.py diff --git a/notebooks/01 - Parsing MPII Dataset.ipynb b/notebooks/01 - Parsing MPII Dataset.ipynb new file mode 100644 index 0000000..76fdd0d --- /dev/null +++ b/notebooks/01 - Parsing MPII Dataset.ipynb @@ -0,0 +1,800 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 01 - Parsing MPII Dataset\n", + "\n", + "- Date: 2022\n", + "- Author: Walid BENBIHI\n", + "- Source: [wbenbihi/hourglasstensorlfow](https://github.com/wbenbihi/hourglasstensorlfow) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Standard imports\n", + "import os\n", + "import sys\n", + "import re\n", + "import json\n", + "sys.path.append(os.path.join('..'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Specific Imports\n", + "import scipy.io\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [], + "source": [ + "# Hourglass Tensorflow Imports\n", + "from hourglass_tensorflow.utils.parsers import mpii as mpii_parser\n", + "\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIAct\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIDataset\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIAnnorect\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIAnnoPoint\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIDatapoint\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIAnnotation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global Variables" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "ROOT_FOLDER = '..'\n", + "DATA_FOLDER = \"data\"\n", + "MPII_MAT = \"mpii.ignore.mat\"\n", + "MPII_FILE = os.path.join(ROOT_FOLDER, DATA_FOLDER, MPII_MAT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Function definition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MPII Documentation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "--------------------------------------------------------------------------- \n", + "MPII Human Pose Dataset, Version 1.0 \n", + "Copyright 2015 Max Planck Institute for Informatics \n", + "Licensed under the Simplified BSD License [see bsd.txt] \n", + "--------------------------------------------------------------------------- \n", + "\n", + "We are making the annotations and the corresponding code freely available for research \n", + "purposes. If you would like to use the dataset for any other purposes please contact \n", + "the authors. \n", + "\n", + "### Introduction\n", + "MPII Human Pose dataset is a state of the art benchmark for evaluation\n", + "of articulated human pose estimation. The dataset includes around\n", + "**25K images** containing over **40K people** with annotated body\n", + "joints. The images were systematically collected using an established\n", + "taxonomy of every day human activities. Overall the dataset covers\n", + "**410 human activities** and each image assigned an activity\n", + "label. Each image was extracted from a YouTube video and provided with\n", + "preceding and following un-annotated frames. In addition, for the test\n", + "set we obtained richer annotations including body part occlusions and\n", + "3D torso and head orientations.\n", + "\n", + "Following the best practices for the performance evaluation benchmarks\n", + "in the literature we withhold the test annotations to prevent\n", + "overfitting and tuning on the test set. We are working on an automatic\n", + "evaluation server and performance analysis tools based on rich test\n", + "set annotations.\n", + "\n", + "### Citing the dataset\n", + "```\n", + "@inproceedings{andriluka14cvpr,\n", + " author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt}\n", + " title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},\n", + " booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},\n", + " year = {2014},\n", + " month = {June}\n", + "}\n", + "```\n", + "\n", + "### Download\n", + "\n", + "-. **Images (12.9 GB)**\n", + " \n", + " http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1.tar.gz\n", + "-. **Annotations (12.5 MB)**\t\n", + " \n", + " http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1_u12.tar.gz\n", + "-. **Videos for each image (25 batches x 17 GB)**\t\n", + "\n", + " http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1_sequences_batch1.tar.gz\n", + " ...