def run(self):
# video = Video(self.video_path)
if self.verbose:
print("Hello, World!")
# print("TESTING: FFMPEG options for video sample enabled!!")
w, h = 2336, 1729
# -ss 00:00:02.00 -t 00:00:00.50
video_reader = FFmpeg(self.video_path, "", (1729, 2336, 1),
" -vf scale={}:{}".format(w, h))
bg_modeler = BG(model="simpleMax", window_size=50, img_shape=(h, w, 1))
fg_modeler = FG(passThrough=True)
pipeline = EZ(video_reader, bg_modeler, fg_modeler).items()
experiment_dir = os.path.join(config.experiment_dir,
str(self.experiment_uuid))
# io.imsave(os.path.join(experiment_dir, "bg.png"), video.extract_background().squeeze())
magic_pixel = 255
magic_pixel_delta = 0.1
segment_number = -1
i = -1
for item in pipeline:
i += 1
if self.stopped():
break
else:
if self.verbose:
print("Processing frame", i)
# raw_frame = video.frame(i)
# frame = video.normal_frame(i)
# print("OLD raw frame stats: shape "+str(raw_frame.shape)+", min/max "+str(np.min(raw_frame))+"/"+str(np.max(raw_frame)))
# print("OLD frame stats: shape "+str(frame.shape)+", min/max "+str(np.min(frame))+"/"+str(np.max(frame)))
raw_frame = item["frame"] / 250.
frame = item["fg"]
# print("NEW raw frame stats: shape "+str(raw_frame.shape)+", min/max "+str(np.min(raw_frame))+"/"+str(np.max(raw_frame)))
# print("NEW frame stats: shape "+str(frame.shape)+", min/max "+str(np.min(frame))+"/"+str(np.max(frame)))
if config.use_magic_pixel_segmentation:
this_frame_magic_pixel = raw_frame[0, 4, 0]
if abs(this_frame_magic_pixel -
magic_pixel) > magic_pixel_delta:
if self.verbose:
print("Segment Boundry Detected:", i)
segment_number += 1
segment = (uuid4(), self.experiment_uuid,
segment_number)
self.db_writer_queue.put((
"execute",
"""
INSERT INTO Segment (segment, experiment, number)
VALUES ($1, $2, $3)
""",
segment,
))
magic_pixel = this_frame_magic_pixel
frame_uuid = uuid4()
frame_insert = (frame_uuid, self.experiment_uuid, segment[0],
i)
self.db_writer_queue.put((
"execute",
"""
INSERT INTO Frame (frame, experiment, segment, number)
VALUES ($1, $2, $3, $4)
""",
frame_insert,
))
# frame = frame[:, crop_side:-crop_side]
self.frame_bytes_norm.put(frame.tobytes())
self.frame_queue.put((frame_uuid, i, frame))
self.frame_bytes_norm.put(None)
for g in range(config.GPUs):
self.frame_queue.put(None)
if self.verbose:
print("VideoFrameGetter Exiting")
async def detect_video(video_file, date, name="Today", notes=""):
cpus = multiprocessing.cpu_count()
experiment_uuid = uuid4()
experiment_day = dateparse(date)
experiment_dir = os.path.join(config.experiment_dir, str(experiment_uuid))
experiment = (experiment_uuid, experiment_day, name, "detection", notes)
try:
print("Creating data directory", experiment_dir)
os.mkdir(experiment_dir)
scaleby = 1
w, h = int(2336 / scaleby), int(1729 / scaleby)
# Reads the source video, outputs frames
print("Launching Video Reader")
video_reader = FFmpeg(
video_file,
"",
(h, w, 1),
"-ss 00:00:02.00 -t 00:00:00.50 -vf scale={}:{}".format(w, h),
[],
False,
FrameData,
)
print("Launching Database processor")
db_proc = DB_Processor(experiment_uuid, experiment_day, name, notes)
print("Launching Entry processor")
entry_proc = Entry(experiment_uuid)
print("Launching Magic pixel processor")
magic_proc = MagicPixel()
print("Launching Rescale processor")
rescale_proc = Rescaler()
# Computes a background for a frame, outputs {"frame": frame, "bg": bg}
print("Launching Background Modeler")
bg_proc = BG(model="simpleMax", window_size=50, img_shape=(h, w, 1))
# Takes a background and a frame, enhances frame to model foreground
print("Launching Foreground Modeler")
fg_proc = FG()
# Takes a foreground frame, binarizes it
print("Launching Binary Mask Processor")
mask_proc = Binary("legacyLabeled")
print("Launching Properties Processor")
prop_proc = Properties()
print("Launching Crop Processor")
crop_proc = Crop_Processor()
# A utility to view video pipeline output
raw_player = RawPlayer()
bg_player = BGPlayer()
fg_player = FGPlayer()
mask_player = MaskPlayer()
crop_player = CropPlayer()
meta_player = MetaPlayer()
# A utility to clean up datagram resources
cleaner = Cleaner()
# Todo
# print("Launching Crop Writer")
# print("Launching Detection Video Writer")
# print("Launching Particle Commmitter")
# /todo
EZ(
video_reader,
entry_proc,
magic_proc,
meta_player,
rescale_proc,
raw_player,
bg_proc,
fg_proc,
mask_proc,
cleaner,
).start().join()
except Exception as e:
print("Uh oh. Something went wrong")
traceback.print_exc()
# wq.push(None)
if os.path.exists(experiment_dir):
print("Removing files from", experiment_dir)
shutil.rmtree(experiment_dir)
else:
pass
# dbwriter.commit()
# wq.push(None)
finally:
print("Fin.")
