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scripts_GAN_HR-LR.py
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scripts_GAN_HR-LR.py
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'''
This script is taken from https://github.com/xinntao/BasicSR/blob/master/codes/scripts/generate_mod_LR_bic.py
Need to understand, but for now using for generating Low res ang bicubic upscale images.
Can be done using much simpler code.
This code is useful for variable size of images but my images are square, so don't Need
this now but useful for later use cases.
'''
import os
import sys
import cv2
import numpy as np
import glob
import shutil
import kornia
import torch
try:
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from utils import imresize_np, calculate_psnr, calculate_ssim
except ImportError:
pass
def generate_mod_LR_bic():
# set parameters
up_scale = 4
mod_scale = 4
# set data dir
# directory structure on sunray server pc
# Need to change later when refactoring, code cleaning and testing.
sourcedir = '/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS'
savedir = '/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS'
saveHRpath = os.path.join(savedir, 'HR', 'x' + str(mod_scale))
saveLRpath = os.path.join(savedir, 'LR', 'x' + str(up_scale))
saveBicpath = os.path.join(savedir, 'Bic', 'x' + str(up_scale))
if not os.path.isdir(sourcedir):
print('Error: No source data found')
exit(0)
if not os.path.isdir(savedir):
os.mkdir(savedir)
if not os.path.isdir(os.path.join(savedir, 'HR')):
os.mkdir(os.path.join(savedir, 'HR'))
if not os.path.isdir(os.path.join(savedir, 'LR')):
os.mkdir(os.path.join(savedir, 'LR'))
if not os.path.isdir(os.path.join(savedir, 'Bic')):
os.mkdir(os.path.join(savedir, 'Bic'))
if not os.path.isdir(saveHRpath):
os.mkdir(saveHRpath)
else:
print('It will cover ' + str(saveHRpath))
if not os.path.isdir(saveLRpath):
os.mkdir(saveLRpath)
else:
print('It will cover ' + str(saveLRpath))
if not os.path.isdir(saveBicpath):
os.mkdir(saveBicpath)
else:
print('It will cover ' + str(saveBicpath))
filepaths = [f for f in os.listdir(sourcedir) if f.endswith('.jpg') and not f.endswith('check.jpg')]
num_files = len(filepaths)
# prepare data with augementation
for i in range(num_files):
filename = filepaths[i]
print('No.{} -- Processing {}'.format(i, filename))
# read image
image = cv2.imread(os.path.join(sourcedir, filename))
width = int(np.floor(image.shape[1] / mod_scale))
height = int(np.floor(image.shape[0] / mod_scale))
# modcrop
if len(image.shape) == 3:
image_HR = image[0:mod_scale * height, 0:mod_scale * width, :]
else:
image_HR = image[0:mod_scale * height, 0:mod_scale * width]
# LR
image_LR = imresize_np(image_HR, 1 / up_scale, True)
# bic
image_Bic = imresize_np(image_LR, up_scale, True)
cv2.imwrite(os.path.join(saveHRpath, filename), image_HR)
cv2.imwrite(os.path.join(saveLRpath, filename), image_LR)
cv2.imwrite(os.path.join(saveBicpath, filename), image_Bic)
def copy_folder_name_for_valid_image():
Dir_HR = "/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/HR/x4/"
Dir_Bic = "/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/Bic/x4/"
Dir_LR = "/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/LR/x4/"
for file in glob.glob("/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved_ESRGAN/val_images/*/"):
img_file = os.path.basename(file[:-1]+'.jpg')
txt_file = os.path.basename(file[:-1]+'.txt')
#print(file)
sourceH = os.path.join(Dir_HR,img_file)
destinationH = os.path.join(Dir_HR, 'valid_img', img_file)
shutil.move(sourceH, destinationH)
sourceHtxt = os.path.join(Dir_HR,txt_file)
