def __init__(self,
projectID=None,
modelID=None,
workers=None,
summaryFile=None):
self.projectID = projectID
self.fileManager = FM(projectID=projectID,
modelID=modelID,
summaryFile=summaryFile)
self.modelID = modelID
if not self._checkProjectID():
raise Exception(projectID + ' is not valid.')
self.workers = workers
def __init__(self):
# 1: Define valid commands and ignore warnings
self.commands = [
'New', 'Restart', 'Stop', 'Rewrite', 'UploadData', 'LocalDelete',
'Snapshots'
]
np.seterr(invalid='ignore')
# 2: Determine which Kinect is attached (This script can handle v1 or v2 Kinects)
self._identifyDevice() #Stored in self.device
self.system = platform.node()
# 3: Create file manager
self.fileManager = FM()
# 4: Download credential files
self.fileManager.downloadData(
self.fileManager.localCredentialSpreadsheet)
self.fileManager.downloadData(self.fileManager.localCredentialDrive)
self.credentialSpreadsheet = self.fileManager.localCredentialSpreadsheet # Rename to make code readable
# 5: Connect to Google Spreadsheets
self._authenticateGoogleSpreadSheets() #Creates self.controllerGS
self._modifyPiGS(error='')
# 6: Start PiCamera
self.camera = PiCamera()
self.camera.resolution = (1296, 972)
self.camera.framerate = 30
self.piCamera = 'True'
# 7: Keep track of processes spawned to convert and upload videofiles
self.processes = []
# 8: Set size of frame
try:
self.r
except AttributeError:
self.r = (0, 0, 640, 480)
# 9: Await instructions
self.monitorCommands()
def __init__(self, all_data):
self.all_data = all_data # Flag to keep all data if desired
# 1: Define valid commands and ignore warnings
self.commands = ['New', 'Restart', 'Stop', 'Rewrite', 'UploadData', 'LocalDelete']
np.seterr(invalid='ignore')
# 2: Determine which depth sensor is attached (This script can handle DepthSense cameras)
self._identifyDevice() #Stored in self.device
self.system = platform.node()
# 3: Create file manager
self.fileManager = FM()
# 4: Start PiCamera
self.camera = PiCamera()
self.camera.resolution = (1296, 972)
self.camera.framerate = 30
self.piCamera = 'True'
# 5: Download credential files
self.fileManager.downloadData(self.fileManager.localCredentialDir)
self.credentialSpreadsheet = self.fileManager.localCredentialSpreadsheet # Rename to make code readable
self._authenticateGoogleSpreadSheets() #Creates self.controllerGS
self._identifyTank() #Stored in self.tankID
self._identifyServiceAccount()
# 6: Keep track of processes spawned to convert and upload videofiles
self.processes = []
# 7: Set size of frame
try:
self.r
except AttributeError:
self.r = (0,0,640,480)
# 9: Await instructions
print('Monitoring commands')
self.monitorCommands()
import argparse, subprocess, pdb, shutil, os
import pandas as pd
from cichlid_bower_tracking.helper_modules.file_manager import FileManager as FM
parser = argparse.ArgumentParser(description='This script is used to manually prepared projects for downstream analysis')
parser.add_argument('--SummaryFile', type = str, help = 'Restrict analysis to projectIDs specified in csv file, which will be rewritten. ProjectIDs must be found in a column called projectID')
parser.add_argument('--Start', type = int)
parser.add_argument('--Total', type = int)
args = parser.parse_args()
fm_obj = FM()
if args.SummaryFile is not None:
summary_file = fm_obj.localAnalysisStatesDir + args.SummaryFile
fm_obj.downloadData(summary_file)
dt = pd.read_csv(summary_file, index_col = False)
projectIDs = list(dt.projectID)
if args.Start is not None:
projectIDs = projectIDs[args.Start: args.Start + args.Total]
else:
projectIDs = fm_obj.getAllProjectIDs()
for projectID in projectIDs:
fm_obj = FM(projectID = projectID)
print(projectID)
lp = fm_obj.lp
main_directory_data = subprocess.run(['rclone', 'lsf', 'cichlidVideo:McGrath/Apps/CichlidPiData/' + '__ProjectData/' + projectID + '/'], capture_output = True, encoding = 'utf-8').stdout.split('\n')
for bad_data in ['AllClips.tar', 'MLClips.tar', 'MLFrames.tar', 'Backgrounds.tar']:
if bad_data in main_directory_data:
parser.add_argument('-i',
'--Initials',
required=True,
type=str,
help='Initials to save annotations')
args = parser.parse_args()
numbers = {}
# Identify projects to run analysis on
if args.ProjectIDs is not None:
projectIDs = args.ProjectIDs # Specified at the command line
for projectID in projectIDs:
numbers[projectID] = args.Number
else:
fm_obj = FM()
summary_file = fm_obj.localAnalysisStatesDir + args.SummaryFile
fm_obj.downloadData(summary_file)
dt = pd.read_csv(summary_file, index_col=False)
projectIDs = list(
dt.projectID) # Only run analysis on projects that need it
for projectID in projectIDs:
if dt.loc[dt.projectID == projectID]['Labeled' + args.DataType] > 0:
numbers[projectID] = dt.loc[dt.projectID == projectID][
'Labeled' + args.DataType]
# To run analysis efficiently, we download and upload data in the background while the main script runs
for projectID, number in numbers.items():
print('Downloading: ' + projectID + ' ' + str(datetime.datetime.now()))
class CichlidTracker:
def __init__(self):
# 1: Define valid commands and ignore warnings
self.commands = [
'New', 'Restart', 'Stop', 'Rewrite', 'UploadData', 'LocalDelete',
'Snapshots'
]
np.seterr(invalid='ignore')
# 2: Determine which Kinect is attached (This script can handle v1 or v2 Kinects)
self._identifyDevice() #Stored in self.device
self.system = platform.node()
# 3: Create file manager
self.fileManager = FM()
# 4: Download credential files
self.fileManager.downloadData(
self.fileManager.localCredentialSpreadsheet)
self.fileManager.downloadData(self.fileManager.localCredentialDrive)
self.credentialSpreadsheet = self.fileManager.localCredentialSpreadsheet # Rename to make code readable
# 5: Connect to Google Spreadsheets
self._authenticateGoogleSpreadSheets() #Creates self.controllerGS
self._modifyPiGS(error='')
# 6: Start PiCamera
self.camera = PiCamera()
self.camera.resolution = (1296, 972)
self.camera.framerate = 30
self.piCamera = 'True'
# 7: Keep track of processes spawned to convert and upload videofiles
self.processes = []
# 8: Set size of frame
try:
self.r
except AttributeError:
self.r = (0, 0, 640, 480)
# 9: Await instructions
self.monitorCommands()
def __del__(self):
# Try to close out files and stop running Kinects
self._modifyPiGS(command='None',
status='Stopped',
error='UnknownError')
if self.piCamera:
if self.camera.recording:
self.camera.stop_recording()
self._print('PiCameraStopped: Time=' +
str(datetime.datetime.now()) + ', File=Videos/' +
str(self.videoCounter).zfill(4) + "_vid.h264")
try:
if self.device == 'kinect2':
self.K2device.stop()
if self.device == 'kinect':
freenect.sync_stop()
freenect.shutdown(self.a)
except AttributeError:
pass
self._closeFiles()
def monitorCommands(self, delta=10):
# This function checks the master Controller Google Spreadsheet to determine if a command was issued (delta = seconds to recheck)
while True:
self._identifyTank() #Stored in self.tankID
command, projectID = self._returnCommand()
if projectID in ['', 'None']:
self._reinstructError('ProjectID must be set')
time.sleep(delta)
continue
if command != 'None':
print(command + '\t' + projectID)
self.fileManager.createProjectData(projectID)
self.runCommand(command, projectID)
