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Notebook for running openpose for my Bangla Sign Language Project in Google Colaboratory
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bslr.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "bslr.ipynb",
"provenance": [],
"toc_visible": true,
"authorship_tag": "ABX9TyPhXTa+sjrpdTSU3tJ6uH0i",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/Mahedi-61/7d54378b2abd49baf02d8040a53fdea5/bslr.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7s-6HrO-5reA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "e752d167-4767-41a4-b001-ad7c93e65c01"
},
"source": [
"!pwd"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"/content\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "L821Bl1J7Ffa",
"colab_type": "code",
"colab": {}
},
"source": [
"! apt update\n",
"! apt install -y sudo libopencv-dev"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4Ae0tUFZ7R67",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"from os.path import exists, join, basename, splitext\n",
"\n",
"git_repo_url = 'https://github.com/CMU-Perceptual-Computing-Lab/openpose.git'\n",
"project_name = splitext(basename(git_repo_url))[0]\n",
"\n",
"# see: https://github.com/CMU-Perceptual-Computing-Lab/openpose/issues/949\n",
"# install new CMake becaue of CUDA10\n",
"!wget -q https://cmake.org/files/v3.13/cmake-3.13.0-Linux-x86_64.tar.gz\n",
"!tar xfz cmake-3.13.0-Linux-x86_64.tar.gz --strip-components=1 -C /usr/local\n",
"# clone openpose\n",
"!git clone -q --depth 1 $git_repo_url\n",
"!sed -i 's/execute_process(COMMAND git checkout master WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}\\/3rdparty\\/caffe)/execute_process(COMMAND git checkout f019d0dfe86f49d1140961f8c7dec22130c83154 WORKING_DIRECTORY ${CMAKE_SOURCE_DIR}\\/3rdparty\\/caffe)/g' openpose/CMakeLists.txt\n",
"# install system dependencies\n",
"!apt-get -qq install -y libatlas-base-dev libprotobuf-dev libleveldb-dev libsnappy-dev libhdf5-serial-dev protobuf-compiler libgflags-dev libgoogle-glog-dev liblmdb-dev opencl-headers ocl-icd-opencl-dev libviennacl-dev\n",
"# install python dependencies\n",
"# build openpose\n",
"!cd openpose && rm -rf build || true && mkdir build && cd build && cmake .. && make -j`nproc`"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "KR2if8mH-1Jk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 126
},
"outputId": "3369acd1-ae6e-40b0-ff95-9579fc1dbf3d"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
"\n",
"Enter your authorization code:\n",
"··········\n",
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gCBYKw-T_Zjy",
"colab_type": "code",
"colab": {}
},
"source": [
"!unzip \"/content/drive/My Drive/50_Words.zip\""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CrT2SYVy_8Lz",
"colab_type": "code",
"colab": {}
},
"source": [
"# python packages\n",
"import os\n",
"import numpy as np\n",
"\n",
"# for training CASIA-B Dataset\n",
"def get_all_video_files_for_train ():\n",
" ls_video_files = []\n",
"\n",
" working_dataset_path = os.path.join(\".\", \"50_Words\")\n",
" ls_word = os.listdir(working_dataset_path)\n",
"\n",
" for word in ls_word:\n",
" word_dir = os.path.join(working_dataset_path, word)\n",
" ls_vids_each_word = os.listdir(word_dir)\n",
"\n",
" for vid_each_word in ls_vids_each_word:\n",
" input_dir = os.path.join(word_dir, vid_each_word)\n",
" ls_video_files.append(input_dir)\n",
"\n",
" return ls_video_files"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7CtJtpJcAyGl",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 55
},
"outputId": "528d1fb1-05e1-4b9e-bb93-c3b1759596bb"
},
"source": [
"ls_video_files = get_all_video_files_for_train()\n",
