-
Notifications
You must be signed in to change notification settings - Fork 292
/
step1b_preprocess_make_train_cubes.py
281 lines (225 loc) · 11.8 KB
/
step1b_preprocess_make_train_cubes.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
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
import settings
import helpers
import glob
import pandas
import ntpath
import numpy
import cv2
import os
CUBE_IMGTYPE_SRC = "_i"
def save_cube_img(target_path, cube_img, rows, cols):
assert rows * cols == cube_img.shape[0]
img_height = cube_img.shape[1]
img_width = cube_img.shape[1]
res_img = numpy.zeros((rows * img_height, cols * img_width), dtype=numpy.uint8)
for row in range(rows):
for col in range(cols):
target_y = row * img_height
target_x = col * img_width
res_img[target_y:target_y + img_height, target_x:target_x + img_width] = cube_img[row * cols + col]
cv2.imwrite(target_path, res_img)
def get_cube_from_img(img3d, center_x, center_y, center_z, block_size):
start_x = max(center_x - block_size / 2, 0)
if start_x + block_size > img3d.shape[2]:
start_x = img3d.shape[2] - block_size
start_y = max(center_y - block_size / 2, 0)
start_z = max(center_z - block_size / 2, 0)
if start_z + block_size > img3d.shape[0]:
start_z = img3d.shape[0] - block_size
start_z = int(start_z)
start_y = int(start_y)
start_x = int(start_x)
res = img3d[start_z:start_z + block_size, start_y:start_y + block_size, start_x:start_x + block_size]
return res
def make_pos_annotation_images():
src_dir = settings.LUNA_16_TRAIN_DIR2D2 + "metadata/"
dst_dir = settings.BASE_DIR_SSD + "luna16_train_cubes_pos/"
for file_path in glob.glob(dst_dir + "*.*"):
os.remove(file_path)
for patient_index, csv_file in enumerate(glob.glob(src_dir + "*_annos_pos.csv")):
patient_id = ntpath.basename(csv_file).replace("_annos_pos.csv", "")
# print(patient_id)
# if not "148229375703208214308676934766" in patient_id:
# continue
df_annos = pandas.read_csv(csv_file)
if len(df_annos) == 0:
continue
images = helpers.load_patient_images(patient_id, settings.LUNA_16_TRAIN_DIR2D2, "*" + CUBE_IMGTYPE_SRC + ".png")
for index, row in df_annos.iterrows():
coord_x = int(row["coord_x"] * images.shape[2])
coord_y = int(row["coord_y"] * images.shape[1])
coord_z = int(row["coord_z"] * images.shape[0])
diam_mm = int(row["diameter"] * images.shape[2])
anno_index = int(row["anno_index"])
cube_img = get_cube_from_img(images, coord_x, coord_y, coord_z, 64)
if cube_img.sum() < 5:
print(" ***** Skipping ", coord_x, coord_y, coord_z)
continue
if cube_img.mean() < 10:
print(" ***** Suspicious ", coord_x, coord_y, coord_z)
save_cube_img(dst_dir + patient_id + "_" + str(anno_index) + "_" + str(diam_mm) + "_1_" + "pos.png", cube_img, 8, 8)
helpers.print_tabbed([patient_index, patient_id, len(df_annos)], [5, 64, 8])
def make_annotation_images_lidc():
src_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/"
dst_dir = settings.BASE_DIR_SSD + "generated_traindata/luna16_train_cubes_lidc/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
for file_path in glob.glob(dst_dir + "*.*"):
os.remove(file_path)
for patient_index, csv_file in enumerate(glob.glob(src_dir + "*_annos_pos_lidc.csv")):
patient_id = ntpath.basename(csv_file).replace("_annos_pos_lidc.csv", "")
df_annos = pandas.read_csv(csv_file)
if len(df_annos) == 0:
continue
images = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*" + CUBE_IMGTYPE_SRC + ".png")
for index, row in df_annos.iterrows():
coord_x = int(row["coord_x"] * images.shape[2])
coord_y = int(row["coord_y"] * images.shape[1])
coord_z = int(row["coord_z"] * images.shape[0])
malscore = int(row["malscore"])
anno_index = row["anno_index"]
anno_index = str(anno_index).replace(" ", "xspacex").replace(".", "xpointx").replace("_", "xunderscorex")
cube_img = get_cube_from_img(images, coord_x, coord_y, coord_z, 64)
if cube_img.sum() < 5:
print(" ***** Skipping ", coord_x, coord_y, coord_z)
continue
if cube_img.mean() < 10:
print(" ***** Suspicious ", coord_x, coord_y, coord_z)
if cube_img.shape != (64, 64, 64):
print(" ***** incorrect shape !!! ", str(anno_index), " - ",(coord_x, coord_y, coord_z))
continue
save_cube_img(dst_dir + patient_id + "_" + str(anno_index) + "_" + str(malscore * malscore) + "_1_pos.png", cube_img, 8, 8)
helpers.print_tabbed([patient_index, patient_id, len(df_annos)], [5, 64, 8])
def make_pos_annotation_images_manual():
src_dir = "resources/luna16_manual_labels/"
dst_dir = settings.BASE_DIR_SSD + "generated_traindata/luna16_train_cubes_manual/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
for file_path in glob.glob(dst_dir + "*_manual.*"):
os.remove(file_path)
for patient_index, csv_file in enumerate(glob.glob(src_dir + "*.csv")):
patient_id = ntpath.basename(csv_file).replace(".csv", "")
if "1.3.6.1.4" not in patient_id:
continue
print(patient_id)
# if not "172845185165807139298420209778" in patient_id:
# continue
df_annos = pandas.read_csv(csv_file)
if len(df_annos) == 0:
