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CreateDataSet.py
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CreateDataSet.py
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'''
Function:
Create dataset
From [binary yuv frame] to [blocks of individual yuv channels]
From [partition information saved from encoder] to [incomplete partition map]
Main functions:
* save_sequence_block_set(): partition the three channels of input yuv file into numpy array. (input of the network)
* save_partition_block_set(): using partition information saved from the encoder to get incomplete partition map. (label of the network)
* concate_seqs(): concatenate the individual yuv block files (generated from different yuv sequences) to be used for training
* concate_partition(): similar as concate_seqs(), but for partition map
Note:
* The reason it's incomplete is that this code only outputs the qt depth map, direction map, and the last layer of bt depth map.
The multi-layer bt depth map needs the code "GenMSBTMap.py" to get.
* To get "partition information saved from the encoder:, reference the Macro definition "Save_Depth_fal" in the VTM codec.
* Functions beyond the four main functions serve as reference.
Author: Aolin Feng
'''
import os
import numpy as np
from matplotlib import pyplot as plt
import torch
import time
# from yuv420 binary file to numpy array
def import_yuv420(file_path, width, height, frm_num, SubSampleRatio=1, show=False, is10bit=False):
fp = open(file_path,'rb')
pixnum = width * height
subnumfrm = (frm_num + SubSampleRatio - 1) // SubSampleRatio # actual frame number after downsampling
if is10bit:
data_type = np.uint16
else:
data_type = np.uint8
y_temp = np.zeros(pixnum*subnumfrm, dtype=data_type)
u_temp = np.zeros(pixnum*subnumfrm // 4, dtype=data_type)
v_temp = np.zeros(pixnum*subnumfrm // 4, dtype=data_type)
for i in range(0, frm_num, SubSampleRatio):
if is10bit:
fp.seek(i * pixnum * 3, 0)
else:
fp.seek(i * pixnum * 3 // 2, 0)
subi = i // SubSampleRatio
y_temp[subi*pixnum : (subi+1)*pixnum] = np.fromfile(fp, dtype=data_type, count=pixnum, sep='')
u_temp[subi*pixnum//4 : (subi+1)*pixnum//4] = np.fromfile(fp, dtype=data_type, count=pixnum//4, sep='')
v_temp[subi*pixnum//4 : (subi+1)*pixnum//4] = np.fromfile(fp, dtype=data_type, count=pixnum//4, sep='')
fp.close()
y = y_temp.reshape((subnumfrm, height, width))
u = u_temp.reshape((subnumfrm, height//2, width//2))
v = v_temp.reshape((subnumfrm, height//2, width//2))
if show:
for i in range(subnumfrm):
print(i)
plt.imshow(y[i, :, :], cmap='gray')
plt.show()
plt.pause(1)
return y, u, v # return frm_num * H * W
def clip_yuv(filename, width, height, startfrm, endfrm):
fp = open(filename,'rb')
frmsize = width * height * 3 // 2
start_loc = startfrm * frmsize
read_size = (endfrm - startfrm + 1) * frmsize
fp.seek(start_loc, 0)
data = fp.read(read_size)
fp.close()
out_fp = open('out_clip_yuv.yuv', 'wb')
out_fp.write(data)
out_fp.close()
def cut_yuv(filename, width, height, block_size, numfrm):
width_cut = int(width // block_size * block_size)
height_cut = int(height // block_size * block_size)
y, u, v = import_yuv420(filename, width, height, numfrm)
out_fp = open('out_cut_yuv.yuv', 'ab')
for i in range(numfrm):
y_cut = y[i, 0:height_cut, 0:width_cut]
u_cut = u[i, 0:height_cut//2, 0:width_cut//2]
v_cut = v[i, 0:height_cut//2, 0:width_cut//2]
print(i, ' ', y_cut.shape)
out_fp.write(y_cut.reshape(-1))
out_fp.write(u_cut.reshape(-1))
