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Map2Partition.py
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Map2Partition.py
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'''
Function:
From partition map (network-output) to partition structure ( able to be used in the VVC encoder)
Main functions:
* get_sequence_partition_for_VTM(qt_map, bt_map, dire_map, is_luma, save_path, frm_num, frm_width, frm_height)
Note:
* The output partition structure includes "partition apperaence" + "qt depth map" + "direction map" in the .txt form
* The output partition structure can be read and used in the VVC encoder to decide the partition structure without using RDO search.
* The target of this code is to get proper partition information that can be easily used in the VVC encoding acceleration.
* This form of representing partition structure is not good (little complicated actually). There should be better ways.
Author: Aolin Feng
'''
#import os
import numpy as np
# import torch
# from matplotlib import pyplot as plt
# from Metrics import eli_structual_error
# import time
# VVC prior information
# qt depth 0->3
# bt depth 0->5 0->4 (chroma)
# direction 0 1 2
def th_round(input_batch, thd):
input_batch = np.where(input_batch >= thd, np.full_like(input_batch, 1), input_batch)
input_batch = np.where(input_batch <= -thd, np.full_like(input_batch, -1), input_batch)
input_batch = np.where((input_batch > -thd) & (input_batch < thd), np.full_like(input_batch, 0),
input_batch)
return input_batch
def delete_tree(root):
if len(root.children) == 0: # no child
del root
else:
children = root.children
for child in children:
delete_tree(child)
# Split_Mode and Search are set for generate combination modes
# example: input [[1,2], [3,4,5]]; output [[1,3],[1,4],[1,5],[2,3],[2,4],[2,5]]
class Split_Node():
def __init__(self, split_type):
self.split_type = split_type
self.children = []
class Search():
def __init__(self, cus_candidate_mode_list):
self.split_root = Split_Node(0)
self.parent_list = []
self.cus_mode_list = cus_candidate_mode_list
self.partition_modes = []
self.cus_modes = []
def get_cus_mode_tree(self):
self.parent_list = [self.split_root]
while len(self.cus_mode_list) != 0:
parent_temp = []
for parent in self.parent_list:
for split_type in self.cus_mode_list[0]:
child = Split_Node(split_type)
parent.children.append(child)
parent_temp.append(child)
self.parent_list = parent_temp
self.cus_mode_list.pop(0)
def bfs(self, node):
self.cus_modes.append(node.split_type)
if len(node.children) == 0:
temp = self.cus_modes[1:]
self.partition_modes.append(temp)
self.cus_modes.pop(-1)
else:
for child in node.children:
self.bfs(child)
self.cus_modes.pop(-1)
def get_partition_modes(self):
self.get_cus_mode_tree()
self.bfs(self.split_root)
return self.partition_modes
class Map_Node():
def __init__(self, bt_map, dire_map, mtt_depth, cus, parent=None):
self.bt_map = bt_map
self.dire_map = dire_map
self.mtt_depth = mtt_depth
self.cus = cus # [x, y, h, w] list
self.children = []
self.parent = parent
class Map_to_Partition():
"""Convert Partition maps to Split flags to Partition vectors"""
def __init__(self, qt_map, msbt_map, msdire_map, chroma_factor, lamb1=0.7, lamb2=0.7, lamb3=1.5, lamb4=0.3, lamb5=0.7):
self.qt_map = qt_map
self.ori_msbt_map = msbt_map
self.ori_msdire_map = msdire_map
self.msbt_map = np.round(msbt_map)
self.msdire_map = th_round(msdire_map, thd=0.5)
