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utilsDetector.py
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utilsDetector.py
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import os
import cv2
import shutil
import pickle
import numpy as np
import json
import sys
import time
from decouple import config
from utils import getOpenPoseMarkerNames, getMMposeMarkerNames, getVideoExtension
from utilsChecker import getVideoRotation
# %%
def runPoseDetector(CameraDirectories, trialRelativePath, pathPoseDetector,
trialName,
CamParamDict=None, resolutionPoseDetection='default',
generateVideo=True, cams2Use=['all'],
poseDetector='OpenPose', bbox_thr=0.8):
# Create list of cameras.
if cams2Use[0] == 'all':
cameras2Use = list(CameraDirectories.keys())
else:
cameras2Use = cams2Use
CameraDirectories_selectedCams = {}
CamParamList_selectedCams = []
for cam in cameras2Use:
CameraDirectories_selectedCams[cam] = CameraDirectories[cam]
CamParamList_selectedCams.append(CamParamDict[cam])
# Get/add video extension.
cameraDirectory = CameraDirectories_selectedCams[cameras2Use[0]]
pathVideoWithoutExtension = os.path.join(cameraDirectory,
trialRelativePath)
extension = getVideoExtension(pathVideoWithoutExtension)
trialRelativePath += extension
for camName in CameraDirectories_selectedCams:
cameraDirectory = CameraDirectories_selectedCams[camName]
print('Running {} for {}'.format(poseDetector, camName))
if poseDetector == 'OpenPose':
runOpenPoseVideo(
cameraDirectory,trialRelativePath,pathPoseDetector, trialName,
resolutionPoseDetection=resolutionPoseDetection,
generateVideo=generateVideo)
elif poseDetector == 'mmpose':
runMMposeVideo(
cameraDirectory,trialRelativePath,pathPoseDetector, trialName,
generateVideo=generateVideo, bbox_thr=bbox_thr)
return extension
# %%
def runOpenPoseVideo(cameraDirectory,fileName,pathOpenPose, trialName,
resolutionPoseDetection='default', generateVideo=True):
trialPrefix, _ = os.path.splitext(os.path.basename(fileName))
videoFullPath = os.path.normpath(os.path.join(cameraDirectory, fileName))
if not os.path.exists(videoFullPath):
exception = "Video upload failed. Make sure all devices are connected to Internet and that your connection is stable."
raise Exception(exception, exception)
outputMediaFolder = "OutputMedia_" + resolutionPoseDetection
outputJsonFolder = "OutputJsons_" + resolutionPoseDetection
outputPklFolder = "OutputPkl_" + resolutionPoseDetection
pathOutputVideo = os.path.join(cameraDirectory, outputMediaFolder,
trialName)
openposeJsonDir = os.path.join(outputJsonFolder, trialName)
pathOutputJsons = os.path.join(cameraDirectory, openposeJsonDir)
pathJsonDir = os.path.join(cameraDirectory, outputJsonFolder)
openposePklDir = os.path.join(outputPklFolder, trialName)
pathOutputPkl = os.path.join(cameraDirectory, openposePklDir)
os.makedirs(pathOutputVideo, exist_ok=True)
os.makedirs(pathOutputJsons, exist_ok=True)
os.makedirs(pathOutputPkl, exist_ok=True)
# Get number of frames.
thisVideo = cv2.VideoCapture(videoFullPath)
nFrameIn = int(thisVideo.get(cv2.CAP_PROP_FRAME_COUNT))
# The video is rewritten, unrotated, and downsampled. There is no
# need to do anything specific for the rotation, just rewriting the video
# unrotates it.
trialPath, _ = os.path.splitext(fileName)
fileName = trialPath + "_rotated.avi"
pathVideoRot = os.path.normpath(os.path.join(cameraDirectory, fileName))
cmd_fr = ' '
# frameRate = np.round(thisVideo.get(cv2.CAP_PROP_FPS))
# if frameRate > 60.0: # previously downsampled for efficiency
# cmd_fr = ' -r 60 '
# frameRate = 60.0
CMD = "ffmpeg -loglevel error -y -i {}{}-q 0 {}".format(
videoFullPath, cmd_fr, pathVideoRot)
videoFullPath = pathVideoRot
trialPrefix = trialPrefix + "_rotated"
if not os.path.exists(pathVideoRot):
os.system(CMD)
# Run OpenPose if this file doesn't exist in outputs
ppPklPath = os.path.join(pathOutputPkl, trialPrefix + '_pp.pkl')
if not os.path.exists(ppPklPath):
c_path = os.getcwd()
command = runOpenPoseCMD(
pathOpenPose, resolutionPoseDetection, cameraDirectory,
fileName, openposeJsonDir, pathOutputVideo, trialPrefix,
generateVideo, videoFullPath, pathOutputJsons)
if not pathOpenPose == "docker":
os.chdir(c_path)
