repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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HDN | HDN-master/toolkit/benchmarks/__init__.py | 0 | 0 | 0 | py | |
HDN | HDN-master/toolkit/benchmarks/POT/video2img.py | import cv2
from hdn.core.config import cfg
import os
if __name__ == "__main__":
# ad_video_root = cfg.BASE.PROJ_PATH + 'demo/videos/dior.mp4' # 'chanel.mp4' 'dior'
ad_video_root = cfg.BASE.PROJ_PATH + 'demo/t5_videos/replace-video/zju-view.mp4' # 'chanel.mp4' 'dior'[demo/videos/t5_videos/replace-video/21centfo... | 1,060 | 38.296296 | 157 | py |
HDN | HDN-master/toolkit/benchmarks/POT/convert2Points.py | '''
convert the homography reuslts to the polygon form
'''
import cv2
import sys,os
import glob
import numpy as np
from hdn.core.config import cfg
import csv
inDir = cfg.BASE.BASE_PATH + "POT/results/TSA-ESM-original-H/"
outDir = cfg.BASE.BASE_PATH + "POT/results/TSA-ESM/"
anno_base_path = cfg.BASE.DATA_ROOT + "SOT/... | 1,690 | 31.519231 | 136 | py |
HDN | HDN-master/toolkit/benchmarks/POT/convert_GOP-ESM_result_form.py | """
This script is for convert the results provided by GOP-ESM to our forms.
GOP-ESM form:
frame ulx uly urx ury lrx lry llx lly
frame00001.jpg 399.0000 150.0000 612.0000 166.0000 595.0000 508.0000 384.0000 504.0000
frame00002.jpg 397.2197 149.7962 610.5286 165.9439 593.6185 508.1770 382.2537 504.2491
frame00003.jpg ... | 2,389 | 36.34375 | 86 | py |
HDN | HDN-master/toolkit/benchmarks/POT/convert2Homograhpy(GOP-ESM).py | import cv2
import sys, os
import glob
import numpy as np
from hdn.core.config import cfg
inDir = cfg.BASE.PROJ_PATH + 'experiments/tracker_homo_config/results/POT/NGF'
outDir = cfg.BASE.BASE_PATH + 'POT/results/NGFHomography/'
if __name__ == "__main__":
if not os.path.isdir(outDir):
os.mkdir(outDir)
... | 1,381 | 34.435897 | 93 | py |
HDN | HDN-master/toolkit/benchmarks/POT/convert2Homography.py | import os
import cv2
import numpy as np
from hdn.core.config import cfg
inDir = cfg.BASE.BASE_PATH + 'siamban_liyang_86/hdn/experiments/tracker_homo_config/results/POT/HDN'
outDir = cfg.BASE.BASE_PATH + 'POT/results/HDNHomography/'
if __name__ == "__main__":
if not os.path.isdir(outDir):
os.mkdir(outDir... | 1,250 | 36.909091 | 116 | py |
HDN | HDN-master/toolkit/benchmarks/POT/generate_json_for_POT.py | import os
import csv
import json
from hdn.core.config import cfg
import argparse
parser = argparse.ArgumentParser(description='POT json')
parser.add_argument('--dataset', default='POT210', type=str, help='POT210 or POT280')
args = parser.parse_args()
if __name__ == "__main__":
anno_path = cfg.BASE.DATA_PATH + "PO... | 3,431 | 36.714286 | 100 | py |
HDN | HDN-master/toolkit/benchmarks/POT/change_pot_result_format.py | import cv2
import os
from hdn.core.config import cfg
if __name__ == "__main__":
import os, shutil
result_path = cfg.BASE.PROJ_PATH + 'experiments/hdn_r50_l234_pot/results/POT/model_otb'
new_result_path = cfg.BASE.PROJ_PATH + 'experiments/hdn_r50_l234_pot/results/POT/model_otb_convert_with_minus'
for i... | 935 | 38 | 114 | py |
HDN | HDN-master/toolkit/benchmarks/POT/video_replace.py | import numpy as np
import os
import csv
import matplotlib.pyplot as plt
import cv2
import os.path as osp
from hdn.core.config import cfg
plot_list = ['V19_7','V14_7','V11_7','V09_7','V07_7', 'V05_7', 'V04_7','V01_7']
# plot_list = ['V30_7','V02_7','V11_7', 'V29_7']
def plotPOT(im_root, res_root, anno_root, plot_root... | 4,236 | 50.048193 | 160 | py |
HDN | HDN-master/toolkit/benchmarks/POT/change_pot_results_name.py | import cv2
import os
from hdn.core.config import cfg
'''This script is using for transform POT dataset from video to img frames.'''
if __name__ == "__main__":
import os, shutil
result_path = cfg.BASE.PROJ_PATH + 'experiments/tracker_homo_config/results/POT/HDN'
for i in os.listdir(result_path):
or... | 539 | 32.75 | 88 | py |
HDN | HDN-master/toolkit/benchmarks/POT/pot_video_to_pic.py | import cv2
import os
from hdn.core.config import cfg
'''This script is used for transform POT dataset from video to img frames.'''