self.chroma_factor = chroma_factor
# *************************** Difficult to understand ******************************************
# par_vec is an array that tries to record the split edges (if is edge, 1; else, 0).
# You can try to understand its function according to the usage of this data structure
# This is a bad design.
self.par_vec = np.zeros((2, 17, 17), dtype=np.uint8)
# ************************************************************************************************
self.out_msdire_map = np.zeros((3, 16, 16), dtype=np.int8)
self.cur_leaf_nodes = [] # store leaf nodes of Map Tree
# lamb indicates several kinds of thresholds
self.lamb1 = lamb1 # control no partition based on depth map
self.lamb2 = lamb2 # judge direction based on direction map
self.lamb3 = lamb3 # control hor or ver
self.lamb4 = lamb4 # control number of minus
self.lamb5 = lamb5 # control number of zero
self.time = 0
def split_cur_map(self, x, y, h, w, split_type):
# split current cu [x,y,h,w]
# split_type: 0 1 2 3 4 no bth btv tth ttv
if split_type == 0:
return [[x, y, h, w]]
elif split_type == 1: # bth
return [[x, y, h//2, w], [x+h//2, y, h//2, w]]
elif split_type == 2: # btv
return [[x, y, h, w//2], [x, y+w//2, h, w//2]]
elif split_type == 3: # tth
return [[x, y, h//4, w], [x+h//4, y, h//2, w], [x+(h*3)//4, y, h//4, w]]
elif split_type == 4: # ttv
return [[x, y, h, w//4], [x, y+w//4, h, w//2], [x, y+(w*3)//4, h, w//4]]
else:
print("Unknown split type!")
def can_split_mode_list(self, x, y, h, w, cur_bt_map, mtt_depth):
"""output candidate split type list for current cu"""
comp_map = self.msbt_map[2, x:x+h, y:y+w] - cur_bt_map[x:x+h, y:y+w]
count_zero = len(np.where(comp_map == 0)[0]) # number of zero
if count_zero >= self.lamb1 * h * w: # no partition
return [0]
count_hor = len(np.where(self.msdire_map[mtt_depth, x:x+h, y:y+w] == 1)[0]) # horizontal unit number of current direction map
count_ver = len(np.where(self.msdire_map[mtt_depth, x:x+h, y:y+w] == -1)[0]) # vertical unit number of current direction map
direction = 0 # 0 1 2 Unknown Horizontal Vertical
if (count_ver + count_hor) >= self.lamb2 * h * w: # non-flat flag within direction map dominates
if count_hor >= self.lamb3 * count_ver:
direction = 1
elif count_ver >= self.lamb3 * count_hor:
direction = 2
initial_split_list = []
for split_mode in [1, 2, 3, 4]:
if split_mode == 1 and (h // (2*self.chroma_factor) == 0 or h % (2*self.chroma_factor) != 0): # bth
continue
if split_mode == 2 and (w // (2*self.chroma_factor) == 0 or w % (2*self.chroma_factor) != 0): # btv
continue
if split_mode == 3 and (h // (4*self.chroma_factor) == 0 or h % (4*self.chroma_factor) != 0): # tth
continue
if split_mode == 4 and (w // (4*self.chroma_factor) == 0 or w % (4*self.chroma_factor) != 0): # ttv
continue
if (split_mode == 1 or split_mode == 3) and direction == 2: # horizontal mode with vertical texture
continue
if (split_mode == 2 or split_mode == 4) and direction == 1: # vertical mode with horizontal texture
continue
initial_split_list.append(split_mode)
candidate_mode_list = [0]
bt_map_temp = np.zeros_like(cur_bt_map, dtype=np.int8)
for split_mode in initial_split_list: # try potential partitions
sub_map_xyhw = self.split_cur_map(x, y, h, w, split_mode)
