def One_Key_Frame_Graphs(
data_folder,
sub_dic,
show_clip=3,
alinged_sub_folder=r'\Results\Final_Aligned_Frames',
Stim_Align_sub_folder=r'\Results\Stim_Frame_Align.pkl'):
result_folder = data_folder + r'\Results'
graph_save_folder = result_folder + r'\Only_Frame_SubGraphs'
OS_Tools.mkdir(result_folder)
OS_Tools.mkdir(graph_save_folder)
stim_path = data_folder + Stim_Align_sub_folder
stim_dic = OS_Tools.Load_Variable(stim_path)
all_tif_name = OS_Tools.Get_File_Name(data_folder + alinged_sub_folder)
graph_num = len(sub_dic)
all_sub_graph_names = list(sub_dic.keys())
for i in range(graph_num):
current_name = all_sub_graph_names[i]
current_a = Frame_ID_Extractor(stim_dic, sub_dic[current_name][0])
current_b = Frame_ID_Extractor(stim_dic, sub_dic[current_name][1])
current_sub_graph, current_t_graph, current_info_dic = Single_Subgraph_Generator(
all_tif_name, current_a, current_b)
current_sub_graph = Graph_Tools.Clip_And_Normalize(
current_sub_graph, show_clip)
current_t_graph = Graph_Tools.Clip_And_Normalize(
current_t_graph, show_clip)
# Save graphs
Graph_Tools.Show_Graph(current_sub_graph, current_name + '_Sub_Graph',
graph_save_folder)
Graph_Tools.Show_Graph(current_t_graph, current_name + '_t_Graph',
graph_save_folder)
OS_Tools.Save_Variable(graph_save_folder, current_name + r'_Sub_Info',
current_info_dic, '.info')
return True
def __init__(self,all_folders):
self.all_folders = all_folders
self.all_save_folders = List_Op.List_Annex(self.all_folders,['Results'])
self.Aligned_frame_folders = List_Op.List_Annex(self.all_save_folders,['Aligned_Frames'])
for i in range(len(self.all_save_folders)):
OS_Tools.mkdir(self.all_save_folders[i])
OS_Tools.mkdir(self.Aligned_frame_folders[i])
self.Before_Align_Tif_Name = []
for i in range(len(self.all_folders)):
current_run_tif = OS_Tools.Get_File_Name(self.all_folders[i])
self.Before_Align_Tif_Name.append(current_run_tif)
def Translation_Alignment(all_folders,
base_mode='global',
input_base=np.array([[0, 0], [0, 0]]),
align_range=20,
align_boulder=20,
before_average=True,
average_std=5,
big_memory_mode=False,
save_aligned_data=False,
graph_shape=(512, 512),
timer=True):
'''
This function will align all tif graphs in input folders. Only translation transaction here. Affine transformation need further discussion.
Parameters
----------
all_folders:(list)
List of all tif folders, elements are strs.
base_mode:('global',int,'input',optional. The default is 'global')
How to select base frame. 'global': use global average as base. int: use average of specific run as base. 'input':Manually input base graph, need to be a 2D-Ndarray.
input_base:(2D-Ndarray,optional. The default is none.)
If base_mode = 'input', input_base must be given. This will be the base for alignment.
align_range:(int,optional. The default is 20)
Max pixel of alignment.
align_boulder:(int,optional. The default is 20)
boulder cut for align. For different graph size, this variable shall be change.
before_average:(bool,optional. The default is True)
Whether before average is done. It can be set False to save time, on this case base graph shall be given.
average_std:(float,optional. The default is 5)
How much std you want for average graph generation. Different std can effect graph effect.
big_memory_mode:(bool,optional. The default is False)
If memory is big enough, use this mode is faster.
save_aligned_data:(bool,optional. The default is False)
Can be true only in big memory mode. This will save all aligned graph in a single 4D-Ndarray file.Save folder is the first folder.
graph_shape:(2-element-turple,optional. The default is (512,512))
Shape of graphs. All input graph must be in same shape.
timer:(bool,optional. The default is True)
Show runtime of function and each procedures.