\n", + " http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1_sequences_batch25.tar.gz\n", + "-. **Image - video mapping (239 KB)**\t\n", + " \n", + " http://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1_sequences_keyframes.mat\n", + "\n", + "### Annotation description \n", + "Annotations are stored in a matlab structure `RELEASE` having following fields\n", + "\n", + "- `.annolist(imgidx)` - annotations for image `imgidx`\n", + " - `.image.name` - image filename\n", + " - `.annorect(ridx)` - body annotations for a person `ridx`\n", + "\t\t - `.x1, .y1, .x2, .y2` - coordinates of the head rectangle\n", + "\t\t - `.scale` - person scale w.r.t. 200 px height\n", + "\t\t - `.objpos` - rough human position in the image\n", + "\t\t - `.annopoints.point` - person-centric body joint annotations\n", + "\t\t - `.x, .y` - coordinates of a joint\n", + "\t\t - `id` - joint id \n", + "[//]: # \"(0 - r ankle, 1 - r knee, 2 - r hip, 3 - l hip, 4 - l knee, 5 - l ankle, 6 - pelvis, 7 - thorax, 8 - upper neck, 9 - head top, 10 - r wrist, 10 - r wrist, 12 - r shoulder, 13 - l shoulder, 14 - l elbow, 15 - l wrist)\"\n", + "\t\t - `is_visible` - joint visibility\n", + " - `.vidx` - video index in `video_list`\n", + " - `.frame_sec` - image position in video, in seconds\n", + " \n", + "- `img_train(imgidx)` - training/testing image assignment \n", + "- `single_person(imgidx)` - contains rectangle id `ridx` of *sufficiently separated* individuals\n", + "- `act(imgidx)` - activity/category label for image `imgidx`\n", + " - `act_name` - activity name\n", + " - `cat_name` - category name\n", + " - `act_id` - activity id\n", + "- `video_list(videoidx)` - specifies video id as is provided by YouTube. To watch video on youtube go to https://www.youtube.com/watch?v=video_list(videoidx) \n", + "\n", + "### Browsing the dataset\n", + "- Please use our online tool for browsing the data\n", + "http://human-pose.mpi-inf.mpg.de/#dataset\n", + "- Red rectangles mark testing images\n", + "\n", + "### References\n", + "- **2D Human Pose Estimation: New Benchmark and State of the Art Analysis.**\n", + "\n", + " Mykhaylo Andriluka, Leonid Pishchulin, Peter Gehler and Bernt Schiele. \n", + "\n", + " IEEE CVPR'14\n", + "- **Fine-grained Activity Recognition with Holistic and Pose based Features.**\n", + "\n", + " Leonid Pishchulin, Mykhaylo Andriluka and Bernt Schiele.\n", + "\n", + " GCPR'14\n", + "\n", + "### Contact\n", + "You can reach us via `@mpi-inf.mpg.de`\n", + "We are looking forward to your feedback. If you have any questions related to the dataset please let us know.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Main Code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since MPII Human Pose Dataset labels are recorded in a MATLAB .mat file, we need to parse it to a clean pandas DataFrame. This format is heavily nested and needs a little bit of exploration to parse it completely" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Load .mat file\n", + "mat = scipy.io.loadmat(MPII_FILE, struct_as_record=False)\n", + "release_mat = mat['RELEASE'][0][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explore Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We check if the `_fieldnames` are correct" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MAT _fieldnames ['annolist', 'img_train', 'version', 'single_person', 'act', 'video_list']\n", + " X Coordinate of from [[610]]\n" + ] + } + ], + "source": [ + "print(\"MAT _fieldnames\",release_mat._fieldnames)\n", + "# Accessing coordinates X of Point 0 from Person 0 in Image 4\n", + "print(\" X Coordinate of from \", release_mat.__dict__['annolist'][0][4].annorect[0][0].annopoints[0][0].point[0][0].x)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# Train/Test Label\n", + "img_train = release_mat.img_train[0]\n", + "# List of Videos\n", + "video_list = release_mat.video_list[0]\n", + "video_list_json = [{'video': {'videoidx':i, 'video_list':item[0]}} for i, item in enumerate(video_list)]\n", + "# Read Data\n", + "mpii_version = release_mat.version[0]\n", + "annolist = release_mat.annolist[0]\n", + "single_person = release_mat.single_person\n", + "act = release_mat.act" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore Act" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Act List Size 24987\n", + "Act List Types {}\n", + "Act List Object Types {}\n", + "Act Fieldnames ['cat_name', 'act_name', 'act_id']\n" + ] + } + ], + "source": [ + "# Explore Object Length and Type\n", + "print(\"Act List Size\", len(act))\n", + "print(\"Act List Types\", {type(i) for i in act})\n", + "print(\"Act List Object Types\", {type(i[0]) for i in act})\n", + "# Get _fieldnames\n", + "print(\"Act Fieldnames\", act[4][0]._fieldnames)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample Act {'act': {'imgidx': 4, 'cat_name': 'sports', 'act_name': ['curling'], 'act_id': 1}}\n" + ] + } + ], + "source": [ + "# Parse Act\n", + "act_json = [\n", + " {\n", + " 'act':{\n", + " 'imgidx':i,\n", + " 'cat_name':elem[0].cat_name[0] if len(elem[0].cat_name) else None,\n", + " 'act_name':elem[0].act_name[0].split(', ') if len(elem[0].act_name) else None,\n", + " 'act_id':elem[0].act_id[0][0]\n", + " }\n", + " } \n", + " for i, elem in enumerate(act)\n", + "]\n", + "print(\"Sample