return experiment_uuid
#sub_frame = (frame_bg["frame"].astype('float32') - frame_bg["bg"].astype('float32')).clip(0,255).astype('uint8')
sub_frame = np.float64(255 - (np.absolute(
np.int16(frame_bg["frame"]) - np.int16(frame_bg["bg"]))))
sub_frame -= sub_frame.min()
sub_frame *= 255.0 / sub_frame.max()
self.put(np.uint8(sub_frame))
scaleby = 1
#w, h = int(2336/scaleby), int(1729/scaleby)
w, h = int(1920 / scaleby), int(1080 / scaleby)
#vid = "../data/source/BU-2015-10-14/T02578.avi"
#vid = "../data/experiments/a5f4681c-9e28-4a49-a370-ccadf6d47ef1/raw.mp4"
vid = "/home/datasets/mot17/train/MOT17-02-DPM/img1/video.mp4"
EZ(
FFmpeg(vid, "-ss 0", (h, w, 3), "-vf scale={}:{}".format(w, h)),
Seq(As(6, BG, model='splitmedian', window_size=20, img_shape=(h, w, 3))),
#Seq(tuple(BG(model='ae', window_size=15, img_shape=(h, w,3), env={"CUDA_VISIBLE_DEVICES":str(i)}) for i in range(4))),
BGPlayer(),
#Seq(As(2, FrameSubtractor)),
Seq(As(2, FrameDivider)),
#Seq(As(16, F)),
VideoPlayer(),
FFmpeg((h, w, 3), [], "../data/ae_bg/b2d.avi",
"-c:v libx264 -preset slow -crf 17", "-y"),
Stamp()).daemonize().watch().start().join()
async def detect_video(video_file, date, name="", notes=""):
cpus = multiprocessing.cpu_count()
experiment_uuid = uuid4()
experiment_day = dateparse(date)
experiment_dir = os.path.join(config.experiment_dir, str(experiment_uuid))
experiment = (experiment_uuid, experiment_day, name, "detection", notes)
method = "TrackingPrefix DetectionMpyx"
def e_handler(e, tb):
print("Exception occured:", e)
print(tb)
try:
# print("Creating data directory", experiment_dir)
os.mkdir(experiment_dir)
w, h = 2336, 1729
ms_metric_cal = 13.94736842
# Reads the source video, outputs frames
# -ss 00:00:02.00 -t 00:00:00.50
video_reader = FFmpeg(
video_file,
"",
(h, w, 1),
"-vf scale={}:{}".format(w, h),
[],
False,
FrameData,
)
csv_proc = CSVWriter(experiment_uuid, experiment_day, name, method,
notes)
db_proc = DBProcessor(experiment_uuid, experiment_day, name, method,
notes)
# Computes a background for a frame, outputs {"frame": frame, "bg": bg}
bg_proc = BG(model="simpleMax", window_size=20, img_shape=(h, w, 1))
fines_proc = BG(
model="rollingMax",
window_size=20,
img_shape=(h, w, 1),
inputProp="div",
saveAs="fines",
)