Returns
-------
bool
Whether new folder is generated.
'''
time_tic_start = time.time()
#%% Step1, generate folders and file cycle.
all_save_folders = List_Op.List_Annex(all_folders, ['Results'])
Aligned_frame_folders = List_Op.List_Annex(all_save_folders,
['Aligned_Frames'])
for i in range(len(all_save_folders)):
OS_Tools.mkdir(all_save_folders[i])
OS_Tools.mkdir(Aligned_frame_folders[i])
Before_Align_Tif_Name = []
for i in range(len(all_folders)):
current_run_tif = OS_Tools.Get_File_Name(all_folders[i])
Before_Align_Tif_Name.append(current_run_tif)
#%% Step2, Generate average map before align.
if before_average == True:
print('Before run averaging ...')
Before_Align_Dics = {
} # This is the dictionary of all run averages. Keys are run id.
total_graph_num = 0 # Counter of graph numbers.
for i in range(len(Before_Align_Tif_Name)):
current_run_graph_num = len(Before_Align_Tif_Name[i])
total_graph_num += current_run_graph_num
current_run_average = Graph_Tools.Average_From_File(
Before_Align_Tif_Name[i])
current_run_average = Graph_Tools.Clip_And_Normalize(
current_run_average, clip_std=average_std)
Before_Align_Dics[i] = (
current_run_average, current_run_graph_num
) # Attention here, data recorded as turple.
Graph_Tools.Show_Graph(
current_run_average, 'Run_Average',
all_save_folders[i]) # Show and save Run Average.
# Then Use Weighted average method to generate global tif.
global_average_graph = np.zeros(shape=np.shape(
Before_Align_Dics[0][0]),
dtype='f8') # Base on shape of graph
for i in range(len(Before_Align_Tif_Name)):
global_average_graph += Before_Align_Dics[i][0].astype(
'f8') * Before_Align_Dics[i][1] / total_graph_num
global_average_graph = Graph_Tools.Clip_And_Normalize(
global_average_graph, clip_std=average_std)
# Then save global average in each run folder.
if len(all_folders) > 1:
for i in range(len(Before_Align_Tif_Name)):
Graph_Tools.Show_Graph(global_average_graph,
'Global_Average',
all_save_folders[i],
show_time=0)
else:
print('Only One run, no global average.')
else:
print('Before average Skipped.')
time_tic_average0 = time.time()
#%% Step3, Core Align Function.
print('Aligning...')
if base_mode == 'global':
base = global_average_graph
elif base_mode == 'input':
base = input_base
elif type(base_mode) == int:
base = Before_Align_Dics[base_mode][0]
else:
raise IOError('Invalid base mode.')
# In big memory mode, save aligned_data in a dictionary file.
if big_memory_mode == True:
All_Aligned_Frame = {}
for i in range(len(Before_Align_Tif_Name)):
All_Aligned_Frame[i] = np.zeros(
shape=(graph_shape + (len(Before_Align_Tif_Name[i]), )),
dtype='u2') # Generate empty graph matrix.
for i in range(len(Before_Align_Tif_Name)): # Cycle all runs
for j in range(len(
Before_Align_Tif_Name[i])): # Cycle all graphs in current run
current_graph = cv2.imread(Before_Align_Tif_Name[i][j],
-1) # Read in current graph.
_, _, current_aligned_graph = Alignment(base,
current_graph,
boulder=align_boulder,
align_range=align_range)
graph_name = Before_Align_Tif_Name[i][j].split(
'\\')[-1][:-4] # Ignore extend name'.tif'.