def video2img(video_path, frame_save_dir):
cap = cv2.VideoCapture(video_path)
suc = cap.isOpened()
frame_count = 0
while suc:
suc, frame = cap.read()
frame... | 1,322 | 34.756757 | 121 | py |
HDN | HDN-master/toolkit/benchmarks/POT/create_pot_dir.py | import cv2
import os
from hdn.core.config import cfg
if __name__ == "__main__":
import os, shutil
anno_path = cfg.BASE.DATA_PATH + "POT_annotation/"
base_path = cfg.BASE.DATA_PATH + "POT_annotation_280/"
for i in range(1,31):
for j in range(1,8):
new_v_dir = base_path+'V%02d_%d'%(i... | 702 | 30.954545 | 75 | py |
HDN | HDN-master/toolkit/benchmarks/POT/plot.py | import numpy as np
import os
import csv
import matplotlib.pyplot as plt
import cv2
import os.path as osp
from hdn.core.config import cfg
def plotPOT(im_root, res_root, anno_root, plot_root,tracker):
for i in range(1, 31):
for j in range(1,8):
seq_name_prefix = "V%02d"%i
seq_name = "V... | 5,329 | 43.416667 | 150 | py |
HDN | HDN-master/toolkit/benchmarks/POT/__init__.py | 0 | 0 | 0 | py | |
HDN | HDN-master/toolkit/benchmarks/POIC/convert_GOP-ESM_POIC_result_form.py | """
This script is for convert the results provided by GOP-ESM to our forms.
GOP-ESM form:
frame ulx uly urx ury lrx lry llx lly
frame00001.jpg 399.0000 150.0000 612.0000 166.0000 595.0000 508.0000 384.0000 504.0000
frame00002.jpg 397.2197 149.7962 610.5286 165.9439 593.6185 508.1770 382.2537 504.2491
frame00003.jpg ... | 2,344 | 35.640625 | 86 | py |
HDN | HDN-master/toolkit/benchmarks/POIC/plot.py | import numpy as np
import os
import csv
import matplotlib.pyplot as plt
import cv2
import os.path as osp
from hdn.core.config import cfg
"""
gt format
frame ulx uly urx ury lrx lry llx lly
frame00001.jpg 207.0010 134.0020 557.9950 122.0010 562.0030 485.0090 198.9940 485.9930
frame00002.jpg 206.9468 134.5056 556.9513 1... | 3,081 | 43.028571 | 155 | py |
HDN | HDN-master/toolkit/benchmarks/POIC/__init__.py | 0 | 0 | 0 | py | |
HDN | HDN-master/toolkit/benchmarks/POIC/generate_json_for_poic.py | """
generate the JSON file for POIC dataset. POIC is our testing dataset.
"""
import os
import csv
import json
import re
import os.path as osp
from hdn.core.config import cfg
if __name__ == "__main__":
POIC_Path = cfg.BASE.DATA_PATH + 'POIC'
POIC_Path_seqs = osp.join(POIC_Path, 'sequences')
POIC_Path_gts = ... | 2,313 | 38.220339 | 127 | py |
HDN | HDN-master/toolkit/benchmarks/UCSB/generate_json_for_ucsb.py | """
generate the JSON file for UCSB dataset. UCSB is our testing dataset. not including the homo anno
"""
import os
import csv
import json
import re
from hdn.core.config import cfg
if __name__ == "__main__":
UCSB_Path = cfg.BASE.DATA_PATH + 'UCSB'
seq_dirs = os.listdir(UCSB_Path)
json_obj = {}
for seq_... | 2,402 | 40.431034 | 168 | py |
HDN | HDN-master/toolkit/benchmarks/UCSB/plot.py | import numpy as np
import os
import csv
import matplotlib.pyplot as plt
import cv2
import os.path as osp
from hdn.core.config import cfg
"""
gt format
frame ulx uly urx ury lrx lry llx lly
frame00001.jpg 207.0010 134.0020 557.9950 122.0010 562.0030 485.0090 198.9940 485.9930
frame00002.jpg 206.9468 134.5056 556.9513 1... | 4,726 | 44.893204 | 155 | py |
HDN | HDN-master/toolkit/benchmarks/UCSB/convert_GOP-ESM_UCSB_result_form.py | """
This script is for convert the results provided by GOP-ESM to our forms.
GOP-ESM form:
frame ulx uly urx ury lrx lry llx lly
frame00001.jpg 399.0000 150.0000 612.0000 166.0000 595.0000 508.0000 384.0000 504.0000
frame00002.jpg 397.2197 149.7962 610.5286 165.9439 593.6185 508.1770 382.2537 504.2491
frame00003.jpg ... | 4,072 | 39.326733 | 94 | py |
HDN | HDN-master/toolkit/datasets/DeepHomo.py | from torch.utils.data import Dataset
import numpy as np
import cv2, torch
import os
def make_mesh(patch_w, patch_h):
x_flat = np.arange(0, patch_w)
x_flat = x_flat[np.newaxis, :]
y_one = np.ones(patch_h)
y_one = y_one[:, np.newaxis]
x_mesh = np.matmul(y_one, x_flat)
y_flat = np.arange(0, patc... | 6,774 | 36.021858 | 118 | py |
HDN | HDN-master/toolkit/datasets/video.py | import os
import cv2
import re
import numpy as np
import json
from glob import glob
class Video(object):
def __init__(self, name, root, video_dir, init_rect, img_names,
gt_rect, attr='0', load_img=False):
self.name = name
self.video_dir = video_dir
self.init_rect = init_rect
... | 5,400 | 37.304965 | 113 | py |
HDN | HDN-master/toolkit/datasets/nfs.py | import json
import os
import numpy as np
from tqdm import tqdm
from glob import glob
from .dataset import Dataset
from .video import Video
class NFSVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rectangle
img_nam... | 2,781 | 34.666667 | 91 | py |
HDN | HDN-master/toolkit/datasets/poic.py | import json
import os
import numpy as np
from PIL import Image
from tqdm import tqdm
from glob import glob
from .dataset import Dataset
from .video import Video
class POICVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rec... | 3,467 | 37.533333 | 118 | py |
HDN | HDN-master/toolkit/datasets/lasot.py | import os
import json
import numpy as np
from tqdm import tqdm
from glob import glob
from .dataset import Dataset
from .video import Video
class LaSOTVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rectangle
img_na... | 3,325 | 33.28866 | 72 | py |
HDN | HDN-master/toolkit/datasets/dataset.py | from tqdm import tqdm
class Dataset(object):
def __init__(self, name, dataset_root):
self.name = name
self.dataset_root = dataset_root
self.videos = None
def __getitem__(self, idx):
if isinstance(idx, str):
return self.videos[idx]
elif isinstance(idx, int):
... | 833 | 25.903226 | 69 | py |
HDN | HDN-master/toolkit/datasets/trackingnet.py | import json
import os
import numpy as np
from tqdm import tqdm
from glob import glob
from .dataset import Dataset
from .video import Video
class TrackingNetVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rectangle
... | 2,819 | 36.105263 | 91 | py |
HDN | HDN-master/toolkit/datasets/uav.py | import os
import json
from tqdm import tqdm
from glob import glob
from .dataset import Dataset
from .video import Video
class UAVVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rectangle
img_names: image names
... | 2,075 | 30.938462 | 72 | py |
HDN | HDN-master/toolkit/datasets/vot.py | import os
import cv2
import json
import numpy as np
from glob import glob
from tqdm import tqdm
from PIL import Image
from .dataset import Dataset
from .video import Video
class VOTVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect... | 7,454 | 37.828125 | 96 | py |
HDN | HDN-master/toolkit/datasets/pot.py | import json
import os
import numpy as np
from PIL import Image
from tqdm import tqdm
from glob import glob
from .dataset import Dataset
from .video import Video
class POTVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rect... | 4,257 | 39.942308 | 118 | py |
HDN | HDN-master/toolkit/datasets/__init__.py | from .vot import VOTDataset, VOTLTDataset
from .otb import OTBDataset
from .uav import UAVDataset
from .lasot import LaSOTDataset
from .nfs import NFSDataset
from .trackingnet import TrackingNetDataset
from .got10k import GOT10kDataset
from .pot import POTDataset
from .DeepHomo import DeepHomoTestDataset, DeepHomoTrain... | 2,009 | 34.892857 | 79 | py |
HDN | HDN-master/toolkit/datasets/got10k.py |
import json
import os
from tqdm import tqdm
from .dataset import Dataset
from .video import Video
class GOT10kVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rectangle
img_names: image names
gt_rect: groun... | 2,754 | 35.733333 | 91 | py |
HDN | HDN-master/toolkit/datasets/ucsb.py | import json
import os
import numpy as np
from PIL import Image
from tqdm import tqdm
from glob import glob
from .dataset import Dataset
from .video import Video
class UCSBVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rec... | 3,393 | 35.891304 | 118 | py |
HDN | HDN-master/toolkit/datasets/otb.py | import json
import os
import numpy as np
from PIL import Image
from tqdm import tqdm
from glob import glob
from .dataset import Dataset
from .video import Video
class OTBVideo(Video):
"""
Args:
name: video name
root: dataset root
video_dir: video directory
init_rect: init rec... | 4,268 | 35.801724 | 81 | py |
HDN | HDN-master/toolkit/visualization/draw_eao.py | import matplotlib.pyplot as plt
import numpy as np
import pickle
from matplotlib import rc
from .draw_utils import COLOR, MARKER_STYLE
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=True)
def draw_eao(result):
fig = plt.figure()
ax = fig.add_subplot(111, projection='polar')... | 1,716 | 33.34 | 86 | py |
HDN | HDN-master/toolkit/visualization/draw_utils.py |
COLOR = ((1, 0, 0),
(0, 1, 0),