destinationHtxt = os.path.join(Dir_HR, 'valid_img', txt_file)
shutil.move(sourceHtxt, destinationHtxt)
sourceB = os.path.join(Dir_Bic,img_file)
destinationB = os.path.join(Dir_Bic, 'valid_img', img_file)
shutil.move(sourceB, destinationB)
sourceBtxt = os.path.join(Dir_Bic,txt_file)
destinationBtxt = os.path.join(Dir_Bic, 'valid_img', txt_file)
shutil.move(sourceBtxt, destinationBtxt)
sourceL = os.path.join(Dir_LR,img_file)
destinationL = os.path.join(Dir_LR, 'valid_img', img_file)
shutil.move(sourceL, destinationL)
sourceLtxt = os.path.join(Dir_LR,txt_file)
destinationLtxt = os.path.join(Dir_LR, 'valid_img', txt_file)
shutil.move(sourceLtxt, destinationLtxt)
def merge_edge():
dir = "/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved/val_images/*/*"
img_SR = sorted(glob.glob(dir+'_216000_SR.png'))
img_lap = sorted(glob.glob(dir+'_216000_lap.png'))
img_lap_learned = sorted(glob.glob(dir+'_216000_lap_learned.png'))
mean = np.array([0.3442, 0.3708, 0.3476])
std = np.array([0.1232, 0.1230, 0.1284])
for i, j, k in zip(img_SR, img_lap, img_lap_learned):
#print(i+'____'+j)
img_SR_1 = cv2.imread(i) / 255
img_lap_1 = cv2.imread(j) / 255
img_lap_learned_1 = cv2.imread(k) / 255
img_SR_1 = np.clip(img_SR_1, 0, 1)
img_lap_1 = np.clip(img_lap_1, 0, 1)
img_lap_learned_1 = np.clip(img_lap_learned_1, 0, 1)
img_SR_1 = (img_SR_1 - mean) / std
img_lap_1 = (img_lap_1 - mean) / std
img_lap_learned_1 = (img_lap_learned_1 - mean) / std
#img_final_SR_enhanced = img_SR_1 + (img_lap_1 - img_lap_learned_1)
img_final_SR_enhanced = img_SR_1 + (img_lap_learned_1 - img_lap_1)
img_final_SR_enhanced = std * img_final_SR_enhanced + mean
img_final_SR_enhanced = np.clip(img_final_SR_enhanced, 0, 1)
img_final_SR_enhanced = (img_final_SR_enhanced * 255.0).round().astype(np.uint8)
folder_name = os.path.dirname(i)
file_name = os.path.basename(folder_name)
img_path = os.path.join(folder_name,file_name+'_216000_img_final_SR_enhanced.png')
#print('_____'+img_path)
#img_final_SR_enhanced = cv2.cvtColor(img_final_SR_enhanced, cv2.COLOR_BGR2RGB)
cv2.imwrite(img_path, img_final_SR_enhanced)
'''
very inefficient code, create a method to send two images at a time
refactor it later..
'''
def calculate_psnr_ssim():
dir = "/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved/"
HR_DIR = "/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/HR/x4/valid_img/*"
bicubic_DIR = "/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/Bic/x4/valid_img/*"
img_GT = sorted(glob.glob(HR_DIR+'.jpg'))
img_final_SR_enhanced_1 = sorted(glob.glob(dir+'/enhanced_SR_images_1/*.png'))
img_final_SR_enhanced_2 = sorted(glob.glob(dir+'/enhanced_SR_images_2/*.png'))
img_final_SR_enhanced_3 = sorted(glob.glob(dir+'/enhanced_SR_images_3/*.png'))
img_final_SR = sorted(glob.glob(dir+'/final_SR_images_216000/*.png'))
img_SR = sorted(glob.glob(dir+'/SR_images/*.png'))
img_SR_combined = sorted(glob.glob(dir+'/combined_SR_images_216000/*.png'))
img_Bic = sorted(glob.glob(bicubic_DIR+'.jpg'))
psnr_enhanced_1 = 0
psnr_enhanced_2 = 0
psnr_enhanced_3 = 0
psnr_final = 0
psnr_SR = 0
psnr_SR_combined = 0
psnr_Bic = 0
ssim_enhanced_1 = 0
ssim_enhanced_2 = 0
ssim_enhanced_3 = 0
ssim_final = 0
ssim_SR = 0
ssim_SR_combined = 0
ssim_Bic = 0
total = len(img_SR)
print(total)
i = 0
for im_gt, im_enhanced_1, im_enhanced_2, im_enhanced_3, im_final, im_SR, \
im_SR_combined, im_Bic in zip(img_GT,
img_final_SR_enhanced_1,
img_final_SR_enhanced_2,
img_final_SR_enhanced_3,
img_final_SR, img_SR, img_SR_combined,
img_Bic):
print(os.path.basename(im_gt)+'--', os.path.basename(im_enhanced_1)+'--',
