-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmIoU.py
259 lines (204 loc) · 10.7 KB
/
mIoU.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
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
import csv
import os
from os.path import join
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
import os
import argparse
from tqdm import tqdm
import archs
from archs import NestedUNet
from utils_metrics import compute_mIoU, show_results
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
import yaml
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--name', default="timestamp_groundtruth_NestedUNet",
help='model name')
parser.add_argument('--num_classes', default=4, type=int,
help='number of classes')
args = parser.parse_args()
return args
def fast_hist(a, b, n):
# --------------------------------------------------------------------------------#
# a是转化成一维数组的标签，形状(H×W,)；b是转化成一维数组的预测结果，形状(H×W,)
# --------------------------------------------------------------------------------#
k = (a >= 0) & (a < n)
# --------------------------------------------------------------------------------#
# np.bincount计算了从0到n**2-1这n**2个数中每个数出现的次数，返回值形状(n, n)
# 返回中，写对角线上的为分类正确的像素点
# --------------------------------------------------------------------------------#
return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)
def per_class_iu(hist):
return np.diag(hist) / np.maximum((hist.sum(1) + hist.sum(0) - np.diag(hist)), 1)
# np.diag(array) 中
# array是一个1维数组时，结果形成一个以一维数组为对角线元素的矩阵
#
# array是一个二维矩阵时，结果输出矩阵的对角线元素
def per_class_PA_Recall(hist):
return np.diag(hist) / np.maximum(hist.sum(1), 1)
def per_class_Precision(hist):
return np.diag(hist) / np.maximum(hist.sum(0), 1)
def per_Accuracy(hist):
return np.sum(np.diag(hist)) / np.maximum(np.sum(hist), 1)
def compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes):
print('Num classes', num_classes)
# -----------------------------------------#
# 创建一个全是0的矩阵，是一个混淆矩阵
# -----------------------------------------#
hist = np.zeros((num_classes, num_classes))
# ------------------------------------------------#
# 获得验证集标签路径列表，方便直接读取
# 获得验证集图像分割结果路径列表，方便直接读取
# ------------------------------------------------#
gt_imgs = [join(gt_dir, x + ".png") for x in png_name_list]
pred_imgs = [join(pred_dir, x + ".png") for x in png_name_list]
# ------------------------------------------------#
# 读取每一个（图片-标签）对
# ------------------------------------------------#
for ind in range(len(gt_imgs)):
# ------------------------------------------------#
# 读取一张图像分割结果，转化成numpy数组
# ------------------------------------------------#
pred = np.array(Image.open(pred_imgs[ind]))
# ------------------------------------------------#
# 读取一张对应的标签，转化成numpy数组
# ------------------------------------------------#
label = np.array(Image.open(gt_imgs[ind]))
# 如果图像分割结果与标签的大小不一样，这张图片就不计算
if len(label.flatten()) != len(pred.flatten()):
print(
'Skipping: len(gt) = {:d}, len(pred) = {:d}, {:s}, {:s}'.format(
len(label.flatten()), len(pred.flatten()), gt_imgs[ind],
pred_imgs[ind]))
continue
# ------------------------------------------------#
# 对一张图片计算21×21的hist矩阵，并累加
# ------------------------------------------------#
hist += fast_hist(label.flatten(), pred.flatten(), num_classes)
# 每计算10张就输出一下目前已计算的图片中所有类别平均的mIoU值
if ind > 0 and ind % 10 == 0:
print('{:d} / {:d}: mIou-{:0.2f}%; mPA-{:0.2f}%; Accuracy-{:0.2f}%'.format(
ind,
len(gt_imgs),
100 * np.nanmean(per_class_iu(hist)),
100 * np.nanmean(per_class_PA_Recall(hist)),
100 * per_Accuracy(hist)
)
)
# ------------------------------------------------#
# 计算所有验证集图片的逐类别mIoU值
# ------------------------------------------------#
IoUs = per_class_iu(hist)
PA_Recall = per_class_PA_Recall(hist)
Precision = per_class_Precision(hist)
# ------------------------------------------------#
# 逐类别输出一下mIoU值
# ------------------------------------------------#
for ind_class in range(num_classes):
print('===>' + name_classes[ind_class] + ':\tIou-' + str(round(IoUs[ind_class] * 100, 2)) \
+ '; Recall (equal to the PA)-' + str(round(PA_Recall[ind_class] * 100, 2)) + '; Precision-' + str(