Act\", act_json[4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore Single Person" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Single Person List Size 24987\n", + "Single Person List Types {}\n", + "Single Person List Object Types {}\n", + "Single Person Fieldnames [array([[1],\n", + " [2]], dtype=uint8)]\n" + ] + } + ], + "source": [ + "# Explore Object Length and Type\n", + "print(\"Single Person List Size\", len(single_person))\n", + "print(\"Single Person List Types\", {type(i) for i in single_person})\n", + "print(\"Single Person List Object Types\", {type(i[0][0]) if 0 not in i[0].shape else type(i[0]) for i in single_person})\n", + "# Get _fieldnames\n", + "print(\"Single Person Fieldnames\", single_person[4])" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample Single Person {'single_person': {'imgidx': 4, 'ridx': [1, 2]}}\n" + ] + } + ], + "source": [ + "#Parse single_person\n", + "single_person_json = [\n", + " {\n", + " 'single_person':{\n", + " 'imgidx':i,\n", + " 'ridx': [elm[0] for elm in item[0]] if 0 not in item[0].shape else None\n", + " }\n", + " }\n", + " for i, item in enumerate(single_person)\n", + "]\n", + "print(\"Sample Single Person\", single_person_json[4])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Explore Annolist" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annolist List Size 24987\n", + "Annolist List Types {}\n", + "Annolist Fieldnames ['image', 'annorect', 'frame_sec', 'vididx']\n" + ] + } + ], + "source": [ + "# Explore Object Length and Type\n", + "print(\"Annolist List Size\", len(annolist))\n", + "print(\"Annolist List Types\", {type(i) for i in annolist})\n", + "# Get _fieldnames\n", + "print(\"Annolist Fieldnames\", annolist[4]._fieldnames)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annolist Person {'annopoint': {'imgidx': 4, 'image': '015601864.jpg', 'annorect': array([[,\n", + " ]],\n", + " dtype=object), 'frame_sec': array([11], dtype=uint8), 'vididx': 1660}}\n" + ] + } + ], + "source": [ + "#Parse annolist\n", + "annolist_parse_json = [\n", + " {\n", + " 'annopoint':{\n", + " 'imgidx':i,\n", + " 'image':item.image[0][0].name[0],\n", + " 'annorect':item.annorect,\n", + " 'frame_sec':item.frame_sec[0] if 0 not in item.frame_sec.shape else None,\n", + " 'vididx':item.vididx[0][0] if 0 not in item.vididx.shape else None,\n", + " }\n", + " }\n", + " for i, item in enumerate(annolist)\n", + "]\n", + "print(\"Annolist Person\", annolist_parse_json[4])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'annopoint': {'imgidx': 2,\n", + " 'image': '073199394.jpg',\n", + " 'annorect': array([[]],\n", + " dtype=object),\n", + " 'frame_sec': None,\n", + " 'vididx': None}},\n", + " {'annopoint': {'imgidx': 3,\n", + " 'image': '059865848.jpg',\n", + " 'annorect': array([[]],\n", + " dtype=object),\n", + " 'frame_sec': None,\n", + " 'vididx': None}},\n", + " {'annopoint': {'imgidx': 4,\n", + " 'image': '015601864.jpg',\n", + " 'annorect': array([[,\n", + " ]],\n", + " dtype=object),\n", + " 'frame_sec': array([11], dtype=uint8),\n", + " 'vididx': 1660}}]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Sample with raw parsing\n", + "annolist_parse_json[2:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Explore Annolist Sample" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Set Variables\n", + "IMAGE_INDEX = 4\n", + "BODY_INDEX = 0\n", + "JOINT_INDEX = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Image 4 contains 2 person(s)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([,\n", + " ],\n", + " dtype=object)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get Persons on the IMAGE_INDEX\n", + "bodies = annolist_parse_json[IMAGE_INDEX]['annopoint']['annorect'][0]\n", + "print(f\"Image {IMAGE_INDEX} contains {len(bodies)} person(s)\")\n", + "bodies\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Annorect structure has the following fields ['x1', 'y1', 'x2', 'y2', 'annopoints', 'scale', 'objpos']\n", + " from has the following bounding box { x1:627, y1:100, x2:706, y2:198 }\n", + " from has Scale=3.021046176409755\n", + " from has ObjPos={ x:594, y:257 }\n", + " from has 16 joints\n", + " from Joints= [{'x': 610, 'y': 187, 'id': 6, 'is_visible': array([[0]], dtype=uint8)}, {'x': 647, 'y': 176, 'id': 7, 'is_visible': array([[1]], dtype=uint8)}, {'x': 637.0201, 'y': 189.8183, 'id': 8, 'is_visible': array([], shape=(0, 0), dtype=uint8)}, {'x': 695.9799, 'y': 108.1817, 'id': 9, 'is_visible': array([], shape=(0, 0), dtype=uint8)}, {'x': 620, 'y': 394, 'id': 0, 'is_visible': array([[1]], dtype=uint8)}, {'x': 616, 'y': 269, 'id': 1, 'is_visible': array([[1]], dtype=uint8)}, {'x': 573, 'y': 185, 'id': 2, 'is_visible': array([[1]], dtype=uint8)}, {'x': 647, 'y': 188, 'id': 3, 'is_visible': array([[0]], dtype=uint8)}, {'x': 