# Get number of frames output video. We count the number of jsons, as
# videos are not written on server.
nFrameOut = len([f for f in os.listdir(pathOutputJsons)
if f.endswith('.json')])
# At high resolution, sometimes OpenPose does not process the full
# video, let's check here and try max 5 times. If still bad, then raise
# an exception.
checknFrames = False
if not resolutionPoseDetection == 'default' and checknFrames:
countFrames = 0
while nFrameIn != nFrameOut:
# Need to get command again, as there is os.chdir(pathOpenPose)
# in the function.
command = runOpenPoseCMD(pathOpenPose, resolutionPoseDetection,
cameraDirectory, fileName,
openposeJsonDir, pathOutputVideo,
trialPrefix, generateVideo,
videoFullPath, pathOutputJsons)
if not pathOpenPose == "docker":
os.chdir(c_path)
nFrameOut = len([f for f in os.listdir(pathOutputJsons)
if f.endswith('.json')])
if countFrames > 4:
print('# frames in {} - # frames out {}'.format(nFrameIn,
nFrameOut))
raise ValueError('OpenPose did not process the full video')
countFrames += 1
# Gather data from jsons in pkl file.
saveJsonsAsPkl(pathOutputJsons, ppPklPath, trialPrefix)
# Delete jsons
shutil.rmtree(pathJsonDir)
return
# %%
def runOpenPoseCMD(pathOpenPose, resolutionPoseDetection, cameraDirectory,
fileName, openposeJsonDir, pathOutputVideo, trialPrefix,
generateVideo, videoFullPath, pathOutputJsons):
rotation = getVideoRotation(videoFullPath)
if rotation in [0,180]:
horizontal = True
else:
horizontal = False
command = None
if resolutionPoseDetection == 'default':
cmd_hr = ' '
elif resolutionPoseDetection == '1x1008_4scales':
if horizontal:
cmd_hr = ' --net_resolution "1008x-1" --scale_number 4 --scale_gap 0.25 '
else:
cmd_hr = ' --net_resolution "-1x1008" --scale_number 4 --scale_gap 0.25 '
elif resolutionPoseDetection == '1x736':
if horizontal:
cmd_hr = ' --net_resolution "736x-1" '
else:
cmd_hr = ' --net_resolution "-1x736" '
elif resolutionPoseDetection == '1x736_2scales':
if horizontal:
cmd_hr = ' --net_resolution "-1x736" --scale_number 2 --scale_gap 0.75 '
else:
cmd_hr = ' --net_resolution "736x-1" --scale_number 2 --scale_gap 0.75 '
if config("DOCKERCOMPOSE", cast=bool, default=False):
vid_path_tmp = "/data/tmp-video.mov"
vid_path = "/data/video_openpose.mov"
# copy the video to vid_path_tmp
shutil.copy(f"{cameraDirectory}/{fileName}", vid_path_tmp)
# rename the video to vid_path
os.rename(vid_path_tmp, vid_path)
try:
# wait until the video is processed (i.e. until the video is removed -- then json should be ready)
start = time.time()
while True:
if not os.path.isfile(vid_path):
break
if start + 60*60 < time.time():
raise Exception("Pose detection timed out. This is unlikely to be your fault, please report this issue on the forum. You can proceed with your data collection (videos are uploaded to the server) and later reprocess errored trials.", 'timeout - openpose')
time.sleep(0.1)
# copy /data/output to openposeJsonDir
os.system("cp /data/output_openpose/* {cameraDirectory}/{openposeJsonDir}/".format(cameraDirectory=cameraDirectory, openposeJsonDir=openposeJsonDir))
except Exception as e:
if len(e.args) == 2: # specific exception
raise Exception(e.args[0], e.args[1])
elif len(e.args) == 1: # generic exception
exception = "Pose detection failed. Verify your setup and try again. Visit https://www.opencap.ai/best-pratices to learn more about data collection and https://www.opencap.ai/troubleshooting for potential causes for a failed neutral pose."