bt_map_temp[:, :] = cur_bt_map[:, :]
split_thres = 0
for sub_map_id in range(len(sub_map_xyhw)): # traverse all sub-blocks
[sub_x, sub_y, sub_h, sub_w] = sub_map_xyhw[sub_map_id]
# comp_map defines the difference between the bt map and the temp bt map;
# the proper partition would bring a small portion of negative values in the map;
# if zero appears in the map, a proper partition would expect the portion of zero values either very large (partition end) or very small
bt_map_temp[sub_x:sub_x + sub_h, sub_y:sub_y + sub_w] += 1
if (split_mode == 3 or split_mode == 4) and (sub_map_id != 1):
# depth +2 in the first and last parts fot tt partition
bt_map_temp[sub_x:sub_x + sub_h, sub_y:sub_y + sub_w] += 1
comp_map = self.msbt_map[mtt_depth, sub_x:sub_x + sub_h, sub_y:sub_y + sub_w] - bt_map_temp[
sub_x:sub_x + sub_h,
sub_y:sub_y + sub_w]
count_minus = len(np.where(comp_map < 0)[0]) # number of minus
count_zero = len(np.where(comp_map == 0)[0]) # number of zero
num_pixel = sub_h * sub_w
if count_minus < num_pixel * self.lamb4 and count_zero > num_pixel * self.lamb5:
# (count_zero < num_pixel * self.lamb5 or count_zero > num_pixel * (1 - self.lamb5)):
# current sub-block gets proper partition
split_thres += 1
if split_thres == len(sub_map_xyhw): # all sub-block get proper partition
candidate_mode_list.append(split_mode)
return candidate_mode_list
def get_candidate_map_tree(self, map_node):
if map_node.mtt_depth >= 3:
return
cur_cus = map_node.cus
cu_num = len(cur_cus)
cur_bt_map = map_node.bt_map
cur_dire_map = map_node.dire_map
cur_mtt_depth = map_node.mtt_depth
cus_candidate_mode_list = []
for i in range(cu_num):
cus_candidate_mode_list.append([]) # store all candidate split modes of every CU
for cu_id in range(cu_num): # traverse all CUs in current map
[cu_x, cu_y, cu_h, cu_w] = cur_cus[cu_id]
candidate_mode_list = self.can_split_mode_list(cu_x, cu_y, cu_h, cu_w, cur_bt_map, cur_mtt_depth)
if len(candidate_mode_list) == 0: # no proper partition for a certain CU
return
cus_candidate_mode_list[cu_id] += candidate_mode_list
# t1 = time.time()
# partition_modes = [] # store all possible combination modes of all CUs
# for comb_mode_id in range(1, 7**cu_num):
# # maximum possible CU modes combination 5**cu_num, 0 means all CUs no partition
# cus_modes = []
# for cu_id in range(cu_num):
# bit_number = 7 << (cu_id * 3)
# mode_id = (comb_mode_id & bit_number) >> (cu_id * 3)
# if mode_id not in cus_candidate_mode_list[cu_id]:
# break
# cus_modes.append(mode_id)
# if len(cus_modes) == cu_num:
# partition_modes.append(cus_modes)
# the function of above annotation codes is equivalent to the following two lines of codes, but inefficient
s = Search(cus_candidate_mode_list)
partition_modes = s.get_partition_modes()
# self.time += time.time() - t1
for cus_modes in partition_modes: # traverse all possible combination cu split modes
child_bt_map = np.zeros_like(cur_bt_map, dtype=np.int8)
child_dire_map = np.zeros_like(cur_dire_map, dtype=np.int8)
child_bt_map[:, :] = cur_bt_map
# child_dire_map[:, :] = cur_dire_map
child_cus = []
for cu_id in range(cu_num): # traverse all cu
[cu_x, cu_y, cu_h, cu_w] = cur_cus[cu_id] # location and size of current CU
cu_mode = cus_modes[cu_id] # split mode of current CU