out_fp.write(v_cut.reshape(-1))
out_fp.close()
# save blocks of y component (with overlap)
def output_block_yuv(file_path, width, height, block_size, in_overlap, numfrm, SubSampleRatio, is10bit=False, save_path=None):
y, u, v = import_yuv420(file_path, width, height, numfrm, SubSampleRatio, is10bit=is10bit)
if is10bit:
y = (np.round(y / 4)).clip(0, 255).astype(np.uint8)
u = (np.round(u / 4)).clip(0, 255).astype(np.uint8)
v = (np.round(v / 4)).clip(0, 255).astype(np.uint8)
block_num_in_width = width // block_size
block_num_in_height = height // block_size
print(block_num_in_width, block_num_in_height)
for id, comp in enumerate([y, u, v]):
if id == 0:
overlap = in_overlap
comp_block_size = block_size
else:
overlap = int(in_overlap / 2)
comp_block_size = block_size // 2
pad_comp = np.zeros((comp.shape[0], comp.shape[1]+overlap, comp.shape[2]+overlap), dtype=np.uint8)
pad_comp[:, overlap:, overlap:] = comp
subnumfrm = comp.shape[0]
block_list = []
for f_num in range(subnumfrm):
for i in range(block_num_in_height):
for j in range(block_num_in_width):
block_list.append(pad_comp
[f_num, i * comp_block_size:(i + 1) * comp_block_size + overlap, j * comp_block_size:(j + 1) * comp_block_size + overlap])
if id == 0:
block_y = np.array(block_list)
elif id == 1:
block_u = np.array(block_list)
else:
block_v = np.array(block_list)
if save_path is not None:
out_fp = open(save_path, "wb")
for i in range(block_y.shape[0]):
out_fp.write(block_y[i].reshape(-1))
out_fp.write(block_u[i].reshape(-1))
out_fp.write(block_v[i].reshape(-1))
out_fp.close()
print('shape of block_y', block_y.shape)
print('shape of block_u', block_u.shape)
print('shape of block_v', block_v.shape)
check_id = 29
plt.figure(0)
plt.imshow(block_v[check_id], cmap='gray')
# plt.figure(1)
# plt.imshow(block_u[check_id], cmap='gray')
# plt.figure(2)
# plt.imshow(block_v[check_id], cmap='gray')
plt.show()
return block_y, block_u, block_v # num_block * block_size * block_size
def save_sequence_block_set():
seqs_info_path = r'.\Cfg\VVC_Test_Sequences.txt'
seqs_root_dir = r'E:\Research\testyuv\VVC'
save_dir = r'E:\VVC-Fast-Partition-DP\Dataset\Test'
seqs_info_fp = open(seqs_info_path, 'r')
# load input sequence information
data = []
for line in seqs_info_fp:
data.append(line.rstrip('\n').split(','))
seqs_info_fp.close()
data = np.array(data)
print(data.shape)
seqs_name = data[:, 0]
seqs_path_name = data[:, 1]
seqs_width = data[:, 2].astype(np.int64) # enough bits for calculating h*w
seqs_height = data[:, 3].astype(np.int64)
seqs_frmnum = data[:, 4].astype(np.int64)
num_seq = 85
for i_seq in range(0, 22):
path = os.path.join(seqs_root_dir, seqs_path_name[i_seq])
print(path, i_seq)
width = seqs_width[i_seq]
height = seqs_height[i_seq]
frmnum = seqs_frmnum[i_seq]
# print(width,height,frmnum)
if i_seq < 8:
is10bit = True
else:
is10bit = False
block_y, block_u, block_v = output_block_yuv(path, width, height, block_size=64, in_overlap=4, numfrm=frmnum, SubSampleRatio=8, is10bit=is10bit)
save_name_y = seqs_name[i_seq] + '_Y_Block68.npy'
save_name_u = seqs_name[i_seq] + '_U_Block34.npy'
save_name_v = seqs_name[i_seq] + '_V_Block34.npy'
save_path_y = os.path.join(save_dir + r'\Block_Y', save_name_y)
save_path_u = os.path.join(save_dir + r'\Block_U', save_name_u)
save_path_v = os.path.join(save_dir + r'\Block_V', save_name_v)
print(save_path_y)
print(save_path_u)
print(save_path_v)
np.save(save_path_y, block_y)
np.save(save_path_u, block_u)
np.save(save_path_v, block_v)
# output blocks of qt depth map, mt depth map and direction map from saved information.
# you can infer the information thta the encoder needs to save from this function.
def output_block_partition_map(file_path, frm_width, frm_height, frm_num, block_size=64, isChroma=False):
depth_fp = open(file_path, 'r')
qtdepth_mat = np.zeros((frm_num, frm_height // 4, frm_width // 4), dtype=np.uint8)
btdepth_mat = np.zeros((frm_num, frm_height // 4, frm_width // 4), dtype=np.uint8)
msdirection_mat = np.zeros((frm_num, 3, frm_height // 4, frm_width // 4), dtype=np.int8)
frm_id = -1
for line in depth_fp:
# x y h w depth qtdepth btdepth mtdepth splitmode
if 'frame' in line:
frm_id += 1
continue
if isChroma:
factor = 2
else:
factor = 1
x = int(line.split(' ')[0]) * factor
y = int(line.split(' ')[1]) * factor
h = int(line.split(' ')[2]) * factor
w = int(line.split(' ')[3]) * factor
depth = int(line.split(' ')[4])
qtdepth = int(line.split(' ')[5])
btdepth = int(line.split(' ')[6])
qtdepth_mat[frm_id, y // 4:(y + h) // 4, x // 4:(x + w) // 4] = qtdepth
btdepth_mat[frm_id, y // 4:(y + h) // 4, x // 4:(x + w) // 4] = btdepth
direction = 0
for i in range(3):
splitmode = int(line.split(' ')[8 + qtdepth + i])
if splitmode == 2 or splitmode == 4: # bth or tth
direction = 1
elif splitmode == 3 or splitmode == 5: # btv or ttv
direction = -1
elif splitmode == 2000:
direction = 0
else:
print('Error!!')
msdirection_mat[frm_id, i, y // 4:(y + h) // 4, x // 4:(x + w) // 4] = direction
qtdepth_mat = qtdepth_mat[:, ::2, ::2] # down sample
# print(qtdepth_mat.shape)
# print(btdepth_mat.shape)
# print(direction_mat.shape)
# bt_plus = qtdepth_mat * 2 + btdepth_mat
# plt.imshow(bt_plus[0], cmap='gray')
# plt.axis('off')
# plt.savefig('bt_plusqt.png', dpi=320, bbox_inches='tight', pad_inches=0)
# plt.show()
# save depth blocks
block_num_in_width = frm_width // block_size
block_num_in_height = frm_height // block_size
qd_block_list = []
bd_block_list = []
msdire_block_list = []
qd_block_size = block_size // 8
md_block_size = block_size // 4
dire_block_size = block_size // 4
for f_num in range(frm_num):
for i in range(block_num_in_height):
for j in range(block_num_in_width):
qd_block_list.append(qtdepth_mat[f_num, i * qd_block_size:(i + 1) * qd_block_size,
j * qd_block_size:(j + 1) * qd_block_size])
bd_block_list.append(btdepth_mat[f_num, i * md_block_size:(i + 1) * md_block_size,
j * md_block_size:(j + 1) * md_block_size])
msdire_block_list.append(msdirection_mat[f_num, :, i * dire_block_size:(i + 1) * dire_block_size,
j * dire_block_size:(j + 1) * dire_block_size])
qtdepth_block = np.array(qd_block_list)
btdepth_block = np.array(bd_block_list)
msdirection_block = np.array(msdire_block_list)
del qtdepth_mat, btdepth_mat, msdirection_mat
print(qtdepth_block.shape)
print(btdepth_block.shape)
print(msdirection_block.shape)
# check_id = 104
# print(qtdepth_block[check_id])
# print(mtdepth_block[check_id])
# print(direction_block[check_id])
return qtdepth_block, btdepth_block, msdirection_block
def save_partition_block_set():
comp = 'Chroma'
is_chroma = False
if comp == 'Chroma':
