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validation.py
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import argparse
import os
import parser
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import cv2
import torch
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import yaml
import albumentations as A
from albumentations.core.composition import Compose
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from PIL import Image
import archs
from dataset import Dataset
from metrics import iou_score
from utils import AverageMeter
import colorsys
"""
需要指定参数：--name project name
"""
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--name', default="project name",
help='model name')
parser.add_argument('--num_classes', default=4, type=int,
help='number of classes')
args = parser.parse_args()
return args
def main():
args = parse_args()
with open('models/%s/config.yml' % args.name, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
print('-' * 20)
for key in config.keys():
print('%s: %s' % (key, str(config[key])))
print('-' * 20)
cudnn.benchmark = True
# create model
print("=> creating model %s" % config['arch'])
model = archs.__dict__[config['arch']](config['num_classes'],
config['input_channels'],
config['deep_supervision'])
model = model.cuda()
model.load_state_dict(torch.load('models/%s/model-X.pth' %
config['name']))
model.eval()
inputdir = r'C:\Users\Data'
# Data loading code
img_ids = glob(os.path.join(inputdir, config['dataset'], 'GFP_original', '*' + config['img_ext']))
img_ids = [os.path.splitext(os.path.basename(p))[0] for p in img_ids]
_, val_img_ids = train_test_split(img_ids, test_size=0.15, random_state=45)
# model.load_state_dict(torch.load('models/%s/model.pth' %
# config['name']))
# model.eval()
val_transform = A.Compose([
A.ToFloat(max_value=1.0),
#A.Resize(config['input_h'], config['input_w']),
#A.Normalize(mean=(0.03684), std=(0.01488), max_pixel_value=1),
# A.FromFloat(max_value=13108.0)
])
val_dataset = Dataset(
img_ids=val_img_ids,
img_dir=os.path.join(inputdir, config['dataset'], 'GFP_original'),
mask_dir=os.path.join(inputdir, config['dataset'], 'groundtruth_unet++'),
img_ext=config['img_ext'],
mask_ext=config['mask_ext'],
num_classes=config['num_classes'],
transform=val_transform)
val_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=config['batch_size'],
shuffle=False,
num_workers=config['num_workers'],
drop_last=False)
avg_meter = AverageMeter()
os.makedirs(os.path.join('outputs', config['name'], 'png_results'), exist_ok=True)
# for c in range(config['num_classes']):
# os.makedirs(os.path.join('outputs', config['name'], str(c)), exist_ok=True)
with torch.no_grad():
for input, target, meta in tqdm(val_loader, total=len(val_loader)):
input = input.cuda()
target = target.cuda()
outimage = []
# compute output
if config['deep_supervision']:
output = model(input)[-1]
else:
output = model(input)
iou = iou_score(output, target)
avg_meter.update(iou, input.size(0))
# output = torch.sigmoid(output).cpu().numpy()
#
pr_soft_last = F.softmax(output.permute(0, 2, 3, 1), dim=-1).cpu().numpy()
pr_soft_last_arg = pr_soft_last.argmax(axis=-1)
colors = [(0, 0, 0), (128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128), (0, 128, 128),
(128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0), (192, 128, 0), (64, 0, 128),
(192, 0, 128),
(64, 128, 128), (192, 128, 128), (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0),
(0, 64, 128),
(128, 64, 12)]
for i in range(np.shape(pr_soft_last_arg)[0]):
seg_img = np.zeros((np.shape(pr_soft_last_arg)[0],
np.shape(pr_soft_last_arg)[1],
np.shape(pr_soft_last_arg)[2], 3))
# ---------------------
# 需要注意的是：cv写图像的时候也是按照BRG，与RGB不一样，因此是需要调整顺序才能获得正确颜色
# ---------------------
for c in range(4):
seg_img[i, :, :, 0] += (
(pr_soft_last_arg[i, :, :] == c) * (colors[c][2])).astype('uint8')
seg_img[i, :, :, 1] += (
(pr_soft_last_arg[i, :, :] == c) * (colors[c][0])).astype('uint8')
seg_img[i, :, :, 2] += (
(pr_soft_last_arg[i, :, :] == c) * (colors[c][1])).astype('uint8')
seggg = seg_img[i]
#seggg = cv2.resize(seggg, (1200, 1200), interpolation=cv2.INTER_LINEAR)
cv2.imwrite(os.path.join('outputs', config['name'], meta['img_id'][i] + '.jpg'),
seggg.astype('uint8'))
segggr = pr_soft_last_arg[i]
#segggr = cv2.resize(segggr, (1200, 1200), interpolation=cv2.INTER_NEAREST_EXACT)
cv2.imwrite(os.path.join('outputs', config['name'],'png_results', meta['img_id'][i] + '.png'),
segggr.astype('uint8'))
# ------------------------------------------------#
# 将新图片转换成Image的形式
# ------------------------------------------------#
# for i in range(len(outimage)):
# cv2.imwrite(os.path.join('outputs', config['name'], meta['img_id'][i] + '.jpg'),
# (np.array(i * 255).astype('uint8')))
print('IoU: %.4f' % avg_meter.avg)
# plot_examples(input, target, model,num_examples=3)
torch.cuda.empty_cache()
#
# def plot_examples(datax, datay, model,num_examples=6):
# fig, ax = plt.subplots(nrows=num_examples, ncols=3, figsize=(18,4*num_examples))
# m = datax.shape[0]
# for row_num in range(num_examples):
# image_indx = np.random.randint(m)
# image_arr = model(datax[image_indx:image_indx+1]).squeeze(0).detach().cpu().numpy()
# ax[row_num][0].imshow(np.transpose(datax[image_indx].cpu().numpy(), (1,2,0))[:,:,0])
# ax[row_num][0].set_title("Orignal Image")
# ax[row_num][1].imshow(np.squeeze((image_arr > 0.40)[0,:,:].astype(int)))
# ax[row_num][1].set_title("Segmented Image localization")
# ax[row_num][2].imshow(np.transpose(datay[image_indx].cpu().numpy(), (1,2,0))[:,:,0])
# ax[row_num][2].set_title("Target image")
# plt.show()
if __name__ == '__main__':
main()