def cvt_dataset(src_dir, dest_dir):
if not os.path.exists(src_dir):
raise Exception('cvt_dataset: src_dir does not exists!')
if not os.path.exists(dest_dir):
os.makedirs(dest_dir)
if os.listdir(dest_dir):
raise Exception('cvt_dataset: dest_dir must be an empty dir!')
for node in os.listdir(src_dir):
if os.path.isdir(os.path.join(src_dir, node)):
cvt_dataset(os.path.join(src_dir, node),
os.path.join(dest_dir, node))
else:
name, ext = os.path.splitext(node)
if ext != '.avi':
continue
frames = utils.extract_frames(os.path.join(src_dir, node))
utils.save_frames(os.path.join(dest_dir, name), frames)
global num_processed
num_processed += 1
if num_processed % 30 == 0:
print('video(%s) has been processed' % num_processed)
def __main():
"""Main function"""
# Parameters declaration and parsing
ap = argparse.ArgumentParser()
ap.add_argument('-of',
'--out_dir',
required=False,
default='.',
help='Output directory')
ap.add_argument('-i', '--input_video', required=True, help='Input video')
ap.add_argument('-t',
'--threshold',
required=False,
default=50,
help='The threshold to decide if two frames are the same')
args = ap.parse_args()
frames = extract_frames(args.input_video)
print('Total frames extracted: %d' % len(frames))
save_images(frames, join(args.out_dir, 'all'))
frames = get_unique_frames(frames, int(args.threshold))
print('Unique frames: %d' % len(frames))
save_images(frames, join(args.out_dir, 'uniques'))
def main(args, callback=None):
uid = uuid.uuid1()
dir_name = './.TEMP-' + str(uid)
os.makedirs(dir_name)
audio_file = dir_name + '/audio.mp3'
extract_audio(args.video, audio_file)
frames_dir = dir_name + '/frames'
os.makedirs(frames_dir)
extract_frames(args.video, frames_dir)
frames_count = len(glob(frames_dir + '/*'))
audio_analyze = analyze(audio_file, frames_count)
process(frames_dir,
audio_analyze,
args.size,
neural=args.neural,
colorize=args.colorize,
brightify=args.brightify,
callback=callback)
construct_video(frames_dir, audio_file, get_fps(args.video), args.output)
if not args.no_clean:
rmtree(dir_name)
def classify(model, input):
yt = YouTube('https://youtube.com/embed/%s?start=%d&end=%d' %
(input['video'], start, end))
video = yt.streams.all()[0]
video_file = video.download(videoPath)
num_segments = 16
print('Extracting frames using ffmpeg...')
frames = extract_frames(video_file, num_segments)
# Prepare input tensor
input = torch.stack([transform(frame) for frame in frames], 1).unsqueeze(0)
# Make video prediction
with torch.no_grad():
logits = model(input)
h_x = F.softmax(logits, 1).mean(dim=0)
probs, idx = h_x.sort(0, True)
# Output the prediction.
return categories[idx[0]]
""" print('RESULT ON ' + video_file)
def main():
parser = argparse.ArgumentParser(
description='Full pipeline to download Youtube video and infer deep trackers.')
