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unet_infer.py
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unet_infer.py
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# author:
# contact: https://github.com/WilliamLambertCN
# datetime:2020/5/28 14:07
"""
文件说明： Unet inference
"""
import os
import time
import torch.nn as nn
import torch
import numpy as np
import torchvision.transforms as transforms
from PIL import Image
from torch.utils.data import DataLoader
from matplotlib import pyplot as plt
from tools.common_tools import set_seed
from tools.my_dataset import SkyDataset
from tools.unet import UNet
def compute_dice(y_pred, y_true):
"""
:param y_pred: 4-d tensor, value = [0,1]
:param y_true: 4-d tensor, value = [0,1]
:return: Dice index 2*TP/(2*TP+FP+FN)=2TP/(pred_P+true_P)
"""
y_pred, y_true = np.array(y_pred), np.array(y_true)
y_pred, y_true = np.round(y_pred).astype(int), np.round(y_true).astype(int)
return np.sum(y_pred[y_true == 1]) * 2.0 / (np.sum(y_pred) + np.sum(y_true))
##########################################################
def unet_infer(demo_path_img, demo, save_result):
"""
Args:
demo_path_img:
demo:
save_result:
Returns:
"""
# demo = True
# demo_path_img = 'd:/MyLearning/DIP/Final_Project/Unet/Demo/1.jpg'
# save_result = True
testset_path = os.path.join("dataset/testset")
checkpoint_load = 'tools/checkpoint_199_epoch.pkl'
shuffle_dataset = True
vis_num = 1000
mask_thres = 0.5
##########################################################
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
set_seed() # 设置随机种子
in_size = 224
if not demo:
testset = SkyDataset(testset_path)
valid_loader = DataLoader(testset, batch_size=1, drop_last=False, shuffle=False)
else:
img_pil = Image.open(demo_path_img).convert('RGB')
original_img = np.array(img_pil)
w, h = img_pil.size
img_pil = img_pil.resize((in_size, in_size), Image.BILINEAR)
img_hwc = np.array(img_pil)
img_chw = img_hwc.transpose((2, 0, 1))
img_chw = torch.from_numpy(img_chw).float()
net = UNet(in_channels=3, out_channels=1, init_features=32) # init_features is 64 in stander uent
net.to(device)
if checkpoint_load is not None:
path_checkpoint = checkpoint_load
checkpoint = torch.load(path_checkpoint)
net.load_state_dict(checkpoint['model_state_dict'])
print('load checkpoint from %s' % path_checkpoint)
else:
raise Exception("\nPlease specify the checkpoint")
net.eval()
with torch.no_grad():
if not demo:
for idx, (inputs, labels) in enumerate(valid_loader):
if idx > vis_num:
break
if torch.cuda.is_available():
inputs, labels = inputs.to(device), labels.to(device)
outputs = net(inputs)
pred = (outputs.cpu().data.numpy() * 255).astype("uint8")
pred_gray = pred.squeeze()
mask_pred = outputs.ge(mask_thres).cpu().data.numpy()
mask_pred_gray = (mask_pred.squeeze() * 255).astype("uint8")
print('idx>>%d, Dice>>%.4f' % (idx, compute_dice(mask_pred, labels.cpu().numpy())))
img_hwc = inputs.cpu().data.numpy()[0, :, :, :].transpose((1, 2, 0)).astype("uint8")
img_label = (labels.cpu().data.numpy()[0, 0, :, :] * 255).astype("uint8")
plt.subplot(221).imshow(img_hwc)
plt.title('%d Original IMG' % idx)
plt.subplot(222).imshow(img_label, cmap="gray")
plt.title('%d Original Label' % idx)
plt.subplot(223).imshow(mask_pred_gray, cmap="gray")
plt.title('%d Binary Label' % idx)
plt.subplot(224).imshow(pred_gray, cmap="gray")
plt.title('%d Raw Label' % idx)
plt.tight_layout()
plt.savefig('results/%d_img' % idx)
plt.show()
plt.close()
if save_result:
pred_gray_img = Image.fromarray(pred_gray)
pred_gray_img.save('results/%d_pred_gray_img.png' % idx)
img_hwc_img = Image.fromarray(img_hwc)
img_hwc_img.save('results/%d_img_hwc.png' % idx)
else:
inputs = img_chw.to(device).unsqueeze(0)
outputs = net(inputs)
pred = (outputs.cpu().data.numpy() * 255).astype("uint8")
pred_gray = pred.squeeze()
mask_pred = outputs.ge(mask_thres).cpu().data.numpy()
mask_pred_gray = (mask_pred.squeeze() * 255).astype("uint8")
img_hwc = inputs.cpu().data.numpy()[0, :, :, :].transpose((1, 2, 0)).astype("uint8")
if save_result:
pred_gray_img = Image.fromarray(pred_gray)
pred_gray_img = pred_gray_img.resize((w, h), Image.BICUBIC)
pred_gray_img.save('d:/MyLearning/DIP/Final_Project/Unet/results/1_pred_gray_img.png')
mask_pred_gray_img = Image.fromarray(mask_pred_gray)
mask_pred_gray_img = mask_pred_gray_img.resize((w, h), Image.BICUBIC)
mask_pred_gray_img.save('d:/MyLearning/DIP/Final_Project/Unet/results/1_mask_pred_gray_img.png')
img_hwc_img = Image.open(demo_path_img).convert('RGB')
img_hwc_img.save('d:/MyLearning/DIP/Final_Project/Unet/results/1_img_hwc_img.png')
# plt.subplot(131).imshow(img_hwc)
# plt.subplot(132).imshow(mask_pred_gray, cmap="gray")
# plt.subplot(133).imshow(pred_gray, cmap="gray")
# plt.show()
# plt.pause(0.5)
# plt.close()
# img_hwc = Image.fromarray(img_hwc)
# img_hwc = img_hwc.resize((w, h), Image.BILINEAR)
# img_hwc = np.array(img_hwc)
mask_pred_gray = Image.fromarray(mask_pred_gray)
mask_pred_gray = mask_pred_gray.resize((w, h), Image.BILINEAR)
mask_pred_gray = np.array(mask_pred_gray)
return original_img, mask_pred_gray