# Utilities for viewing various stages of processing.
raw_player = RawPlayer()
bg_player = BGPlayer()
fg_player = FGPlayer()
mask_player = MaskPlayer()
crop_player = CropPlayer()
meta_player = MetaPlayer()
# Number of processes to spawn for particular processors
n_prop_procs = 10
n_crop_procs = 4
n_binary = 5
n_crop_writer = 10
n_csv_procs = 10
n_deblur_procs = 20
# Main pipeline
EZ(
video_reader,
Counter(),
Entry(experiment_uuid, ms_metric_cal),
MagicPixel(),
Rescaler(scale=1 / 4),
Counter("Rescaled"),
bg_proc,
FG(saveAs="div"),
fines_proc,
Counter("fines"),
FG("finesDivision", saveAs="fg"),
Seq(
# As(n_deblur_procs, Deblur),
As(n_binary, Binary, "legacyLabeled"),
As(n_prop_procs, Properties),
As(n_crop_procs, Crop_Processor),
As(n_crop_writer, CropWriter, experiment_dir),
Counter("crops"),
As(
n_csv_procs,
CSVWriter,
experiment_uuid,
experiment_day,
name,
method,
notes,
),
Counter("csv"),
),
db_proc,
Counter("db"),
Passthrough(),
PerformanceMonitor({
"Properties": n_prop_procs,
"Crop_Processor": n_crop_procs,
"Binary": n_binary,
"CropWriter": n_crop_writer,
"CSVWriter": n_csv_procs,
"Deblur": n_deblur_procs,
}),
Counter("performance"),
# raw_player,
# bg_player,
# fg_player,
# mask_player,
# MetaPlayer(),
ImageSampleWriter(str(experiment_dir),
["bg", "div", "fg", "raw", "mask", "fines"]),
Counter("samplewriter"),
Cleaner(),
# qsize=10,
).catch(e_handler).start().join()
except Exception as e:
print("Uh oh. Something went wrong")
traceback.print_exc()
if os.path.exists(experiment_dir):
print("Removing files from", experiment_dir)
shutil.rmtree(experiment_dir)
else:
pass
finally:
pass
# print("Fin.")
return experiment_uuid
def run(self):
# video = Video(self.video_path)
if self.verbose:
print("Hello, World!")
# print("TESTING: FFMPEG options for video sample enabled!!")
w, h = 2336, 1729
# -ss 00:00:02.00 -t 00:00:00.50
video_reader = FFmpeg(
self.video_path,
"",
(1729, 2336, 1),
" -ss 00:00:02.00 -t 00:00:00.50 -vf scale={}:{}".format(w, h),
)
bg_modeler = BG(model="simpleMax", window_size=50, img_shape=(h, w, 1))
fg_modeler = FG(passThrough=True)
pipeline = EZ(video_reader, bg_modeler, fg_modeler).items()
experiment_dir = os.path.join(config.experiment_dir, str(self.experiment_uuid))
# io.imsave(os.path.join(experiment_dir, "bg.png"), video.extract_background().squeeze())
magic_pixel = 255
magic_pixel_delta = 0.1
segment_number = -1
i = -1
for item in pipeline:
i += 1
if self.stopped():
break
else:
if self.verbose:
print("Processing frame", i)
# raw_frame = video.frame(i)
# frame = video.normal_frame(i)
# print("OLD raw frame stats: shape "+str(raw_frame.shape)+", min/max "+str(np.min(raw_frame))+"/"+str(np.max(raw_frame)))
# print("OLD frame stats: shape "+str(frame.shape)+", min/max "+str(np.min(frame))+"/"+str(np.max(frame)))
raw_frame = item["frame"] / 250.
frame = item["fg"]
# print("NEW raw frame stats: shape "+str(raw_frame.shape)+", min/max "+str(np.min(raw_frame))+"/"+str(np.max(raw_frame)))
# print("NEW frame stats: shape "+str(frame.shape)+", min/max "+str(np.min(frame))+"/"+str(np.max(frame)))
if config.use_magic_pixel_segmentation:
this_frame_magic_pixel = raw_frame[0, 4, 0]
print("magic:", this_frame_magic_pixel)
if abs(this_frame_magic_pixel - magic_pixel) > magic_pixel_delta:
if self.verbose:
print("Segment Boundry Detected:", i)
segment_number += 1
segment = (uuid4(), self.experiment_uuid, segment_number)
s = "{}\t{}\t{}\n"
with open(
"/tmp/{}_segment.csv".format(self.experiment_uuid), "a"
) as f:
for g in [segment]:
f.write(s.format(g[0], g[1], g[2]))
magic_pixel = this_frame_magic_pixel
frame_uuid = uuid4()
frame_insert = (frame_uuid, self.experiment_uuid, segment[0], i)
s = "{}\t{}\t{}\t{}\n"
with open("/tmp/{}_frame.csv".format(self.experiment_uuid), "a") as f:
for g in [frame_insert]:
f.write(s.format(g[0], g[1], g[2], g[3]))
# frame = frame[:, crop_side:-crop_side]
# import cv2
# cv2.imwrite('/home/mot/tmp/fg_'+str(uuid4())+'.png',frame)
self.frame_bytes_norm.put(frame.tobytes())
self.frame_queue.put((frame_uuid, i, frame))
self.frame_bytes_norm.put(None)
for g in range(config.GPUs):
self.frame_queue.put(None)
if self.verbose:
print("VideoFrameGetter Exiting")
async def detect_video(video_file, date = "NOW()", name = "Today", notes = ""):
