frames_folder, traces_folder, log_path, stat_path):

print('STARTING VIDEO ANALYSIS:\n')

#temporary folder to save filtered image before labeling

output_folder_mean = path_out+'toLabel/'

output_folder = path_out+'pooled/'

if not os.path.exists(output_folder_mean):

os.makedirs(output_folder_mean)

if not os.path.exists(output_folder):

os.makedirs(output_folder)

#setup parameters

signals = 0

total_traces=0

start = time.time()

n_frames = TriggerFunctions.TotalFrames(frames_folder)

image_prototype=Image.open(str(frames_folder)+video_name+'-1.png')

matrix_prototype=np.asarray(image_prototype.convert('L'))

print("Number of frames: "+str(n_frames)+'\n')

log_text=open(log_path, "a")

log_text.write("Number of frames analyzed: "+str(n_frames)+'\n')

log_text.close()

#Normalized mean and variance matrices

matrix_mean, matrix_var = TriggerFunctions.TotalMeanVar(frames_folder, matrix_prototype, n_frames)

#Saving mean and variance matrices

plt.matshow(matrix_var)

plt.colorbar()

plt.savefig(str(stat_path)+'var_'+str(video_name)+'.png')

plt.matshow(matrix_mean)

plt.colorbar()

plt.savefig(str(stat_path)+'mean_'+str(video_name)+'.png')

#LOOP ON FRAMES

for file_raw in tqdm(os.listdir(frames_folder)):

if not file_raw.endswith('.png'):

continue

if (file_raw.endswith('-1.png') or file_raw.endswith('-2.png')

or file_raw.endswith('-3.png') or file_raw.endswith('-4.png')):

continue

raw_image = Image.open(str(frames_folder)+file_raw)

matrix_raw=np.asarray(raw_image.convert('L'))

#Selecting relevant frames via discriminant value

if not TriggerFunctions.ImageSelectingByDiscrimination(matrix_raw/255., matrix_mean, matrix_var, discriminant_thr, verbose = True):

continue

signals+=1

log_text=open(log_path, "a")

log_text.write('\nSelected file number '+str(signals)+': '+str(file_raw)+'\n')

log_text.close()

print('\nSelected file number '+str(signals)+': '+str(file_raw)+'\n')

raw_file_name, dot, formato = file_raw.partition('.')

temp_file_name = raw_file_name.split('-')

number_frame=int(temp_file_name[-1])

Filters.ReducingResolution(frames_folder, file_raw, output_folder, 'raw_pooled.png', max_pool)

raw_pooled = Image.open(str(output_folder)+'raw_pooled.png')

matrix_raw_pooled=np.asarray(raw_pooled.convert('L'))

matrix_mean_pooled = np.zeros_like(matrix_raw_pooled)

#SUBTRACTING BACKGROUNDS PROCEDURE

#counting the number of background frames

n_backgrounds=0

start_partial=time.time()

for file in os.listdir(frames_folder):

if (file.startswith(video_name)):

if (file.endswith('-1.png') or file.endswith('-2.png')

or file.endswith('-3.png') or file.endswith('-4.png')

or file == file_raw):

continue

file_name, dot, extension = file.partition('.')

file_name1 = file_name.split('-')

number=int(file_name1[-1])

if (number%3):

continue

n_backgrounds += 1

log_text=open(log_path, "a")

log_text.write('Total number of background images processed: '+str(n_backgrounds)+'\n')

log_text.close()

print('Total number of background images processed: '+str(n_backgrounds)+'\n')

for file in os.listdir(frames_folder):

if (file.startswith(video_name)):

if (file.endswith('-1.png') or file.endswith('-2.png')

or file.endswith('-3.png') or file.endswith('-4.png')

or file == file_raw):

continue

file_name, dot, extension = file.partition('.')

file_name1 = file_name.split('-')

number=int(file_name1[-1])

if (number%3):

continue

#background manipulation before subtracting

Filters.Denoising1(frames_folder, file, output_folder, 'bkg_denoising1.png', denoising1_bkg)

Filters.Dilation(output_folder, 'bkg_denoising1.png', output_folder, 'bkg_dilationed.png', dilation_bkg)

#raw_image manipulation before subtracting

Filters.Denoising1(frames_folder, file_raw, output_folder, 'raw_denoising1.png', denoising1)

#background subtraction

Filters.BackgroundSubtraction(output_folder, 'raw_denoising1.png', 'bkg_dilationed.png', output_folder, 'subtracted.png')

#manipulation after subtracting

Filters.Denoising2(output_folder, 'subtracted.png', output_folder, 'sub_denoising2.png', denoising2_sub)

Filters.ReducingResolution(output_folder, 'sub_denoising2.png', output_folder, 'pool_'+str(file), max_pool)

image_pooled = Image.open(str(output_folder)+'pool_'+str(file))

matrix_pooled=np.asarray(image_pooled.convert('L'))

matrix_mean_pooled=np.add(matrix_mean_pooled*1., matrix_pooled*1./n_backgrounds)

#meaning subtracted images

image_mean = Image.fromarray(np.uint8(matrix_mean_pooled))

image_mean.save(str(output_folder_mean)+'mean_'+str(raw_file_name)+'.png')

#binarization

Filters.Denoising2(output_folder_mean, 'mean_'+str(raw_file_name)+'.png', output_folder_mean, 'mean_'+str(raw_file_name)+'_den2.png', denoising2)

#LABELING

temp_folder_1 = path_out+'temp1/'

temp_folder_2 = path_out+'temp2/'

if os.path.exists(temp_folder_1):

shutil.rmtree(temp_folder_1)

if os.path.exists(temp_folder_2):

shutil.rmtree(temp_folder_2)

os.makedirs(temp_folder_1)

os.makedirs(temp_folder_2)

#first labeling

n1=Filters.HiLoLabeling(output_folder_mean, 'mean_'+str(raw_file_name)+'_den2.png', traces_folder, temp_folder_1,

gauss_radius_1, loCcThr_1, hiCcThr_1)

#second labeling

n2=0

for temp_cc in os.listdir(temp_folder_1):

q=Filters.HiLoLabeling(temp_folder_1, temp_cc, traces_folder, temp_folder_2,

gauss_radius_2, loCcThr_2, hiCcThr_2)

n2+=q

#deleting temporary folders

shutil.rmtree(temp_folder_1)

shutil.rmtree(temp_folder_2)

total_cc=n1+n2 #+n3

total_traces+=total_cc

log_text=open(log_path, "a")

'''

log_text.write("Number of traces finded: "+str(total_cc)+'\n')

log_text.write("Time required for one frame to be analyzed: "+str(delta_partial)+' sec \n'+

"Integrated time from the beginning: "+str(delta_integrated)+' sec \n')

log_text.close()

print("Number of traces finded: "+str(total_cc)+'\n')

print("Time required for one frame to be analyzed: "+str(delta_partial)+' sec \n'+

"Integrated time from the beginning: "+str(delta_integrated)+' sec \n')

'''

stop=time.time()

delta=stop-start

log_text=open(log_path, "a")

log_text.write("\nTotal time for the execution: "+str(delta)+' sec\n\n'+

"Total number of frames selected for the analysis: "+str(signals)+'\n')

log_text.write("Total traces:"+str(total_traces)+str('\n\n\n'))

log_text.close()

print("Total time for the execution: "+str(delta)+' sec \n\n'+

"Total number of frames selected for the analysis: "+str(signals)+'\n')

print("Total traces:"+str(total_traces)+str('\n\n\n'))

#deleting temporary folders

shutil.rmtree(output_folder_mean)