os.path.basename(im_enhanced_2)+'--', os.path.basename(im_enhanced_3)+'--',
os.path.basename(im_final)+'--', os.path.basename(im_SR)+'--',
os.path.basename(im_SR_combined)+'--', os.path.basename(im_Bic))
image_gt = cv2.imread(im_gt)
image_enhanced_1 = cv2.imread(im_enhanced_1)
image_enhanced_2 = cv2.imread(im_enhanced_2)
image_enhanced_3 = cv2.imread(im_enhanced_3)
image_final = cv2.imread(im_final)
image_SR = cv2.imread(im_SR)
image_SR_combined = cv2.imread(im_SR_combined)
image_Bic = cv2.imread(im_Bic)
psnr_enhanced_1 += calculate_psnr(image_gt, image_enhanced_1)
psnr_enhanced_2 += calculate_psnr(image_gt, image_enhanced_2)
psnr_enhanced_3 += calculate_psnr(image_gt, image_enhanced_3)
psnr_final += calculate_psnr(image_gt, image_final)
psnr_SR += calculate_psnr(image_gt, image_SR)
psnr_SR_combined += calculate_psnr(image_gt, image_SR_combined)
psnr_Bic += calculate_psnr(image_gt, image_Bic)
ssim_enhanced_1 += calculate_ssim(image_gt, image_enhanced_1)
ssim_enhanced_2 += calculate_ssim(image_gt, image_enhanced_2)
ssim_enhanced_3 += calculate_ssim(image_gt, image_enhanced_3)
ssim_final += calculate_ssim(image_gt, image_final)
ssim_SR += calculate_ssim(image_gt, image_SR)
ssim_SR_combined += calculate_ssim(image_gt, image_SR_combined)
ssim_Bic += calculate_ssim(image_gt, image_Bic)
i += 1
print(i)
avg_psnr_enhanced_1, avg_psnr_enhanced_2, avg_psnr_enhanced_3, avg_psnr_final, \
avg_psnr_SR, avg_psnr_SR_combined, avg_psnr_Bic = (psnr_enhanced_1 / total,
psnr_enhanced_2 / total,
psnr_enhanced_3 / total,
psnr_final / total,
psnr_SR / total,
psnr_SR_combined / total,
psnr_Bic / total)
avg_ssim_enhanced_1, avg_ssim_enhanced_2, avg_ssim_enhanced_3, avg_ssim_final, \
avg_ssim_SR, avg_ssim_SR_combined, avg_ssim_Bic = (ssim_enhanced_1 / total,
ssim_enhanced_2 / total,
ssim_enhanced_3 / total,
ssim_final / total,
ssim_SR / total,
ssim_SR_combined / total,
ssim_Bic / total)
text_file = open("/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved/Output_216000.txt", "a")
print("Enhanced PSNR_1: %4.2f" % avg_psnr_enhanced_1)
text_file.write("Enhanced PSNR_1: %4.2f \n" % avg_psnr_enhanced_1)
print("Enhanced PSNR_2: %4.2f" % avg_psnr_enhanced_2)
text_file.write("Enhanced PSNR_2: %4.2f \n" % avg_psnr_enhanced_2)
print("Enhanced PSNR_3: %4.2f" % avg_psnr_enhanced_3)
text_file.write("Enhanced PSNR_3: %4.2f \n" % avg_psnr_enhanced_3)
print("Final PSNR: %4.2f"%avg_psnr_final)
text_file.write("Final PSNR: %4.2f \n"%avg_psnr_final)
print("SR PSNR: %4.2f"%avg_psnr_SR)
text_file.write("SR PSNR: %4.2f \n"%avg_psnr_SR)
print("SR PSNR_combined: %4.2f"%avg_psnr_SR_combined)
text_file.write("SR PSNR_combined: %4.2f \n"%avg_psnr_SR_combined)
print("Bic PSNR: %4.2f"%avg_psnr_Bic)
text_file.write("Bic PSNR: %4.2f \n"%avg_psnr_Bic)
print("Enhanced SSIM_1: %5.4f"%avg_ssim_enhanced_1)
text_file.write("Enhanced SSIM_1: %5.4f \n"%avg_ssim_enhanced_1)
print("Enhanced SSIM_2: %5.4f"%avg_ssim_enhanced_2)
text_file.write("Enhanced SSIM_2: %5.4f \n"%avg_ssim_enhanced_2)
print("Enhanced SSIM_3: %5.4f"%avg_ssim_enhanced_3)
text_file.write("Enhanced SSIM_3: %5.4f \n"%avg_ssim_enhanced_3)
print("Final SSIM: %5.4f"%avg_ssim_final)
text_file.write("Final SSIM: %5.4f \n"%avg_ssim_final)
print("SR SSIM: %5.4f"%avg_ssim_SR)
text_file.write("SR SSIM: %5.4f \n"%avg_ssim_SR)
print("SR SSIM_combined: %5.4f"%avg_ssim_SR_combined)
text_file.write("SR SSIM_combined: %5.4f \n"%avg_ssim_SR_combined)
print("Bic SSIM: %5.4f"%avg_ssim_Bic)
text_file.write("Bic SSIM: %5.4f \n"%avg_ssim_Bic)
text_file.close()
def calculate_psnr_ssim_ESRGAN():
dir = "/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved_ESRGAN/val_images/*/*"