is_chroma = True
seqs_info_path = r'.\Cfg\Training_Sequences.txt'
# seqs_root_dir = r'E:\Research\testyuv\Dataset\Train'
partition_root_dir = r'E:\VVC-Fast-Partition-DP\Dataset\Partition_Info\TestSeq\DepthSaving'
save_dir = r'E:\VVC-Fast-Partition-DP\Dataset'
seqs_info_fp = open(seqs_info_path, 'r')
data = []
for line in seqs_info_fp:
if "end!!!!" in line:
break
data.append(line.rstrip('\n').split(','))
seqs_info_fp.close()
data = np.array(data)
print(data.shape)
seqs_name = data[:, 0]
seqs_path_name = data[:, 1]
seqs_width = data[:, 2].astype(np.int64) # enough bits for calculating h*w
seqs_height = data[:, 3].astype(np.int64)
seqs_frmnum = data[:, 4].astype(np.int64)
sub_frmnum_list = []
for i in range(91):
SubSampleRatio = 8
sub_frmnum = (seqs_frmnum[i] + SubSampleRatio - 1) // SubSampleRatio
sub_frmnum_list.append(sub_frmnum)
for qp in [22, 27, 32, 37]:
concate_qt_block = np.zeros((1, 8, 8), dtype=np.uint8)
concate_bt_block = np.zeros((1, 16, 16), dtype=np.uint8)
concate_direction_block = np.zeros((1, 3, 16, 16), dtype=np.int8)
for i_seq in range(88, 91):
partition_path = os.path.join(partition_root_dir, seqs_name[i_seq] + '_QP' + str(qp) + '_' + comp + '_Partition.txt')
print(partition_path)
width = seqs_width[i_seq]
height = seqs_height[i_seq]
sub_frmnum = sub_frmnum_list[i_seq]
qtdepth_block, btdepth_block, msdirection_block = output_block_partition_map(file_path=partition_path,
frm_width=width,
frm_height=height,
frm_num=sub_frmnum,
block_size=64,
isChroma=is_chroma)
block_num = qtdepth_block.shape[0]
if block_num < 5000:
sub_num = 500
else:
sub_num = 5000
concate_qt_block = np.concatenate((concate_qt_block, qtdepth_block), axis=0)
concate_bt_block = np.concatenate((concate_bt_block, btdepth_block), axis=0)
concate_direction_block = np.concatenate((concate_direction_block, msdirection_block), axis=0)
concate_qt_block = concate_qt_block[1:]
concate_bt_block = concate_bt_block[1:]
concate_direction_block = concate_direction_block[1:]
save_qt_path = os.path.join(save_dir, 'Validate_' + comp + '_QP' + str(qp) + '_QTdepth_Block8' + '.npy')
save_bt_path = os.path.join(save_dir, 'Validate_' + comp + '_QP' + str(qp) + '_BTdepth_Block16' + '.npy')
save_direction_path = os.path.join(save_dir, 'Validate_' + comp + '_QP' + str(qp) + '_MSdirection_Block16' + '.npy')
print(save_qt_path)
print(save_bt_path)
print(save_direction_path)
print(concate_qt_block.shape)
print(concate_bt_block.shape)
print(concate_direction_block.shape)
print(concate_qt_block.dtype, concate_bt_block.dtype, concate_direction_block.dtype)
np.save(save_qt_path, concate_qt_block)
np.save(save_bt_path, concate_bt_block)
np.save(save_direction_path, concate_direction_block)
def concate_seqs(comp='Y'): # comp = 'Y' or 'UV'
if comp == 'Y':
block_size = 68
else:
block_size = 34
seqs_info_path = r'.\Cfg\VVC_Test_Sequences.txt'
root_dir = r'E:\VVC-Fast-Partition-DP\Dataset\Test'
seqs_info_fp = open(seqs_info_path, 'r')
data = []
for line in seqs_info_fp:
data.append(line.rstrip('\n').split(','))
seqs_info_fp.close()
data = np.array(data)
print(data.shape)
seqs_name = data[:, 0]