"print(ls_video_files)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"['./50_Words/Growth/4.mp4', './50_Words/Growth/1.mp4', './50_Words/Growth/2.mp4', './50_Words/Growth/3.mp4', './50_Words/Picture/4.mp4', './50_Words/Picture/1.mp4', './50_Words/Picture/2.mp4', './50_Words/Picture/3.mp4', './50_Words/Pencil/4.mp4', './50_Words/Pencil/1.mp4', './50_Words/Pencil/2.mp4', './50_Words/Pencil/3.mp4', './50_Words/Clean/4.mp4', './50_Words/Clean/1.mp4', './50_Words/Clean/2.mp4', './50_Words/Clean/3.mp4', './50_Words/Meat/4.mp4', './50_Words/Meat/1.mp4', './50_Words/Meat/2.mp4', './50_Words/Meat/3.mp4', './50_Words/Snake/4.mp4', './50_Words/Snake/1.mp4', './50_Words/Snake/2.mp4', './50_Words/Snake/3.mp4', './50_Words/Short/4.mp4', './50_Words/Short/1.mp4', './50_Words/Short/2.mp4', './50_Words/Short/3.mp4', './50_Words/Me/4.mp4', './50_Words/Me/1.mp4', './50_Words/Me/2.mp4', './50_Words/Me/3.mp4', './50_Words/Food/4.mp4', './50_Words/Food/1.mp4', './50_Words/Food/2.mp4', './50_Words/Food/3.mp4', './50_Words/Wet/4.mp4', './50_Words/Wet/1.mp4', './50_Words/Wet/2.mp4', './50_Words/Wet/3.mp4', './50_Words/Fish/4.mp4', './50_Words/Fish/1.mp4', './50_Words/Fish/2.mp4', './50_Words/Fish/3.mp4', './50_Words/March/4.mp4', './50_Words/March/1.mp4', './50_Words/March/2.mp4', './50_Words/March/3.mp4', './50_Words/Degrade/4.mp4', './50_Words/Degrade/1.mp4', './50_Words/Degrade/2.mp4', './50_Words/Degrade/3.mp4', './50_Words/Table/4.mp4', './50_Words/Table/1.mp4', './50_Words/Table/2.mp4', './50_Words/Table/3.mp4', './50_Words/Telephone/4.mp4', './50_Words/Telephone/1.mp4', './50_Words/Telephone/2.mp4', './50_Words/Telephone/3.mp4', './50_Words/Small/4.mp4', './50_Words/Small/1.mp4', './50_Words/Small/2.mp4', './50_Words/Small/3.mp4', './50_Words/Goodbye/4.mp4', './50_Words/Goodbye/1.mp4', './50_Words/Goodbye/2.mp4', './50_Words/Goodbye/3.mp4', './50_Words/Sit/4.mp4', './50_Words/Sit/1.mp4', './50_Words/Sit/2.mp4', './50_Words/Sit/3.mp4', './50_Words/Mobile/4.mp4', './50_Words/Mobile/1.mp4', './50_Words/Mobile/2.mp4', './50_Words/Mobile/3.mp4', './50_Words/Good/4.mp4', './50_Words/Good/1.mp4', './50_Words/Good/2.mp4', './50_Words/Good/3.mp4', './50_Words/Rose_Color/4.mp4', './50_Words/Rose_Color/1.mp4', './50_Words/Rose_Color/2.mp4', './50_Words/Rose_Color/3.mp4', './50_Words/Caram/4.mp4', './50_Words/Caram/1.mp4', './50_Words/Caram/2.mp4', './50_Words/Caram/3.mp4', './50_Words/Thanks/4.mp4', './50_Words/Thanks/1.mp4', './50_Words/Thanks/2.mp4', './50_Words/Thanks/3.mp4', './50_Words/Book/4.mp4', './50_Words/Book/1.mp4', './50_Words/Book/2.mp4', './50_Words/Book/3.mp4', './50_Words/Together/4.mp4', './50_Words/Together/1.mp4', './50_Words/Together/2.mp4', './50_Words/Together/3.mp4', './50_Words/Come/4.mp4', './50_Words/Come/1.mp4', './50_Words/Come/2.mp4', './50_Words/Come/3.mp4', './50_Words/Bread/4.mp4', './50_Words/Bread/1.mp4', './50_Words/Bread/2.mp4', './50_Words/Bread/3.mp4', './50_Words/Quick/4.mp4', './50_Words/Quick/1.mp4', './50_Words/Quick/2.mp4', './50_Words/Quick/3.mp4', './50_Words/Fate/4.mp4', './50_Words/Fate/1.mp4', './50_Words/Fate/2.mp4', './50_Words/Fate/3.mp4', './50_Words/Exercise_Book/4.mp4', './50_Words/Exercise_Book/1.mp4', './50_Words/Exercise_Book/2.mp4', './50_Words/Exercise_Book/3.mp4', './50_Words/February/4.mp4', './50_Words/February/1.mp4', './50_Words/February/2.mp4', './50_Words/February/3.mp4', './50_Words/Listen/4.mp4', './50_Words/Listen/1.mp4', './50_Words/Listen/2.mp4', './50_Words/Listen/3.mp4', './50_Words/Break/4.mp4', './50_Words/Break/1.mp4', './50_Words/Break/2.mp4', './50_Words/Break/3.mp4', './50_Words/Chair/4.mp4', './50_Words/Chair/1.mp4', './50_Words/Chair/2.mp4', './50_Words/Chair/3.mp4', './50_Words/Procession/4.mp4', './50_Words/Procession/1.mp4', './50_Words/Procession/2.mp4', './50_Words/Procession/3.mp4', './50_Words/Door/4.mp4', './50_Words/Door/1.mp4', './50_Words/Door/2.mp4', './50_Words/Door/3.mp4', './50_Words/Large/4.mp4', './50_Words/Large/1.mp4', './50_Words/Large/2.mp4', './50_Words/Large/3.mp4', './50_Words/Up/4.mp4', './50_Words/Up/1.mp4', './50_Words/Up/2.mp4', './50_Words/Up/3.mp4', './50_Words/Bird/4.mp4', './50_Words/Bird/1.mp4', './50_Words/Bird/2.mp4', './50_Words/Bird/3.mp4', './50_Words/More/4.mp4', './50_Words/More/1.mp4', './50_Words/More/2.mp4', './50_Words/More/3.mp4', './50_Words/Egg/4.mp4', './50_Words/Egg/1.mp4', './50_Words/Egg/2.mp