self._modifyPiGS(status='AwaitingCommand')
time.sleep(delta)
def runCommand(self, command, projectID):
# This function is used to run a specific command found int he master Controller Google Spreadsheet
self.projectID = projectID
# Rename files to make code more readable
self.projectDirectory = self.fileManager.localProjectDir
self.loggerFile = self.fileManager.localLogfile
self.frameDirectory = self.fileManager.localFrameDir
self.videoDirectory = self.fileManager.localVideoDir
self.backupDirectory = self.fileManager.localBackupDir
if command not in self.commands:
self._reinstructError(command +
' is not a valid command. Options are ' +
str(self.commands))
if command == 'Stop':
if self.piCamera:
if self.camera.recording:
self.camera.stop_recording()
self._print('PiCameraStopped: Time: ' +
str(datetime.datetime.now()) +
',,File: Videos/' +
str(self.videoCounter).zfill(4) + "_vid.h264")
command = [
'python3', 'unit_scripts/process_video.py',
self.videoDirectory + str(self.videoCounter).zfill(4) +
'_vid.h264'
]
command += [str(self.camera.framerate[0]), self.projectID]
self._print(command)
self.processes.append(subprocess.Popen(command))
try:
if self.device == 'kinect2':
self.K2device.stop()
if self.device == 'kinect':
freenect.sync_stop()
freenect.shutdown(self.a)
except:
self._print('ErrorStopping kinect')
self._closeFiles()
self._modifyPiGS(command='None', status='AwaitingCommand')
return
if command == 'UploadData':
self._modifyPiGS(command='None')
self._uploadFiles()
return
if command == 'LocalDelete':
if os.path.exists(self.projectDirectory):
shutil.rmtree(self.projectDirectory)
self._modifyPiGS(command='None', status='AwaitingCommand')
return
self._modifyPiGS(command='None', status='Running', error='')
if command == 'New':
# Project Directory should not exist. If it does, report error
if os.path.exists(self.projectDirectory):
self._reinstructError(
'New command cannot be run if ouput directory already exists. Use Rewrite or Restart'
)
if command == 'Rewrite':
if os.path.exists(self.projectDirectory):
shutil.rmtree(self.projectDirectory)
os.makedirs(self.projectDirectory)
if command in ['New', 'Rewrite']:
self.masterStart = datetime.datetime.now()
if command == 'New':
os.makedirs(self.projectDirectory)
os.makedirs(self.frameDirectory)
os.makedirs(self.videoDirectory)
os.makedirs(self.backupDirectory)
#self._createDropboxFolders()
self.frameCounter = 1
self.videoCounter = 1
if command == 'Restart':
logObj = LP(self.loggerFile)
self.masterStart = logObj.master_start
#self.r = logObj.bounding_shape
self.frameCounter = logObj.lastFrameCounter + 1
self.videoCounter = logObj.lastVideoCounter + 1
if self.system != logObj.system or self.device != logObj.device or self.piCamera != logObj.camera:
self._reinstructError(
'Restart error. System, device, or camera does not match what is in logfile'
)
self.lf = open(self.loggerFile, 'a')
self._modifyPiGS(start=str(self.masterStart))
if command in ['New', 'Rewrite']:
self._print('MasterStart: System: ' + self.system + ',,Device: ' +
self.device + ',,Camera: ' + str(self.piCamera) +
',,Uname: ' + str(platform.uname()) + ',,TankID: ' +
self.tankID + ',,ProjectID: ' + self.projectID)
self._print('MasterRecordInitialStart: Time: ' +
str(self.masterStart))
self._print(
'PrepFiles: FirstDepth: PrepFiles/FirstDepth.npy,,LastDepth: PrepFiles/LastDepth.npy,,PiCameraRGB: PiCameraRGB.jpg,,DepthRGB: DepthRGB.jpg'
)
picamera_settings = {
'AnalogGain': str(self.camera.analog_gain),
'AWB_Gains': str(self.camera.awb_gains),
'AWB_Mode': str(self.camera.awb_mode),
'Brightness': str(self.camera.brightness),
'ClockMode': str(self.camera.clock_mode),
'Contrast': str(self.camera.contrast),
'Crop': str(self.camera.crop),
'DigitalGain': str(self.camera.digital_gain),
'ExposureCompensation': str(self.camera.exposure_compensation),
'ExposureMode': str(self.camera.exposure_mode),
'ExposureSpeed': str(self.camera.exposure_speed),
'FrameRate': str(self.camera.framerate),
'ImageDenoise': str(self.camera.image_denoise),
'MeterMode': str(self.camera.meter_mode),
'RawFormat': str(self.camera.raw_format),
'Resolution': str(self.camera.resolution),
'Saturation': str(self.camera.saturation),
'SensorMode': str(self.camera.sensor_mode),
'Sharpness': str(self.camera.sharpness),
'ShutterSpeed': str(self.camera.shutter_speed),
'VideoDenoise': str(self.camera.video_denoise),
'VideoStabilization': str(self.camera.video_stabilization)
}
self._print('PiCameraSettings: ' + ',,'.join([
x + ': ' + picamera_settings[x]
for x in sorted(picamera_settings.keys())
]))
#self._createROI(useROI = False)
else:
self._print('MasterRecordRestart: Time: ' +
str(datetime.datetime.now()))
# Start kinect
self._start_kinect()
# Diagnose speed
self._diagnose_speed()
# Capture data
self.captureFrames()
def captureFrames(self,
frame_delta=5,
background_delta=5,
max_frames=20,
stdev_threshold=20):
current_background_time = datetime.datetime.now()
current_frame_time = current_background_time + datetime.timedelta(
seconds=60 * frame_delta)
command = ''
while True:
self._modifyPiGS(command='None', status='Running', error='')
# Grab new time
now = datetime.datetime.now()
# Fix camera if it needs to be
if self.piCamera:
if self._video_recording() and not self.camera.recording:
self.camera.capture(self.videoDirectory +
str(self.videoCounter).zfill(4) +
"_pic.jpg")
self.camera.start_recording(
self.videoDirectory + str(self.videoCounter).zfill(4) +
"_vid.h264",
bitrate=7500000)
self._print('PiCameraStarted: FrameRate: ' +
str(self.camera.framerate) + ',,Resolution: ' +
str(self.camera.resolution) + ',,Time: ' +
str(datetime.datetime.now()) +
',,VideoFile: Videos/' +
str(self.videoCounter).zfill(4) +
'_vid.h264,,PicFile: Videos/' +
str(self.videoCounter).zfill(4) + '_pic.jpg')
elif not self._video_recording() and self.camera.recording:
self.camera.stop_recording()
self._print('PiCameraStopped: Time: ' +
str(datetime.datetime.now()) +
',, File: Videos/' +
str(self.videoCounter).zfill(4) + "_vid.h264")
#self._print(['rclone', 'copy', self.videoDirectory + str(self.videoCounter).zfill(4) + "_vid.h264"])
command = [
'python3', 'unit_scripts/process_video.py',
self.videoDirectory + str(self.videoCounter).zfill(4) +
'_vid.h264'
]
command += [str(self.camera.framerate[0]), self.projectID]
self._print(command)
self.processes.append(subprocess.Popen(command))
self.videoCounter += 1
# Capture a frame and background if necessary
if now > current_background_time:
if command == 'Snapshots':
out = self._captureFrame(current_frame_time,
max_frames=max_frames,
stdev_threshold=stdev_threshold,
snapshots=True)
else:
out = self._captureFrame(current_frame_time,
max_frames=max_frames,
stdev_threshold=stdev_threshold)
if out is not None:
current_background_time += datetime.timedelta(
seconds=60 * background_delta)
subprocess.Popen([
'python3', 'unit_scripts/drive_updater.py', self.loggerFile
])
else:
if command == 'Snapshots':
out = self._captureFrame(current_frame_time,
max_frames=max_frames,
stdev_threshold=stdev_threshold,
snapshots=True)
else:
out = self._captureFrame(current_frame_time,
max_frames=max_frames,
stdev_threshold=stdev_threshold)
current_frame_time += datetime.timedelta(seconds=60 * frame_delta)
self._modifyPiGS(status='Running')