raise Exception(exception, exception)
elif pathOpenPose == "docker":
command = "docker run --gpus=1 -v {}:/openpose/data stanfordnmbl/openpose-gpu\
/openpose/build/examples/openpose/openpose.bin\
--video /openpose/data/{}\
--display 0\
--write_json /openpose/data/{}\
--render_pose 0{}".format(cameraDirectory, fileName,
openposeJsonDir, cmd_hr)
else:
os.chdir(pathOpenPose)
pathVideoOut = os.path.join(pathOutputVideo,
trialPrefix + 'withKeypoints.avi')
if not generateVideo:
command = ('bin\OpenPoseDemo.exe --video {} --write_json {} --render_threshold 0.5 --display 0 --render_pose 0{}'.format(
videoFullPath, pathOutputJsons, cmd_hr))
else:
command = ('bin\OpenPoseDemo.exe --video {} --write_json {} --render_threshold 0.5 --display 0{}--write_video {}'.format(
videoFullPath, pathOutputJsons, cmd_hr, pathVideoOut))
if command:
os.system(command)
return
# %%
def runMMposeVideo(
cameraDirectory, fileName, pathMMpose, trialName,
generateVideo=True, bbox_thr=0.8,
model_config_person='faster_rcnn_r50_fpn_coco.py',
model_ckpt_person='faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth',
model_config_pose='hrnet_w48_coco_wholebody_384x288_dark_plus.py',
model_ckpt_pose='hrnet_w48_coco_wholebody_384x288_dark-f5726563_20200918.pth',
):
trialPrefix, _ = os.path.splitext(os.path.basename(fileName))
videoFullPath = os.path.normpath(os.path.join(cameraDirectory, fileName))
pathOutputVideo = os.path.join(cameraDirectory,"OutputMedia_mmpose_" +
str(bbox_thr), trialName)
mmposeBoxDir = os.path.join("OutputBox_mmpose", trialName)
pathOutputBox = os.path.join(cameraDirectory, mmposeBoxDir)
mmposePklDir = os.path.join("OutputPkl_mmpose_" + str(bbox_thr),
trialName)
pathOutputPkl = os.path.join(cameraDirectory, mmposePklDir)
os.makedirs(pathOutputVideo, exist_ok=True)
os.makedirs(pathOutputBox, exist_ok=True)
os.makedirs(pathOutputPkl, exist_ok=True)
# Get frame rate.
thisVideo = cv2.VideoCapture(videoFullPath)
# frameRate = np.round(thisVideo.get(cv2.CAP_PROP_FPS))
# The video is rewritten, unrotated, and downsampled. There is no
# need to do anything specific for the rotation, just rewriting the video
# unrotates it.
trialPath, _ = os.path.splitext(fileName)
fileName = trialPath + "_rotated.avi"
pathVideoRot = os.path.normpath(os.path.join(cameraDirectory, fileName))
cmd_fr = ' '
# if frameRate > 60.0:
# cmd_fr = ' -r 60 '
# frameRate = 60.0
CMD = "ffmpeg -loglevel error -y -i {}{}-q 0 {}".format(
videoFullPath, cmd_fr, pathVideoRot)
videoFullPath = pathVideoRot
trialPrefix = trialPrefix + "_rotated"
if not os.path.exists(pathVideoRot):
os.system(CMD)
pklPath = os.path.join(pathOutputPkl, trialPrefix + '.pkl')
ppPklPath = os.path.join(pathOutputPkl, trialPrefix + '_pp.pkl')
# Run pose detector if this file doesn't exist in outputs
if not os.path.exists(ppPklPath):
if config("DOCKERCOMPOSE", cast=bool, default=False):
vid_path_tmp = "/data/tmp-video.mov"
vid_path = "/data/video_mmpose.mov"
# copy the video to vid_path_tmp
shutil.copy(f"{cameraDirectory}/{fileName}", vid_path_tmp)
# rename the video to vid_path
os.rename(vid_path_tmp, vid_path)
try:
# wait until the video is processed (i.e. until the video is removed -- then json should be ready)
start = time.time()
while True:
if not os.path.isfile(vid_path):
break
if start + 60*60 < time.time():
raise Exception("Pose detection timed out. This is unlikely to be your fault, please report this issue on the forum. You can proceed with your data collection (videos are uploaded to the server) and later reprocess errored trials.", 'timeout - hrnet')
time.sleep(0.1)
# copy /data/output to pathOutputPkl
os.system("cp /data/output_mmpose/* {pathOutputPkl}/".format(pathOutputPkl=pathOutputPkl))
pkl_path_tmp = os.path.join(pathOutputPkl, 'human.pkl')
os.rename(pkl_path_tmp, pklPath)
except Exception as e:
if len(e.args) == 2: # specific exception
raise Exception(e.args[0], e.args[1])
elif len(e.args) == 1: # generic exception
exception = "Pose detection failed. Verify your setup and try again. Visit https://www.opencap.ai/best-pratices to learn more about data collection and https://www.opencap.ai/troubleshooting for potential causes for a failed neutral pose."