Graph_Tools.Show_Graph(current_aligned_graph,
graph_name,
Aligned_frame_folders[i],
show_time=0)
if big_memory_mode == True:
All_Aligned_Frame[i][:, :, j] = current_aligned_graph
print('Align Finished, generating average graphs...')
time_tic_align_finish = time.time()
#%% Step4, After Align Average
After_Align_Graphs = {}
if big_memory_mode == True: # Average can be faster.
temp_global_average_after_align = np.zeros(shape=graph_shape,
dtype='f8')
for i in range(len(All_Aligned_Frame)):
current_run_average = Graph_Tools.Clip_And_Normalize(
np.mean(All_Aligned_Frame[i], axis=2),
clip_std=average_std) # Average run graphs, in type 'u2'
After_Align_Graphs[i] = (current_run_average,
len(All_Aligned_Frame[i][0, 0, :]))
temp_global_average_after_align += After_Align_Graphs[i][0].astype(
'f8') * After_Align_Graphs[i][1] / total_graph_num
global_average_after_align = Graph_Tools.Clip_And_Normalize(
temp_global_average_after_align, clip_std=average_std)
else: # Traditional ways.
temp_global_average_after_align = np.zeros(shape=graph_shape,
dtype='f8')
for i in range(len(Aligned_frame_folders)):
current_run_names = OS_Tools.Get_File_Name(
Aligned_frame_folders[i])
current_run_average = Graph_Tools.Average_From_File(
current_run_names)
current_run_average = Graph_Tools.Clip_And_Normalize(
current_run_average, clip_std=average_std)
After_Align_Graphs[i] = (current_run_average,
len(current_run_names))
temp_global_average_after_align += After_Align_Graphs[i][0].astype(
'f8') * After_Align_Graphs[i][1] / total_graph_num
global_average_after_align = Graph_Tools.Clip_And_Normalize(
temp_global_average_after_align, clip_std=average_std)
# After average, save aligned graph in each save folder.
for i in range(len(all_save_folders)):
current_save_folder = all_save_folders[i]
Graph_Tools.Show_Graph(After_Align_Graphs[i][0],
'Run_Average_After_Align', current_save_folder)
if i == 0: # Show global average only once.
global_show_time = 5000
else:
global_show_time = 0
if len(all_folders) > 1:
Graph_Tools.Show_Graph(global_average_after_align,
'Global_Average_After_Align',
current_save_folder,
show_time=global_show_time)
time_tic_average1 = time.time()
#%% Step5, save and timer
if save_aligned_data == True:
OS_Tools.Save_Variable(all_save_folders[0], 'All_Aligned_Frame_Data',
All_Aligned_Frame)
if timer == True:
whole_time = time_tic_average1 - time_tic_start
before_average_time = time_tic_average0 - time_tic_start
align_time = time_tic_align_finish - time_tic_average0
after_average_time = time_tic_average1 - time_tic_align_finish
print('Total Time = ' + str(whole_time) + ' s.')
print('Before Average Time = ' + str(before_average_time) + ' s.')
print('Align Time = ' + str(align_time) + ' s.')
print('After Average Time = ' + str(after_average_time) + ' s.')
return True
def Tremble_Comparision(before_folder,
after_folder,
boulder_ignore=20,
cut_shape=(9, 9),
mask_thres=0):
# Initialization
save_folder = after_folder + r'\Results'
OS_Tools.mkdir(save_folder)
save_folder = save_folder + r'\Tremble_Compare'
OS_Tools.mkdir(save_folder)
row_num = cut_shape[0]
col_num = cut_shape[1]
frac_num = row_num * col_num
cov_matrix_dic = {}
var_matrix_dic = {}
variation = np.zeros(shape=(row_num, col_num, 2), dtype='f8')
variation_change = np.zeros(shape=(row_num, col_num), dtype='f8')
variation_prop = np.zeros(shape=(row_num, col_num), dtype='f8')
# Calculation Begins
before_schematic, before_frac_center = Tremble_Evaluator(
before_folder,
boulder_ignore=boulder_ignore,
cut_shape=cut_shape,
mask_thres=mask_thres)
after_schematic, after_frac_center = Tremble_Evaluator(
after_folder,
boulder_ignore=boulder_ignore,
cut_shape=cut_shape,
mask_thres=mask_thres)
fig, ax = plt.subplots(row_num, col_num,
figsize=(30, 28)) # Initialize graphs
fig.suptitle('Mass Center Distribution', fontsize=54)
# Cycle all fracture,get scatter map and variance
for i in range(frac_num):
# Graph_Plot
current_row = i % row_num
current_col = i // row_num
ax[current_row, current_col].scatter(before_frac_center[i, :, 1],
before_frac_center[i, :, 0],
s=1,
c='r')
ax[current_row, current_col].scatter(after_frac_center[i, :, 1],
after_frac_center[i, :, 0],
s=1,
c='g')