seqs_path_name = data[:, 1]
# seqs_width = data[:, 2].astype(np.int64) # enough bits for calculating h*w
# seqs_height = data[:, 3].astype(np.int64)
# seqs_frmnum = data[:, 4].astype(np.int64)
num_seq = 85
concate_block = np.zeros((1, block_size, block_size), dtype=np.uint8)
for i_seq in range(22):
file_name = seqs_name[i_seq] + '_' + comp + '_Block' + str(block_size) + '.npy'
file_path = os.path.join(root_dir + r'\Block_' + comp, file_name)
block_comp = np.load(file_path)
# print(file_path)
# print(block_comp.shape)
block_num = block_comp.shape[0]
concate_block = np.concatenate((concate_block, block_comp), axis=0)
concate_block = concate_block[1:]
save_path = os.path.join(root_dir, 'Test_' + comp + '_Block' + str(block_size) + '.npy')
print(save_path)
print(concate_block.shape)
print(concate_block.dtype)
np.save(save_path, concate_block)
def concate_partition():
comp = 'Luma'
seqs_info_path = r'.\Cfg\Training_Sequences.txt'
root_dir = r'E:\VVC-Fast-Partition-DP\Dataset'
seqs_info_fp = open(seqs_info_path, 'r')
data = []
for line in seqs_info_fp:
data.append(line.rstrip('\n').split(','))
seqs_info_fp.close()
data = np.array(data)
print(data.shape)
seqs_name = data[:, 0]
seqs_path_name = data[:, 1]
seqs_width = data[:, 2].astype(np.int64) # enough bits for calculating h*w
seqs_height = data[:, 3].astype(np.int64)
seqs_frmnum = data[:, 4].astype(np.int64)
for qp in [22, 27, 32, 37]:
concate_qt_block = np.zeros((1, 8, 8), dtype=np.uint8)
concate_bt_block = np.zeros((1, 16, 16), dtype=np.uint8)
concate_direction_block = np.zeros((1, 3, 16, 16), dtype=np.int8)
for i_seq in range(82, 85): # need to change
qt_name = seqs_name[i_seq] + '_QTdepth_QP' + str(qp) + '_' + comp + '.npy'
bt_name = seqs_name[i_seq] + '_BTdepth_QP' + str(qp) + '_' + comp + '.npy'
direction_name = seqs_name[i_seq] + '_MSDirection_QP' + str(qp) + '_' + comp + '.npy'
qt_path = os.path.join(root_dir + r'\Partition_' + comp, qt_name)
bt_path = os.path.join(root_dir + r'\Partition_' + comp, bt_name)
direction_path = os.path.join(root_dir + r'\Partition_' + comp, direction_name)
print(direction_path)
qt_block = np.load(qt_path)
bt_block = np.load(bt_path)
msdirection_block = np.load(direction_path)
print(qt_block.dtype, bt_block.dtype, msdirection_block.dtype)
print(seqs_name[i_seq], msdirection_block.shape)
block_num = msdirection_block.shape[0]
if block_num < 5000:
sub_num = 500
else:
sub_num = 5000
concate_qt_block = np.concatenate((concate_qt_block, qt_block[0:sub_num]), axis=0)
concate_bt_block = np.concatenate((concate_bt_block, bt_block[0:sub_num]), axis=0)
concate_direction_block = np.concatenate((concate_direction_block, msdirection_block), axis=0)
concate_qt_block = concate_qt_block[1:]
concate_bt_block = concate_bt_block[1:]
concate_direction_block = concate_direction_block[1:]
save_qt_path = os.path.join(root_dir, 'TestSub_' + comp + '_QP' + str(qp) + '_QTdepth_Block8' + '.npy')
save_bt_path = os.path.join(root_dir, 'TestSub_' + comp + '_QP' + str(qp) + '_BTdepth_Block16' + '.npy')
save_direction_path = os.path.join(root_dir + r'\MSDirection_Map', 'Validation_' + comp + '_QP' + str(qp) + '_MSDirection_Block16' + '.npy')