# Check google doc to determine if recording has changed.
try:
command, projectID = self._returnCommand()
except KeyError:
continue
if command != 'None':
if command == 'Snapshots':
self._modifyPiGS(command='None',
status='Writing Snapshots')
continue
else:
break
else:
self._modifyPiGS(error='')
def _authenticateGoogleSpreadSheets(self):
scope = [
"https://spreadsheets.google.com/feeds",
"https://www.googleapis.com/auth/spreadsheets"
]
credentials = ServiceAccountCredentials.from_json_keyfile_name(
self.credentialSpreadsheet, scope)
for i in range(0, 3): # Try to autheticate three times before failing
try:
gs = gspread.authorize(credentials)
except:
continue
try:
self.controllerGS = gs.open('Controller')
pi_ws = self.controllerGS.worksheet('RaspberryPi')
except:
continue
try:
headers = pi_ws.row_values(1)
except:
continue
column = headers.index('RaspberryPiID') + 1
try:
pi_ws.col_values(column).index(platform.node())
return True
except ValueError:
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80))
ip = s.getsockname()[0]
s.close()
try:
pi_ws.append_row([
platform.node(), ip, '', '', '', '', '', 'None',
'Stopped', 'Error: Awaiting assignment of TankID',
str(datetime.datetime.now())
])
except:
continue
return True
except:
continue
time.sleep(2)
return False
def _identifyDevice(self):
try:
global freenect
import freenect
self.a = freenect.init()
if freenect.num_devices(self.a) == 0:
kinect = False
elif freenect.num_devices(self.a) > 1:
self._initError(
'Multiple Kinect1s attached. Unsure how to handle')
else:
kinect = True
except ImportError:
kinect = False
try:
global rs
import pyrealsense2 as rs
ctx = rs.context()
if len(ctx.devices) == 0:
realsense = False
if len(ctx.devices) > 1:
self._initError(
'Multiple RealSense devices attached. Unsure how to handle'
)
else:
realsense = True
except ImportError:
realsense = False
if kinect and realsense:
self._initError(
'Kinect1 and RealSense devices attached. Unsure how to handle')
elif not kinect and not realsense:
self._initError('No depth sensor attached')
elif kinect:
self.device = 'kinect'
else:
self.device = 'realsense'
def _identifyTank(self):
while True:
self._authenticateGoogleSpreadSheets(
) # link to google drive spreadsheet stored in self.controllerGS
pi_ws = self.controllerGS.worksheet('RaspberryPi')
headers = pi_ws.row_values(1)
raPiID_col = headers.index('RaspberryPiID') + 1
for i in range(5):
try:
row = pi_ws.col_values(raPiID_col).index(
platform.node()) + 1
break
except:
continue
col = headers.index('TankID')
if pi_ws.row_values(row)[col] not in ['None', '']:
self.tankID = pi_ws.row_values(row)[col]
for i in range(5):
try:
self._modifyPiGS(capability='Device=' + self.device +
',Camera=' + str(self.piCamera),
status='AwaitingCommand')
return
except:
continue
return
else:
self._modifyPiGS(error='Awaiting assignment of TankID')
time.sleep(5)
def _initError(self, message):
try:
self._modifyPiGS(command='None',
status='Stopped',
error='InitError: ' + message)
except:
pass
self._print('InitError: ' + message)
raise TypeError
def _reinstructError(self, message):
self._modifyPiGS(command='None',
status='AwaitingCommands',
error='InstructError: ' + message)
# Update google doc to indicate error
self.monitorCommands()
def _print(self, text):
try:
print(text, file=self.lf, flush=True)
except:
pass
print(text, file=sys.stderr, flush=True)
def _returnRegColor(self, crop=True):
# This function returns a registered color array
if self.device == 'kinect':
out = freenect.sync_get_video()[0]
if self.device == 'realsense':
frames = self.pipeline.wait_for_frames()
color_frame = frames.get_color_frame()
out = np.asanyarray(color_frame.get_data())
if crop:
return out[self.r[1]:self.r[1] + self.r[3],
self.r[0]:self.r[0] + self.r[2]]
else:
return out
def _returnDepth(self):
# This function returns a float64 npy array containing one frame of data with all bad data as NaNs
if self.device == 'kinect':
data = freenect.sync_get_depth()[0].astype('float64')
data[data == 2047] = np.nan # 2047 indicates bad data from Kinect
return data[self.r[1]:self.r[1] + self.r[3],
self.r[0]:self.r[0] + self.r[2]]
if self.device == 'realsense':
depth_frame = self.pipeline.wait_for_frames().get_depth_frame(
).as_depth_frame()
data = np.asanyarray(depth_frame.data) * depth_frame.get_units(
) # Convert to meters
data[data == 0] = np.nan # 0 indicates bad data from RealSense
data[data >
1] = np.nan # Anything further away than 1 m is a mistake
return data[self.r[1]:self.r[1] + self.r[3],
self.r[0]:self.r[0] + self.r[2]]
def _returnCommand(self):
if not self._authenticateGoogleSpreadSheets():
raise KeyError
# link to google drive spreadsheet stored in self.controllerGS
while True:
try:
pi_ws = self.controllerGS.worksheet('RaspberryPi')
headers = pi_ws.row_values(1)
piIndex = pi_ws.col_values(headers.index('RaspberryPiID') +
1).index(platform.node())
command = pi_ws.col_values(headers.index('Command') +
1)[piIndex]
projectID = pi_ws.col_values(headers.index('ProjectID') +
1)[piIndex]
return command, projectID
except gspread.exceptions.RequestError:
continue
def _modifyPiGS(self,
start=None,
command=None,
status=None,
IP=None,
capability=None,
error=None):
while not self._authenticateGoogleSpreadSheets(
): # link to google drive spreadsheet stored in self.controllerGS
continue
try:
pi_ws = self.controllerGS.worksheet('RaspberryPi')
headers = pi_ws.row_values(1)
row = pi_ws.col_values(headers.index('RaspberryPiID') + 1).index(
platform.node()) + 1
if start is not None:
column = headers.index('MasterStart') + 1
pi_ws.update_cell(row, column, start)
if command is not None:
column = headers.index('Command') + 1
pi_ws.update_cell(row, column, command)
if status is not None:
column = headers.index('Status') + 1
pi_ws.update_cell(row, column, status)
if error is not None:
column = headers.index('Error') + 1
pi_ws.update_cell(row, column, error)
if IP is not None:
column = headers.index('IP') + 1
pi_ws.update_cell(row, column, IP)
if capability is not None:
column = headers.index('Capability') + 1
pi_ws.update_cell(row, column, capability)
column = headers.index('Ping') + 1
pi_ws.update_cell(row, column, str(datetime.datetime.now()))
except gspread.exceptions.RequestError as e:
self._print('GoogleError: Time: ' + str(datetime.datetime.now()) +
',,Error: ' + str(e))
except TypeError:
self._print(
'GoogleError: Time: ' + str(datetime.datetime.now()) +
',,Error: Unknown. Gspread does not handle RequestErrors properly'
)
def _video_recording(self):
if datetime.datetime.now().hour >= 8 and datetime.datetime.now(
).hour <= 18:
return True
else:
return False
def _start_kinect(self):
if self.device == 'kinect':
freenect.sync_get_depth() #Grabbing a frame initializes the device
freenect.sync_get_video()
elif self.device == 'realsense':
# Create a context object. This object owns the handles to all connected realsense devices
self.pipeline = rs.pipeline()
# Configure streams
config = rs.config()
config.enable_stream(rs.stream.depth, rs.format.z16, 30)
config.enable_stream(rs.stream.color, rs.format.rgb8, 30)
# Start streaming
self.pipeline.start(config)
frames = self.pipeline.wait_for_frames()
depth = frames.get_depth_frame()
self.r = (0, 0, depth.width, depth.height)
def _diagnose_speed(self, time=10):
print('Diagnosing speed for ' + str(time) + ' seconds.',
file=sys.stderr)
delta = datetime.timedelta(seconds=time)
start_t = datetime.datetime.now()
counter = 0
while True:
depth = self._returnDepth()
counter += 1
if datetime.datetime.now() - start_t > delta:
break
#Grab single snapshot of depth and save it
depth = self._returnDepth()
np.save(self.projectDirectory + 'Frames/FirstFrame.npy', depth)
#Grab a bunch of depth files to characterize the variability
data = np.zeros(shape=(50, self.r[3], self.r[2]))
for i in range(0, 50):
data[i] = self._returnDepth()