# After plot, calculate cov matrix and variance.
before_cov = np.cov(before_frac_center[i, :, :].T)
after_cov = np.cov(after_frac_center[i, :, :].T)
cov_matrix_dic[i] = (before_cov, after_cov)
before_eig, _ = np.linalg.eig(before_cov)
after_eig, _ = np.linalg.eig(after_cov)
before_var = np.round(before_eig.sum(), 4)
after_var = np.round(after_eig.sum(), 4)
variation[current_row, current_col, 0] = before_var
variation[current_row, current_col, 1] = after_var
variation_change[current_row, current_col] = before_var - after_var
variation_prop[current_row,
current_col] = (before_var - after_var) / before_var
# Text annotate
anchored_text = AnchoredText('Before variance:' + str(before_var) +
'\n After variance:' + str(after_var),
loc='lower left')
ax[current_row, current_col].add_artist(anchored_text)
# After this, save figures and matrixs.
var_matrix_dic['Before'] = variation[:, :, 0]
var_matrix_dic['After'] = variation[:, :, 1]
Graph_Tools.Show_Graph(before_schematic, 'Before_Schematic', save_folder)
Graph_Tools.Show_Graph(after_schematic, 'After_Schematic', save_folder)
fig.savefig(save_folder + '\Scatter Plots.png', dpi=330)
OS_Tools.Save_Variable(save_folder, 'cov_matrix', cov_matrix_dic)
OS_Tools.Save_Variable(save_folder, 'variance_matrix', var_matrix_dic)
# Calculate variance change and plot variance map.
# Before variance map
plt.clf()
fig2 = plt.figure(figsize=(15, 15))
plt.title('Before Align Variance', fontsize=36)
fig2 = sns.heatmap(variation[:, :, 0],
cmap='bwr',
annot=True,
annot_kws={"size": 20},
square=True,
yticklabels=False,
xticklabels=False,
center=0)
fig2.figure.savefig(save_folder + '\Before_Variance.png', dpi=330)
# After variance map
plt.clf()
fig2 = plt.figure(figsize=(15, 15))
plt.title('After Align Variance', fontsize=36)
fig2 = sns.heatmap(variation[:, :, 1],
cmap='bwr',
annot=True,
annot_kws={"size": 20},
square=True,
yticklabels=False,
xticklabels=False,
center=0)
fig2.figure.savefig(save_folder + '\After_Variance.png', dpi=330)
# Variance change map
plt.clf()
fig2 = plt.figure(figsize=(15, 15))
plt.title('Variance Change', fontsize=36)
fig2 = sns.heatmap(variation_change,
cmap='bwr',
annot=True,
annot_kws={"size": 20},
square=True,
yticklabels=False,
xticklabels=False,
center=0)
fig2.figure.savefig(save_folder + '\Variance_Change.png', dpi=330)
# Variance change propotion map
plt.clf()
fig2 = plt.figure(figsize=(15, 15))
plt.title('Variance Change Propotion', fontsize=36)
fig2 = sns.heatmap(variation_prop,
cmap='bwr',
annot=True,
annot_kws={"size": 20},
square=True,
yticklabels=False,
xticklabels=False,
center=0)
fig2.figure.savefig(save_folder + '\Variance_Change_Prop.png', dpi=330)
return cov_matrix_dic, var_matrix_dic
import My_Wheels.OS_Tools_Kit as OS_Tools
import cv2
data_folder = [r'E:\Test_Data\2P\201222_L76_2P']
run_list = [
'1-001', # Spon
'1-008', # OD
'1-010', # G8
'1-011', # RGLum4
'1-014' # Spon After
]
all_runs = List_Tools.List_Annex(data_folder, run_list)
#%% Add 3 list for run01 to fit ROI change.
