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Commit 9b02e4db by 王肇一
Options

mrnet

parent ca3e4a87
Showing with 1021 additions and 196 deletions
app.py
...@@ -42,7 +42,7 @@ def step_1(net, args, device, list, position): ...@@ -42,7 +42,7 @@ def step_1(net, args, device, list, position):
cv.imwrite('data/masks/' + fn[0] + '/' + fn[1], result) cv.imwrite('data/masks/' + fn[0] + '/' + fn[1], result)
def step_2(list, position): def step_2(list, position=1):
for num, dir in enumerate(list): for num, dir in enumerate(list):
#df = pd.DataFrame(columns = ('ug', 'iter', 'id', 'size', 'area_mean', 'back_mean', 'Intensity (a. u.)')) #df = pd.DataFrame(columns = ('ug', 'iter', 'id', 'size', 'area_mean', 'back_mean', 'Intensity (a. u.)'))
values = [] values = []
...@@ -52,20 +52,21 @@ def step_2(list, position): ...@@ -52,20 +52,21 @@ def step_2(list, position):
img = cv.imread('data/imgs/' + dir + '/' + name, 0) img = cv.imread('data/imgs/' + dir + '/' + name, 0)
mask = cv.imread('data/masks/' + dir + '/' + name, 0) mask = cv.imread('data/masks/' + dir + '/' + name, 0)
value = get_subarea_info(img, mask) value, count = get_subarea_info(img, mask)
ug = 0.0 ug = 0.0
if str.lower(match_group.group(1)).endswith('ug'): if str.lower(match_group.group(1)).endswith('ug'):
ug = float(str.lower(match_group.group(1))[:-2]) ug = float(str.lower(match_group.group(1))[:-2])
elif str.lower(match_group.group(1))== 'd2o':
ug = 0
elif str.lower(match_group.group(1)) == 'd2o': elif str.lower(match_group.group(1)) == 'd2o':
ug = 0
elif str.lower(match_group.group(1)) == 'lb':
ug = -1 ug = -1
iter = str.lower(match_group.group(2)) values.append({'Intensity (a. u.)': value, 'ug': ug, 'count': count})
values.append({'Intensity (a. u.)':value,'ug':ug,'iter':iter})
df = pd.DataFrame(values) df = pd.DataFrame(values)
df.sort_values('ug', inplace = True) df.sort_values('ug', inplace = True)
baseline = df[df['ug'] == 0]['Intensity (a. u.)'].mean()*0.62 df.replace(-1, 'lb', inplace = True)
df.replace(0, 'd2o', inplace = True)
baseline = df[df['ug'] == 'd2o']['Intensity (a. u.)'].mean()*0.62
sns.set_style("darkgrid") sns.set_style("darkgrid")
sns.catplot(x = 'ug', y = 'Intensity (a. u.)', kind = 'bar', palette = 'vlag', data = df) sns.catplot(x = 'ug', y = 'Intensity (a. u.)', kind = 'bar', palette = 'vlag', data = df)
#sns.swarmplot(x = "ug", y = "Intensity (a. u.)", data = df, size = 2, color = ".3", linewidth = 0) #sns.swarmplot(x = "ug", y = "Intensity (a. u.)", data = df, size = 2, color = ".3", linewidth = 0)
......
