notebook for openpose
This commit is contained in:
Ruben van de Ven
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c92a9fb824
commit
ec5d553e75
1 changed files with 433 additions and 0 deletions
433
app/openpose.ipynb
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433
app/openpose.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4268bbde",
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"metadata": {},
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"outputs": [],
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"source": [
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"#hide\n",
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"import pyopenpose as op\n",
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"import ipywidgets\n",
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"from ipywebrtc import CameraStream\n",
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"import cv2 \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9b530e24",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Defaulting to user installation because normal site-packages is not writeable\n",
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"Requirement already satisfied: tqdm in ./.local/lib/python3.8/site-packages (4.64.1)\n"
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]
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}
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],
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"source": [
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"!pip3 install tqdm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "47bbe21b",
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"metadata": {},
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"outputs": [],
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"source": [
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" from tqdm.notebook import tqdm\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8185becc",
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"metadata": {},
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"source": [
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"# Jupyter notebook for OpenPose experiments\n",
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"\n",
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"Read any image or video from the `/data` directory and render the output here "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "cb916658",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wed Nov 2 15:54:10 2022 \r\n",
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"+-----------------------------------------------------------------------------+\r\n",
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"| NVIDIA-SMI 470.141.03 Driver Version: 470.141.03 CUDA Version: 11.4 |\r\n",
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"|-------------------------------+----------------------+----------------------+\r\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n",
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"| | | MIG M. |\r\n",
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"|===============================+======================+======================|\r\n",
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"| 0 NVIDIA GeForce ... On | 00000000:08:00.0 Off | N/A |\r\n",
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"| 0% 41C P5 61W / 350W | 21MiB / 24265MiB | 0% Default |\r\n",
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"| | | N/A |\r\n",
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"+-------------------------------+----------------------+----------------------+\r\n",
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" \r\n",
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"+-----------------------------------------------------------------------------+\r\n",
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"| Processes: |\r\n",
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"| GPU GI CI PID Type Process name GPU Memory |\r\n",
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"| ID ID Usage |\r\n",
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"|=============================================================================|\r\n",
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"+-----------------------------------------------------------------------------+\r\n"
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]
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}
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],
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"source": [
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"!nvidia-smi"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "5222af69",
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"metadata": {},
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"outputs": [],
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"source": [
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"# CameraStream.facing_user(audio=False)\n",
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"# requires SSL website... :-("
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "266062c5",
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"metadata": {},
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"outputs": [],
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"source": [
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"params = dict()\n",
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"params[\"model_folder\"] = \"/openpose/models/\"\n",
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"# params[\"face\"] = True\n",
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"params[\"hand\"] = True\n",
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"# params[\"heatmaps_add_parts\"] = True\n",
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"# params[\"heatmaps_add_bkg\"] = True\n",
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"# params[\"heatmaps_add_PAFs\"] = True\n",
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"# params[\"heatmaps_scale\"] = 3\n",
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"# params[\"upsampling_ratio\"] = 1\n",
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"# params[\"body\"] = 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "f034fee8",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Starting OpenPose Python Wrapper...\n",
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"Auto-detecting all available GPUs... Detected 1 GPU(s), using 1 of them starting at GPU 0.\n"
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]
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}
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],
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"source": [
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"# Starting OpenPose\n",
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"opWrapper = op.WrapperPython()\n",
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"opWrapper.configure(params)\n",
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"opWrapper.start()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "37f999b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# upload = ipywidgets.FileUpload()\n",
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"# display(upload)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "de658948",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "1b4208da",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# # Process Image\n",
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"# datum = op.Datum()\n",
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"# imageToProcess = cv2.imread(args[0].image_path)\n",
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"# datum.cvInputData = imageToProcess\n",
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"# opWrapper.emplaceAndPop(op.VectorDatum([datum]))\n",
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"\n",
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"# # Display Image\n",
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"# print(\"Body keypoints: \\n\" + str(datum.poseKeypoints))\n",
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"# print(\"Face keypoints: \\n\" + str(datum.faceKeypoints))\n",
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"# print(\"Left hand keypoints: \\n\" + str(datum.handKeypoints[0]))\n",
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"# print(\"Right hand keypoints: \\n\" + str(datum.handKeypoints[1]))\n",
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"# cv2.imwrite(\"/data/result_body.jpg\",datum.cvOutputData)\n",
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"\n",
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"# print(dir(datum))\n",
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"# # cv2.imshow(\"OpenPose 1.7.0 - Tutorial Python API\", datum.cvOutputData)\n",