round(Precision[ind_class] * 100, 2)))
# -----------------------------------------------------------------#
# 在所有验证集图像上求所有类别平均的mIoU值，计算时忽略NaN值
# -----------------------------------------------------------------#
print('===> mIoU: ' + str(round(np.nanmean(IoUs) * 100, 2)) + '; mPA: ' + str(
round(np.nanmean(PA_Recall) * 100, 2)) + '; Accuracy: ' + str(round(per_Accuracy(hist) * 100, 2)))
return np.array(hist, np.int64), IoUs, PA_Recall, Precision
def adjust_axes(r, t, fig, axes):
bb = t.get_window_extent(renderer=r)
text_width_inches = bb.width / fig.dpi
current_fig_width = fig.get_figwidth()
new_fig_width = current_fig_width + text_width_inches
propotion = new_fig_width / current_fig_width
x_lim = axes.get_xlim()
axes.set_xlim([x_lim[0], x_lim[1] * propotion])
def draw_plot_func(values, name_classes, plot_title, x_label, output_path, tick_font_size=12, plt_show=True):
fig = plt.gcf()
axes = plt.gca()
plt.barh(range(len(values)), values, color='royalblue')
plt.title(plot_title, fontsize=tick_font_size + 2)
plt.xlabel(x_label, fontsize=tick_font_size)
plt.yticks(range(len(values)), name_classes, fontsize=tick_font_size)
r = fig.canvas.get_renderer()
for i, val in enumerate(values):
str_val = " " + str(val)
if val < 1.0:
str_val = " {0:.2f}".format(val)
t = plt.text(val, i, str_val, color='royalblue', va='center', fontweight='bold')
if i == (len(values) - 1):
adjust_axes(r, t, fig, axes)
fig.tight_layout()
fig.savefig(output_path)
if plt_show:
plt.show()
plt.close()
def show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes, tick_font_size=12):
draw_plot_func(IoUs, name_classes, "mIoU = {0:.2f}%".format(np.nanmean(IoUs) * 100), "Intersection over Union", \
os.path.join(miou_out_path, "mIoU.png"), tick_font_size=tick_font_size, plt_show=True)
print("Save mIoU out to " + os.path.join(miou_out_path, "mIoU.png"))
draw_plot_func(PA_Recall, name_classes, "mPA = {0:.2f}%".format(np.nanmean(PA_Recall) * 100), "Pixel Accuracy", \
os.path.join(miou_out_path, "mPA.png"), tick_font_size=tick_font_size, plt_show=False)
print("Save mPA out to " + os.path.join(miou_out_path, "mPA.png"))
draw_plot_func(PA_Recall, name_classes, "mRecall = {0:.2f}%".format(np.nanmean(PA_Recall) * 100), "Recall", \
os.path.join(miou_out_path, "Recall.png"), tick_font_size=tick_font_size, plt_show=False)
print("Save Recall out to " + os.path.join(miou_out_path, "Recall.png"))
draw_plot_func(Precision, name_classes, "mPrecision = {0:.2f}%".format(np.nanmean(Precision) * 100), "Precision", \
os.path.join(miou_out_path, "Precision.png"), tick_font_size=tick_font_size, plt_show=False)
print("Save Precision out to " + os.path.join(miou_out_path, "Precision.png"))
with open(os.path.join(miou_out_path, "confusion_matrix.csv"), 'w', newline='') as f:
writer = csv.writer(f)
writer_list = []
writer_list.append([' '] + [str(c) for c in name_classes])
for i in range(len(hist)):
writer_list.append([name_classes[i]] + [str(x) for x in hist[i]])
writer.writerows(writer_list)
print("Save confusion_matrix out to " + os.path.join(miou_out_path, "confusion_matrix.csv"))
if __name__ == "__main__":
num_classes = 4
# --------------------------------------------#
# 区分的种类，和json_to_dataset里面的一样
# --------------------------------------------#
name_classes = ["_background_", "border", "Cell", "Nucleus"]
# -------------------------------------------------------#
# 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
# -------------------------------------------------------#
pred_dir = r'C:\Users\outputs\project_name\png_results'
pred_ids = os.listdir(pred_dir)
png_name_list = []
for name in pred_ids:
name = name.split('.')[0]
png_name_list.append(name)
gt_dir = r"C:\Users\groundtruth_png"
miou_out_path = r"C:\outputs\project_name\mIoU"
os.makedirs(miou_out_path, exist_ok=True)
image_ids = png_name_list
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()
unet = model.eval()
print("Load model done.")
print("Get miou.")
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.")
show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes)