def test_stimulator(StimulatorClass,
InterfaceClass,
remove_db_file=True,
*args,
**kwargs):
tmp = tempfile.mkstemp(suffix="_ethoscope_test.db")[1]
print("Making a tmp db: " + tmp)
cam = MovieVirtualCamera(VIDEO, drop_each=15)
rb = SleepMonitorWithTargetROIBuilder()
rois = rb.build(cam)
cam.restart()
connection = HardwareConnection(InterfaceClass)
try:
# stimulators = [MockSDStimulator(connection,min_inactive_time= 10) for _ in rois ]
stimulators = [
StimulatorClass(connection, *args, **kwargs) for _ in rois
]
mon = Monitor(cam, AdaptiveBGModel, rois, stimulators=stimulators)
drawer = DefaultDrawer(draw_frames=DRAW_FRAMES)
with SQLiteResultWriter(tmp, rois) as rw:
mon.run(result_writer=rw, drawer=drawer)
# cred = {"name": "ethoscope_db",
# "user": "******",
# "password": "******"}
# with ResultWriter( cred , rois) as rw:
# mon.run(result_writer=rw, drawer=drawer)
finally:
if remove_db_file:
print("Removing temp db (" + tmp + ")")
os.remove(tmp)
else:
print("db file lives in (" + tmp + ")")
connection.stop()
def test_API(self):
random.seed(1)
cam = MovieVirtualCamera(VIDEO)
rb = SleepMonitorWithTargetROIBuilder()
rois = rb.build(cam)
hc = HardwareConnection(MockInterface)
stimulators = [MockStimulator(hc) for _ in rois]
cam.restart()
mon = Monitor(cam, AdaptiveBGModel, rois, stimulators)
drawer = DefaultDrawer(draw_frames=DRAW_FRAMES)
tmp = tempfile.mkstemp(suffix="_ethoscope_test.db")[1]
try:
print("Making a tmp db: " + tmp)
with SQLiteResultWriter(tmp, rois) as rw:
mon.run(result_writer=rw, drawer=drawer)
except:
self.fail("testAPI raised ExceptionType unexpectedly!")
finally:
hc.stop()
cam._close()
print("Removing temp db (" + tmp + ")")
os.remove(tmp)
def __init__(self, port=None):
self._serial = MockSerial()
class MockSDInterface(MockLynxMotionInterface, SleepDepriverInterface):
pass
class MockSDStimulator(SleepDepStimulator):
_HardwareInterfaceClass = MockSDInterface
tmp = tempfile.mkstemp(suffix="_ethoscope_test.db")[1]
print("Making a tmp db: " + tmp)
cam = MovieVirtualCamera(VIDEO, drop_each=15)
rb = SleepMonitorWithTargetROIBuilder()
rois = rb.build(cam)
cam.restart()
connection = HardwareConnection(MockSDInterface)
stimulators = [
MockSDStimulator(connection, min_inactive_time=10) for _ in rois
]
mon = Monitor(cam, AdaptiveBGModel, rois, stimulators=stimulators)
drawer = DefaultDrawer(draw_frames=DRAW_FRAMES)
try:
with SQLiteResultWriter(tmp, rois) as rw:
mon.run(result_writer=rw, drawer=drawer)
finally:
#sub_grey = grey[st_y:st_y+50, st_x:st_x+50]
contours,hierarchy = cv2.findContours(thr_im, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 0:
continue
x,y,w,h = cv2.boundingRect(contours[0])
sub_grey= sub_grey[y : y + h, x : x + w]
# cv2.imshow("t",sub_grey)
# cv2.waitKey(30)
sub_grey= cv2.resize(sub_grey, (24,24))
x_arrstr = np.char.mod('%i', sub_grey)
out = ", ".join(list(x_arrstr.flatten())) + "\n"
self._path.write(out)
# change these three variables according to how you name your input/output files
INPUT_VIDEO = "/home/quentin/comput/ethoscope-git/src/ethoscope/tests/integration_server_tests/test_video.mp4"
cam = MovieVirtualCamera(INPUT_VIDEO)
nm = NegativeMaker()
nm.run(cam)
parser.add_option("-p",
"--prefix",
dest="prefix",
help="The prefix for result dir")
(options, args) = parser.parse_args()
option_dict = vars(options)
INPUT = option_dict["input"]
OUTPUT = os.path.splitext(INPUT)[0] + ".db"
OUTPUT = option_dict["prefix"] + "/" + OUTPUT
try:
os.makedirs(os.path.dirname(OUTPUT))
except OSError:
pass
print INPUT + " ===> " + OUTPUT
cam = MovieVirtualCamera(INPUT)
rois = SleepMonitorWithTargetROIBuilder().build(cam)
drawer = DefaultDrawer(draw_frames=True)
mon = Monitor(cam, AdaptiveBGModel, rois)
#fixme
date = datetime.datetime.strptime("2016-05-03_08-25-02",
"%Y-%m-%d_%H-%M-%S")
ts = int(calendar.timegm(date.timetuple()))
#todo parse metadata from filename
# metadata = {}
with SQLiteResultWriter(OUTPUT, rois) as rw:
mon.run(rw, drawer)