HR_DIR = "/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/HR/x4/valid_img/"
img_SR = sorted(glob.glob(dir+'_300000.png'))
psnr_SR = 0
ssim_SR = 0
total = len(img_SR)
print(total)
i = 0
for im_SR in img_SR:
print(os.path.basename(im_SR)+'--')
im_gt = os.path.basename(im_SR)
im_gt = im_gt.rsplit('_', 1)[0]+".jpg"
im_gt = os.path.join(HR_DIR, im_gt)
print(im_gt)
image_SR = cv2.imread(im_SR)
image_SR = cv2.cvtColor(image_SR, cv2.COLOR_BGR2RGB)
cv2.imwrite(im_SR, image_SR)
image_gt = cv2.imread(im_gt)
image_SR = cv2.imread(im_SR)
psnr_SR += calculate_psnr(image_gt, image_SR)
ssim_SR += calculate_ssim(image_gt, image_SR)
i += 1
print(i)
avg_psnr_SR = psnr_SR / total
avg_ssim_SR = ssim_SR / total
text_file = open("/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved_ESRGAN/Output.txt", "a")
print("SR PSNR: %4.2f"%avg_psnr_SR)
text_file.write("SR PSNR: %4.2f \n"%avg_psnr_SR)
print("SR SSIM: %5.4f"%avg_ssim_SR)
text_file.write("SR SSIM: %5.4f \n"%avg_ssim_SR)
def separate_generated_image_for_test():
dir = "/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved/val_images/*/*"
dir_ESRGAN = "/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved_EEGAN_separate/val_images/*/*"
dir_save = "/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved/"
img_final_SR = sorted(glob.glob(dir+'_216000_final_SR.png'))
img_SR = sorted(glob.glob(dir+'_216000_SR.png'))
#img_enhanced_SR = sorted(glob.glob(dir_ESRGAN+'_400000_img_final_SR_enhanced.png'))
for im_final_SR, im_SR in zip(img_final_SR, img_SR):
#for im_final_SR in img_final_SR:
image_final_SR = cv2.imread(im_final_SR)
image_SR = cv2.imread(im_SR)
#image_enhanced_SR = cv2.imread(im_enhanced_SR)
final_SR_Dir = os.path.basename(im_final_SR)
final_SR_Dir = final_SR_Dir.rsplit('_', 3)[0]+".png"
final_SR_Dir = os.path.join(dir_save, 'final_SR_images_216000', final_SR_Dir)
cv2.imwrite(final_SR_Dir, image_final_SR)
SR_Dir = os.path.basename(im_SR)
SR_Dir = SR_Dir.rsplit('_', 2)[0]+".png"
SR_Dir = os.path.join(dir_save, 'combined_SR_images_216000', SR_Dir)
cv2.imwrite(SR_Dir, image_SR)
'''
enhanced_SR_Dir = os.path.basename(im_enhanced_SR)
enhanced_SR_Dir = enhanced_SR_Dir.rsplit('_', 5)[0]+".png"
enhanced_SR_Dir = os.path.join(dir_save, 'enhanced_SR_images', enhanced_SR_Dir)
cv2.imwrite(enhanced_SR_Dir, image_enhanced_SR)
'''
def calculate_lap_edge():
HR_DIR = "/home/jakaria/Super_Resolution/Datasets/COWC/DetectionPatches_256x256/Potsdam_ISPRS/HR/x4/valid_img/*"
dir_save = "/home/jakaria/Super_Resolution/Filter_Enhance_Detect/saved/lap_edges_GT"
img_GT = sorted(glob.glob(HR_DIR+'.jpg'))
mean = np.array([0.3442, 0.3708, 0.3476])
std = np.array([0.1232, 0.1230, 0.1284])
for i in img_GT:
#print(i+'____'+j)
img_gt = cv2.imread(i) / 255
img_gt = np.clip(img_gt, 0, 1)
img_gt = (img_gt - mean) / std
img_gt = img_gt.transpose(2, 0, 1)
img_gt = np.expand_dims(img_gt, axis=0)
img_gt = torch.Tensor(img_gt)
img_gt = kornia.laplacian(img_gt, 3)
img_gt = img_gt.squeeze()
img_gt = img_gt.numpy()
img_gt = img_gt.transpose(1, 2, 0)
img_gt = std * img_gt + mean
img_gt = np.clip(img_gt, 0, 1)
img_gt = (img_gt * 255.0).round().astype(np.uint8)
file_name = os.path.basename(i)
img_path = os.path.join(dir_save,file_name)
#print('_____'+img_path)
#img_final_SR_enhanced = cv2.cvtColor(img_final_SR_enhanced, cv2.COLOR_BGR2RGB)
cv2.imwrite(img_path, img_gt)
if __name__ == "__main__":
generate_mod_LR_bic()
#copy_folder_name_for_valid_image()
#merge_edge()
#calculate_psnr_ssim_ESRGAN() #not working expected, use the other methods.
#separate_generated_image_for_test()
#calculate_psnr_ssim()
#calculate_lap_edge()