counts = np.count_nonzero(~np.isnan(data), axis=0)
std = np.nanstd(data, axis=0)
np.save(self.projectDirectory + 'Frames/FirstDataCount.npy', counts)
np.save(self.projectDirectory + 'Frames/StdevCount.npy', std)
self._print('DiagnoseSpeed: Rate: ' + str(counter / time))
self._print(
'FirstFrameCaptured: FirstFrame: Frames/FirstFrame.npy,,GoodDataCount: Frames/FirstDataCount.npy,,StdevCount: Frames/StdevCount.npy'
)
def _captureFrame(self,
endtime,
max_frames=40,
stdev_threshold=.05,
snapshots=False):
# Captures time averaged frame of depth data
sums = np.zeros(shape=(self.r[3], self.r[2]))
n = np.zeros(shape=(self.r[3], self.r[2]))
stds = np.zeros(shape=(self.r[3], self.r[2]))
current_time = datetime.datetime.now()
if current_time >= endtime:
return
counter = 1
all_data = np.empty(shape=(int(max_frames), self.r[3], self.r[2]))
all_data[:] = np.nan
for i in range(0, max_frames):
all_data[i] = self._returnDepth()
current_time = datetime.datetime.now()
if snapshots:
self._print('SnapshotCaptured: NpyFile: Frames/Snapshot_' +
str(counter).zfill(6) + '.npy,,Time: ' +
str(current_time) + ',,GP: ' +
str(np.count_nonzero(~np.isnan(all_data[i]))))
np.save(
self.projectDirectory + 'Frames/Snapshot_' +
str(counter).zfill(6) + '.npy', all_data[i])
counter += 1
if current_time >= endtime:
break
time.sleep(10)
med = np.nanmean(all_data, axis=0)
std = np.nanstd(all_data, axis=0)
med[np.isnan(std)] = np.nan
med[std > stdev_threshold] = np.nan
std[std > stdev_threshold] = np.nan
counts = np.count_nonzero(~np.isnan(all_data), axis=0)
med[counts < 3] = np.nan
std[counts < 3] = np.nan
color = self._returnRegColor()
self._print('FrameCaptured: NpyFile: Frames/Frame_' +
str(self.frameCounter).zfill(6) +
'.npy,,PicFile: Frames/Frame_' +
str(self.frameCounter).zfill(6) + '.jpg,,Time: ' +
str(endtime) + ',,NFrames: ' + str(i) + ',,AvgMed: ' +
'%.2f' % np.nanmean(med) + ',,AvgStd: ' +
'%.2f' % np.nanmean(std) + ',,GP: ' +
str(np.count_nonzero(~np.isnan(med))))
np.save(
self.projectDirectory + 'Frames/Frame_' +
str(self.frameCounter).zfill(6) + '.npy', med)
matplotlib.image.imsave(
self.projectDirectory + 'Frames/Frame_' +
str(self.frameCounter).zfill(6) + '.jpg', color)
self.frameCounter += 1
return med
def _uploadFiles(self):
self._modifyPiGS(status='Finishing converting and uploading of videos')
for p in self.processes:
p.communicate()
for movieFile in os.listdir(self.videoDirectory):
if '.h264' in movieFile:
command = [
'python3', 'unit_scripts/process_video.py',
self.videoDirectory + movieFile
]
command += [str(self.camera.framerate[0]), self.projectID]
self._print(command)
self.processes.append(subprocess.Popen(command))
for p in self.processes:
p.communicate()
self._modifyPiGS(status='Creating prep files')
# Move files around as appropriate
prepDirectory = self.projectDirectory + 'PrepFiles/'
shutil.rmtree(prepDirectory) if os.path.exists(prepDirectory) else None
os.makedirs(prepDirectory)
lp = LP(self.loggerFile)
self.frameCounter = lp.lastFrameCounter + 1
videoObj = [
x for x in lp.movies
if x.startTime.hour >= 8 and x.startTime.hour <= 20
][0]
subprocess.call([
'cp', self.projectDirectory + videoObj.pic_file,
prepDirectory + 'PiCameraRGB.jpg'
])
subprocess.call([
'cp', self.projectDirectory + lp.movies[-1].pic_file,
prepDirectory + 'LastPiCameraRGB.jpg'
])
# Find depthfile that is closest to the video file time
depthObj = [x for x in lp.frames if x.time > videoObj.startTime][0]
subprocess.call([
'cp', self.projectDirectory + depthObj.pic_file,
prepDirectory + 'DepthRGB.jpg'
])
if not os.path.isdir(self.frameDirectory):
self._modifyPiGS(status='Error: ' + self.frameDirectory +
' does not exist.')
return
subprocess.call([
'cp', self.frameDirectory + 'Frame_000001.npy',
prepDirectory + 'FirstDepth.npy'
])
subprocess.call([
'cp', self.frameDirectory + 'Frame_' +
str(self.frameCounter - 1).zfill(6) + '.npy',
prepDirectory + 'LastDepth.npy'
])
try:
self._modifyPiGS(status='Uploading data to cloud')
self.fileManager.uploadData(self.frameDirectory, tarred=True)
#print(prepDirectory)
self.fileManager.uploadData(prepDirectory)
#print(self.videoDirectory)
self.fileManager.uploadData(self.videoDirectory)
#print(self.loggerFile)
self.fileManager.uploadData(self.loggerFile)
self._modifyPiGS(error='UploadSuccessful, ready for delete')
except Exception as e:
print('UploadError: ' + str(e))
self._modifyPiGS(error='UploadFailed, Need to rerun')
raise Exception
def _closeFiles(self):
try:
self._print('MasterRecordStop: ' + str(datetime.datetime.now()))
self.lf.close()
except AttributeError:
pass
try:
if self.system == 'mac':
self.caff.kill()
except AttributeError:
pass
class ProjectPreparer():
# This class takes in a projectID and runs all the appropriate analysis
def __init__(self,
projectID=None,
modelID=None,
workers=None,
summaryFile=None):
self.projectID = projectID
self.fileManager = FM(projectID=projectID,
modelID=modelID,
summaryFile=summaryFile)
self.modelID = modelID
if not self._checkProjectID():
raise Exception(projectID + ' is not valid.')
self.workers = workers
def _checkProjectID(self):
if self.projectID is None:
return True
projectIDs = subprocess.run([
'rclone', 'lsf', self.fileManager.cloudMasterDir + '__ProjectData/'
],
capture_output=True,
encoding='utf-8').stdout.split()
if self.projectID + '/' in projectIDs:
return True
else:
return False
def downloadData(self, dtype, videoIndex=None):
self.fileManager.downloadProjectData(dtype, videoIndex)
def uploadData(self,
dtype,
videoIndex=None,
delete=False,
no_upload=False):
print(no_upload)
self.fileManager.uploadProjectData(dtype, videoIndex, delete,
no_upload)
def runPrepAnalysis(self):
prp_obj = PrP(self.fileManager)
prp_obj.validateInputData()
prp_obj.prepData()
def runDepthAnalysis(self):
dp_obj = DP(self.fileManager)
dp_obj.validateInputData()
dp_obj.createSmoothedArray()
dp_obj.createRGBVideo()
def runClusterAnalysis(self, videoIndex):
if videoIndex is None:
videos = list(range(len(self.fileManager.lp.movies)))
else:
videos = [videoIndex]
for videoIndex in videos:
cp_obj = CP(self.fileManager, videoIndex, self.workers)
cp_obj.validateInputData()
cp_obj.runClusterAnalysis()
def run3DClassification(self):
tdcp_obj = TDCP(self.fileManager)
tdcp_obj.validateInputData()
tdcp_obj.predictLabels()
tdcp_obj.createSummaryFile()
def manuallyLabelVideos(self, initials, number):
mlv_obj = MLVP(self.fileManager, initials, number)
mlv_obj.validateInputData()
mlv_obj.labelVideos()
def manuallyLabelFrames(self, initials, number):
mlf_obj = MLFP(self.fileManager, initials, number)
mlf_obj.validateInputData()
mlf_obj.labelFrames()
def createModel(self, MLtype, projectIDs, gpu):
if MLtype == '3DResnet':
tdm_obj = TDMP(self.fileManager, projectIDs, self.modelID, gpu)
tdm_obj.validateInputData()
tdm_obj.create3DModel()
def runMLFishDetection(self):
pass
def runSummaryCreation(self):
sp_obj = SP(self.fileManager)
sp_obj.createFullSummary()
def backupAnalysis(self):
uploadCommands = set()
uploadFiles = [
x for x in os.listdir(self.fileManager.localUploadDir)
if 'UploadData' in x
]
for uFile in uploadFiles:
with open(self.fileManager.localUploadDir + uFile) as f:
line = next(f)
for line in f:
tokens = line.rstrip().split(',')
tokens[2] = bool(int(tokens[2]))
uploadCommands.add(tuple(tokens))
for command in uploadCommands:
self.fileManager.uploadData(command[0], command[1], command[2])
for uFile in uploadFiles:
subprocess.run(
['rm', '-rf', self.fileManager.localUploadDir + uFile])
self.fileManager.uploadData(self.fileManager.localAnalysisLogDir,
self.fileManager.cloudAnalysisLogDir,
False)
subprocess.run(['rm', '-rf', self.projFileManager.localMasterDir])
def localDelete(self):
subprocess.run(['rm', '-rf', self.fileManager.localProjectDir])
def createUploadFile(self, uploads):
with open(
self.fileManager.localUploadDir + 'UploadData_' +
str(datetime.datetime.now().timestamp()) + '.csv', 'w') as f:
