-
Notifications
You must be signed in to change notification settings - Fork 2
/
test.py
172 lines (142 loc) · 6.35 KB
/
test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
Project Name: unet
File Name: test.py
Author: chenming
Create Date: 2022/2/7
Description：
-------------------------------------------------
"""
import os
import time
from matplotlib import pyplot as plt
# from pytorch_grad_cam import GradCAM
# from pytorch_grad_cam.utils.image import show_cam_on_image
# from pytorch_grad_cam.utils.model_targets import SemanticSegmentationTarget
from torch.nn import Conv2d
from torch.utils.tensorboard import SummaryWriter
from torchvision.models.segmentation import deeplabv3_resnet50, fcn_resnet50, lraspp_mobilenet_v3_large
from tqdm import tqdm
from model.enet import ENet
from utils.utils_metrics import compute_mIoU, show_results
import glob
import numpy as np
import torch
import os
from model.Models import AttU_Net, AttU_Net_min
import cv2
from model.unet_model import UNet
def cal_miou(test_dir="./images/cracks/Test_Images",
pred_dir="./images/cracks/results", gt_dir="./images/cracks/Test_Labels"):
# ---------------------------------------------------------------------------#
# miou_mode用于指定该文件运行时计算的内容
# miou_mode为0代表整个miou计算流程，包括获得预测结果、计算miou。
# miou_mode为1代表仅仅获得预测结果。
# miou_mode为2代表仅仅计算miou。
# ---------------------------------------------------------------------------#
miou_mode = 0
# ------------------------------#
# 分类个数+1、如2+1
# ------------------------------#
num_classes = 2
# --------------------------------------------#
# 区分的种类，和json_to_dataset里面的一样
# --------------------------------------------#
name_classes = ["background", "crack"]
# name_classes = ["_background_","cat","dog"]
# -------------------------------------------------------#
# 指向VOC数据集所在的文件夹
# 默认指向根目录下的VOC数据集
# -------------------------------------------------------#
# 计算结果和gt的结果进行比对
# 加载模型
if miou_mode == 0 or miou_mode == 1:
if not os.path.exists(pred_dir):
os.makedirs(pred_dir)
print("Load model.")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# net = UNet(n_channels=1, n_classes=1)
# net = AttU_Net(img_ch=1, output_ch=1)
net = AttU_Net(img_ch=1, output_ch=1) # todo: change the model
# net = deeplabv3_resnet50(num_classes=1)
# net = fcn_resnet50(num_classes=1)
# net.backbone.conv1 = Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
# net = ENet(num_classes=1, in_channels=1)
# net = lraspp_mobilenet_v3_large(num_classes=1)
# #
# # # net.classifier._modules['6'] = nn.Linear(4096, 4)#for vgg16, alexnet
# # net.backbone.conv1 = Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) # for vgg16, alexnet
#
# net.backbone._modules['0']._modules['0'] = Conv2d(1, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1),
# bias=False)
# 将网络拷贝到deivce中
net.to(device=device)
# 加载模型参数
net.load_state_dict(torch.load('best_model.pth', map_location=device)) # todo
# 测试模式
net.eval()
print("Load model done.")
# target_layers = [net.down4.maxpool_conv]
img_names = os.listdir(test_dir)
image_ids = [image_name.split(".")[0] for image_name in img_names]
# kk=0
# writer = SummaryWriter('logs/test')
print("Get predict result.")
# with GradCAM(model=net, target_layers=target_layers, use_cuda=torch.cuda.is_available()) as cam:
times=[]
for image_id in tqdm(image_ids):
image_path = os.path.join(test_dir, image_id + ".jpg")
label_path = os.path.join(gt_dir, image_id + ".png")
label = cv2.imread(label_path)
img = cv2.imread(image_path)
origin_shape = img.shape
# print(origin_shape)
# 转为灰度图
img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
img = cv2.resize(img, (256, 256))
label = cv2.resize(label, (256,256))
label = (label/255).astype(int)
# print(type(label))
# 转为batch为1，通道为1，大小为512*512的数组
img = img.reshape(1, 1, img.shape[0], img.shape[1])
# 转为tensor
img_tensor = torch.from_numpy(img)
# 将tensor拷贝到device中，只用cpu就是拷贝到cpu中，用cuda就是拷贝到cuda中。
img_tensor = img_tensor.to(device=device, dtype=torch.float32)
# print(img_tensor.size())
# 预测
starttime = time.time()
pred = net(img_tensor)
endtime = time.time()
times.append(endtime-starttime)
# print(pred.size())
# 提取结果
#------------
# pred=pred['out']
#----------
pred = np.array(pred.data.cpu()[0])[0]
pred[pred >= 0.5] = 255
pred[pred < 0.5] = 0
pred = cv2.resize(pred, (origin_shape[1], origin_shape[0]), interpolation=cv2.INTER_NEAREST)
cv2.imwrite(os.path.join(pred_dir, image_id + ".png"), pred)
# targets = [SemanticSegmentationTarget(0, label)]
# grayscale_cam = cam(input_tensor=img_tensor, targets=targets)[0,:]
# cam_image = show_cam_on_image(img, grayscale_cam, use_rgb=True)
# plt.imshow(cam_image)
# plt.show()
print("Get predict result done.")
print(np.mean(times))
if miou_mode == 0 or miou_mode == 2:
print("Get miou.")
print(gt_dir)
print(pred_dir)
print(num_classes)
print(name_classes)
hist, IoUs, PA_Recall, Precision = compute_mIoU(gt_dir, pred_dir, image_ids, num_classes,
name_classes) # 执行计算mIoU的函数
print("Get miou done.")
miou_out_path = "results/"
show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes)
if __name__ == '__main__':
cal_miou()