661, 'y': 221, 'id': 4, 'is_visible': array([[1]], dtype=uint8)}, {'x': 656, 'y': 231, 'id': 5, 'is_visible': array([[1]], dtype=uint8)}, {'x': 606, 'y': 217, 'id': 10, 'is_visible': array([[1]], dtype=uint8)}, {'x': 553, 'y': 161, 'id': 11, 'is_visible': array([[1]], dtype=uint8)}, {'x': 601, 'y': 167, 'id': 12, 'is_visible': array([[1]], dtype=uint8)}, {'x': 692, 'y': 185, 'id': 13, 'is_visible': array([[1]], dtype=uint8)}, {'x': 693, 'y': 240, 'id': 14, 'is_visible': array([[1]], dtype=uint8)}, {'x': 688, 'y': 313, 'id': 15, 'is_visible': array([[1]], dtype=uint8)}]\n" + ] + } + ], + "source": [ + "# Explore Annorect _fieldnames\n", + "print(\"Annorect structure has the following fields\", bodies[BODY_INDEX]._fieldnames)\n", + "# Get Person Bounding Box\n", + "print(f\" from has the following bounding box {{ x1:{bodies[BODY_INDEX].x1[0][0]}, y1:{bodies[BODY_INDEX].y1[0][0]}, x2:{bodies[BODY_INDEX].x2[0][0]}, y2:{bodies[BODY_INDEX].y2[0][0]} }}\")\n", + "# Get the scale attribute\n", + "print(f\" from has Scale={bodies[BODY_INDEX].scale[0][0]}\")\n", + "# Get the objpos attribute\n", + "print(f\" from has ObjPos={{ x:{bodies[BODY_INDEX].objpos[0][0].x[0][0]}, y:{bodies[BODY_INDEX].objpos[0][0].y[0][0]} }}\")\n", + "# Get the joints\n", + "print(f\" from has {len(bodies[BODY_INDEX].annopoints[0][0].point[0])} joints\")\n", + "print(f\" from Joints=\", [\n", + " dict(x = j.x[0, 0], y=j.y[0, 0], id=j.id[0, 0], is_visible=j.is_visible)\n", + " for j in bodies[BODY_INDEX].annopoints[0][0].point[0]\n", + "])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parse MPII" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This package contains utility functions to help you parse the MPII Dataset\n", + "\n", + "```python\n", + "from hourglass_tensorflow.utils.parsers import mpii as mpii_parser\n", + "\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_mpii\n", + "\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_objpos\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_is_visible\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_annopoints\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_additional_annorect_item\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_annorect_item\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_annorect\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_annolist\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_img_train\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_single_person\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_video_list\n", + "from hourglass_tensorflow.utils.parsers.mpii import parse_act\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'index': 3,\n", + " 'frame_sec': None,\n", + " 'image': '059865848.jpg',\n", + " 'vididx': None,\n", + " 'annorect': [{'index': 0,\n", + " 'objpos': {'x': 684, 'y': 309},\n", + " 'scale': 4.928480496055553}]},\n", + " {'index': 4,\n", + " 'frame_sec': 11,\n", + " 'image': '015601864.jpg',\n", + " 'vididx': 1660,\n", + " 'annorect': [{'index': 0,\n", + " 'annopoints': [{'id': 6, 'x': 610, 'y': 187, 'is_visible': 0},\n", + " {'id': 7, 'x': 647, 'y': 176, 'is_visible': 1},\n", + " {'id': 8, 'x': 637, 'y': 189},\n", + " {'id': 9, 'x': 695, 'y': 108},\n", + " {'id': 0, 'x': 620, 'y': 394, 'is_visible': 1},\n", + " {'id': 1, 'x': 616, 'y': 269, 'is_visible': 1},\n", + " {'id': 2, 'x': 573, 'y': 185, 'is_visible': 1},\n", + " {'id': 3, 'x': 647, 'y': 188, 'is_visible': 0},\n", + " {'id': 4, 'x': 661, 'y': 221, 'is_visible': 1},\n", + " {'id': 5, 'x': 656, 'y': 231, 'is_visible': 1},\n", + " {'id': 10, 'x': 606, 'y': 217, 'is_visible': 1},\n", + " {'id': 11, 'x': 553, 'y': 161, 'is_visible': 1},\n", + " {'id': 12, 'x': 601, 'y': 167, 'is_visible': 1},\n", + " {'id': 13, 'x': 692, 'y': 185, 'is_visible': 1},\n", + " {'id': 14, 'x': 693, 'y': 240, 'is_visible': 1},\n", + " {'id': 15, 'x': 688, 'y': 313, 'is_visible': 1}],\n", + " 'objpos': {'x': 594, 'y': 257},\n", + " 'scale': 3.021046176409755,\n", + " 'x1': 627,\n", + " 'y1': 627,\n", + " 'x2': 706,\n", + " 'y2': 706},\n", + " {'index': 1,\n", + " 'annopoints': [{'id': 6, 'x': 979, 'y': 221, 'is_visible': 0},\n", + " {'id': 7, 'x': 906, 'y': 190, 'is_visible': 0},\n", + " {'id': 8, 'x': 912, 'y': 190},\n", + " {'id': 9, 'x': 830, 'y': 182},\n", + " {'id': 0, 'x': 895, 'y': 293, 'is_visible': 1},\n", + " {'id': 1, 'x': 910, 'y': 279, 'is_visible': 1},\n", + " {'id': 2, 'x': 945, 'y': 223, 'is_visible': 0},\n", + " {'id': 3, 'x': 1012, 'y': 218, 'is_visible': 1},\n", + " {'id': 4, 'x': 961, 'y': 315, 'is_visible': 1},\n", + " {'id': 5, 'x': 960, 'y': 403, 'is_visible': 1},\n", + " {'id': 10, 'x': 871, 'y': 304, 'is_visible': 1},\n", + " {'id': 11, 'x': 883, 'y': 229, 'is_visible': 1},\n", + " {'id': 12, 