cli/32to8.ipynb
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 2,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -20,8 +20,8 @@ ...@@ -20,8 +20,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"inputdir = '../trans/'\n", "inputdir = './trans/'\n",
"outputdir = '../trans_out/'\n", "outputdir = './trans_out/'\n",
"for name in filter(lambda x: x!='.DS_Store',os.listdir(inputdir)):\n", "for name in filter(lambda x: x!='.DS_Store',os.listdir(inputdir)):\n",
" img = cv.imread(inputdir+name,flags = cv.IMREAD_ANYDEPTH)\n", " img = cv.imread(inputdir+name,flags = cv.IMREAD_ANYDEPTH)\n",
" norm = cv.normalize(img, None, 0, 255, cv.NORM_MINMAX, cv.CV_8U)\n", " norm = cv.normalize(img, None, 0, 255, cv.NORM_MINMAX, cv.CV_8U)\n",
...@@ -30,15 +30,519 @@ ...@@ -30,15 +30,519 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20191231-S.aureus ATCC 29213 Amipicillin\n",
"Step size0Dwell time50 S.a atcc 29213 1h d2o-2 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 1ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 2ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 1ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 4ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 2ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 2ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin LB 852nm 40mw 400mw tune 43.08 0088.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 4ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin LB 852nm 40mw 400mw tune 43.08 0089.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 2ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.0625ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.125ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.5ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.125ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.0625ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.0625ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.25ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.125ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.5ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.125ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.5ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin D2O 852nm 40mw 400mw tune 43.08 0089.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin D2O 852nm 40mw 400mw tune 43.08 0088.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.125ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Ampicillin 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"20191231-S.a atcc 29213 Vancomycin\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.5ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.5ug 852nm 40mw 400mw tune 43.08 006.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.5ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.5ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 2ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 4ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 2ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 4ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 2ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 1ug 852nm 40mw 400mw tune 43.08 006.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 4ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 4ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time10 S.a atcc 29213 Vancomycin 4ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 2ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 1ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 1h d2o-2 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 1ug 852nm 40mw 400mw tune 43.08 008.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.125ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.125ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin LB 852nm 40mw 400mw tune 43.08 0088.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.125ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 0.125ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 8ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 8ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 8ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin 8ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"20191231-E. faecium ATCC 29212 Linezolid\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 16ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 16ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 16ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 4ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 2ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 1ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 4ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Lb 852nm 40mw 400mw tune 43.08 0077.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 2ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 2ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 1ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 2ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 0.25 852nm 40mw 400mw tune 43.08 0088.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 0.25 852nm 40mw 400mw tune 43.08 0077.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 0.5ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 0.5ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 0.25 852nm 40mw 400mw tune 43.08 0099.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 8ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 8ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 8ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Line 8ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"20191231-E. faecium ATCC 29212 Amipicillin\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 2ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 2ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 2ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Lb 852nm 40mw 400mw tune 43.08 0077.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 1ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 2ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 1ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 8ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 16ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 0.5ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 8ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 8ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 16ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 0.5ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Amp 0.5ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"20191231-E. faecium ATCC 29212 Tigecycline\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.0625ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.5ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.0625ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.0625ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.0625ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.5ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Lb 852nm 40mw 400mw tune 43.08 0077.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 1ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.125ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.03125ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.03125ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.125ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.03125ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.125ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.25ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.03125ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.03125ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.125ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Tig 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"20191231-E. faecium ATCC 29212 Vancomycin\n",
"Step size0Dwell time50 E.faecium atcc 29212 Vancomycin 2ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 16ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 16ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 8ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Vancomycin 2ug 852nm 40mw 400mw tune 43.08 002.tif\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step size0Dwell time50 E.faecium atcc 29212 Van 16ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Vancomycin 2ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 8ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 16ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 8ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Vancomycin 2ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 8ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Lb 852nm 40mw 400mw tune 43.08 0077.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 0.5ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 4ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 1ug 852nm 40mw 400mw tune 43.08 006.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 1ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 1ug 852nm 40mw 400mw tune 43.08 003 .tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 1ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 0.5ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 4ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 4ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 1ug 852nm 40mw 400mw tune 43.08 007-1.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Van 4ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Clinical Sample K. p Gentamicin\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 4ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p LB 0ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 4ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 4ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 4ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 32ug 852nm 30mw 300mw tune 43.00 003_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 64ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 64ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 32ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 64ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 32ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 64ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 32ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 16ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 8ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 16ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 128ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 8ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 128ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 8ug 852nm 30mw 300mw tune 43.00 002_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 16ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 128ug 852nm 30mw 300mw tune 43.00 005.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Gentamicin 128ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"K.p Ampicillcin\n",