print('Local,Cloud,Tar', file=f)
for upload in uploads:
print(upload[0] + ',' + upload[1] + ',' + str(upload[2]),
file=f)
def createAnalysisUpdate(self, aType, procObj):
now = datetime.datetime.now()
with open(
self.fileManager.localAnalysisLogDir + 'AnalysisUpdate_' +
str(now.timestamp()) + '.csv', 'w') as f:
print('ProjectID,Type,Version,Date', file=f)
print(self.projectID + ',' + aType + ',' + procObj.__version__ +
'_' + os.getenv('USER') + ',' + str(now),
file=f)
f = open(fileManager.localProjectDir + 'VideoProcessLog.txt', 'a')
if indent:
print(' ' + str(datetime.datetime.now()) + ': ' + str(message), file = f)
else:
print(str(datetime.datetime.now()) + ': ' + str(message), file = f)
f.close()
parser = argparse.ArgumentParser()
parser.add_argument('VideoFile', type = str, help = 'Name of h264 file to be processed')
parser.add_argument('Framerate', type = float, help = 'Video framerate')
parser.add_argument('ProjectID', type = str, help = 'Video framerate')
args = parser.parse_args()
fileManager = FM(projectID = args.ProjectID)
if '.h264' not in args.VideoFile:
logPrinter(args.VideoFile + ' not an h264 file', indent = False)
raise Exception(args.VideoFile + ' not an h264 file')
# Convert h264 to mp4
if os.path.exists(args.VideoFile.replace('.h264', '.mp4')):
logPrinter(args.VideoFile.replace('.h264', '.mp4') + ' already exits. Deleting')
subprocess.run(['rm', '-f', args.VideoFile.replace('.h264', '.mp4')])
command = ['ffmpeg', '-r', str(args.Framerate), '-i', args.VideoFile, '-threads', '1', '-c:v', 'copy', '-r', str(args.Framerate), args.VideoFile.replace('.h264', '.mp4')]
logPrinter('Beginning conversion of video: ' + args.VideoFile.split('/')[-1], indent = False)
logPrinter(command)
ffmpeg_output = subprocess.run(command, capture_output = True)
try:
import argparse, subprocess
from cichlid_bower_tracking.helper_modules.object_labeler import AnnotationDisagreements as AD
from cichlid_bower_tracking.helper_modules.file_manager import FileManager as FM
parser = argparse.ArgumentParser()
parser.add_argument('User1', type = str, help = 'Initials of user annotations to compare')
parser.add_argument('User2', type = str, help = 'Initials user annotations to compare')
parser.add_argument('ProjectID', type = str, help = 'Project to analyze')
parser.add_argument('-p', '--Practice', action = 'store_true', help = 'Use if you dont want to save annotations')
args = parser.parse_args()
fm_obj = FM(projectID = args.ProjectID)
fm_obj.downloadData(fm_obj.localLabeledClipsProjectDir, tarred = True)
fm_obj.downloadData(fm_obj.localBoxedFishFile)
obj = AD(self.fileManager.localLabeledFramesProjectDir, self.fileManager.localBoxedFishFile, args.ProjectID, args.User1, args.User2)
ad_obj = AD(fm_obj.localBoxedFishDir + args.ProjectID + '/', temp_dt, args.ProjectID, args.User1, args.User2, args.All)
# Redownload csv in case new annotations have been added
fm_obj.downloadData(fm_obj.localBoxedFishFile)
old_dt = pd.read_csv(fm_obj.localBoxedFishFile, index_col = 0)
new_dt = pd.read_csv(temp_dt)
old_dt = old_dt.append(new_dt, sort = 'False').drop_duplicates(subset = ['ProjectID', 'Framefile', 'User', 'Sex', 'Box'], keep = 'last').sort_values(by = ['ProjectID', 'Framefile'])
old_dt.to_csv(fm_obj.localBoxedFishFile, sep = ',', columns = ['ProjectID', 'Framefile', 'Nfish', 'Sex', 'Box', 'CorrectAnnotation','User', 'DateTime'])
if not args.Practice:
from cichlid_bower_tracking.helper_modules.file_manager import FileManager as FM
parser = argparse.ArgumentParser(
description=
'This script is used to determine analysis states for each project.')
parser.add_argument(
'--SummaryFile',
type=str,
help=
'Restrict analysis to projectIDs specified in csv file, which will be rewritten. ProjectIDs must be found in a column called projectID'
)
args = parser.parse_args()
fm_obj = FM(summaryFile=args.SummaryFile)
if args.SummaryFile is not None:
summary_file = fm_obj.localSummaryFile
fm_obj.downloadData(summary_file)
dt = pd.read_csv(summary_file, index_col=False)
projectIDs = list(dt.projectID)
else:
fm_obj.createDirectory(fm_obj.localAnalysisStatesDir)
summary_file = fm_obj.localAnalysisStatesDir + 'AllProjects.csv'
projectIDs = fm_obj.getAllProjectIDs()
dt = pd.DataFrame(columns=[
'projectID', 'tankID', 'StartingFiles', 'Prep', 'Depth', 'Cluster',
'ClusterClassification', 'LabeledVideos', 'LabeledFrames', 'Summary'
])
import subprocess, gspread, pdb
from cichlid_bower_tracking.helper_modules.file_manager import FileManager as FM
import pandas as pd
# Requires ttab https://www.npmjs.com/package/ttab#manual-installation
fileManager = FM()
fileManager.downloadData(fileManager.localCredentialDir)
gs = gspread.service_account(filename=fileManager.localCredentialSpreadsheet)
controllerGS = gs.open('Controller')
pi_ws = controllerGS.worksheet('RaspberryPi')
data = pi_ws.get_all_values()
dt = pd.DataFrame(data[1:], columns=data[0])
for row in dt.RaspberryPiID:
print(row)
#subprocess.run(['ssh-keygen', '-t', 'rsa', '-f', '~/.ssh/id_rsa'])
#subprocess.run(['ssh-copy-id', 'pi@' + row + '.biosci.gatech.edu'])
for row in dt.RaspberryPiID:
subprocess.run(
['ttab', '-t', row, 'ssh', 'pi@' + row + '.biosci.gatech.edu'])
help='ModelID to use to classify clusters with')
parser.add_argument(
'--Force',
type=bool,
default=False,
help=
'if True, run the analysis even if the summary file indicates is has already been run. Default False'
)
args = parser.parse_args()
# Identify projects to run analysis on
if args.ProjectIDs is not None:
projectIDs = args.ProjectIDs # Specified at the command line
else:
fm_obj = FM(summaryFile=args.SummaryFile)
fm_obj.downloadData(fm_obj.localSummaryFile)
dt = pd.read_csv(fm_obj.localSummaryFile)
if args.Force:
projectIDs = list(dt.projectID)
else:
projectIDs = list(
dt[dt[args.AnalysisType] ==
False].projectID) # Only run analysis on projects that need it
if args.Workers is None:
workers = os.cpu_count()
else:
workers = args.Workers
class CichlidTracker:
def __init__(self, all_data):
self.all_data = all_data # Flag to keep all data if desired
# 1: Define valid commands and ignore warnings
self.commands = ['New', 'Restart', 'Stop', 'Rewrite', 'UploadData', 'LocalDelete']
np.seterr(invalid='ignore')
# 2: Determine which depth sensor is attached (This script can handle DepthSense cameras)
self._identifyDevice() #Stored in self.device
self.system = platform.node()
# 3: Create file manager
self.fileManager = FM()
# 4: Start PiCamera
self.camera = PiCamera()
self.camera.resolution = (1296, 972)
self.camera.framerate = 30
self.piCamera = 'True'
# 5: Download credential files
self.fileManager.downloadData(self.fileManager.localCredentialDir)
self.credentialSpreadsheet = self.fileManager.localCredentialSpreadsheet # Rename to make code readable
self._authenticateGoogleSpreadSheets() #Creates self.controllerGS
self._identifyTank() #Stored in self.tankID
self._identifyServiceAccount()
# 6: Keep track of processes spawned to convert and upload videofiles
self.processes = []
# 7: Set size of frame
try:
self.r
except AttributeError:
self.r = (0,0,640,480)
# 9: Await instructions
print('Monitoring commands')
self.monitorCommands()
def __del__(self):
# Try to close out files and stop running Kinects
self._modifyPiGS('Command','None', ping = False)
self._modifyPiGS('Status','Stopped', ping = False)
self._modifyPiGS('Error','UnknownError', ping = False)
if self.piCamera:
if self.camera.recording:
self.camera.stop_recording()
self._print('PiCameraStopped: Time=' + str(datetime.datetime.now()) + ', File=Videos/' + str(self.videoCounter).zfill(4) + "_vid.h264")
if self.device == 'realsense':
self.pipeline.stop()
self._closeFiles()
def monitorCommands(self, delta = 20):
# This function checks the master Controller Google Spreadsheet to determine if a command was issued (delta = seconds to recheck)
self._modifyPiGS('Status', 'AwaitingCommand')