child_map_xyhw = self.split_cur_map(cu_x, cu_y, cu_h, cu_w, cu_mode)
child_cus += child_map_xyhw
if cu_mode == 0: # no partition
child_dire_map[cu_x:cu_x+cu_h, cu_y:cu_y+cu_w] = 0
continue
elif cu_mode == 1 or cu_mode == 3: # horizontal
child_dire_map[cu_x:cu_x + cu_h, cu_y:cu_y + cu_w] = 1
elif cu_mode == 2 or cu_mode == 4: # vertical
child_dire_map[cu_x:cu_x + cu_h, cu_y:cu_y + cu_w] = -1
for sub_block_id in range(len(child_map_xyhw)): # traverse all sub-blocks
[sub_x, sub_y, sub_h, sub_w] = child_map_xyhw[sub_block_id]
child_bt_map[sub_x:sub_x + sub_h, sub_y:sub_y + sub_w] += 1
if (cu_mode == 3 or cu_mode == 4) and (sub_block_id != 1):
# depth +2 in the first and last parts
child_bt_map[sub_x:sub_x + sub_h, sub_y:sub_y + sub_w] += 1
child_map_node = Map_Node(bt_map=child_bt_map, dire_map=child_dire_map, mtt_depth=cur_mtt_depth+1, cus=child_cus, parent=map_node)
self.get_candidate_map_tree(child_map_node)
map_node.children.append(child_map_node)
def get_leaf_nodes(self, map_node):
if len(map_node.children) == 0: # no children node
self.cur_leaf_nodes.append(map_node)
else:
for child_node in map_node.children:
self.get_leaf_nodes(child_node)
def print_tree(self, map_node, depth):
print('**********************')
print('node', depth)
print(map_node.mtt_depth)
print(map_node.bt_map)
print(map_node.cus)
print(len(map_node.children))
print('**********************')
if len(map_node.children) != 0:
for child_node in map_node.children:
self.print_tree(child_node, depth+1)
def set_bt_partition_vector(self, x, y, h, w):
init_bt_map = np.zeros((16, 16), dtype=np.int8)
init_dire_map = np.zeros((16, 16), dtype=np.int8)
map_root = Map_Node(bt_map=init_bt_map, dire_map=init_dire_map, mtt_depth=0, cus=[[x, y, h, w]])
self.get_candidate_map_tree(map_root) # build Map Tree
# self.print_tree(map_root, 0)
self.cur_leaf_nodes = []
self.get_leaf_nodes(map_root) # get lead nodes list of Map Tree
error_list = []
for node2 in self.cur_leaf_nodes:
node1 = node2.parent
node0 = node1.parent
bt_map0 = node0.bt_map # best bt map
bt_map1 = node1.bt_map
bt_map2 = node2.bt_map
dire_map0 = node0.dire_map # best bt map
dire_map1 = node1.dire_map
dire_map2 = node2.dire_map
error = np.sum(np.abs(bt_map0[x:x + h, y:y + w] - self.ori_msbt_map[0, x:x + h, y:y + w])) + \
np.sum(np.abs(bt_map1[x:x + h, y:y + w] - self.ori_msbt_map[1, x:x + h, y:y + w])) + \
np.sum(np.abs(bt_map2[x:x + h, y:y + w] - self.ori_msbt_map[2, x:x + h, y:y + w])) + \
0.8*(np.sum(np.abs(dire_map0[x:x + h, y:y + w] - self.ori_msdire_map[0, x:x + h, y:y + w])) +
np.sum(np.abs(dire_map1[x:x + h, y:y + w] - self.ori_msdire_map[1, x:x + h, y:y + w])) +
np.sum(np.abs(dire_map2[x:x + h, y:y + w] - self.ori_msdire_map[2, x:x + h, y:y + w])))
error_list.append(error)
min_index = error_list.index(min(error_list))
best_node2 = self.cur_leaf_nodes[min_index]
best_node1 = best_node2.parent
best_node0 = best_node1.parent
best_dire_map0 = best_node0.dire_map
best_dire_map1 = best_node1.dire_map
best_dire_map2 = best_node2.dire_map
self.out_msdire_map[0, x:x+h, y:y+w] = best_dire_map0[x:x+h, y:y+w]
self.out_msdire_map[1, x:x+h, y:y+w] = best_dire_map1[x:x+h, y:y+w]
self.out_msdire_map[2, x:x+h, y:y+w] = best_dire_map2[x:x+h, y:y+w]