parser.add_argument('--overwrite', action='store_true',
help='remove existing folder')
# parser.add_argument('--YT_ID', type=str, default=None,
# help='ID from YT')
# parser.add_argument('--start', type=int, default=0,
# help='starting time of the frames')
# parser.add_argument('--duration', type=int, default=None,
# help='duration time of the frames')
# parser.add_argument('--ID', type=int, default=0,
# help='ID of the sequence')
parser.add_argument('--path', type=str, default="/home/$USER/Documents/Videos",
help='where to save the sequence/video/results')
parser.add_argument('--CSV', type=str, default="TrackingNet 2.0 Test Set Extension - Final TrackingNet2.0.csv",
help='where to save the sequence/video/results')
args = parser.parse_args()
List_Sequences = os.path.join(args.path, args.CSV)
df = pd.read_csv(List_Sequences)
# print(df)
for i, data in df.iterrows():
# print(data)
# print(data["Youtube_ID"])
if i>6:
# if (isinstance(data["Object_ID"], float)):
# print(int(data["Object_ID"]))
print(data)
args.YT_ID = data["Youtube_ID"]
args.start = int(data["Start Time"])
args.duration = int(data["Duration"])
args.ID = int(data["Object_ID"])
print(args)
# # remove previous BB
# if args.overwrite:
# if os.path.exists(first_BB_path):
# shutil.rmtree(first_BB_path)
# define all paths
sequence_ID = args.YT_ID + "_" + str(args.ID)
full_video_path = os.path.join(args.path, "Videos", args.YT_ID+'.mp4')
sequence_path = os.path.join(args.path, "Sequences", sequence_ID)
cut_video_path = os.path.join(sequence_path, 'video.mp4')
frame_path = os.path.join(sequence_path, 'frames')
frame_BB_path = os.path.join(sequence_path, 'frames_BB')
first_BB_path = os.path.join(sequence_path, "initial_BB.txt")
tracking_results_path = os.path.join(sequence_path, 'results')
video_BB_path = os.path.join(sequence_path, 'video_BB.mkv')
# Download the video
download_video(args.YT_ID, full_video_path)
#Cut the video
cut_video(full_video_path, cut_video_path, args.start, args.duration)
#extract frames
extract_frames(cut_video_path, frame_path)
# draw the first Bounding box
if not os.path.exists(first_BB_path):
draw_first_BB(sequence_path, frame_path, first_BB_path)
def run_single_sequence(args):
# define all paths
sequence_ID = args.YT_ID + "_" + str(args.ID)
full_video_path = os.path.join(args.path, "Videos", args.YT_ID + '.mp4')
sequence_path = os.path.join(args.path, "Sequences", sequence_ID)
cut_video_path = os.path.join(sequence_path, 'video.mp4')
frame_path = os.path.join(sequence_path, 'frames')
frame_BB_path = os.path.join(sequence_path, 'frames_BB')
first_BB_path = os.path.join(sequence_path, "initial_BB.txt")
tracking_results_path = os.path.join(sequence_path, 'results')
video_BB_path = os.path.join(sequence_path, 'video_BB.mkv')
# remove previous results
if (args.overwrite):
# if os.path.exists(sequence_path) and os.path.isdir(sequence_path):
# # shutil.rmtree(sequence_path)
if os.path.exists(cut_video_path):
os.remove(cut_video_path)
if os.path.exists(video_BB_path):
os.remove(video_BB_path)
if os.path.exists(frame_path) and os.path.isdir(frame_path):
shutil.rmtree(frame_path)
if os.path.exists(frame_BB_path) and os.path.isdir(frame_BB_path):
shutil.rmtree(frame_BB_path)
if os.path.exists(tracking_results_path) and os.path.isdir(
tracking_results_path):
shutil.rmtree(tracking_results_path)
print(sequence_ID)
# Download the video
download_video(args.YT_ID, full_video_path)
#Cut the video
cut_video(full_video_path, cut_video_path, args.start, args.duration)
#extract frames
extract_frames(cut_video_path, frame_path)
first_BB_path_shared = os.path.join(
"/run/user/1001/gvfs/smb-share:server=10.68.74.21,share=tn2",
"Sequences", sequence_ID, "initial_BB.txt")
# draw the first Bounding box
if not os.path.exists(first_BB_path) and os.path.exists(
first_BB_path_shared):
first_BB_path_shared = os.path.join(
"/run/user/1001/gvfs/smb-share:server=10.68.74.21,share=tn2",
"Sequences", sequence_ID, "initial_BB.txt")
# first_BB_path_shared = os.path.join(
# "/home/giancos/Documents/Videos", "Sequences", sequence_ID, "initial_BB.txt")
# if os.path.exists(first_BB_path_shared):
shutil.copyfile(first_BB_path_shared, first_BB_path)