(1, 0, 1),
(1, 1, 0),
(0 , 162/255, 232/255),
(0.5, 0.5, 0.5),
(0, 0, 1),
(0, 1, 1),
(136/255, 0 , 21/255),
(255/255, 127/255, 39/255),
(0, 0, 0))
CB91_Blue = '#2CBDFE'
CB91_Green = '#47DBCD'
CB9... | 863 | 24.411765 | 95 | py |
HDN | HDN-master/toolkit/visualization/draw_f1.py | import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rc
from .draw_utils import COLOR, LINE_STYLE
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=True)
def draw_f1(result, bold_name=None):
# drawing f1 contour
fig, ax = plt.subplots()
for f1 in np.ar... | 2,176 | 35.283333 | 80 | py |
HDN | HDN-master/toolkit/visualization/__init__.py | from .draw_f1 import draw_f1
from .draw_success_precision import draw_success_precision
from .draw_eao import draw_eao
| 119 | 29 | 58 | py |
HDN | HDN-master/toolkit/visualization/draw_success_precision.py | import matplotlib.pyplot as plt
import numpy as np
from .draw_utils import COLOR, LINE_STYLE
def draw_success_precision(success_ret, name, videos, attr, precision_ret=None,
norm_precision_ret=None, bold_name=None, axis=[0, 1]):
# success plot
fig, ax = plt.subplots()
ax.grid(b=True)
ax.set_asp... | 5,042 | 42.852174 | 91 | py |
HDN | HDN-master/toolkit/visualization/draw_homo_success_precision.py | import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rc
from .draw_utils import COLOR, LINE_STYLE
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
rc('text', usetex=False)
def draw_success_precision(success_ret, name, videos, attr, precision_ret=None,
norm... | 6,817 | 43.562092 | 110 | py |
HDN | HDN-master/toolkit/visualization/draw_homo_success_precision_tex.py | import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rc
import os
from .draw_utils import COLOR, LINE_STYLE, COLOR2, LINE_STYLE2
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
font1 = {
'family':'sans-serif',
# 'sans-serif':['Helvetica']
# 'family': 'TImes New Roman',
... | 11,201 | 44.352227 | 150 | py |
HDN | HDN-master/toolkit/utils/misc.py | """
@author fangyi.zhang@vipl.ict.ac.cn
"""
import numpy as np
def determine_thresholds(confidence, resolution=100):
"""choose threshold according to confidence
Args:
confidence: list or numpy array or numpy array
reolution: number of threshold to choose
Restures:
threshold: n... | 889 | 27.709677 | 82 | py |
HDN | HDN-master/toolkit/utils/statistics.py | """
@author fangyi.zhang@vipl.ict.ac.cn
"""
import numpy as np
from . import region
# import shapely
from shapely.geometry import Polygon
def calculate_failures(trajectory):
""" Calculate number of failures
Args:
trajectory: list of bbox
Returns:
num_failures: number of failures
... | 8,557 | 30.814126 | 95 | py |
HDN | HDN-master/toolkit/utils/__init__.py | from . import region
from .statistics import *
| 47 | 15 | 25 | py |
HDN | HDN-master/training_dataset/got_10k/gen_json_old.py | from os.path import join
from os import listdir
import json
import numpy as np
import math
print('loading json (raw got10k info), please wait 20 seconds~')
got10k = json.load(open('got10k.json', 'r'))
def check_size(frame_sz, bbox):
min_ratio = 0.1
max_ratio = 0.75
# only accept objects >10% and <75% of ... | 2,674 | 35.643836 | 118 | py |
HDN | HDN-master/training_dataset/got_10k/mkdir_got_train.py | import cv2
import os
import argparse
import glob
import numpy as np
from os.path import join
from os import listdir
from hdn.core.config import cfg
'''This script is using for create GOT dataset for training.'''
parser = argparse.ArgumentParser()
parser.add_argument('--dir',type=str, default='GOT10k', help='your got_... | 1,783 | 33.980392 | 106 | py |
HDN | HDN-master/training_dataset/got_10k/parse_got10k.py | # -*- coding:utf-8 -*-
# ! ./usr/bin/env python
# __author__ = 'zzp'
import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
from hdn.core.config import cfg
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dir',type=str, default='./GOT_10k', help=... | 2,219 | 34.238095 | 117 | py |
HDN | HDN-master/training_dataset/got_10k/gen_json.py | from os.path import join
from os import listdir
import json
import numpy as np
import math
# In this version, I neglect the rotation angle. cause GOT doesn't have poly gts.(TODO here this change may induce the problem in training for siamldes)
print('loading json (raw got10k info), please wait 20 seconds~')
got10k = j... | 3,015 | 36.7 | 152 | py |
HDN | HDN-master/training_dataset/got_10k/par_crop.py | from os.path import join, isdir, exists
from os import listdir, mkdir, makedirs
import cv2
import numpy as np
import glob
from concurrent import futures
import sys
import time
got10k_base_path = './GOT_10k'
sub_sets = sorted({'train_data', 'val_data'})
# Print iterations progress (thanks StackOverflow)
def printPro... | 5,016 | 39.788618 | 122 | py |
HDN | HDN-master/training_dataset/got_10k/visual.py | import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
visual = True
GOT_10k_base_path = './GOT_10k'
sub_sets = sorted({'train_data', 'val_data'})
for sub_set in sub_sets:
sub_set_base_path = join(GOT_10k_base_path, sub_set)
for video_set in sorted(listdir(sub... | 1,501 | 33.930233 | 117 | py |
HDN | HDN-master/training_dataset/pot/mkdir_pot_train.py | import cv2
import os
from hdn.core.config import cfg
'''This script is using for create POT dataset for training.'''