# ************************* debug fal **************************
# num_min = error_list.count(min(error_list))
# print("Number of optimal map: ", num_min)
# best_bt_map = self.cur_leaf_nodes[min_index].bt_map # best bt map
best_cus = self.cur_leaf_nodes[min_index].cus # best partition
# print('*********************************************************')
# print('error_list', error_list, np.min(error_list))
# print('x, y, h, w', x, y, h, w)
# print(best_bt_map[x:x + h, y:y + w])
# print(self.cur_leaf_nodes[2].cus)
delete_tree(map_root)
for cu in best_cus:
[cu_x, cu_y, cu_h, cu_w] = cu
for i_w in range(cu_w): # set CU horizontal edges
self.par_vec[0, cu_x, cu_y + i_w] = 1
self.par_vec[0, cu_x + cu_h, cu_y + i_w] = 1
for i_h in range(cu_h): # set CU vertical edges
self.par_vec[1, cu_x + i_h, cu_y] = 1
self.par_vec[1, cu_x + i_h, cu_y + cu_w] = 1
def set_partition_vector(self, depth, qx, qy):
cur_qt_depth = self.qt_map[qx, qy]
sub_map_size = 8 >> depth
if cur_qt_depth == depth: # end QT partition [2*qx:2*qx + 2*sub_map_size, 2*qy:2*qy + 2*sub_map_size]
self.set_bt_partition_vector(2*qx, 2*qy, 2*sub_map_size, 2*sub_map_size)
return
elif cur_qt_depth > depth: # carry on QT partition
for i in range(sub_map_size * 2):
# set qt node partition
self.par_vec[0, 2 * qx + sub_map_size, 2 * qy + i] = 1 # horizontal
self.par_vec[1, 2 * qx + i, 2 * qy + sub_map_size] = 1 # vertical
for i_offset in range(2):
for j_offset in range(2):
self.set_partition_vector(depth + 1, qx + i_offset * sub_map_size // 2, qy + j_offset * sub_map_size // 2)
return
def get_partition(self):
self.set_partition_vector(0, 0, 0)
return self.par_vec, self.out_msdire_map
def map_to_parititon(qt_map, bt_map, dire_map, chroma_factor):
# start_time = time.time()
partition = Map_to_Partition(qt_map, bt_map, dire_map, chroma_factor)
p, d = partition.get_partition()
# total_time = time.time() - start_time
return p[0][:16, :16], p[1][:16, :16], d
def get_sequence_partition_for_VTM(qt_map, bt_map, dire_map, is_luma, save_path, frm_num, frm_width, frm_height):
# partition maps --> partition edge vector + sequence qt depth map + sequence direction map
# dire_map = np.where(dire_map < 0, np.ones_like(dire_map) * 2, dire_map)
chroma_factor = 2
if is_luma:
chroma_factor = 1
if save_path is not None:
out_file = open(save_path, 'w')
block_num_in_height = frm_height // 64
block_num_in_width = frm_width // 64
seq_partition_hor_mat = np.zeros((frm_num, block_num_in_height * 16, block_num_in_width * 16))
seq_partition_ver_mat = np.zeros((frm_num, block_num_in_height * 16, block_num_in_width * 16))
seq_qt_map = np.zeros((frm_num, block_num_in_height * 8, block_num_in_width * 8))
seq_dire_map = np.zeros((frm_num, 3, block_num_in_height * 16, block_num_in_width * 16))
for frm_id in range(frm_num):
print("Frame ", frm_id)
frm_block_id = frm_id * block_num_in_height * block_num_in_width
for block_x in range(block_num_in_height):
for block_y in range(block_num_in_width):
block_id = frm_block_id + block_x * block_num_in_width + block_y
hor_mat, ver_mat, out_dire_map = map_to_parititon(qt_map[block_id], bt_map[block_id], dire_map[block_id], chroma_factor)
seq_partition_hor_mat[frm_id, block_x * 16:(block_x + 1) * 16, block_y * 16:(block_y + 1) * 16] = hor_mat