# elif: args.just_BB
# else:
# print("please Draw!")
# draw_first_BB(sequence_path, frame_path, first_BB_path, sequence_ID)
# if args.just_BB:
# draw_first_BB(sequence_path, frame_path,
# first_BB_path, sequence_ID)
# draw BB if asked to
if args.draw_BB:
if (not os.path.exists(first_BB_path)):
os.system(f"xdg-open {cut_video_path}")
draw_first_BB(sequence_path, frame_path, first_BB_path,
sequence_ID)
if args.run_trackers:
# if at that stage, no BB, then need to draw one
if not os.path.exists(first_BB_path):
draw_first_BB(sequence_path, frame_path, first_BB_path,
sequence_ID)
# Run trackers bsed on pysot
run_tracker_pysot(args.YT_ID, args.ID, args.path,
tracking_results_path, args.overwrite)
# Run trackers bsed on pytracking
run_tracker_pytracking(frame_path, sequence_path,
tracking_results_path, sequence_ID,
args.overwrite)
# show result bounding boxes
result_BB(tracking_results_path, frame_path, frame_BB_path,
sequence_ID, args.YT_ID, args.overwrite)
if args.play_video:
# create results on video
result_video(frame_BB_path, video_BB_path, args.overwrite)
# run video
os.system(f"xdg-open {video_BB_path}")
sleep(args.duration + args.sleep_between_videos)
import utils
import torch
import sys, os
from torchvision import transforms
from artnet import ARTNet
from PIL import Image
labels = ['nonporn', 'p**n']
assert len(sys.argv) == 3, 'Insufficient number of argument'
v = utils.extract_frames(sys.argv[2], 'samples')
transform = transforms.Compose([
transforms.Resize((112, 112)),
transforms.RandomCrop((112, 112)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
frames = [Image.open(os.path.join(v, f)) for f in os.listdir(v)]
frames = [transform(f) for f in frames]
tensors = []
for i in range(0, len(frames), 16):
tensors.append(torch.stack(frames[i:i + 16]))
model = ARTNet()
model.load_state_dict(torch.load(sys.argv[1]))
model = model.to('cuda')
for tensor in tensors:
tensor = tensor.to('cuda')
tensor = tensor.unsqueeze(0)
else:
categories = models.load_categories('category_momentsv2.txt')
# Load the video frame transform
transform = models.load_transform()
# Obtain video frames
if args.frame_folder is not None:
print('Loading frames in {}'.format(args.frame_folder))
import glob
# here make sure after sorting the frame paths have the correct temporal order
frame_paths = sorted(glob.glob(os.path.join(args.frame_folder, '*.jpg')))
frames = load_frames(frame_paths)
else:
print('Extracting frames using ffmpeg...')
frames = extract_frames(args.video_file, args.num_segments)
# Prepare input tensor
if 'resnet3d50' in args.arch:
# [1, num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames], 1).unsqueeze(0)
else:
# [num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames])
# Make video prediction
with torch.no_grad():
logits = model(input)
h_x = F.softmax(logits, 1).mean(dim=0)
probs, idx = h_x.sort(0, True)
# Get dataset categories
categories = models.load_categories()
# Load the video frame transform
transform = models.load_transform()
# Obtain video frames
if args.frame_folder is not None:
print('Loading frames in {}'.format(args.frame_folder))
import glob
# here make sure after sorting the frame paths have the correct temporal order
frame_paths = sorted(glob.glob(os.path.join(args.frame_folder, '*.jpg')))
frames = load_frames(frame_paths)
else:
print('Extracting frames using ffmpeg...')
frames = extract_frames(args.video_file, args.num_segments,
args.start_frame, args.subsample_rate)
# Prepare input tensor
if args.arch == 'resnet3d50':
# [1, num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames], 1).unsqueeze(0)
else:
# [num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames])
# Make video prediction
with torch.no_grad():
logits = model(input)
h_x = F.softmax(logits, 1).mean(dim=0)
probs, idx = h_x.sort(0, True)
#categories = models.load_categories()
# Load the video frame transform
transform = models.load_transform()
# Obtain video frames
if args.frame_folder is not None:
print('Loading frames in {}'.format(args.frame_folder))
import glob
# here make sure after sorting the frame paths have the correct temporal order
frame_paths = sorted(glob.glob(os.path.join(args.frame_folder, '*.jpg')))
print(frame_paths)
frames = load_frames(frame_paths)
else:
print('Extracting frames using ffmpeg...')