if __name__ == "__main__":
import os, shutil
video_type_num = 1 #select num of trans types
start_type = 1
start_v = 1#1 16
end_v = 31# 31 17
# video2img(video_path, frame_save_di... | 2,744 | 45.525424 | 113 | py |
HDN | HDN-master/training_dataset/pot/gen_json.py | from os.path import join
from os import listdir
import json
import numpy as np
import cv2
import math
print('loading json (raw pot info), please wait 20 seconds~')
pot = json.load(open('pot.json', 'r'))
def check_size(frame_sz, bbox):
min_ratio = 0.1
max_ratio = 0.75
# only accept objects >10% and <75% of... | 6,634 | 47.430657 | 189 | py |
HDN | HDN-master/training_dataset/pot/parse_pot.py | # -*- coding:utf-8 -*-
# ! ./usr/bin/env python
# __author__ = 'zzp'
import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dir',type=str, default=cfg.BASE.BASE_PATH + 'hdn_liyang/hdn/training_dat... | 2,571 | 35.742857 | 142 | py |
HDN | HDN-master/training_dataset/pot/par_crop.py | from os.path import join, isdir, exists
from os import listdir, mkdir, makedirs
import cv2
import numpy as np
import glob
from concurrent import futures
import sys
import time
import math
pot_base_path = './POT_train'
# Print iterations progress (thanks StackOverflow)
def printProgress(iteration, total, prefix='', s... | 5,514 | 39.255474 | 117 | py |
HDN | HDN-master/training_dataset/pot/create_pot_test_list.py | from os.path import join
from os import listdir
import json
import numpy as np
num_types = 1
test_list = []
start_type = 1
for i in range(25,31):
for j in range(start_type,num_types+1):
print('i,j',i,j)
test_list.append('V%02d_%d\n'%(i,j))
# for i in range(17, 31):
# for j in range(start_type, ... | 543 | 22.652174 | 50 | py |
HDN | HDN-master/training_dataset/pot/__init__.py | 0 | 0 | 0 | py | |
HDN | HDN-master/training_dataset/got_homo/mkdir_got_train.py | import cv2
import os
import argparse
import glob
import numpy as np
from os.path import join
from os import listdir
'''This script is using for create GOT dataset for training.'''
parser = argparse.ArgumentParser()
parser.add_argument('--dir',type=str, default='GOT10k', help='your got_10k data dir')
args = parser.pars... | 2,662 | 40.609375 | 113 | py |
HDN | HDN-master/training_dataset/got_homo/parse_got10k.py | # -*- coding:utf-8 -*-
# ! ./usr/bin/env python
# __author__ = 'zzp'
import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dir',type=str, default='./GOT_10k', help='your got_10k data dir')
args =... | 2,098 | 33.409836 | 117 | py |
HDN | HDN-master/training_dataset/got_homo/gen_json.py | from os.path import join
from os import listdir
import json
import numpy as np
import math
print('loading json (raw got10k info), please wait 20 seconds~')
got10k = json.load(open('got10k.json', 'r'))
def check_size(frame_sz, bbox):
min_ratio = 0.1
max_ratio = 0.75
# only accept objects >10% and <75% of ... | 2,889 | 37.533333 | 118 | py |
HDN | HDN-master/training_dataset/got_homo/par_crop.py | from os.path import join, isdir, exists
from os import listdir, mkdir, makedirs
import cv2
import numpy as np
import glob
from concurrent import futures
import sys
import time
got10k_base_path = './GOT_10k'
sub_sets = sorted({'train_data', 'val_data'})
# Print iterations progress (thanks StackOverflow)
def printPro... | 5,205 | 39.046154 | 122 | py |
HDN | HDN-master/training_dataset/got_homo/visual.py | import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
visual = True
GOT_10k_base_path = './GOT_10k'
sub_sets = sorted({'train_data', 'val_data'})
for sub_set in sub_sets:
sub_set_base_path = join(GOT_10k_base_path, sub_set)
for video_set in sorted(listdir(sub... | 1,501 | 33.930233 | 117 | py |
HDN | HDN-master/training_dataset/yt_bb/gen_json.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import json
from os.path import join, exists
import pandas as pd
# The data sets to be downloaded
d_sets = ['yt_bb_detection_validation', 'yt_bb_detection_train']
# Column names for detection CSV files
col_names = ['youtube_id', 't... | 2,284 | 30.736111 | 88 | py |
HDN | HDN-master/training_dataset/yt_bb/par_crop.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from subprocess import check_call
from concurrent import futures
import os
from os.path import join
import sys
import cv2
import pandas as pd
import numpy as np
# The data sets to be downloaded
d_sets = ['yt_bb_detection_validation'... | 6,079 | 36.763975 | 114 | py |
HDN | HDN-master/training_dataset/yt_bb/visual.py | import glob
import pandas as pd
import numpy as np
import cv2
visual = True
col_names = ['youtube_id', 'timestamp_ms', 'class_id', 'class_name',
'object_id', 'object_presence', 'xmin', 'xmax', 'ymin', 'ymax']
df = pd.DataFrame.from_csv('yt_bb_detection_validation.csv', header=None, index_col=False)