#cv2.imwrite(os.path.join(self._path, "positive_%09d.png" % (self._idx)), sub_grey)
self._idx += 1
class ABGMImageSaver(AdaptiveBGModel):
fg_model = ObjectModelImageSaver()
# change these three variables according to how you name your input/output files
INPUT_VIDEO = "/home/quentin/comput/ethoscope-git/src/ethoscope/tests/integration_server_tests/test_video.mp4"
OUTPUT_VIDEO = "/tmp/my_output.avi"
OUTPUT_DB = "/tmp/results.db"
# We use a video input file as if it was a "camera"
cam = MovieVirtualCamera(INPUT_VIDEO)
# here, we generate ROIs automatically from the targets in the images
roi_builder = SleepMonitorWithTargetROIBuilder()
rois = roi_builder.build(cam)
# Then, we go back to the first frame of the video
cam.restart()
# we use a drawer to show inferred position for each animal, display frames and save them as a video
drawer = DefaultDrawer(OUTPUT_VIDEO, draw_frames=True)
# We build our monitor
monitor = Monitor(cam, ABGMImageSaver, rois)
# Now everything ius ready, we run the monitor with a result writer and a drawer
with SQLiteResultWriter(OUTPUT_DB, rois) as rw:
def main(argv):
parser = ArgumentParser(
description='Runs an Ethoscope machinery on the given video file,' +
' which is meant to be a recording of single daphnia moving' +
' in a bunch of wells.' +
' The area of each well is determined by non-black regions' +
' in the supplied regions of interest (ROI) image file.' +
' Optionally an output video may be produced, documenting the ' +
' detection of animals.')
parser.add_argument("-i",
"--input-video",
dest="inp_video_filename",
required=True,
help="The video file to be processed.",
metavar='<input video filename>')
parser.add_argument("-o",
"--output-db",
dest="db_filename",
required=True,
help="Create Sqlite DB file for storing results.",
metavar='<output DB filename>')
parser.add_argument(
"-r",
"--roi_image",
dest="roi_filename",
required=True,
help="Create Sqlite DB file DB_FILENAME for storing results.",
metavar='<roi image>')
parser.add_argument("-a",
"--output-video",
dest="outp_video_filename",
help="The annotated output video file.",
metavar='<output video filename>')
parser.add_argument(
"-b",
"--single-roi-video",
dest="single_roi_video_filename",
help=
"For debugging purpose a video file of a single roi may be produced.",
metavar='<single roi video filename>')
parser.add_argument(
"-n",
"--single-roi-video-roi-nbr",
dest="single_roi_video_roi_nbr",
type=int,
default=0,
help="Select the number of the roi to produce a debugging video from."
+ " If not specified, roi 0 is the default.",
metavar='<single roi video roi number>')
args = parser.parse_args()
# change these variables according to how you name your input/output files
INPUT_DATA_DIR = "/home/lukas/tmp/AAA-Video/"
OUTPUT_DATA_DIR = "/home/lukas/tmp/ethoscope/"
#ROI_IMAGE = INPUT_DATA_DIR + "irbacklit_20200109_1525_4x4x6_squaremask_valued.png"
ROI_IMAGE = INPUT_DATA_DIR + "4xWellplates4x6_registred_squaremask_tight.png"
#INPUT_VIDEO = INPUT_DATA_DIR + "Basler_acA5472-17um__23065142__20200109_152536071.mp4"
INPUT_VIDEO = INPUT_DATA_DIR + "Basler_acA5472-17um__23065142__20200205_172106124.mp4"
logfile = OUTPUT_DATA_DIR + '20200205.log'
OUTPUT_VIDEO = OUTPUT_DATA_DIR + "20200205.avi"
OUTPUT_DB = OUTPUT_DATA_DIR + "results20200205.db"
#logfile = OUTPUT_DATA_DIR + 'results.log'
#OUTPUT_VIDEO = OUTPUT_DATA_DIR + "output.avi"
#OUTPUT_DB = OUTPUT_DATA_DIR + "output.db"
dbgImgWinSizeX = 2200
dbgImgWinSizeY = 1500
# setup logging
logging.basicConfig(filename=logfile, level=logging.INFO)
#logging.basicConfig(filename=logfile, level=logging.DEBUG)
# define a Handler which writes INFO messages or higher to the sys.stderr
console = logging.StreamHandler()
console.setLevel(logging.INFO)
# add the handler to the root logger
logging.getLogger('').addHandler(console)
# Make the ethoscope packages accessible
package_path = os.path.join(os.path.dirname(sys.path[0]), '')
logging.info("path of ethoscope package: %s" % package_path)
sys.path.insert(0, package_path)