'x': 888, 'y': 174, 'is_visible': 0},\n", + " {'id': 13, 'x': 924, 'y': 206, 'is_visible': 1},\n", + " {'id': 14, 'x': 1013, 'y': 203, 'is_visible': 1},\n", + " {'id': 15, 'x': 955, 'y': 263, 'is_visible': 1}],\n", + " 'objpos': {'x': 952, 'y': 222},\n", + " 'scale': 2.472116502109073,\n", + " 'x1': 841,\n", + " 'y1': 841,\n", + " 'x2': 902,\n", + " 'y2': 902}]}]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Parse data with utility functions\n", + "## `remove_null_keys` is a keyword argument that remove None values when set to True\n", + "parsed_img_train = mpii_parser.parse_img_train(img_train, remove_null_keys=False)\n", + "parsed_video_list = mpii_parser.parse_video_list(video_list, remove_null_keys=False)\n", + "parsed_annolist = mpii_parser.parse_annolist(annolist, remove_null_keys=False)\n", + "parsed_single_person = mpii_parser.parse_single_person(single_person, remove_null_keys=False)\n", + "parsed_act = mpii_parser.parse_act(act[0], remove_null_keys=False)\n", + "# Sample\n", + "parsed_annolist[3:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The previously parsed arrays are stored as records of generic dictionaries. To make linting and autocompletion available to all, `pydantic.BaseModel` classes are available\n", + "\n", + "```python\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIAct\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIDataset\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIAnnorect\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIAnnoPoint\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIDatapoint\n", + "from hourglass_tensorflow.utils.parsers.mpii import MPIIAnnotation\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MPIIAnnotation(index=4, annorect=[MPIIAnnorect(index=0, annopoints=[MPIIAnnoPoint(id=6, x=610, y=187, is_visible=0), MPIIAnnoPoint(id=7, x=647, y=176, is_visible=1), MPIIAnnoPoint(id=8, x=637, y=189, is_visible=None), MPIIAnnoPoint(id=9, x=695, y=108, is_visible=None), MPIIAnnoPoint(id=0, x=620, y=394, is_visible=1), MPIIAnnoPoint(id=1, x=616, y=269, is_visible=1), MPIIAnnoPoint(id=2, x=573, y=185, is_visible=1), MPIIAnnoPoint(id=3, x=647, y=188, is_visible=0), MPIIAnnoPoint(id=4, x=661, y=221, is_visible=1), MPIIAnnoPoint(id=5, x=656, y=231, is_visible=1), MPIIAnnoPoint(id=10, x=606, y=217, is_visible=1), MPIIAnnoPoint(id=11, x=553, y=161, is_visible=1), MPIIAnnoPoint(id=12, x=601, y=167, is_visible=1), MPIIAnnoPoint(id=13, x=692, y=185, is_visible=1), MPIIAnnoPoint(id=14, x=693, y=240, is_visible=1), MPIIAnnoPoint(id=15, x=688, y=313, is_visible=1)], objpos=MPIIObjPos(x=594, y=257), scale=3.021046176409755, x1=627, y1=627, x2=706, y2=706, head_r11=None, head_r12=None, head_r13=None, head_r21=None, head_r22=None, head_r23=None, head_r31=None, head_r32=None, head_r33=None, part_occ1=None, part_occ10=None, part_occ2=None, part_occ3=None, part_occ4=None, part_occ5=None, part_occ6=None, part_occ7=None, part_occ8=None, part_occ9=None, torso_r11=None, torso_r12=None, torso_r13=None, torso_r21=None, torso_r22=None, torso_r23=None, torso_r31=None, torso_r32=None, torso_r33=None), MPIIAnnorect(index=1, annopoints=[MPIIAnnoPoint(id=6, x=979, y=221, is_visible=0), MPIIAnnoPoint(id=7, x=906, y=190, is_visible=0), MPIIAnnoPoint(id=8, x=912, y=190, is_visible=None), MPIIAnnoPoint(id=9, x=830, y=182, is_visible=None), MPIIAnnoPoint(id=0, x=895, y=293, is_visible=1), MPIIAnnoPoint(id=1, x=910, y=279, is_visible=1), MPIIAnnoPoint(id=2, x=945, y=223, is_visible=0), MPIIAnnoPoint(id=3, x=1012, y=218, is_visible=1), MPIIAnnoPoint(id=4, x=961, y=315, is_visible=1), MPIIAnnoPoint(id=5, x=960, y=403, is_visible=1), MPIIAnnoPoint(id=10, x=871, y=304, is_visible=1), MPIIAnnoPoint(id=11, x=883, y=229, is_visible=1), MPIIAnnoPoint(id=12, x=888, y=174, is_visible=0), MPIIAnnoPoint(id=13, x=924, y=206, is_visible=1), MPIIAnnoPoint(id=14, x=1013, y=203, is_visible=1), MPIIAnnoPoint(id=15, x=955, y=263, is_visible=1)], objpos=MPIIObjPos(x=952, y=222), scale=2.472116502109073, x1=841, y1=841, x2=902, y2=902, head_r11=None, head_r12=None, head_r13=None, head_r21=None, head_r22=None, head_r23=None, head_r31=None, head_r32=None, head_r33=None, part_occ1=None, part_occ10=None, part_occ2=None, part_occ3=None, part_occ4=None, part_occ5=None, part_occ6=None, part_occ7=None, part_occ8=None, part_occ9=None, torso_r11=None, torso_r12=None, torso_r13=None, torso_r21=None, torso_r22=None, torso_r23=None, torso_r31=None, torso_r32=None, torso_r33=None)], frame_sec=11, image='015601864.jpg', vididx=1660)" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let's use one BaseModel to make parsed_annolist as a record of structured object\n", + "structured_annolist = [MPIIAnnotation.parse_obj(an) for an in