# note: "NOW()" may not work.
cpus = multiprocessing.cpu_count()
experiment_uuid = uuid4()
experiment_day = dateparse(date)
experiment_dir = os.path.join(config.experiment_dir, str(experiment_uuid))
experiment = (experiment_uuid, experiment_day, name, "detection", notes)
try:
print("Creating data directory", experiment_dir)
os.mkdir(experiment_dir)
print("Launching Database Writer")
dbwriter = DBWriter()
wq = dbwriter.input()
dbwriter.start()
scaleby = 1
w, h = int(2336/scaleby), int(1729/scaleby)
video_reader = FFmpeg(video_file, "", (h, w,3), "-vf scale={}:{}".format(w,h))
raw_compressor = FFmpeg((h, w, 3), "", os.path.join(experiment_dir, "raw.mp4"), "-c:v libx264 -preset slow -crf 17 -movflags +faststart")
bg_model = Seq(As(6, BG, model='splitmedian', window_size=20, img_shape=(h, w,3)))
EZ(
video_reader, {raw_compressor,
VideoPlayer()}
).start().watch().join()
"""
# TODO:
EZ(video_reader, {raw_compressor,
[segment_detector, bg_model, foreground_extraction, {extraction_compressor,
[segmentation_mask, {mask_compressor,
[segmentation_properties, get_crops, {crop_writer,
[crop_classifier, write_crops]
]}]}]]}}).start().watch().join()
"""
except Exception as e:
print("Uh oh. Something went wrong")
traceback.print_exc()
dbwriter.rollback()
wq.push(None)
if os.path.exists(experiment_dir):
print("Removing files from", experiment_dir)
shutil.rmtree(experiment_dir)
else:
dbwriter.commit()
wq.push(None)
finally:
print("Fin.")
return experiment_uuid
async def detect_video(video_file, date, name="", notes=""):
cpus = multiprocessing.cpu_count()
experiment_uuid = uuid4()
experiment_day = dateparse(date)
experiment_dir = os.path.join(config.experiment_dir, str(experiment_uuid))
experiment = (experiment_uuid, experiment_day, name, "detection", notes)
method = "detection"
try:
print("Creating data directory", experiment_dir)
os.mkdir(experiment_dir)
w, h = 2336, 1729
# Reads the source video, outputs frames
# -ss 00:00:02.00 -t 00:00:00.50
print("Launching Video Reader")
video_reader = FFmpeg(
video_file,
"",
(h, w, 1),
" -vf scale={}:{}".format(w, h),
[],
False,
FrameData,
)
print("Launching Database processor")
csv_proc = CSVWriter(experiment_uuid, experiment_day, name, method,
notes)
db_proc = DBProcessor(experiment_uuid, experiment_day, name, method,
notes)
# Computes a background for a frame, outputs {"frame": frame, "bg": bg}
print("Launching Background Modeler")
bg_proc = BG(model="simpleMax", window_size=20, img_shape=(h, w, 1))
# Utilities for viewing various stages of processing.
raw_player = RawPlayer()
bg_player = BGPlayer()
fg_player = FGPlayer()
mask_player = MaskPlayer()
crop_player = CropPlayer()
meta_player = MetaPlayer()
# A utility to clean up datagram resources
cleaner = Cleaner()
# Number of processes to spawn for particular processors
n_prop_procs = 10
n_crop_procs = 4
n_binary = 5
n_crop_writer = 10
n_csv_procs = 5
# Main pipeline
EZ(
video_reader,
Counter(),
Entry(experiment_uuid),
MagicPixel(),
Rescaler(scale=1 / 1),
bg_proc,
FG(),
Seq(
As(n_binary, Binary, "legacyLabeled"),
As(n_prop_procs, Properties),
As(n_crop_procs, Crop_Processor),
As(n_crop_writer, CropWriter, experiment_dir),
As(
n_csv_procs,
CSVWriter,
experiment_uuid,
experiment_day,
name,
method,
notes,
),
),
db_proc,
Passthrough(),
PerformanceMonitor({
"Properties": n_prop_procs,
"Crop_Processor": n_crop_procs,
"Binary": n_binary,
"CropWriter": n_crop_writer,
"CSVWriter": n_csv_procs,
}),
# raw_player,
# bg_player,
# fg_player,
# mask_player,
cleaner,
# qsize=10,
).start().join()
except Exception as e:
print("Uh oh. Something went wrong")
traceback.print_exc()
if os.path.exists(experiment_dir):
print("Removing files from", experiment_dir)
shutil.rmtree(experiment_dir)
else:
pass
finally:
print("Fin.")
return experiment_uuid