-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest.py
243 lines (206 loc) · 9 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
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
Project Name: unet
File Name: test.py
Author: chenming
Create Date: 2022/2/7
Description：
-------------------------------------------------
"""
import os
import time
import torch.nn as nn
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.transformer_seg import SETRModel
from setr.SETR import SETR_PUP
from model.enet import ENet, ENet_improved, ENet_crop2
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,R2AttU_Net, R2U_Net
import cv2
from model.unet_model import UNet
from model.FCN import FCN32s
from model.ESCNet_min import ESCNet_min
from model.DeepCrack import DeepCrack
# def cal_miou(test_dir="./images/cracks/Test_Images_DeepCrack",
# pred_dir="./images/cracks/results", gt_dir="./images/cracks/Test_Labels_DeepCrack"):
def cal_miou(test_dir="./images/patholes/Test_Images",
pred_dir="./images/patholes/results", gt_dir="./images/patholes/Test_Labels"):
# def cal_miou(test_dir="./images/cracks/Test_Images_CFD",
# pred_dir="./images/cracks/results", gt_dir="./images/cracks/Test_Labels_CFD"):
# def cal_miou(test_dir="./images/cracks/Test_Images_GAPS384",
# pred_dir="./images/cracks/results", gt_dir="./images/cracks/Test_Labels_GAPS384"):
# ---------------------------------------------------------------------------#
# 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", "pathole"]
# 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')
# 加载网络，图片单通道，分类为1。
# net = DeepCrack(num_classes=1)
# net = ESCNet_min(num_classes=1, in_channels=1)
# net = FCN32s(n_class=1)
# net.conv1_1 = nn.Conv2d(1, 64, 3, padding=100)
# net = UNet(n_channels=1, n_classes=1)
# net = R2AttU_Net(in_ch=1, out_ch=1, t=2)
# net = R2U_Net(img_ch=1, output_ch=1, t=2)
# net = ENet_improved(num_classes=1, in_channels=1)
# net = ENet_crop2(num_classes=1, in_channels=1)
# net = AttU_Net(img_ch=1, output_ch=1)
# net = SETRModel(patch_size=(16, 16),
# in_channels=1,
# out_channels=1,
# hidden_size=1024,
# num_hidden_layers=6,
# num_attention_heads=16,
# decode_features=[512, 256, 128, 64])
net = SETR_PUP(
img_dim=256,
patch_dim=16,
num_channels=1,
num_classes=1,
embedding_dim=768,
num_heads=12,
num_layers=12,
hidden_dim=3072,
dropout_rate=0.1,
attn_dropout_rate=0.1
)
# net = AttU_Net(img_ch=1, output_ch=1)
# 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)
# print(img_names)
image_ids=[]
for image_name in img_names:
if image_name[-3:]=='jpg':
# print(image_name[:-4])
image_ids.append(image_name[:-4])
# image_ids =image_ids.append(image_name.split(".")[0])
# 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=0
starttime = time.time()
i=0
for image_id in tqdm(image_ids):
i=i+1
image_path = os.path.join(test_dir, image_id + ".jpg")
# print(image_path)
label_path = os.path.join(gt_dir, image_id + ".png")
# print(label_path)
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.cvtColor(label, cv2.COLOR_BGR2GRAY)
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()
# deltatime = endtime-starttime
# times = times+deltatime
# 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)
# print(origin_shape[1], origin_shape[0])
# print(pred.size())
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()
endtime = time.time()
print("Get predict result done.")
print('==================')
print('i=',i)
print('total:', endtime-starttime)
print('mean:',(endtime-starttime)/i)
# print('total-time',times)
# print('mean-time', times/i)
print('==================')
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()