parsed_annolist]\n", + "structured_annolist[4]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.9 ('hourglass-tensorflow-Pl5p0fL5-py3.9')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.9" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "be2d5d21ec23e1ab010cf74c72177a0b2359c60c6692a150b0c617aebb7cf1ed" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/__init__.py b/notebooks/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/poetry.lock b/poetry.lock index e668134..566d615 100644 --- a/poetry.lock +++ b/poetry.lock @@ -14,6 +14,37 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "argon2-cffi" +version = "21.3.0" +description = "The secure Argon2 password hashing algorithm." +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +argon2-cffi-bindings = "*" + +[package.extras] +dev = ["pre-commit", "cogapp", "tomli", "coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "sphinx-notfound-page", "furo"] +docs = ["sphinx", "sphinx-notfound-page", "furo"] +tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"] + +[[package]] +name = "argon2-cffi-bindings" +version = "21.2.0" +description = "Low-level CFFI bindings for Argon2" +category = "dev" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +cffi = ">=1.0.1" + +[package.extras] +dev = ["pytest", "cogapp", "pre-commit", "wheel"] +tests = ["pytest"] + [[package]] name = "asttokens" version = "2.0.8" @@ -69,6 +100,21 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "beautifulsoup4" +version = "4.11.1" +description = "Screen-scraping library" +category = "dev" +optional = false +python-versions = ">=3.6.0" + +[package.dependencies] +soupsieve = ">1.2" + +[package.extras] +lxml = ["lxml"] +html5lib = ["html5lib"] + [[package]] name = "black" version = "22.6.0" @@ -202,6 +248,22 @@ sdist = ["setuptools_rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] test = ["pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] +[[package]] +name = "cycler" +version = "0.11.0" +description = "Composable style cycles" +category = "dev" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "debugpy" +version = "1.6.3" +description = "An implementation of the Debug Adapter Protocol for Python" +category = "dev" +optional = false +python-versions = ">=3.7" + [[package]] name = "decorator" version = "5.1.1" @@ -210,6 +272,14 @@ category = "dev" optional = false python-versions = ">=3.5" +[[package]] +name = "defusedxml" +version = "0.7.1" +description = "XML bomb protection for Python stdlib modules" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" + [[package]] name = "dill" version = "0.3.5.1" @@ -245,6 +315,14 @@ category = "dev" optional = false python-versions = ">=3.5,<4.0" +[[package]] +name = "entrypoints" +version = "0.4" +description = "Discover and load entry points from installed packages." +category = "dev" +optional = false +python-versions = ">=3.6" + [[package]] name = "etils" version = "0.7.1" @@ -282,6 +360,17 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "fastjsonschema" +version = "2.16.1" +description = "Fastest Python implementation of JSON schema" +category = "dev" +optional = false +python-versions = "*" + +[package.extras] +devel = ["colorama", "jsonschema", "json-spec", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"] + [[package]] name = "filelock" version = "3.8.0" @@ -315,6 +404,28 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "fonttools" +version = "4.35.0" +description = "Tools to manipulate font files" +category = "dev" +optional = false +python-versions = ">=3.7" + +[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 (>=14.0.0)", "xattr"] +graphite = ["lz4 (>=1.7.4.2)"] +interpolatable = ["scipy", "munkres"] +lxml = ["lxml (>=4.0,<5)"] +pathops = ["skia-pathops (>=0.5.0)"] +plot = ["matplotlib"] +repacker = ["uharfbuzz (>=0.23.0)"] +symfont = ["sympy"] +type1 = ["xattr"] +ufo = ["fs (>=2.2.0,<3)"] +unicode = ["unicodedata2 (>=14.0.0)"] +woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] + [[package]] name = "gast" version = "0.4.0" @@ -496,6 +607,30 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "ipykernel" +version = "6.15.1" +description = "IPython Kernel for Jupyter" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +appnope = {version = "*", markers = "platform_system == \"Darwin\""} +debugpy = ">=1.0" +ipython = ">=7.23.1" +jupyter-client = ">=6.1.12" +matplotlib-inline = ">=0.1" +nest-asyncio = "*" +packaging = "*" +psutil = "*" +pyzmq = ">=17" +tornado = ">=6.1" +traitlets = ">=5.1.0" + +[package.extras] +test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=6.0)"] + [[package]] name = "ipython" version = "8.4.0" @@ -531,6 +666,33 @@ qtconsole = ["qtconsole"] test = ["pytest (<7.1)", "pytest-asyncio", "testpath"] test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.19)", "pandas", "trio"] +[[package]] +name = "ipython-genutils" +version = "0.2.0" +description = "Vestigial utilities from IPython" +category = "dev" +optional = false +python-versions = "*" + +[[package]] +name = "ipywidgets" +version = "7.7.1" +description = "IPython HTML widgets for Jupyter" +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +ipykernel = ">=4.5.1" +ipython = {version = ">=4.0.0", markers = "python_version >= \"3.3\""} +ipython-genutils = ">=0.2.0,<0.3.0" +jupyterlab-widgets = {version = ">=1.0.0", markers = "python_version >= \"3.6\""} +traitlets = ">=4.3.1" +widgetsnbextension = ">=3.6.0,<3.7.0" + +[package.extras] +test = ["mock", "pytest-cov", "pytest (>=3.6.0)"] + [[package]] name = "jedi" version = "0.18.1" @@ -558,6 +720,122 @@ python-versions = ">=3.7" trio = ["async-generator", "trio"] test = ["async-timeout", "trio", "testpath", "pytest-asyncio (>=0.17)", "pytest-trio", "pytest"] +[[package]] +name = "jinja2" +version = "3.1.2" +description = "A very fast and expressive template engine." +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +MarkupSafe = ">=2.0" + +[package.extras] +i18n = ["Babel (>=2.7)"] + +[[package]] +name = "jsonschema" +version = "4.10.0" +description = "An implementation of JSON Schema validation for Python" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +attrs = ">=17.4.0" +pyrsistent = ">=0.14.0,<0.17.0 || >0.17.0,<0.17.1 || >0.17.1,<0.17.2 || >0.17.2" + +[package.extras] +format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"] +format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"] + +[[package]] +name = "jupyter" +version = "1.0.0" +description = "Jupyter metapackage. Install all the Jupyter components in one go." +category = "dev" +optional = false +python-versions = "*" + +[package.dependencies] +ipykernel = "*" +ipywidgets = "*" +jupyter-console = "*" +nbconvert = "*" +notebook = "*" +qtconsole = "*" + +[[package]] +name = "jupyter-client" +version = "7.3.4" +description = "Jupyter protocol implementation and client libraries" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +entrypoints = "*" +jupyter-core = ">=4.9.2" +nest-asyncio = ">=1.5.4" +python-dateutil = ">=2.8.2" +pyzmq = ">=23.0" +tornado = ">=6.0" +traitlets = "*" + +[package.extras] +doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"] +test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"] + +[[package]] +name = "jupyter-console" +version = "6.4.4" +description = "Jupyter terminal console" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +ipykernel = "*" +ipython = "*" +jupyter-client = ">=7.0.0" +prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0" +pygments = "*" + +[package.extras] +test = ["pexpect"] + +[[package]] +name = "jupyter-core" +version = "4.11.1" +description = "Jupyter core package. A base package on which Jupyter projects rely." +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""} +traitlets = "*" + +[package.extras] +test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] + +[[package]] +name = "jupyterlab-pygments" +version = "0.2.2" +description = "Pygments theme using JupyterLab CSS variables" +category = "dev" +optional = false +python-versions = ">=3.7" + +[[package]] +name = "jupyterlab-widgets" +version = "1.1.1" +description = "A JupyterLab extension." +category = "dev" +optional = false +python-versions = ">=3.6" + [[package]] name = "keras" version = "2.9.0" @@ -601,6 +879,14 @@ SecretStorage = {version = ">=3.2", markers = "sys_platform == \"linux\""} docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"] testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"] +[[package]] +name = "kiwisolver" +version = "1.4.4" +description = "A fast implementation of the Cassowary constraint solver" +category = "dev" +optional = false +python-versions = ">=3.7" + [[package]] name = "libclang" version = "14.0.6" @@ -624,6 +910,20 @@ win32-setctime = {version = ">=1.0.0", markers = "sys_platform == \"win32\""} [package.extras] dev = ["sphinx-rtd-theme (>=0.4.3)", "sphinx-autobuild (>=0.7.1)", "Sphinx (>=4.1.1)", "isort (>=5.1.1)", "black (>=19.10b0)", "pytest-cov (>=2.7.1)", "pytest (>=4.6.2)", "tox (>=3.9.0)", "flake8 (>=3.7.7)", "docutils (==0.16)", "colorama (>=0.3.4)"] +[[package]] +name = "lxml" +version = "4.9.1" +description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API." +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*" + +[package.extras] +cssselect = ["cssselect (>=0.7)"] +html5 = ["html5lib"] +htmlsoup = ["beautifulsoup4"] +source = ["Cython (>=0.29.7)"] + [[package]] name = "markdown" version = "3.4.1" @@ -646,6 +946,25 @@ category = "dev" optional = false python-versions = ">=3.7" +[[package]] +name = "matplotlib" +version = "3.5.3" +description = "Python