import cv2
# import the bricks from ethoscope package
# Use a mask image to define rois. Mask image must have black background, every non-black
# region defines a roi.
from ethoscope.roi_builders.img_roi_builder import ImgMaskROIBuilder
from ethoscope.core.monitor import Monitor
from ethoscope.trackers.adaptive_bg_tracker import AdaptiveBGModel
from ethoscope.utils.io import SQLiteResultWriter
from ethoscope.hardware.input.cameras import MovieVirtualCamera
from ethoscope.drawers.drawers import DefaultDrawer
# Generate ROIs from the mask image
logging.info("reading roi mask")
roi_builder = ImgMaskROIBuilder(args.roi_filename)
logging.info("building rois from \"%s\"" % args.roi_filename)
roi_builder.build(
None) # use image already loaded by ImgMaskROIBuilder instance
rois = roi_builder.gridSort(50, 50)
#for r in rois:
# print("Roi %d: value: %d, (%d,%d)" % (r.idx, r._value, r._rectangle[0], r._rectangle[1]))
# We use a video input file as if it were a camera
cam = MovieVirtualCamera(args.inp_video_filename)
logging.info("Loading \"%s\"-encoded movie with %d FPS of duration %d s." %
(cam.fourcc, cam.frames_per_sec,
cam._total_n_frames / cam.frames_per_sec))
# we use a drawer to show inferred position for each animal, display frames and save them as a video
do_draw_frames = False
if args.outp_video_filename is not None:
do_draw_frames = True
drawer = DefaultDrawer(args.outp_video_filename,
draw_frames=do_draw_frames,
framesWinSizeX=dbgImgWinSizeX,
framesWinSizeY=dbgImgWinSizeY)
# We build our monitor
#monitor = Monitor(cam, AdaptiveBGModel, rois)
if args.single_roi_video_filename is not None:
monitor = Monitor(
cam,
AdaptiveBGModel,
rois,
dbg_roi_value=args.single_roi_video_roi_nbr,
dbg_roi_video_filename=args.single_roi_video_filename)
else:
monitor = Monitor(cam, AdaptiveBGModel, rois)
# Now everything is ready, we run the monitor with a result writer and a drawer
logging.info("run monitor with drawer")
with SQLiteResultWriter(args.db_filename, rois) as rw:
monitor.run(rw, drawer)
import optparse
import os
import cv2
OUT_DIR = "/data/ethoscope_female_videos/rois/"
IN_AVI_FILE = "/data/ethoscope_female_videos/whole_2016-04-18_14-06-08_035aeeee10184bb39b0754e75cef7900__1920x1080@25_00000.mp4"
REF_ROI_IMG = "/data/ethoscope_female_videos/whole_2016-04-18_14-06-08_035aeeee10184bb39b0754e75cef7900__1920x1080@25_00000.png"
MAX_TIME = 50 * 3600 # 50h
ROI_TO_EXCLUDE = {}
if __name__ == "__main__":
parser = optparse.OptionParser()
cam = MovieVirtualCamera(IN_AVI_FILE, use_wall_clock=False)
#ref = cv2.imread(REF_ROI_IMG)
roi_builder = ImgMaskROIBuilder(REF_ROI_IMG)
# rois = roi_builder.build(REF_ROI_IMG)
rois = roi_builder.build(cam)
rois.sort(key=lambda r: r.get_feature_dict()["x"])
for i, r in enumerate(rois):
r.set_value(i + 1)
for r in rois:
if r.value in ROI_TO_EXCLUDE:
continue
d = r.get_feature_dict()