self._modifyPiGS('Error', '', ping = False)
while True:
command, projectID = self._returnCommand()
if projectID in ['','None']:
self._reinstructError('ProjectID must be set')
time.sleep(delta)
continue
if command not in ['None',None]:
print(command + '\t' + projectID)
self.fileManager.createProjectData(projectID)
self.runCommand(command, projectID)
time.sleep(delta)
def runCommand(self, command, projectID):
# This function is used to run a specific command found in the master Controller Google Spreadsheet
self.projectID = projectID
# Rename files to make code more readable
self.projectDirectory = self.fileManager.localProjectDir
self.loggerFile = self.fileManager.localLogfile
self.googleErrorFile = self.fileManager.localProjectDir + 'GoogleErrors.txt'
self.frameDirectory = self.fileManager.localFrameDir
self.videoDirectory = self.fileManager.localVideoDir
self.backupDirectory = self.fileManager.localBackupDir
if command not in self.commands:
self._reinstructError(command + ' is not a valid command. Options are ' + str(self.commands))
if command == 'Stop':
if self.piCamera:
if self.camera.recording:
self.camera.stop_recording()
self._print('PiCameraStopped: Time: ' + str(datetime.datetime.now()) + ',,File: Videos/' + str(self.videoCounter).zfill(4) + "_vid.h264")
command = ['python3', 'unit_scripts/process_video.py', self.videoDirectory + str(self.videoCounter).zfill(4) + '_vid.h264']
command += [str(self.camera.framerate[0]), self.projectID]
self._print(command)
self.processes.append(subprocess.Popen(command))
try:
if self.device == 'realsense':
self.pipeline.stop()
except Exception as e:
self._googlePrint(e)
self._print('ErrorStopping kinect')
self._closeFiles()
self._modifyPiGS('Command', 'None', ping = False)
self._modifyPiGS('Status', 'AwaitingCommand', ping = False)
return
if command == 'UploadData':
self._modifyPiGS('Command', 'None')
self._uploadFiles()
return
if command == 'LocalDelete':
if os.path.exists(self.projectDirectory):
shutil.rmtree(self.projectDirectory)
self._modifyPiGS('Command', 'None', ping = False)
self._modifyPiGS('Status', 'AwaitingCommand', ping = False)
return
self._modifyPiGS('Command', 'None', ping = False)
self._modifyPiGS('Status', 'Running', ping = False)
self._modifyPiGS('Error', '', ping = False)
if command == 'New':
# Project Directory should not exist. If it does, report error
if os.path.exists(self.projectDirectory):
self._reinstructError('New command cannot be run if ouput directory already exists. Use Rewrite or Restart')
if command == 'Rewrite':
if os.path.exists(self.projectDirectory):
shutil.rmtree(self.projectDirectory)
if command in ['New','Rewrite']:
self.masterStart = datetime.datetime.now()
os.makedirs(self.projectDirectory)
os.makedirs(self.frameDirectory)
os.makedirs(self.videoDirectory)
os.makedirs(self.backupDirectory)
#self._createDropboxFolders()
self.frameCounter = 1
self.videoCounter = 1
if command == 'Restart':
logObj = LP(self.loggerFile)
self.masterStart = logObj.master_start
self.frameCounter = logObj.lastFrameCounter + 1
self.videoCounter = logObj.lastVideoCounter + 1
if self.system != logObj.system or self.device != logObj.device:
self._reinstructError('Restart error. LogData: ' + ','.join([str(x) for x in [logObj.system,logObj.device,logObj.camera]]) + ',, SystemData: ' + ','.join([str(x) for x in [self.system, self.device, self.camera]]))
return
if self.device != 'None':
row, column, ping_column = self._getRowColumn('Image')
subprocess.Popen(['python3', 'unit_scripts/drive_updater.py', self.loggerFile, str(row), str(column)])
pass
self.lf = open(self.loggerFile, 'a', buffering = 1) # line buffered
self.g_lf = open(self.googleErrorFile, 'a', buffering = 1)
self._modifyPiGS('MasterStart',str(self.masterStart), ping = False)
if command in ['New', 'Rewrite']:
self._print('MasterStart: System: '+self.system + ',,Device: ' + self.device + ',,Camera: ' + str(self.piCamera) + ',,Uname: ' + str(platform.uname()) + ',,TankID: ' + self.tankID + ',,ProjectID: ' + self.projectID)
self._print('MasterRecordInitialStart: Time: ' + str(self.masterStart))
self._print('PrepFiles: FirstDepth: PrepFiles/FirstDepth.npy,,LastDepth: PrepFiles/LastDepth.npy,,PiCameraRGB: PiCameraRGB.jpg,,DepthRGB: DepthRGB.jpg')
picamera_settings = {'AnalogGain': str(self.camera.analog_gain), 'AWB_Gains': str(self.camera.awb_gains),
'AWB_Mode': str(self.camera.awb_mode), 'Brightness': str(self.camera.brightness),
'ClockMode': str(self.camera.clock_mode), 'Contrast': str(self.camera.contrast),
'Crop': str(self.camera.crop),'DigitalGain': str(self.camera.digital_gain),
'ExposureCompensation': str(self.camera.exposure_compensation),'ExposureMode': str(self.camera.exposure_mode),
'ExposureSpeed': str(self.camera.exposure_speed),'FrameRate': str(self.camera.framerate),
'ImageDenoise': str(self.camera.image_denoise),'MeterMode': str(self.camera.meter_mode),
'RawFormat': str(self.camera.raw_format), 'Resolution': str(self.camera.resolution),
'Saturation': str(self.camera.saturation),'SensorMode': str(self.camera.sensor_mode),
'Sharpness': str(self.camera.sharpness),'ShutterSpeed': str(self.camera.shutter_speed),
'VideoDenoise': str(self.camera.video_denoise),'VideoStabilization': str(self.camera.video_stabilization)}
self._print('PiCameraSettings: ' + ',,'.join([x + ': ' + picamera_settings[x] for x in sorted(picamera_settings.keys())]))
#self._createROI(useROI = False)
else:
self._print('MasterRecordRestart: Time: ' + str(datetime.datetime.now()))
# Start kinect
if self.device != 'None':
self._start_kinect()
# Diagnose speed
self._diagnose_speed()
# Capture data
self.captureFrames()
def captureFrames(self, frame_delta = 5, background_delta = 5):
current_background_time = datetime.datetime.now()
current_frame_time = current_background_time + datetime.timedelta(seconds = 60 * frame_delta)
command = ''
while True:
# Grab new time
now = datetime.datetime.now()
# Fix camera if it needs to be
if self.piCamera:
if self._video_recording() and not self.camera.recording:
self.camera.capture(self.videoDirectory + str(self.videoCounter).zfill(4) + "_pic.jpg")
self._print('PiCameraStarted: FrameRate: ' + str(self.camera.framerate) + ',,Resolution: ' + str(self.camera.resolution) + ',,Time: ' + str(datetime.datetime.now()) + ',,VideoFile: Videos/' + str(self.videoCounter).zfill(4) + '_vid.h264,,PicFile: Videos/' + str(self.videoCounter).zfill(4) + '_pic.jpg')
self.camera.start_recording(self.videoDirectory + str(self.videoCounter).zfill(4) + "_vid.h264", bitrate=7500000)
elif not self._video_recording() and self.camera.recording:
self._print('PiCameraStopped: Time: ' + str(datetime.datetime.now()) + ',, File: Videos/' + str(self.videoCounter).zfill(4) + "_vid.h264")
self.camera.stop_recording()
#self._print(['rclone', 'copy', self.videoDirectory + str(self.videoCounter).zfill(4) + "_vid.h264"])
command = ['python3', 'unit_scripts/process_video.py', self.videoDirectory + str(self.videoCounter).zfill(4) + '_vid.h264']
command += [str(self.camera.framerate[0]), self.projectID]
self._print(command)
self.processes.append(subprocess.Popen(command))
self.videoCounter += 1
# Capture a frame and background if necessary
if self.device != 'None':
if now > current_background_time:
out = self._captureFrame(current_frame_time, keep_all_data = self.all_data)
if out is not None:
current_background_time += datetime.timedelta(seconds = 60 * background_delta)
row, column, ping_column = self._getRowColumn('Image')
subprocess.Popen(['python3', 'unit_scripts/drive_updater.py', self.loggerFile, str(row), str(column)])
else:
out = self._captureFrame(current_frame_time, stdev_threshold = stdev_threshold, keep_all_data = self.all_data)
else:
while datetime.datetime.now() < current_frame_time:
time.sleep(5)
current_frame_time += datetime.timedelta(seconds = 60 * frame_delta)