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"# cv2.waitKey(0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3c994aa0",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "402ad8b9",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"id": "275242c6",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Frames per second : 24.0 FPS\n",
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"Frame count : 500.0\n"
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]
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}
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],
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"source": [
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"vid_capture = cv2.VideoCapture('/data/0001-0500.mp4')\n",
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"\n",
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"# Create a video capture object, in this case we are reading the video from a file\n",
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"if (vid_capture.isOpened() == False):\n",
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" print(\"Error opening the video file\")\n",
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"# Read fps and frame count\n",
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"else:\n",
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" # Get frame rate information\n",
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" # You can replace 5 with CAP_PROP_FPS as well, they are enumerations\n",
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" fps = vid_capture.get(5)\n",
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" print('Frames per second : ', fps,'FPS')\n",
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" \n",
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" # Get frame count\n",
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" # You can replace 7 with CAP_PROP_FRAME_COUNT as well, they are enumerations\n",
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" frame_count = vid_capture.get(7)\n",
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" print('Frame count : ', frame_count)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"id": "b0149d40",
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"metadata": {},
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"outputs": [],
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"source": [
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"frame_size = (int(vid_capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(vid_capture.get(cv2.CAP_PROP_FRAME_HEIGHT\n",
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")))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"id": "2a536a5f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(1920, 1080)"
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]
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},
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"frame_size"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"id": "66003b23",
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"metadata": {},
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"outputs": [],
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"source": [
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"output = cv2.VideoWriter(\n",
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" '/data/output.mp4',\n",
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" # see http://mp4ra.org/#/codecs for codecs\n",
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"# cv2.VideoWriter_fourcc('m','p','4','v'),\n",
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"# cv2.VideoWriter_fourcc(*'mp4v'),\n",
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"# cv2.VideoWriter_fourcc('a','v','1','C'),\n",
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" cv2.VideoWriter_fourcc(*'vp09'),\n",
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"# cv2.VideoWriter_fourcc(*'avc1'),\n",
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" fps,\n",
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" frame_size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"id": "6ed91535",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "633b01b6f8474017ab2ba3d454b3adea",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/500.0 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Stream ended\n"
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]
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}
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],
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"source": [
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"# See also:\n",
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"# with tqdm.wrapattr(file_obj, \"read\", total=file_obj.size) as fobj:\n",
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"# ... while True:\n",
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"# ... chunk = fobj.read(chunk_size)\n",
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"# ... if not chunk:\n",
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"# ... break\n",
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"t = tqdm(total=frame_count) # Initialise\n",
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"while(vid_capture.isOpened()):\n",
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" t.update(1)\n",
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"# with tqdm.wrapattr(vid_capture, \"read\", total=frame_count) as vid_obj:\n",
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"# # vid_capture.read() methods returns a tuple, first element is a bool \n",
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" # and the second is frame\n",
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"# while True:\n",
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" ret, frame = vid_capture.read()\n",
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" if ret == True:\n",
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" datum = op.Datum()\n",
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" datum.cvInputData = frame\n",
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" opWrapper.emplaceAndPop(op.VectorDatum([datum]))\n",
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"\n",
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" # Display Image\n",
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" # print(\"Body keypoints: \\n\" + str(datum.poseKeypoints))\n",
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" # print(\"Face keypoints: \\n\" + str(datum.faceKeypoints))\n",
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" # print(\"Left hand keypoints: \\n\" + str(datum.handKeypoints[0]))\n",
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" # print(\"Right hand keypoints: \\n\" + str(datum.handKeypoints[1]))\n",
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" #cv2.imwrite(f\"/data/out/result_body_scale{i:03d}.jpg\",datum.cvOutputData)\n",
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" output.write(datum.cvOutputData)\n",
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" else:\n",
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" print(\"Stream ended\")\n",
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" break\n",
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"t.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"id": "27580409",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Release the objects\n",
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"vid_capture.release()\n",
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"output.release()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"id": "6f3ad121",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "9b632f75a46f4306bcadddf38ac4887e",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Video(value=b'\\x00\\x00\\x00\\x1cftypisom\\x00\\x00\\x02\\x00isomiso2mp41\\x00\\x00\\x00\\x08free\\x00U\\x0c\\xe0...')"
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]
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},
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"execution_count": 47,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# display(HTML(\"\"\"<video width=\"100\" height=\"100\" controls><source src=\"/data/output.mp4\" type=\"video/mp4\"></video>\"\"\"))\n",
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"ipywidgets.Video.from_file('/data/outputs.mp4')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6d97230b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# from IPython.display import Video\n",
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"# Video('/data/outputs.mp4', embed=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0b462645",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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