print(save_direction_path)
print(concate_qt_block.shape)
print(concate_bt_block.shape)
print(concate_direction_block.shape)
print(concate_qt_block.dtype, concate_bt_block.dtype, concate_direction_block.dtype)
np.save(save_qt_path, concate_qt_block)
np.save(save_bt_path, concate_bt_block)
np.save(save_direction_path, concate_direction_block)
def generate_cfg():
root_dir = r'G:\Research\testyuv\Dataset\Train'
cfg_dir = r'.\Cfg'
seqs_info_path = r'.\Cfg\Training_Sequences.txt'
seqs_info_fp = open(seqs_info_path, 'w')
for count, item in enumerate(os.listdir(root_dir)):
if count >= 500:
break
if 'yuv' in item:
seq_name = item.rstrip('.yuv')
seq_size = item.rstrip('.yuv').split('_')[-3]
seq_width = seq_size.split('x')[0]
seq_height = seq_size.split('x')[1]
seq_frm_rate = item.rstrip('.yuv').split('_')[-2]
seq_frm_num = item.rstrip('.yuv').split('_')[-1]
# print(seq_name)
seq_cfg_path = os.path.join(cfg_dir, seq_name + '.cfg')
seq_cfg_fp = open(seq_cfg_path, 'w')
seq_cfg_fp.write('#======== File I/O =========\n')
seq_cfg_fp.write(
'InputFile : //C01/share/data/origCfp/SequenceData/Train/' + seq_name + '.yuv\n')
seq_cfg_fp.write('InputBitDepth : 8 # Input bitdepth\n')
seq_cfg_fp.write('FrameRate : ' + seq_frm_rate + ' # Frame per second\n')
seq_cfg_fp.write('FrameSkip : 0 # Number of frames to be skipped in input\n')
seq_cfg_fp.write('SourceWidth : ' + seq_width + ' # Input frame width\n')
seq_cfg_fp.write('SourceHeight : ' + seq_height + ' # Input frame height\n')
seq_cfg_fp.write('FramesToBeEncoded : ' + seq_frm_num + ' # Number of frames to be coded\n')
seq_cfg_fp.write('\n')
seq_cfg_fp.write('Level : 4.1\n')
seq_cfg_fp.close()
seqs_info_fp.write(
seq_name + ',' + seq_width + ',' + seq_height + ',' + seq_frm_num + ',' + seq_frm_rate + '\n')
# check data
file_path = os.path.join(root_dir, item)
file_size = os.path.getsize(file_path)
cal_file_size = int(seq_width) * int(seq_height) * int(seq_frm_num) * 1.5
if file_size == cal_file_size:
print('True')
else:
print('*******************False')
seqs_info_fp.close()
def load_sequences_info():
num = 82
seqs_info_path = r"E:\VVC-Fast-Partition-DP\Code\Cfg\Training_Sequences.txt"
seqs_info_fp = open(seqs_info_path, 'r')
data = []
for line in seqs_info_fp:
if "end!!!!" in line:
break
data.append(line.rstrip('\n').split(','))
seqs_info_fp.close()
data = np.array(data)
print(data.shape)
seqs_name = data[:num, 0]
seqs_path_name = data[:num, 1]
seqs_width = data[:num, 2].astype(np.int64) # enough bits for calculating h*w
seqs_height = data[:num, 3].astype(np.int64)
seqs_frmnum = data[:num, 4].astype(np.int64)
sub_frmnum_list = []
for i in range(num):
SubSampleRatio = 30
if i >= 79:
SubSampleRatio = 1
# SubSampleRatio = 8
sub_frmnum = (seqs_frmnum[i] + SubSampleRatio - 1) // SubSampleRatio
sub_frmnum_list.append(sub_frmnum)
return seqs_path_name, seqs_width, seqs_height, sub_frmnum_list
if __name__ == '__main__':
print("Start...")
# save_sequence_block_set()
# save_partition_block_set()
# concate_seqs()
# concate_partition()
print("End!!!")