4', './50_Words/Egg/3.mp4', './50_Words/Khoda_Hafez/4.mp4', './50_Words/Khoda_Hafez/1.mp4', './50_Words/Khoda_Hafez/2.mp4', './50_Words/Khoda_Hafez/3.mp4', './50_Words/Black/4.mp4', './50_Words/Black/1.mp4', './50_Words/Black/2.mp4', './50_Words/Black/3.mp4', './50_Words/Bitter/4.mp4', './50_Words/Bitter/1.mp4', './50_Words/Bitter/2.mp4', './50_Words/Bitter/3.mp4', './50_Words/Remember/4.mp4', './50_Words/Remember/1.mp4', './50_Words/Remember/2.mp4', './50_Words/Remember/3.mp4', './50_Words/Exercise/4.mp4', './50_Words/Exercise/1.mp4', './50_Words/Exercise/2.mp4', './50_Words/Exercise/3.mp4', './50_Words/January/4.mp4', './50_Words/January/1.mp4', './50_Words/January/2.mp4', './50_Words/January/3.mp4', './50_Words/Hearing_Impaired/4.mp4', './50_Words/Hearing_Impaired/1.mp4', './50_Words/Hearing_Impaired/2.mp4', './50_Words/Hearing_Impaired/3.mp4', './50_Words/Salam/4.mp4', './50_Words/Salam/1.mp4', './50_Words/Salam/2.mp4', './50_Words/Salam/3.mp4', './50_Words/Tiger/4.mp4', './50_Words/Tiger/1.mp4', './50_Words/Tiger/2.mp4', './50_Words/Tiger/3.mp4']\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KAGK5_hMBE18",
"colab_type": "code",
"colab": {}
},
"source": [
"import cv2\n",
"\n",
"def gen_video_frames(ls_video_files):\n",
" for video_file in ls_video_files:\n",
" #print(video_file)\n",
"\n",
" v = video_file.split(\"/\")[-2:]\n",
"\n",
" out_word_dir = os.path.join(\"frames\", v[0])\n",
" os.makedirs(out_word_dir, exist_ok = True)\n",
"\n",
" file_name = v[1].split(\".\")[0]\n",
" #print(file_name)\n",
"\n",
" out_vid_dir = os.path.join(out_word_dir, file_name)\n",
" os.makedirs(out_vid_dir, exist_ok = True)\n",
"\n",
" # making all frame sequence per subject\n",
" # capturing video\n",
" vidcap = cv2.VideoCapture(video_file)\n",
" success, image = vidcap.read()\n",
"\n",
" if(vidcap.isOpened() == True):\n",
" count = 0\n",
" success = True\n",
"\n",
" while success:\n",
" success, image = vidcap.read()\n",
" count += 1\n",
" print('reading a new frame: ', success)\n",
"\n",
" if(success == True):\n",
" #save frame as JPEG file\n",
" cv2.imwrite(os.path.join(out_vid_dir, (\"%03d.jpg\") % count), image) "
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NXmMqHSsBrPI",
"colab_type": "code",
"colab": {}
},
"source": [
"ls_video_files = get_all_video_files_for_train()\n",
"print(len(ls_video_files))\n",
"\n",
"# making video frames for given videos\n",
"gen_video_frames(ls_video_files)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "6J7KOpBfF-bj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "10cb930e-f828-4450-99cb-663fd0b25ed2"
},
"source": [
"!pwd"
],
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/openpose\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XLJTCDuDGyLV",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np\n",
"# running openpose for dataset\n",
"def run_openpose(word_list):\n",
"\n",
" # considering each subject\n",
" for word_ID in word_list:\n",
"\n",
" word_dir = os.path.join(\"../frames\", word_ID)\n",
" ls_vids_each_word = os.listdir(word_dir)\n",
"\n",
" \n",
" for vid_each_word in ls_vids_each_word:\n",
" vid_dir = os.path.join(word_dir, vid_each_word)\n",
" \n",
" # save_dir for saving pose keypoints data\n",
" save_dir = os.path.join(\"../pose\", word_ID, vid_each_word)\n",
" os.makedirs(save_dir, exist_ok = True)\n",
"\n",
" print(\"\\ncalculationg pose...\")\n",
" os.system(\"./build/examples/openpose/openpose.bin --image_dir \" + \n",
" vid_dir + \" --number_people_max 1 \" + \" --write_json \" + \n",
" save_dir + \" --display 0 --render_pose 0 --hand\")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5GgGCBBRHzdL",
"colab_type": "code",
"colab": {}
},
"source": [
"word_list = os.listdir(\"../frames\")\n",
"print(word_list)\n",
"run_openpose(word_list)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "C-K-Oi3daKZJ",
"colab_type": "code",
"colab": {}
},
"source": [
"!tar -czf t3.tar.gz pose\n"
],
"execution_count": 0,
"outputs": []
}
]
}
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