"Step size0Dwell time50 Clinical Sample K. p LB 0ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 8ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 8ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 8ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 16ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 16ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 16ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 16ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 16ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 16ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 16ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 256ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 64ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 32ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 32ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 256ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 256ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 32ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 64ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 64ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 256ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 64ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 32ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 128ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 128ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 128ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ampicillcin 128ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"20191231-E. faecium ATCC 29212 Erythromycin\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 0.5ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 2ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 4ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 2ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 1ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 4ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 2ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Lb 852nm 40mw 400mw tune 43.08 0077.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 0.25ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 8ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 8ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 8ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ery 8ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"20191231-E. faecium ATCC 29212 Levofloxacin\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 2ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.125ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 2ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 4ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 4ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 2ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.125ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.125ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 1ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 4ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 2ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 4ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Lb 852nm 40mw 400mw tune 43.08 0077.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Ampicillin D2O 852nm 40mw 400mw tune 43.08 005.tif\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step size0Dwell time50 E.faecium atcc 29212 Levo 8ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 1ug 852nm 40mw 400mw tune 43.08 0099.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.5ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 8ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 8ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 1ug 852nm 40mw 400mw tune 43.08 0088.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 1ug 852nm 40mw 400mw tune 43.08 0077.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 0.5ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 E.faecium atcc 29212 Levo 4ug 852nm 40mw 400mw tune 43.08 003-.tif\n",
"20191230-S.a ATCC 29213 Erythromycin\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.03125ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 1h d2o-2 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.03125ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.03125ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.5ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.125ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.5ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.125ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin LB 852nm 40mw 400mw tune 43.08 0088.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.125ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.0625ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 2ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 1ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.0625ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 2ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 2ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Eryth 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"20191230-S.a atcc 29213 Levofloxacin\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.125ug 852nm 40mw 400mw tune 43.08 006.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.125ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.125ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 1h d2o-2 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 1ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 2ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 2ug 852nm 40mw 400mw tune 43.08 006.tif\n",
"Step size0Dwell time50 S.a atcc 29213 1h d2o-2 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 2ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 1ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 1h d2o-2 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 1ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.0625ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.5ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.0625ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.5ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.0625ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.0625ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin LB 852nm 40mw 400mw tune 43.08 0089.tif\n",
"Step size0Dwell time10 S.a atcc 29213 Levofloxacin 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.25ug 852nm 40mw 400mw tune 43.08 003-1.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Levofloxacin 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"20191230-S.aureus ATCC 29213 Linezolid\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 16ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 1ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 2ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 2ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 1ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.5ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 4ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 4ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.5ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 16ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 1ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 4ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 16ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.5ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 16ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 4ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.5ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 1h d2o-2 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.25ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.25ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.25ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 8ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 8ug 852nm 40mw 400mw tune 43.08 001.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 8ug 852nm 40mw 400mw tune 43.08 003.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Vancomycin LB 852nm 40mw 400mw tune 43.08 0088.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.25ug 852nm 40mw 400mw tune 43.08 004.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 0.25ug 852nm 40mw 400mw tune 43.08 005.tif\n",
"Step size0Dwell time50 S.a atcc 29213 Linezolid 8ug 852nm 40mw 400mw tune 43.08 002.tif\n",
"K.p Tobramycin Clinical Sample\n",