# Check google doc to determine if recording has changed.
try:
command, projectID = self._returnCommand()
except KeyError:
continue
if command != 'None' and command is not None:
break
else:
self._modifyPiGS('Error', '')
def _authenticateGoogleSpreadSheets(self):
# scope = [
# "https://spreadsheets.google.com/feeds",
# "https://www.googleapis.com/auth/spreadsheets"
# ]
# credentials = ServiceAccountCredentials.from_json_keyfile_name(self.credentialSpreadsheet, scope)
# Get IP address
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
s.connect(("8.8.8.8", 80))
self.IP = s.getsockname()[0]
s.close()
for i in range(0,3): # Try to autheticate three times before failing
try:
# gs = gspread.authorize(credentials)
gs = gspread.service_account(filename=self.credentialSpreadsheet)
except Exception as e:
self._googlePrint(e)
continue
try:
self.controllerGS = gs.open('Controller')
self.pi_ws = self.controllerGS.worksheet('RaspberryPi')
data = self.pi_ws.get_all_values()
dt = pd.DataFrame(data[1:], columns = data[0])
except Exception as e:
self._googlePrint(e)
continue
try:
if len(dt.loc[dt.RaspberryPiID == platform.node()]) == 0:
self.pi_ws.append_row([platform.node(),self.IP,'','','','','','None','Stopped','Error: Awaiting assignment of TankID',str(datetime.datetime.now())])
return True
else:
return True
except Exception as e:
self._googlePrint(e)
continue
time.sleep(2)
return False
def _identifyDevice(self):
try:
global rs
import pyrealsense2 as rs
ctx = rs.context()
if len(ctx.devices) == 0:
self.device = 'None'
elif len(ctx.devices) > 1:
self._initError('Multiple RealSense devices attached. Unsure how to handle')
else:
self.device = 'realsense'
except Exception:
self.device = 'None'
def _identifyTank(self):
while True:
tankID = self._getPiGS('TankID')
if tankID not in ['None','']:
self.tankID = tankID
self._modifyPiGS('Capability', 'Device=' + self.device + ',Camera=' + str(self.piCamera), ping = False)
#self._modifyPiGS('Status', 'AwaitingCommand')
break
else:
self._modifyPiGS('Error','Awaiting assignment of TankID')
time.sleep(20)
def _identifyServiceAccount(self):
while True:
serviceAccount = self._getPiGS('ServiceAccount')
if serviceAccount not in ['None','']:
self.serviceAccount = serviceAccount
self.credentialSpreadsheet = self.credentialSpreadsheet.replace('_1.json', '_' + self.serviceAccount + '.json')
self._authenticateGoogleSpreadSheets() #Creates self.controllerGS
break
else:
self._modifyPiGS('Error','Awaiting assignment of ServiceAccount')
time.sleep(20)
def _initError(self, message):
try:
self._modifyPiGS('Command', 'None')
self._modifyPiGS('Status', 'Stopped', ping = False)
self._modifyPiGS('Error', 'InitError: ' + message, ping = False)
except Exception as e:
self._googlePrint(e)
pass
self._print('InitError: ' + message)
raise TypeError
def _reinstructError(self, message):
try:
#self._modifyPiGS('Command', 'None')
#self._modifyPiGS('Status', 'AwaitingCommands', ping = False)
self._modifyPiGS('Error', 'InstructError: ' + message, ping = False)
self._print(message)
except Exception as e:
self._googlePrint(e)
pass
# Update google doc to indicate error
self.monitorCommands()
def _print(self, text):
#temperature = subprocess.run(['/opt/vc/bin/vcgencmd','measure_temp'], capture_output = True)
try:
print(str(text), file = self.lf, flush = True)
except Exception as e:
pass
print(str(text), file = sys.stderr, flush = True)
def _googlePrint(self, e):
try:
print(str(datetime.datetime.now()) + ': ' + str(type(e)) + ': ' + str(e), file = self.g_lf, flush = True)
time.sleep(20)
except AttributeError as e2: # log file not created yet so just print to stderr
print(str(datetime.datetime.now()) + ': ' + str(type(e)) + ': ' + str(e), flush = True)
time.sleep(20)
def _returnRegColor(self, crop = True):
# This function returns a registered color array
if self.device == 'kinect':
out = freenect.sync_get_video()[0]
if self.device == 'realsense':
frames = self.pipeline.wait_for_frames(1000)
color_frame = frames.get_color_frame()
out = np.asanyarray(color_frame.get_data())
if crop:
return out[self.r[1]:self.r[1]+self.r[3], self.r[0]:self.r[0]+self.r[2]]
else:
return out
def _returnDepth(self):
# This function returns a float64 npy array containing one frame of data with all bad data as NaNs
if self.device == 'realsense':
frames = self.pipeline.wait_for_frames(1000)
frames = self.align.process(frames)
depth_frame = frames.get_depth_frame().as_depth_frame()
#except RuntimeError:
# self._googlePrint('No frame received from Kinect. Restarting')
# self._start_kinect()
# depth_frame = self.pipeline.wait_for_frames(1000).get_depth_frame().as_depth_frame()
data = np.asanyarray(depth_frame.data)*depth_frame.get_units() # Convert to meters
data[data==0] = np.nan # 0 indicates bad data from RealSense
data[data>1] = np.nan # Anything further away than 1 m is a mistake
return data[self.r[1]:self.r[1]+self.r[3], self.r[0]:self.r[0]+self.r[2]]
def _returnCommand(self):
command, projectID = self._getPiGS(['Command','ProjectID'])
return command, projectID
def _getPiGS(self, column_names):
# Make this compatible with both lists and also strings
if not isinstance(column_names, list):
column_names = [column_names]
for i in range(3):
try:
#print('Read request: ' + str(datetime.datetime.now()))
data = self.pi_ws.get_all_values()
except gspread.exceptions.APIError as e:
if e.response.status_code == 429:
# Read requests per minute exceeded
self._googlePrint('Read requests per minute exceeded')
continue
elif e.response.status_code == 500:
self._googlePrint('Internal error encountered')
continue
elif e.response.status_code == 404:
self._googlePrint('Requested entity was not found')
continue
else:
self._googlePrint('gspread error of unknown nature: ' + str(e))
continue
except requests.exceptions.ReadTimeout as e:
self._googlePrint('Requests read timeout error encountered')
continue
except Exception as e:
self._googlePrint(f'uncaught exception in _getPiGS: {str(e)}')
except requests.exceptions.ConnectionError as e:
self._googlePrint('Requests connection error encountered')
continue
dt = pd.DataFrame(data[1:], columns = data[0])
self.dt = dt
out_data = []
for column_name in column_names:
if column_name not in dt.columns:
self._googlePrint('Cant find column name in Controller: ' + column_name)
raise Exception
try:
cell = dt.loc[(dt.RaspberryPiID == platform.node())&(dt.IP == self.IP),column_name]
except AttributeError as error:
pdb.set_trace()
if len(cell) > 1:
self._googlePrint('Multiple rows in the Controller with the same ID and IP. Using 1st')
self._modifyPiGS('Error', 'InstructError: Multiple rows with the same IP/ID', ping = False)
out_data.append(cell.values[0])
if len(out_data) == 1:
return out_data[0]
else:
return out_data
self._googlePrint('Failed contancting controller for three tries')
return [None]*len(column_names)
def _getRowColumn(self, column_name):
column = self.dt.columns.get_loc(column_name)
ping_column = self.dt.columns.get_loc('Ping')
row = pd.Index((self.dt.RaspberryPiID == platform.node())&(self.dt.IP == self.IP)).get_loc(True)
return (row + 2, column + 1, ping_column + 1) # 0 vs 1 indexing for pandas vs gspread + column names aren't in the pandas dataframe
def _modifyPiGS(self, column_name, new_value, ping = True):
for i in range(3):
try:
row, column, ping_column = self._getRowColumn(column_name)
#print('Write request: ' + str(datetime.datetime.now()))
self.pi_ws.update_cell(row, column, new_value)
if ping:
#print('Write request: ' + str(datetime.datetime.now()))
self.pi_ws.update_cell(row, ping_column, str(datetime.datetime.now()))
break
except gspread.exceptions.APIError as e:
if e.response.status_code == 429:
# Read requests per minute exceeded
self._googlePrint('Read requests per minute exceeded')
continue
elif e.response.status_code == 500:
self._googlePrint('Internal error encountered')
continue
elif e.response.status_code == 404:
self._googlePrint('Requested entity was not found')
continue
else:
self._googlePrint('gspread error of unknown nature: ' + str(e))
continue
except requests.exceptions.ReadTimeout:
self._googlePrint('Read timeout error')
continue
def _video_recording(self):
if datetime.datetime.now().hour >= 8 and datetime.datetime.now().hour <= 18:
return True
else:
return False
def _start_kinect(self):
if self.device == 'realsense':
# Create a context object. This object owns the handles to all connected realsense devices
self.pipeline = rs.pipeline()
self.align = rs.align(rs.stream.color)
# Configure streams
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.rgb8, 30)
# Start streaming
self.profile = self.pipeline.start(config)
#device = self.profile.get_device()
#depth_sensor = device.first_depth_sensor()
#device.hardware_reset()
frames = self.pipeline.wait_for_frames(1000)
depth = frames.get_depth_frame()
self.r = (0,0,depth.width,depth.height)
def _diagnose_speed(self, time = 10):
print('Diagnosing speed for ' + str(time) + ' seconds.', file = sys.stderr)
delta = datetime.timedelta(seconds = time)
start_t = datetime.datetime.now()
counter = 0
while True:
depth = self._returnDepth()
counter += 1
if datetime.datetime.now() - start_t > delta:
break
#Grab single snapshot of depth and save it
depth = self._returnDepth()
np.save(self.projectDirectory +'Frames/FirstFrame.npy', depth)
#Grab a bunch of depth files to characterize the variability
data = np.zeros(shape = (50, self.r[3], self.r[2]))
for i in range(0, 50):
data[i] = self._returnDepth()
counts = np.count_nonzero(~np.isnan(data), axis = 0)
std = np.nanstd(data, axis = 0)
np.save(self.projectDirectory +'Frames/FirstDataCount.npy', counts)
np.save(self.projectDirectory +'Frames/StdevCount.npy', std)
self._print('DiagnoseSpeed: Rate: ' + str(counter/time))
self._print('FirstFrameCaptured: FirstFrame: Frames/FirstFrame.npy,,GoodDataCount: Frames/FirstDataCount.npy,,StdevCount: Frames/StdevCount.npy')
def _captureFrame(self, endtime, max_frames = 40, stdev_threshold = .05, count_threshold = 10, keep_all_data = False):
# Captures time averaged frame of depth data
sums = np.zeros(shape = (self.r[3], self.r[2]))
n = np.zeros(shape = (self.r[3], self.r[2]))
stds = np.zeros(shape = (self.r[3], self.r[2]))
current_time = datetime.datetime.now()
if current_time >= endtime:
self._print('Frame without data')
return
counter = 1
all_data = np.empty(shape = (int(max_frames), self.r[3], self.r[2]))
all_data[:] = np.nan
for i in range(0, max_frames):
all_data[i] = self._returnDepth()
current_time = datetime.datetime.now()
counter += 1
if current_time >= endtime:
break
time.sleep(10)
if (endtime.minute > 0 and endtime.minute <= 5) or keep_all_data:
self._print('AllDataCaptured: NpyFile: Frames/AllData_' + str(self.frameCounter).zfill(6) + '.npy,,PicFile: Frames/Frame_' + str(self.frameCounter).zfill(6) + '.jpg,,Time: ' + str(endtime) + ',,NFrames: ' + str(i))
np.save(self.projectDirectory +'Frames/AllData_' + str(self.frameCounter).zfill(6) + '.npy', all_data)
bad_all_pixels = np.count_nonzero(np.isnan(all_data))
good_all_pixels = np.count_nonzero(~np.isnan(all_data))
med = np.nanmedian(all_data, axis = 0)
std = np.nanstd(all_data, axis = 0)
med[np.isnan(std)] = np.nan
bad_std_avg_pixels = (std > stdev_threshold).sum()
med[std > stdev_threshold] = np.nan
std[std > stdev_threshold] = np.nan
counts = np.count_nonzero(~np.isnan(all_data), axis = 0)
bad_count_avg_pixels = (counts<count_threshold).sum()
med[counts < count_threshold] = np.nan
std[counts < count_threshold] = np.nan
color = self._returnRegColor()
outstring = 'FrameCaptured: NpyFile: Frames/Frame_' + str(self.frameCounter).zfill(6) + '.npy,,PicFile: Frames/Frame_' + str(self.frameCounter).zfill(6) + '.jpg,,'
outstring += 'Time: ' + str(endtime) + ',,NFrames: ' + str(i) + ',,AvgMed: '+ '%.2f' % np.nanmean(med) + ',,AvgStd: ' + '%.2f' % np.nanmean(std) + ',,'
outstring += 'GP: ' + str(np.count_nonzero(~np.isnan(med))) + ',,AllPixelsBad: ' + str(bad_all_pixels) + ',,AllPixelsGood: ' + str(good_all_pixels) + ',,'
outstring += 'FilteredStdPixels: ' + str(bad_std_avg_pixels) + ',,FilteredCountPixels: ' + str(bad_count_avg_pixels) + ',,LOF: ' + str(self._video_recording())
self._print(outstring)
np.save(self.projectDirectory +'Frames/Frame_' + str(self.frameCounter).zfill(6) + '.npy', med)
matplotlib.image.imsave(self.projectDirectory+'Frames/Frame_' + str(self.frameCounter).zfill(6) + '.jpg', color)
self.frameCounter += 1
return med
def _uploadFiles(self):
self._modifyPiGS('Status', 'Finishing converting and uploading of videos')
for p in self.processes:
p.communicate()
for movieFile in os.listdir(self.videoDirectory):
if '.h264' in movieFile:
command = ['python3', 'unit_scripts/process_video.py', self.videoDirectory + movieFile]
command += [str(self.camera.framerate[0]), self.projectID]
self._print(command)
self.processes.append(subprocess.Popen(command))
for p in self.processes:
p.communicate()
self._modifyPiGS('Status','Creating prep files')
# Move files around as appropriate
prepDirectory = self.projectDirectory + 'PrepFiles/'
shutil.rmtree(prepDirectory) if os.path.exists(prepDirectory) else None
os.makedirs(prepDirectory)
lp = LP(self.loggerFile)
self.frameCounter = lp.lastFrameCounter + 1
videoObj = [x for x in lp.movies if x.startTime.hour >= 8 and x.startTime.hour <= 20][0]
subprocess.call(['cp', self.projectDirectory + videoObj.pic_file, prepDirectory + 'PiCameraRGB.jpg'])
subprocess.call(['cp', self.projectDirectory + lp.movies[-1].pic_file, prepDirectory + 'LastPiCameraRGB.jpg'])
# Find depthfile that is closest to the video file time
if self.device != 'None':
depthObj = [x for x in lp.frames if x.time > videoObj.startTime][0]
subprocess.call(['cp', self.projectDirectory + depthObj.pic_file, prepDirectory + 'DepthRGB.jpg'])
if not os.path.isdir(self.frameDirectory):
self._modifyPiGS('Status', 'Error: ' + self.frameDirectory + ' does not exist.')
return
subprocess.call(['cp', self.frameDirectory + 'Frame_000001.npy', prepDirectory + 'FirstDepth.npy'])
subprocess.call(['cp', self.frameDirectory + 'Frame_' + str(self.frameCounter-1).zfill(6) + '.npy', prepDirectory + 'LastDepth.npy'])
try:
self._modifyPiGS('Status', 'Uploading data to cloud')
if self.device != 'None':
self.fileManager.uploadData(self.frameDirectory, tarred = True)
#print(prepDirectory)
self.fileManager.uploadData(prepDirectory)
#print(self.videoDirectory)
self.fileManager.uploadData(self.videoDirectory)
#print(self.loggerFile)
self.fileManager.uploadData(self.loggerFile)
self._modifyPiGS('Error','UploadSuccessful, ready for delete')
except Exception as e:
print('UploadError: ' + str(e))
self._modifyPiGS('Error','UploadFailed, Need to rerun')
raise Exception
def _closeFiles(self):
try:
self._print('MasterRecordStop: ' + str(datetime.datetime.now()))
self.lf.close()
except AttributeError:
pass
try:
if self.system == 'mac':
self.caff.kill()
except AttributeError:
pass