df.c... | 1,376 | 32.585366 | 90 | py |
HDN | HDN-master/training_dataset/yt_bb/checknum.py | import pandas as pd
import glob
col_names = ['youtube_id', 'timestamp_ms', 'class_id', 'class_name',
'object_id', 'object_presence', 'xmin', 'xmax', 'ymin', 'ymax']
sets = ['yt_bb_detection_validation', 'yt_bb_detection_train']
for subset in sets:
df = pd.DataFrame.from_csv('./'+ subset +'.csv', hea... | 1,041 | 37.592593 | 82 | py |
HDN | HDN-master/training_dataset/pot_e2e/mkdir_pot_train.py | import cv2
import os
from hdn.core.config import cfg
'''This script is using for create POT dataset for training.'''
if __name__ == "__main__":
import os, shutil
video_type_num = 7 #select num of trans types
start_type = 1
start_v = 1#1 16
end_v = 31# 31 17
# video2img(video_path, frame_save_d... | 2,890 | 42.149254 | 113 | py |
HDN | HDN-master/training_dataset/pot_e2e/gen_json_old.py | from os.path import join
from os import listdir
import json
import numpy as np
import cv2
import math
# In this version (pot_e2e), I neglect the rotation angle. cause for e2e training, all the pairs were generate from one image.
print('loading json (raw pot info), please wait 20 seconds~')
pot = json.load(open('pot.jso... | 6,718 | 48.043796 | 189 | py |
HDN | HDN-master/training_dataset/pot_e2e/gen_json.py | from os.path import join
from os import listdir
import json
import numpy as np
import cv2
import math
# In this version (pot_e2e), I neglect the rotation angle. cause for e2e training, all the pairs were generate from one image.
print('loading json (raw pot info), please wait 20 seconds~')
pot = json.load(open('pot.jso... | 3,018 | 38.207792 | 126 | py |
HDN | HDN-master/training_dataset/pot_e2e/parse_pot.py | # -*- coding:utf-8 -*-
# ! ./usr/bin/env python
# __author__ = 'zzp'
import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
import argparse
from hdn.core.config import cfg
parser = argparse.ArgumentParser()
parser.add_argument('--dir',type=str, default=cfg.BASE.BASE_PATH... | 2,610 | 36.3 | 150 | py |
HDN | HDN-master/training_dataset/pot_e2e/gen_json_unsup.py | from os.path import join
from os import listdir
import json
import numpy as np
import cv2
import math
# In this version (pot_e2e), I neglect the rotation angle. cause for e2e training, all the pairs were generate from one image.
print('loading json (raw pot info), please wait 20 seconds~')
pot = json.load(open('pot.jso... | 3,030 | 38.363636 | 126 | py |
HDN | HDN-master/training_dataset/pot_e2e/par_crop.py | from os.path import join, isdir, exists
from os import listdir, mkdir, makedirs
import cv2
import numpy as np
import glob
from concurrent import futures
import sys
import time
import math
pot_base_path = './POT_train_e2e'
# Print iterations progress (thanks StackOverflow)
def printProgress(iteration, total, prefix='... | 5,543 | 39.173913 | 117 | py |
HDN | HDN-master/training_dataset/pot_e2e/create_pot_test_list.py | from os.path import join
from os import listdir
import json
import numpy as np
num_types = 7
test_list = []
start_type = 1
for i in range(22,31):
for j in range(start_type,num_types+1):
if j != 3 and j != 7:
continue
print('i,j',i,j)
test_list.append('V%02d_%d\n'%(i,j))
# for i ... | 598 | 22.96 | 54 | py |
HDN | HDN-master/training_dataset/pot_e2e/__init__.py | 0 | 0 | 0 | py | |
HDN | HDN-master/training_dataset/det/gen_json.py | from os.path import join, isdir
from os import mkdir
import glob
import xml.etree.ElementTree as ET
import json
js = {}
VID_base_path = './ILSVRC'
ann_base_path = join(VID_base_path, 'Annotations/DET/train/')
sub_sets = ('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i')
count = 0
for sub_set in sub_sets:
sub_set_base_p... | 1,757 | 33.470588 | 98 | py |
HDN | HDN-master/training_dataset/det/par_crop.py | from os.path import join, isdir
from os import mkdir, makedirs
import cv2
import numpy as np
import glob
import xml.etree.ElementTree as ET
from concurrent import futures
import time
import sys
# Print iterations progress (thanks StackOverflow)
def printProgress(iteration, total, prefix='', suffix='', decimals=1, bar... | 4,601 | 39.725664 | 112 | py |
HDN | HDN-master/training_dataset/det/visual.py | from os.path import join
from os import listdir
import cv2
import numpy as np
import glob
import xml.etree.ElementTree as ET
visual = False
color_bar = np.random.randint(0, 255, (90, 3))
VID_base_path = './ILSVRC'
ann_base_path = join(VID_base_path, 'Annotations/DET/train/')
img_base_path = join(VID_base_path, 'Data/... | 1,760 | 38.133333 | 91 | py |
HDN | HDN-master/training_dataset/pot_homo/mkdir_pot_train.py | import cv2
import os
'''This script is using for create POT dataset for training.'''