raise Exception(exception, exception)
else:
c_path = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.join(c_path, 'mmpose'))
from utilsMMpose import detection_inference, pose_inference
# Run human detection.
pathModelCkptPerson = os.path.join(pathMMpose, model_ckpt_person)
bboxPath = os.path.join(pathOutputBox, trialPrefix + '.pkl')
full_model_config_person = os.path.join(c_path, 'mmpose',
model_config_person)
detection_inference(full_model_config_person, pathModelCkptPerson,
videoFullPath, bboxPath)
# Run pose detection.
pathModelCkptPose = os.path.join(pathMMpose, model_ckpt_pose)
videoOutPath = os.path.join(pathOutputVideo,
trialPrefix + 'withKeypoints.mp4')
full_model_config_pose = os.path.join(c_path, 'mmpose',
model_config_pose)
pose_inference(full_model_config_pose, pathModelCkptPose,
videoFullPath, bboxPath, pklPath, videoOutPath,
bbox_thr=bbox_thr, visualize=generateVideo)
# Post-process data to have OpenPose-like file structure.
arrangeMMposePkl(pklPath, ppPklPath)
# This is a hack to be able to use pose pickle files already saved in the
# database. In some cases, we saved pklPath instead of ppPklPath:
# https://github.com/stanfordnmbl/opencap-core/pull/100/files.
# We here identify these cases and re-run post processing.
else:
open_file = open(ppPklPath, "rb")
frames = pickle.load(open_file)
open_file.close()
isData = any([('pose_keypoints_2d' in element[0].keys()) for element in frames if len(element)>0])
if not isData:
os.rename(ppPklPath, pklPath)
arrangeMMposePkl(pklPath, ppPklPath)
# %%
def arrangeMMposePkl(poseInferencePklPath, outputPklPath):
open_file = open(poseInferencePklPath, "rb")
frames = pickle.load(open_file)
open_file.close()
markersMMpose = getMMposeMarkerNames()
markersOpenPose = getOpenPoseMarkerNames()
data4pkl = []
for c_frame, frame in enumerate(frames):
data4people = []
for c, person in enumerate(frame):
coordinates = person['preds_with_flip'].tolist()
c_coord_out = np.zeros((25*3,))
for c_m, marker in enumerate(markersOpenPose):
if marker == "midHip":
leftHip = coordinates[markersMMpose.index("LHip")]
rightHip = coordinates[markersMMpose.index("RHip")]
c_coord = []
# Mid point between both hips
c_coord.append((leftHip[0] + rightHip[0]) / 2)
c_coord.append((leftHip[1] + rightHip[1]) / 2)
# Lowest confidence
c_coord.append(np.min([leftHip[2], rightHip[2]]))
elif marker == "Neck":
leftShoulder = coordinates[
markersMMpose.index("LShoulder")]
rightShoulder = coordinates[
markersMMpose.index("RShoulder")]
c_coord = []
# Mid point between both shoulders
c_coord.append((leftShoulder[0] + rightShoulder[0]) / 2)
c_coord.append((leftShoulder[1] + rightShoulder[1]) / 2)
# Lowest confidence
c_coord.append(np.min([leftShoulder[2],
rightShoulder[2]]))
else:
c_coord = coordinates[markersMMpose.index(marker)]
idx_out = np.arange(c_m*3, c_m*3+3)
c_coord_out[idx_out,] = c_coord
c_dict = {}
c_dict['person_id'] = [c]
c_dict['pose_keypoints_2d'] = c_coord_out.tolist()
data4people.append(c_dict)
data4pkl.append(data4people)
with open(outputPklPath, 'wb') as f:
pickle.dump(data4pkl, f)
return
# %%
def saveJsonsAsPkl(json_directory, outputPklPath, videoName):
nFrames = 0
for file in os.listdir(json_directory):
if videoName + "_000" in file: # not great
nFrames += 1
data4pkl = []
for frame in sorted(os.listdir(json_directory)):
image_json = os.path.join(json_directory,frame)
if not os.path.isfile(image_json):
break
with open(image_json) as data_file:
data = json.load(data_file)
data4people = []
for person_idx in range(len(data['people'])):
person = data['people'][person_idx]
keypoints = person['pose_keypoints_2d']
c_dict = {}
c_dict['person_id'] = [person_idx]
c_dict['pose_keypoints_2d'] = keypoints
data4people.append(c_dict)
data4pkl.append(data4people)
with open(outputPklPath, 'wb') as f:
pickle.dump(data4pkl, f)
return