run_1 = all_runs[0]
run1_all_tif = OS_Tools.Get_File_Name(run_1)
save_path = run_1 + r'\shape_extended'
OS_Tools.mkdir(save_path)
for i in range(len(run1_all_tif)):
current_graph = cv2.imread(run1_all_tif[i], -1)
extended_graph = Graph_Tools.Boulder_Extend(
current_graph, [0, 0, 0, 3]) # 3 pix on the right.
current_graph_name = run1_all_tif[i].split('\\')[-1]
Graph_Tools.Show_Graph(extended_graph,
current_graph_name,
save_path,
show_time=0)
#%% Then align Run01_Spon.
from My_Wheels.Translation_Align_Function import Translation_Alignment
Translation_Alignment([all_runs[0] + r'\shape_extended'],
graph_shape=(325, 324))
#%% Then Use this base to align other runs.
base = cv2.imread(
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 30 14:37:51 2021
@author: ZR
"""
from My_Wheels.Standard_Parameters.Stim_Name_Tools import Stim_ID_Combiner
from My_Wheels.Cell_Processor import Cell_Processor
import My_Wheels.OS_Tools_Kit as ot
import matplotlib.pyplot as plt
day_folder = r'K:\Test_Data\2P\210320_L76_2P'
save_folder = day_folder + r'\_All_Results'
ot.mkdir(save_folder)
#%% Analyze Run05-G16 First.
G16_CP = Cell_Processor(day_folder, 'Run005')
all_cell_name = G16_CP.all_cell_names
Ori_IDs = Stim_ID_Combiner('G16_Oriens')
sub_sf = save_folder + r'\G16_Oriens'
ot.mkdir(sub_sf)
for i in range(len(all_cell_name)):
_, raw_data, _ = G16_CP.Single_Cell_Response_Data(Ori_IDs,
all_cell_name[i])
ot.Save_Variable(sub_sf, all_cell_name[i], raw_data, '.raw')
test_fig = G16_CP.Average_Response_Map()
test_fig.savefig(sub_sf + r'\\' + all_cell_name[i] + '_Response.png',
dpi=180)
plt.clf()
#%% Then directions
Dir_IDs = Stim_ID_Combiner('G16_Dirs')
def Standard_Stim_Processor(data_folder,
stim_folder,
sub_dic,
alinged_sub_folder=r'\Results\Aligned_Frames',
show_clip=3,
tuning_graph=False,
cell_method='Default',
filter_method='Gaussian',
LP_Para=((5, 5), 1.5),
HP_Para=False,
spike_train_path='Default',
spike_train_filter_para=(False, False),
spike_train_filter_method=False):
'''
Generate subtraction graph, cell graph and tuning graphs if requred.
Parameters
----------
data_folder : (str)
Run folder.
stim_folder : (str)
Stim file folder or Frame_Stim_Align File folder. Pre align is advised.
sub_dic : (Dic)
Subtraction dicionary. This can be generated from My_Wheels.Standard_Parameters
show_clip : (float), optional
Clip of graph show. The default is 3.
tuning_graph : (bool), optional
Whether we generate tuning graph of each cells. The default is False.
cell_method : (str), optional
Cell find method. You can input cell file path here. The default is 'Default'.
filter_method : (str), optional
False to skip filter. Kernel function of graph filtering. The default is 'Gaussian'.
LP_Para : (turple), optional
False to skip. Low pass filter of graph. The default is ((5,5),1.5).