frames = extract_frames(name, args.num_segments)
# Prepare input tensor
if args.arch == 'resnet3d50':
# [1, num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames], 1).unsqueeze(0)
else:
# [num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames])
# Make video prediction
with torch.no_grad():
logits = model(input)
h_x = F.softmax(logits, 1).mean(dim=0)
probs, idx = h_x.sort(0, True)
def load_video(video_hash):
yt = YouTube('https://youtube.com/embed/%s?start=%d&end=%d' %
(video_hash, start, end))
video = yt.streams.all()[0]
name = video.download('/tmp')
# Load model
model = models.load_model(arch)
av_categories = pd.read_csv('CVS_Actions(NEW).csv',
delimiter=';').values.tolist()
trax = pd.read_csv('audioTracks_urls.csv')
# Get dataset categories
#categories = models.load_categories()
# Load the video frame transform
transform = models.load_transform()
# Obtain video frames
if frame_folder is not None:
print('Loading frames in {}'.format(frame_folder))
import glob
# here make sure after sorting the frame paths have the correct temporal order
frame_paths = sorted(glob.glob(os.path.join(frame_folder, '*.jpg')))
print(frame_paths)
frames = load_frames(frame_paths)
else:
print('Extracting frames using ffmpeg...')
frames = extract_frames(name, num_segments)
# Prepare input tensor
if arch == 'resnet3d50':
# [1, num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames],
1).unsqueeze(0)
else:
# [num_frames, 3, 224, 224]
input = torch.stack([transform(frame) for frame in frames])
# Make video prediction
with torch.no_grad():
logits = model(input)
h_x = F.softmax(logits, 1).mean(dim=0)
probs, idx = h_x.sort(0, True)
# Output the prediction.
print('RESULT ON ' + name)
y = float(av_categories[idx[0]][1]) * 125
x = float(av_categories[idx[0]][2]) * 125
trax = trax.assign(
dist=lambda row: np.sqrt((x - row.valence)**2 + (y - row.energy)**2))
print('min', trax['dist'].min())
best = trax.nsmallest(100, 'dist')
print(best)
rand = randint(0, 9)
print(rand)
choice = best.iloc[rand, [1, 2, 5]]
print('choice', choice)
song = 'valence: ' + str(x) + ' arousal: ' + str(
y) + " " + choice[0] + ' ' + choice[1]
print(song)
print(x, y)
for i in range(0, 5):
print('{:.3f} -> {} ->{}'.format(probs[i], idx[i],
av_categories[idx[i]]))
print('result cutegories', av_categories[idx[i]][0],
av_categories[idx[i]][1])
#r = requests.get(match.iloc[0,2], allow_redirects=True)
r = requests.get(choice[2], allow_redirects=True)
open('./tmp/preview.mp3', 'wb').write(r.content)
# Render output frames with prediction text.
rendered_output = './tmp/' + video_hash + '_' + str(x) + '_' + str(
y) + '.mp4'
if rendered_output is not None:
clip = VideoFileClip(name).subclip(30, 60)
audioclip = AudioFileClip('./tmp/preview.mp3')
txt_clip = TextClip(song, fontsize=16, color='white')
clip_final = clip.set_audio(audioclip)
video = CompositeVideoClip([clip_final, txt_clip])
video.set_duration(30).write_videofile(rendered_output)
from utils import make_video_from_frames, extract_frames
from inference import Infer
from datetime import datetime
import os
import segmentation_models_pytorch as smp
input_file = "ufc234_gastelum_bisping_1080p_nosound_cut.mp4"
output_file = "test.mp4"
intermediate_dir = "video_frames"
intermediate_dir2 = "video_frames_processed"
print("Frame extraction")
now = datetime.now()
extract_frames(input_file, intermediate_dir)
print(datetime.now() - now)
print("Inference")
num_batches = 70
model = smp.Unet("se_resnext50_32x4d")
model.cuda()
for i in range(0, num_batches):
inferer1 = Infer(
rez_dir=intermediate_dir2,
image_folder=intermediate_dir,
batch_size=2,
num_batches=num_batches,
batch_id=i,
threshold=0.5,
)
inferer1.inference(model)
print(datetime.now() - now)
print("Making video")
make_video_from_frames(frame_dir=os.path.join(intermediate_dir2, "mask"),