continue
images = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*" + CUBE_IMGTYPE_SRC + ".png")
for index, row in df_annos.iterrows():
coord_x = int(row["x"] * images.shape[2])
coord_y = int(row["y"] * images.shape[1])
coord_z = int(row["z"] * images.shape[0])
diameter = int(row["d"] * images.shape[2])
node_type = int(row["id"])
malscore = int(diameter)
malscore = min(25, malscore)
malscore = max(16, malscore)
anno_index = index
cube_img = get_cube_from_img(images, coord_x, coord_y, coord_z, 64)
if cube_img.sum() < 5:
print(" ***** Skipping ", coord_x, coord_y, coord_z)
continue
if cube_img.mean() < 10:
print(" ***** Suspicious ", coord_x, coord_y, coord_z)
if cube_img.shape != (64, 64, 64):
print(" ***** incorrect shape !!! ", str(anno_index), " - ",(coord_x, coord_y, coord_z))
continue
save_cube_img(dst_dir + patient_id + "_" + str(anno_index) + "_" + str(malscore) + "_1_" + ("pos" if node_type == 0 else "neg") + ".png", cube_img, 8, 8)
helpers.print_tabbed([patient_index, patient_id, len(df_annos)], [5, 64, 8])
def make_candidate_auto_images(candidate_types=[]):
dst_dir = settings.BASE_DIR_SSD + "generated_traindata/luna16_train_cubes_auto/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
for candidate_type in candidate_types:
for file_path in glob.glob(dst_dir + "*_" + candidate_type + ".png"):
os.remove(file_path)
for candidate_type in candidate_types:
if candidate_type == "falsepos":
src_dir = "resources/luna16_falsepos_labels/"
else:
src_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/"
for index, csv_file in enumerate(glob.glob(src_dir + "*_candidates_" + candidate_type + ".csv")):
patient_id = ntpath.basename(csv_file).replace("_candidates_" + candidate_type + ".csv", "")
print(index, ",patient: ", patient_id, " type:", candidate_type)
# if not "148229375703208214308676934766" in patient_id:
# continue
df_annos = pandas.read_csv(csv_file)
if len(df_annos) == 0:
continue
images = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*" + CUBE_IMGTYPE_SRC + ".png", exclude_wildcards=[])
row_no = 0
for index, row in df_annos.iterrows():
coord_x = int(row["coord_x"] * images.shape[2])
coord_y = int(row["coord_y"] * images.shape[1])
coord_z = int(row["coord_z"] * images.shape[0])
anno_index = int(row["anno_index"])
cube_img = get_cube_from_img(images, coord_x, coord_y, coord_z, 48)
if cube_img.sum() < 10:
print("Skipping ", coord_x, coord_y, coord_z)
continue
# print(cube_img.sum())
try:
save_cube_img(dst_dir + patient_id + "_" + str(anno_index) + "_0_" + candidate_type + ".png", cube_img, 6, 8)
except Exception as ex:
print(ex)
row_no += 1
max_item = 240 if candidate_type == "white" else 200
if candidate_type == "luna":
max_item = 500
if row_no > max_item:
break
def make_pos_annotation_images_manual_ndsb3():
src_dir = "resources/ndsb3_manual_labels/"
dst_dir = settings.BASE_DIR_SSD + "generated_traindata/ndsb3_train_cubes_manual/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
train_label_df = pandas.read_csv("resources/stage1_labels.csv")
train_label_df.set_index(["id"], inplace=True)
for file_path in glob.glob(dst_dir + "*.*"):
os.remove(file_path)
for patient_index, csv_file in enumerate(glob.glob(src_dir + "*.csv")):
patient_id = ntpath.basename(csv_file).replace(".csv", "")
if "1.3.6.1.4.1" in patient_id:
continue
cancer_label = train_label_df.loc[patient_id]["cancer"]
df_annos = pandas.read_csv(csv_file)
if len(df_annos) == 0:
continue
images = helpers.load_patient_images(patient_id, settings.NDSB3_EXTRACTED_IMAGE_DIR, "*" + CUBE_IMGTYPE_SRC + ".png")
anno_index = 0
for index, row in df_annos.iterrows():
pos_neg = "pos" if row["id"] == 0 else "neg"
coord_x = int(row["x"] * images.shape[2])
coord_y = int(row["y"] * images.shape[1])
coord_z = int(row["z"] * images.shape[0])
malscore = int(round(row["dmm"]))
anno_index += 1
cube_img = get_cube_from_img(images, coord_x, coord_y, coord_z, 64)
if cube_img.sum() < 5:
print(" ***** Skipping ", coord_x, coord_y, coord_z)
continue
if cube_img.mean() < 10:
print(" ***** Suspicious ", coord_x, coord_y, coord_z)
if cube_img.shape != (64, 64, 64):
print(" ***** incorrect shape !!! ", str(anno_index), " - ",(coord_x, coord_y, coord_z))
continue
print(patient_id)
assert malscore > 0 or pos_neg == "neg"
save_cube_img(dst_dir + "ndsb3manual_" + patient_id + "_" + str(anno_index) + "_" + pos_neg + "_" + str(cancer_label) + "_" + str(malscore) + "_1_pn.png", cube_img, 8, 8)
helpers.print_tabbed([patient_index, patient_id, len(df_annos)], [5, 64, 8])
if __name__ == "__main__":
if not os.path.exists(settings.BASE_DIR_SSD + "generated_traindata/"):
os.mkdir(settings.BASE_DIR_SSD + "generated_traindata/")
if True:
make_annotation_images_lidc()
if True:
make_pos_annotation_images_manual()
# if False:
# make_pos_annotation_images() # not used anymore
if True:
make_candidate_auto_images(["falsepos", "luna", "edge"])
if True:
make_pos_annotation_images_manual_ndsb3() # for second model