HP_Para : (turple), optional
False to skip. High pass filter of graph. Big HP can be very slow!. The default is False.
spike_train_path : (str), optional
Path of spike train.'Default' will generate spike train directly. The default is 'Default'.
spike_train_filter_para : (turple), optional
Signal filter bandpass propotion of spike train. Please be sure if you need this. The default is (False,False).
spike_train_filter_method : (str), optional
False to skip. Method of signal filtering. The default is False.
Returns
-------
None.
'''
# Path Cycle.
from Cell_Find_From_Graph import On_Off_Cell_Finder
work_folder = data_folder + r'\Results'
OS_Tools.mkdir(work_folder)
aligned_frame_folder = data_folder + alinged_sub_folder
OS_Tools.mkdir(aligned_frame_folder)
# Step1, align graphs. If already aligned, just read
if not os.listdir(aligned_frame_folder): # if this is a new folder
print('Aligned data not found. Aligning here..')
Translation_Alignment([data_folder])
aligned_all_tif_name = np.array(
OS_Tools.Get_File_Name(aligned_frame_folder)
) # Use numpy array, this is easier for slice.
# Step2, get stim fram align matrix. If already aligned, just read in aligned dictionary.
file_detector = len(stim_folder.split('.'))
if file_detector == 1: # Which means input is a folder
print('Frame Stim not Aligned, aligning here...')
from My_Wheels.Stim_Frame_Align import Stim_Frame_Align
_, Frame_Stim_Dic = Stim_Frame_Align(stim_folder)
else: # Input is a file
Frame_Stim_Dic = OS_Tools.Load_Variable(stim_folder)
# Step3, get cell information
if cell_method == 'Default': # meaning we will use On-Off graph to find cell.
print('Cell information not found. Finding here..')
cell_dic = On_Off_Cell_Finder(aligned_all_tif_name,
Frame_Stim_Dic,
filter_method=filter_method,
LP_Para=LP_Para,
HP_Para=HP_Para)
else:
cell_dic = OS_Tools.Load_Variable(cell_method)
# Step4, calculate spike_train.
if spike_train_path != 'Default':
dF_F_train = OS_Tools.Load_Variable(spike_train_path)
else: # meaning we need to calculate spike train from the very begining.
_, dF_F_train = Spike_Train_Generator(
aligned_all_tif_name,
cell_dic['All_Cell_Information'],
Base_F_type='nearest_0',
stim_train=Frame_Stim_Dic['Original_Stim_Train'],
LP_Para=LP_Para,
HP_Para=HP_Para,
filter_method=filter_method)
#Step5, filt spike trains.
if spike_train_filter_method != False: # Meaning we need to do train filter.
for i in range(len(dF_F_train)):
dF_F_train[i] = My_Filter.Signal_Filter(dF_F_train,
spike_train_filter_method,
spike_train_filter_para)
# Step6, get each frame graph and cell graph.
all_graph_keys = list(sub_dic.keys())
for i in range(len(sub_dic)):
output_folder = work_folder + r'\Subtraction_Graphs'
current_key = all_graph_keys[i]
current_sub_list = sub_dic[current_key]
A_conds = current_sub_list[0] # condition of A graph
B_conds = current_sub_list[1] # condition of B graph
A_IDs = []
B_IDs = []
for i in range(len(A_conds)):
A_IDs.extend(Frame_Stim_Dic[A_conds[i]])
for i in range(len(B_conds)):
B_IDs.extend(Frame_Stim_Dic[B_conds[i]])