from hdn.core.config import cfg
if __name__ == "__main__":
import os, shutil
video_type_num = 7 #select num of trans types
start_type = 1
start_v = 1#1 16
end_v = 31# 31 17
# video2img(video_path, frame_save_dir... | 3,024 | 46.265625 | 113 | py |
HDN | HDN-master/training_dataset/pot_homo/gen_json.py | from os.path import join
from os import listdir
import json
import numpy as np
import cv2
import math
print('loading json (raw pot info), please wait 20 seconds~')
pot = json.load(open('pot.json', 'r'))
def check_size(frame_sz, bbox):
min_ratio = 0.1
max_ratio = 0.75
# only accept objects >10% and <75% of... | 4,542 | 42.266667 | 122 | py |
HDN | HDN-master/training_dataset/pot_homo/parse_pot.py | # -*- coding:utf-8 -*-
# ! ./usr/bin/env python
# __author__ = 'zzp'
import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
import os.path as osp
import argparse
import os
from hdn.core.config import cfg
parser = argparse.ArgumentParser()
parser.add_argument('--dir',type=... | 2,712 | 36.680556 | 152 | py |
HDN | HDN-master/training_dataset/pot_homo/par_crop.py | from os.path import join, isdir, exists
from os import listdir, mkdir, makedirs
import cv2
import numpy as np
import glob
from concurrent import futures
import sys
import time
import math
pot_base_path = './POT_train_homo'
# Print iterations progress (thanks StackOverflow)
def printProgress(iteration, total, prefix=... | 6,860 | 43.264516 | 147 | py |
HDN | HDN-master/training_dataset/pot_homo/create_pot_test_list.py | from os.path import join
from os import listdir
import json
import numpy as np
num_types = 7
test_list = []
start_type = 1
for i in range(30,31):
for j in range(start_type,num_types+1):
if j != 3 and j != 7:
continue
print('i,j',i,j)
test_list.append('V%02d_%d\n'%(i,j))
# for i ... | 599 | 23 | 55 | py |
HDN | HDN-master/training_dataset/pot_homo/__init__.py | 0 | 0 | 0 | py | |
HDN | HDN-master/training_dataset/lasot/gen_json.py | from os.path import join
from os import listdir
import json
import numpy as np
print('loading json (raw lasot info), please wait 20 seconds~')
lasot = json.load(open('lasot.json', 'r'))
def check_size(frame_sz, bbox):
min_ratio = 0.1
max_ratio = 0.75
# only accept objects >10% and <75% of the total frame
... | 2,303 | 36.770492 | 148 | py |
HDN | HDN-master/training_dataset/lasot/parse_lasot.py | # -*- coding:utf-8 -*-
# ! ./usr/bin/env python
# __author__ = 'zzp'
import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dir',type=str, default=cfg.BASE.BASE_PATH + 'hdn_liyang/hdn/training_dat... | 2,022 | 33.288136 | 141 | py |
HDN | HDN-master/training_dataset/lasot/par_crop.py | from os.path import join, isdir, exists
from os import listdir, mkdir, makedirs
import cv2
import numpy as np
import glob
from concurrent import futures
import sys
import time
lasot_base_path = './LaSOT'
# Print iterations progress (thanks StackOverflow)
def printProgress(iteration, total, prefix='', suffix='', dec... | 4,776 | 39.483051 | 117 | py |
HDN | HDN-master/training_dataset/lasot/visual.py | import cv2
import json
import glob
import numpy as np
from os.path import join
from os import listdir
visual = True
LaSOT_base_path = './LaSOT'
for video_set in sorted(listdir(LaSOT_base_path)):
if 'txt' not in video_set:
videos = sorted(listdir(join(LaSOT_base_path, video_set)))
for vi, video in... | 1,383 | 33.6 | 110 | py |
HDN | HDN-master/training_dataset/coco14/gen_json.py | from pycocotools.coco import COCO
from os.path import join
import json
import math
dataDir = '.'
count = 0
for dataType in ['val2014', 'train2014']:
dataset = dict()
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)
n_imgs = len(coco.imgs)
print('n_imgs', n_... | 1,846 | 36.693878 | 127 | py |
HDN | HDN-master/training_dataset/coco14/par_crop.py | from pycocotools.coco import COCO
import cv2
import numpy as np
from os.path import join, isdir
from os import mkdir, makedirs
from concurrent import futures
import sys
import time
# Print iterations progress (thanks StackOverflow)
def printProgress(iteration, total, prefix='', suffix='', decimals=1, barLength=100):
... | 4,516 | 40.440367 | 112 | py |
HDN | HDN-master/training_dataset/coco14/visual.py | from pycocotools.coco import COCO
import cv2
import numpy as np
color_bar = np.random.randint(0, 255, (90, 3))
visual = True
dataDir = '.'
dataType = 'val2014'
annFile = '{}/annotations/instances_{}.json'.format(dataDir,dataType)
coco = COCO(annFile)
for img_id in coco.imgs:
img = coco.loadImgs(img_id)[0]
a... | 822 | 26.433333 | 75 | py |
HDN | HDN-master/training_dataset/coco14/pycocotools/setup.py | from distutils.core import setup
from Cython.Build import cythonize
from distutils.extension import Extension
import numpy as np
# To compile and install locally run "python setup.py build_ext --inplace"
# To install library to Python site-packages run "python setup.py build_ext install"
ext_modules = [
Extension... | 710 | 27.44 | 84 | py |
HDN | HDN-master/training_dataset/coco14/pycocotools/cocoeval.py | __author__ = 'tsungyi'
import numpy as np
import datetime
import time
from collections import defaultdict
from . import mask as maskUtils
import copy
class COCOeval:
# Interface for evaluating detection on the Microsoft COCO dataset.
#
# The usage for CocoEval is as follows:
# cocoGt=..., cocoDt=... ... | 24,143 | 44.213483 | 118 | py |
HDN | HDN-master/training_dataset/coco14/pycocotools/__init__.py | __author__ = 'tylin'
| 21 | 10 | 20 | py |
HDN | HDN-master/training_dataset/coco14/pycocotools/coco.py | __author__ = 'tylin'
__version__ = '2.0'
# Interface for accessing the Microsoft COCO dataset.
# Microsoft COCO is a large image dataset designed for object detection,
# segmentation, and caption generation. pycocotools is a Python API that
# assists in loading, parsing and visualizing the annotations in COCO.
# Pleas... | 18,476 | 40.993182 | 128 | py |
HDN | HDN-master/training_dataset/coco14/pycocotools/mask.py | __author__ = 'tsungyi'
#import pycocotools._mask as _mask
from . import _mask
# Interface for manipulating masks stored in RLE format.
#
# RLE is a simple yet efficient format for storing binary masks. RLE
# first divides a vector (or vectorized image) into a series of piecewise
# constant regions and then for each p... | 4,613 | 42.942857 | 100 | py |
HDN | HDN-master/training_dataset/coco/gen_json.py | from pycocotools.coco import COCO
from os.path import join
import json
import math
dataDir = '.'
count = 0
for dataType in ['val2014', 'train2014']:
dataset = dict()
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)
n_imgs = len(coco.imgs)
print('n_imgs', n... | 1,844 | 35.9 | 128 | py |
HDN | HDN-master/training_dataset/coco/par_crop.py | from pycocotools.coco import COCO
import cv2
import numpy as np
from os.path import join, isdir
from os import mkdir, makedirs
from concurrent import futures
import sys
import time
# Print iterations progress (thanks StackOverflow)
def printProgress(iteration, total, prefix='', suffix='', decimals=1, barLength=100):
... | 4,503 | 40.703704 | 112 | py |
HDN | HDN-master/training_dataset/coco/visual.py | from pycocotools.coco import COCO
import cv2
import numpy as np
color_bar = np.random.randint(0, 255, (90, 3))
visual = True
dataDir = '.'
dataType = 'val2017'
annFile = '{}/annotations/instances_{}.json'.format(dataDir,dataType)
coco = COCO(annFile)
for img_id in coco.imgs:
img = coco.loadImgs(img_id)[0]
a... | 822 | 26.433333 | 75 | py |
HDN | HDN-master/training_dataset/coco/pycocotools/setup.py | from distutils.core import setup
from Cython.Build import cythonize
from distutils.extension import Extension
import numpy as np
# To compile and install locally run "python setup.py build_ext --inplace"
# To install library to Python site-packages run "python setup.py build_ext install"
ext_modules = [
Extension... | 710 | 27.44 | 84 | py |
HDN | HDN-master/training_dataset/coco/pycocotools/cocoeval.py | __author__ = 'tsungyi'
import numpy as np
import datetime
import time
from collections import defaultdict
from . import mask as maskUtils
import copy
class COCOeval:
# Interface for evaluating detection on the Microsoft COCO dataset.
#
# The usage for CocoEval is as follows:
# cocoGt=..., cocoDt=... ... | 24,143 | 44.213483 | 118 | py |
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