plotting package" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +cycler = ">=0.10" +fonttools = ">=4.22.0" +kiwisolver = ">=1.0.1" +numpy = ">=1.17" +packaging = ">=20.0" +pillow = ">=6.2.0" +pyparsing = ">=2.2.1" +python-dateutil = ">=2.7" +setuptools_scm = ">=4,<7" + [[package]] name = "matplotlib-inline" version = "0.1.3" @@ -665,6 +984,14 @@ category = "dev" optional = false python-versions = ">=3.6" +[[package]] +name = "mistune" +version = "0.8.4" +description = "The fastest markdown parser in pure Python" +category = "dev" +optional = false +python-versions = "*" + [[package]] name = "mypy-extensions" version = "0.4.3" @@ -673,6 +1000,83 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "nbclient" +version = "0.6.6" +description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." +category = "dev" +optional = false +python-versions = ">=3.7.0" + +[package.dependencies] +jupyter-client = ">=6.1.5" +nbformat = ">=5.0" +nest-asyncio = "*" +traitlets = ">=5.2.2" + +[package.extras] +sphinx = ["autodoc-traits", "mock", "moto", "myst-parser", "Sphinx (>=1.7)", "sphinx-book-theme"] +test = ["black", "check-manifest", "flake8", "ipykernel", "ipython (<8.0.0)", "ipywidgets (<8.0.0)", "mypy", "pip (>=18.1)", "pre-commit", "pytest (>=4.1)", "pytest-asyncio", "pytest-cov (>=2.6.1)", "setuptools (>=60.0)", "testpath", "twine (>=1.11.0)", "xmltodict"] + +[[package]] +name = "nbconvert" +version = "6.5.3" +description = "Converting Jupyter Notebooks" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +beautifulsoup4 = "*" +bleach = "*" +defusedxml = "*" +entrypoints = ">=0.2.2" +jinja2 = ">=3.0" +jupyter-core = ">=4.7" +jupyterlab-pygments = "*" +lxml = "*" +MarkupSafe = ">=2.0" +mistune = 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"testpath", "check-manifest"] + +[[package]] +name = "nest-asyncio" +version = "1.5.5" +description = "Patch asyncio to allow nested event loops" +category = "dev" +optional = false +python-versions = ">=3.5" + [[package]] name = "nodeenv" version = "1.7.0" @@ -681,6 +1085,36 @@ category = "dev" optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*" +[[package]] +name = "notebook" +version = "6.4.12" +description = "A web-based notebook environment for interactive computing" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +argon2-cffi = "*" +ipykernel = "*" +ipython-genutils = "*" +jinja2 = "*" +jupyter-client = ">=5.3.4" +jupyter-core = ">=4.6.1" +nbconvert = ">=5" +nbformat = "*" +nest-asyncio = ">=1.5" +prometheus-client = "*" +pyzmq = ">=17" +Send2Trash = ">=1.8.0" +terminado = ">=0.8.3" +tornado = ">=6.1" +traitlets = ">=4.2.1" + +[package.extras] +docs = ["sphinx", "nbsphinx", 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[[package]] name = "ptyprocess" version = "0.7.0" @@ -979,6 +1455,14 @@ python-versions = ">=3.6.8" [package.extras] diagrams = ["railroad-diagrams", "jinja2"] +[[package]] +name = "pyrsistent" +version = "0.18.1" +description = "Persistent/Functional/Immutable data structures" +category = "dev" +optional = false +python-versions = ">=3.7" + [[package]] name = "pytest" version = "7.1.2" @@ -1062,6 +1546,14 @@ category = "main" optional = false python-versions = "*" +[[package]] +name = "pywin32" +version = "304" +description = "Python for Window Extensions" +category = "dev" +optional = false +python-versions = "*" + [[package]] name = "pywin32-ctypes" version = "0.2.0" @@ -1070,6 +1562,14 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "pywinpty" +version = "2.0.7" +description = "Pseudo terminal support for Windows from Python." +category = "dev" +optional = false +python-versions = ">=3.7" + [[package]] name = "pyyaml" version = "6.0" @@ -1078,6 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hourglass_tensorflow.utils.parsers import MPIIDatapoint +from hourglass_tensorflow.utils.parsers import parse_mpii +from hourglass_tensorflow.utils.writers import common_write +from hourglass_tensorflow.utils.parsers.htf import from_train_mpii_to_htf_data + +MAT_FILE = "data/mpii.ignore.mat" +HTF_JSON = "data/htf.ignore.json" + +if __name__ == "__main__": + # Parse file as list of records + logger.info(f"Parse MPII data at {MAT_FILE}") + data: List[Dict] = parse_mpii( + mpii_annot_file=MAT_FILE, + test_parsing=False, + verify_len=False, + return_as_struct=False, + zip_struct=True, + remove_null_keys=False, + ) + # Cast records as List[MPIIDatapoint] + logger.info("Cast data as record of structures") + datapoints = [MPIIDatapoint.parse_obj(datapoint) for datapoint in data] + logger.info("Convert from MPII to HTF") + htf_data, _ = from_train_mpii_to_htf_data(data=datapoints, require_stats=True) + # Write Transform data + logger.info(f"Write HTF data to {HTF_JSON}") + common_write(htf_data, HTF_JSON)