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main_test_drunet_captures.py
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main_test_drunet_captures.py
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import os.path
import math
import argparse
import time
import random
import numpy as np
from collections import OrderedDict
import logging
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import torch
from utils import utils_logger
from utils import utils_image as util
from utils import utils_option as option
from utils.utils_dist import get_dist_info, init_dist
from data.select_dataset import define_Dataset
from models.select_model import define_Model
import warnings
warnings.filterwarnings('ignore')
# OCR metrics
# First, must install Tesseract: https://tesseract-ocr.github.io/tessdoc/Installation.html
# Second, install CER/WER and tesseract python wrapper libraries
# pip install pybind11
# pip install fastwer
# pip install pytesseract
import pytesseract
import fastwer
def calculate_cer_wer(img_E, img_H):
# Transcribe ground-truth image to text
text_H = pytesseract.image_to_string(img_H).strip().replace('\n',' ')
# Transcribe estimated image to text
text_E = pytesseract.image_to_string(img_E).strip().replace('\n',' ')
cer = fastwer.score_sent(text_E, text_H, char_level=True)
wer = fastwer.score_sent(text_E, text_H)
return cer, wer
'''
# --------------------------------------------
# Testing code for DRUNet
# --------------------------------------------
# Kai Zhang ([email protected])
# github: https://github.com/cszn/KAIR
# --------------------------------------------
# Adapted by Emilio Martínez ([email protected])
'''
def main(json_path='options/train_drunet_finetuning.json'):
'''
# ----------------------------------------
# Step--1 (prepare opt)
# ----------------------------------------
'''
parser = argparse.ArgumentParser()
parser.add_argument('--opt', type=str, default=json_path, help='Path to option JSON file.')
parser.add_argument('--launcher', default='pytorch', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--dist', default=False)
opt = option.parse(parser.parse_args().opt, is_train=True)
opt['dist'] = parser.parse_args().dist
# ----------------------------------------
# configure logger
# ----------------------------------------
logger_name = 'test'
utils_logger.logger_info(logger_name, os.path.join(opt['path']['log'], logger_name + '.log'))
logger = logging.getLogger(logger_name)
logger.info(option.dict2str(opt))
# ----------------------------------------
# distributed settings
# ----------------------------------------
if opt['dist']:
init_dist('pytorch')
opt['rank'], opt['world_size'] = get_dist_info()
opt = option.dict_to_nonedict(opt)
model_path = opt['path']['pretrained_netG']
model_epoch = (model_path.split('/')[-1]).split('_G')[0]
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"\nDevice: {device}\n")
opt_netG = opt['netG']
in_nc = opt_netG['in_nc']
out_nc = opt_netG['out_nc']
nc = opt_netG['nc']
nb = opt_netG['nb']
act_mode = opt_netG['act_mode']
bias = opt_netG['bias']
from models.network_unet import UNetRes as net
model = net(in_nc=in_nc, out_nc=out_nc, nc=nc, nb=nb, act_mode=act_mode, bias=bias)
model.load_state_dict(torch.load(model_path), strict=True)
model.eval()
for k, v in model.named_parameters():
v.requires_grad = False
model = model.to(device)
logger.info('Model path: {:s}'.format(model_path))
number_parameters = sum(map(lambda x: x.numel(), model.parameters()))
logger.info('Params number: {}'.format(number_parameters))
"""
# ----------------------------------------
# Step--3 (load paths)
# ----------------------------------------
"""
L_paths = util.get_image_paths(opt['datasets']['test']['dataroot_L'])
noise_sigma = opt['datasets']['test']['sigma_test']
for phase, dataset_opt in opt['datasets'].items():
if phase == 'test':
test_set = define_Dataset(dataset_opt)
test_loader = DataLoader(test_set, batch_size=1,
shuffle=False, num_workers=1,
drop_last=False, pin_memory=True)
'''
# ----------------------------------------
# Step--4 (main test)
# ----------------------------------------
'''
avg_psnr = 0.0
avg_ssim = 0.0
avg_loss = 0.0
avg_edgeJaccard = 0.0
avg_cer = 0.0
avg_wer = 0.0
idx = 0
for test_data in test_loader:
idx += 1
image_name_ext = os.path.basename(test_data['L_path'][0])
img_name, ext = os.path.splitext(image_name_ext)
# Inference
## With abs value/max-entropy thresholding
# L_visual = test_data['L']
# L_img = util.tensor2uint(L_visual)
# E_img = np.abs(L_img[:,:,0] + 1j*L_img[:,:,1])
# E_img = (255 * (E_img/np.max(E_img))).astype("uint8")
# E_img = util.max_entropy_init(L_img) # using global thresholding
H_visual = test_data['H']
H_img = util.tensor2uint(H_visual)
# With drunet
# Load image
E_visual = model(test_data['L'].cuda())
E_img = util.tensor2uint(E_visual)
H_visual = test_data['H']
H_img = util.tensor2uint(H_visual)
# -----------------------
# save estimated image E
# -----------------------
img_dir = os.path.join(opt['path']['images'], img_name)
util.mkdir(img_dir)
save_img_path = os.path.join(img_dir, '{:s}_E.png'.format(img_name))
util.imsave(E_img, save_img_path)
# -----------------------
# calculate PSNR and SSIM
# -----------------------
current_psnr = util.calculate_psnr(E_img, H_img)
current_ssim = util.calculate_ssim(E_img, H_img)
current_edgeJaccard = util.calculate_edge_jaccard(E_img, H_img)
current_cer, current_wer = calculate_cer_wer(E_img, H_img)
logger.info('{:->4d}--> {:>10s} | PSNR = {:<4.2f}dB ; SSIM = {:.3f} ; edgeJaccard = {:.3f} ; CER = {:.3f}% ; WER = {:.3f}%'.format(idx, image_name_ext, current_psnr, current_ssim, current_edgeJaccard, current_cer, current_wer))
avg_psnr += current_psnr
avg_ssim += current_ssim
avg_edgeJaccard += current_edgeJaccard
avg_cer += current_cer
avg_wer += current_wer
avg_psnr = avg_psnr / idx
avg_ssim = avg_ssim / idx
avg_edgeJaccard = avg_edgeJaccard / idx
avg_cer = avg_cer / idx
avg_wer = avg_wer / idx
# testing log
logger.info('[Average metrics] PSNR : {:<4.2f}dB, SSIM = {:.3f} : edgeJaccard = {:.3f} : CER = {:.3f}% : WER = {:.3f}%'.format(avg_psnr, avg_ssim, avg_edgeJaccard, avg_cer, avg_wer))
if __name__ == '__main__':
main()