# Get frame maps.
current_sub_graph, current_t_graph, current_F_info = Single_Subgraph_Generator(
aligned_all_tif_name, A_IDs, B_IDs, filter_method, LP_Para,
HP_Para)
current_sub_graph = Graph_Tools.Clip_And_Normalize(
current_sub_graph, show_clip)
Graph_Tools.Show_Graph(current_sub_graph, current_key + '_SubGraph',
output_folder)
current_t_graph = Graph_Tools.Clip_And_Normalize(
current_t_graph, show_clip)
Graph_Tools.Show_Graph(current_t_graph, current_key + '_T_Graph',
output_folder)
OS_Tools.Save_Variable(output_folder,
current_key + '_Sub_Info',
current_F_info,
extend_name='.info')
# Get cell maps
cell_info = cell_dic['All_Cell_Information']
current_cell_sub_graph, current_cell_t_graph, current_cell_info = Single_Cellgraph_Generator(
dF_F_train, cell_info, show_clip, A_IDs, B_IDs)
Graph_Tools.Show_Graph(current_cell_sub_graph,
current_key + '_Cell_SubGraph', output_folder)
Graph_Tools.Show_Graph(current_cell_t_graph,
current_key + '_Cell_T_Graph', output_folder)
OS_Tools.Save_Variable(output_folder,
current_key + '_Cell_Info',
current_cell_info,
extend_name='.info')
#Step7, calculate cell tuning graph.
if tuning_graph == True:
print('Not finished yet.')
def Affine_Aligner_Gaussian(data_folder,
base_graph,
window_size=1,
max_point=50000,
good_match_prop=0.3,
dist_lim=120,
match_checker=1,
sector_num=4,
write_file=False,
save_folder='Default'):
if save_folder == 'Default':
save_folder = data_folder + r'\Results'
aligned_tif_folder = save_folder + r'\Affined_Frames'
OS_Tools.mkdir(save_folder)
OS_Tools.mkdir(aligned_tif_folder)
all_tif_name = OS_Tools.Get_File_Name(data_folder)
graph_num = len(all_tif_name)
graph_shape = cv2.imread(all_tif_name[0], -1).shape
height, width = graph_shape
origin_tif_matrix = np.zeros(shape=graph_shape + (graph_num, ), dtype='u2')
# Read in all tif name.
for i in range(graph_num):
origin_tif_matrix[:, :, i] = cv2.imread(all_tif_name[i], -1)
# Then get window slipped average graph.
if window_size == 1:
slipped_average_matrix = origin_tif_matrix
else:
slipped_average_matrix = Filters.Window_Average(
origin_tif_matrix, window_size=window_size)
# Use slip average to get deformation parameters.
aligned_tif_matrix = np.zeros(shape=origin_tif_matrix.shape, dtype='u2')
h_dic = {} # Deformation parameters
for i in range(graph_num):
target = slipped_average_matrix[:, :, i]
_, current_h = Affine_Core_Point_Equal(target,
base_graph,
max_point=max_point,
good_match_prop=good_match_prop,
sector_num=sector_num,
dist_lim=dist_lim,
match_checker=match_checker)
h_dic[i] = current_h
current_deformed_graph = cv2.warpPerspective(
origin_tif_matrix[:, :, i], current_h, (width, height))
Graph_Tools.Show_Graph(current_deformed_graph,
all_tif_name[i].split('\\')[-1],
aligned_tif_folder,
show_time=0,
graph_formation='')
aligned_tif_matrix[:, :, i] = current_deformed_graph
OS_Tools.Save_Variable(save_folder, 'Deform_H', h_dic)
if write_file == True:
OS_Tools.Save_Variable(save_folder, 'Affine_Aligned_Graphs',
aligned_tif_matrix)
# At last, generate average graphs
graph_before_align = origin_tif_matrix.mean(axis=2).astype('u2')
graph_after_align = aligned_tif_matrix.mean(axis=2).astype('u2')
graph_before_align = Graph_Tools.Clip_And_Normalize(graph_before_align,
clip_std=5)
graph_after_align = Graph_Tools.Clip_And_Normalize(graph_after_align,
clip_std=5)
Graph_Tools.Show_Graph(graph_before_align, 'Graph_Before_Affine',
save_folder)
Graph_Tools.Show_Graph(graph_after_align, 'Graph_After_Affine',
save_folder)
return True