seq_partition_ver_mat[frm_id, block_x * 16:(block_x + 1) * 16, block_y * 16:(block_y + 1) * 16] = ver_mat
seq_qt_map[frm_id, block_x * 8:(block_x + 1) * 8, block_y * 8:(block_y + 1) * 8] = qt_map[block_id]
seq_dire_map[frm_id, :, block_x * 16:(block_x + 1) * 16, block_y * 16:(block_y + 1) * 16] = out_dire_map
if save_path is not None:
hor_vec = seq_partition_hor_mat[frm_id].reshape(-1).astype(np.uint8)
ver_vec = seq_partition_ver_mat[frm_id].reshape(-1).astype(np.uint8)
qtdepth_vec = seq_qt_map[frm_id].reshape(-1).astype(np.uint8)
dire_vec = seq_dire_map[frm_id].reshape(-1).astype(np.int8)
for i in range(hor_vec.size): # horizontal edge vector
out_file.write(str(hor_vec[i]) + '\n')
for i in range(ver_vec.size): # vertical edge vector
out_file.write(str(ver_vec[i]) + '\n')
for i in range(qtdepth_vec.size): # qt depth vector
out_file.write(str(qtdepth_vec[i]) + '\n')
for i in range(dire_vec.size): # direction vector
out_file.write(str(dire_vec[i]) + '\n')
# print(hor_vec.size)
# print(qtdepth_vec.size)
# print(dire_vec.size)
if save_path is not None:
out_file.close()
# check_frm_id = 0
# partition = np.clip(seq_partition_hor_mat[check_frm_id] + seq_partition_ver_mat[check_frm_id], a_min=0, a_max=1)
# plt.imshow(partition, cmap='gray')
# plt.axis("off")
# # plt.savefig("Partition.png", dpi=640, bbox_inches='tight', pad_inches=0)
# plt.show()
# print(seq_qt_map[check_frm_id, 0:8, 0:8])
# print(seq_dire_map[check_frm_id, :, 0:16, 0:16])
# print(seq_qt_map[check_frm_id, 8:16, 8:16])
# print(seq_dire_map[check_frm_id, :, 16:32, 16:32])
# if __name__ == '__main__':
# print('start running ...')
# # qt_path = r"E:\VVC-Fast-Partition-DP\Output\Test3\Test_Luma_QP22_QTdepth.npy"
# # bt_path = r"E:\VVC-Fast-Partition-DP\Output\Test3\Test_Luma_QP22_MSBTdepth.npy"
# # dire_path = r"E:\VVC-Fast-Partition-DP\Output\Test3\Test_Luma_QP22_MSdirection.npy"
#
# qt_path = r"E:\VVC-Fast-Partition-DP\Output\Test4\Test_Luma_QP32_QTdepth.npy"
# bt_path = r"E:\VVC-Fast-Partition-DP\Output\Test4\Test_Luma_QP32_MSBTdepth.npy"
# dire_path = r"E:\VVC-Fast-Partition-DP\Output\Test4\Test_Luma_QP32_MSdirection.npy"
#
# qt_out_batch = np.load(qt_path)[551700:551700+3000]
# bt_out_batch = np.load(bt_path)[551700:551700+3000]
# dire_out_batch = np.load(dire_path)[551700:551700+3000]
#
# print(qt_out_batch.shape)
# print(bt_out_batch.shape)
# print(dire_out_batch.shape)
#
# bt_out_batch = torch.FloatTensor(bt_out_batch)
# qt_out_batch = torch.FloatTensor(qt_out_batch).cuda()
# bt_out_batch = torch.clamp(torch.round(bt_out_batch), min=0, max=5).cpu().numpy()
# qt_out_batch = eli_structual_error(qt_out_batch).cpu().numpy().squeeze(axis=1)
# # dire_out_batch_cla = dire_out_batch_cla.cpu().numpy()
# print(qt_out_batch.shape)
# print(bt_out_batch.shape)
# print(dire_out_batch.shape)
#
# # save_name = seq_name + '_' + comp + '_QP' + str(qp) + "_PartitionMat.txt"
# # print("Save Name: ", save_name)
# save_path = r"E:\VVC-Fast-Partition-DP\Output\BasketballDrive_1920x1080_50_Luma_QP32_PartitionMat.txt"
# get_sequence_partition_for_VTM(qt_map=qt_out_batch, bt_map=bt_out_batch, dire_map=dire_out_batch, is_luma=True, save_path=save_path,
# frm_num=3, frm_width=1920, frm_height=1080)
# del qt_out_batch, bt_out_batch, dire_out_batch
#
# print("End Process")