"Step size0Dwell time50 Clinical Sample K. p LB 0ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 4ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 64ug 852nm 30mw 300mw tune 43.00 002_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 4ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 64ug 852nm 30mw 300mw tune 43.00 003_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 4ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 16ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 16ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 16ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 16ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 32ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 32ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 64ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 8ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 64ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 32ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 8ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 8ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 8ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 128ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 128ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 128ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 32ug 852nm 30mw 300mw tune 43.00 002_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Tobramycin 4ug 852nm 30mw 300mw tune 43.00 001_over-exposure.tif\n",
"K.p Ceftazidime Clinical Sample\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 256ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p LB 0ug 852nm 30mw 300mw tune 43.00 002.tif\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 256ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 64ug 852nm 30mw 300mw tune 43.00 003_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 256ug 852nm 30mw 300mw tune 43.00 005.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 256ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 128ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 128ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 16ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 128ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 32ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 64ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 512ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 32ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 32ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 512ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 512ug 852nm 30mw 300mw tune 43.00 001_over-expousre.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 64ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 32ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 512ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Ceftazidime 64ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Clinical Sample K. p Levofloxacin\n",
"Step size0Dwell time50 Clinical Sample K. p LB 0ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 64ug 852nm 30mw 300mw tune 43.00 002_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 8ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 8ug 852nm 30mw 300mw tune 43.00 005.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levo 32ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levo 32ug 852nm 30mw 300mw tune 43.00 001_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 64ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 8ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 64ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 8ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 8ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levo 32ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 64ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levo 32ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 16ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 16ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 16ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 16ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 4ug 852nm 30mw 300mw tune 43.00 005.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 2ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 4ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 4ug 852nm 30mw 300mw tune 43.00 004_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 2ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 4ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 2ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 4ug 852nm 30mw 300mw tune 43.00 001_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Levofloxacin 2ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Clinical Sample K. p Imipenem\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 32ug 852nm 30mw 300mw tune 43.00 004_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p LB 0ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 128ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 128ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 128ug 852nm 30mw 300mw tune 43.00 004_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 4ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 8ug 852nm 30mw 300mw tune 43.00 001_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 128ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 4ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 128ug 852nm 30mw 300mw tune 43.00 005.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 4ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 16ug 852nm 30mw 300mw tune 43.00 004.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 16ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 16ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 4ug 852nm 30mw 300mw tune 43.00 002_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 64ug 852nm 30mw 300mw tune 43.00 002_over-exposure.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 32ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 32ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 32ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 64ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 32ug 852nm 30mw 300mw tune 43.00 005.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 8ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 8ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 64ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p Imipenem 16ug 852nm 30mw 300mw tune 43.00 002_over-exposure.tif\n",
"20200121-Clinical Sample K. p isolated from positive blood bottle\n",
"Step size0Dwell time50 Clinical Sample K. p LB 0ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 001.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 003.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 002.tif\n",
"Step size0Dwell time50 Clinical Sample K. p D2O 0ug 852nm 30mw 300mw tune 43.00 004.tif\n"
]
}
],
"source": [ "source": [
"inputdir = '../trans/'\n", "inputdir = './trans/'\n",
"outputdir = '../trans_out/'\n", "outputdir = './trans_out/'\n",
"for path in filter(lambda x: x!='.DS_Store',os.listdir(inputdir)):\n", "for path in filter(lambda x: x!='.DS_Store',os.listdir(inputdir)):\n",
" os.makedirs(outputdir+path)\n", " os.makedirs(outputdir+path)\n",
" print(path)\n",
" for name in filter(lambda x: x!='.DS_Store',os.listdir(inputdir+path+'/')):\n", " for name in filter(lambda x: x!='.DS_Store',os.listdir(inputdir+path+'/')):\n",
" print(name)\n",
" img = cv.imread(inputdir+path+'/'+name,flags = cv.IMREAD_ANYDEPTH)\n", " img = cv.imread(inputdir+path+'/'+name,flags = cv.IMREAD_ANYDEPTH)\n",
" norm = cv.normalize(img, None, 0, 255, cv.NORM_MINMAX, cv.CV_8U)\n", " norm = cv.normalize(img, None, 0, 255, cv.NORM_MINMAX, cv.CV_8U)\n",
" cv.imwrite(outputdir+path+'/'+name,norm)" " cv.imwrite(outputdir+path+'/'+name,norm)"
...@@ -117,6 +621,20 @@ ...@@ -117,6 +621,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"inputdir = '../data/train_imgs/'\n",
"outputdir = '../data/'\n",
"for name in filter(lambda x: x!='.DS_Store',os.listdir(inputdir)):\n",
" img = cv.imread(inputdir+name,flags = cv.IMREAD_ANYDEPTH)\n",
" rgb = cv.cvtColor(img,cv.COLOR_GRAY2RGB)\n",
" cv.imwrite(outputdir+name,rgb)"
]
},
{
"cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
......
mrnet/__init__.py
#!/usr/bin/env python #!/usr/bin/env python
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
\ No newline at end of file from .train import train_net
from .mrnet_module import MultiUnet
\ No newline at end of file
mrnet/mrnet_module.py
...@@ -2,8 +2,8 @@ ...@@ -2,8 +2,8 @@
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
import torch import torch
import torch.nn.functional as F
from torch import nn from torch import nn
from .mrnet_parts import MultiResBlock,ResPath,TransCompose
class MultiUnet(nn.Module): class MultiUnet(nn.Module):
...@@ -14,80 +14,60 @@ class MultiUnet(nn.Module): ...@@ -14,80 +14,60 @@ class MultiUnet(nn.Module):
self.bilinear = bilinear self.bilinear = bilinear
self.inconv = nn.Sequential( self.inconv = nn.Sequential(
nn.Conv2d(n_channels, 16, kernel_size = 3, stride = 1, padding_mode = 'same'), nn.Conv2d(n_channels, 8, kernel_size = 3, stride = 1, padding_mode = 'same'),
nn.BatchNorm2d(8), nn.BatchNorm2d(8),
nn.ReLU(inplace = True) nn.ReLU(inplace = True)
) )
self.res1 = MultiResBlock(16, 32) self.res1 = MultiResBlock(8, 32)
self.res2 = MultiResBlock(self.res1.outc, 32*2) self.res2 = MultiResBlock(self.res1.outc, 32*2)
self.res3 = MultiResBlock(self.res2.outc, 32*4) self.res3 = MultiResBlock(self.res2.outc, 32*4)
self.res4 = MultiResBlock(self.res3.outc, 32*8) self.res4 = MultiResBlock(self.res3.outc, 32*8)
self.res5 = MultiResBlock(self.res4.outc, 32*16) self.res5 = MultiResBlock(self.res4.outc, 32*16)
self.res6 = MultiResBlock(self.res5.outc, 32*8) self.path1_9 = ResPath(self.res1.outc, 32, 4)
self.res7 = MultiResBlock(self.res6.outc, 32*4) self.path2_8 = ResPath(self.res2.outc, 32*2, 3)
self.res8 = MultiResBlock(self.res7.outc, 32*2) self.path3_7 = ResPath(self.res3.outc, 32*4, 2)
self.res9 = MultiResBlock(self.res8.outc, 32) self.path4_6 = ResPath(self.res4.outc, 32*8, 1)
self.up6 = TransCompose(self.res5.outc, 32 * 8)
self.res6 = MultiResBlock(self.up6.outc*2, 32 * 8)
self.up7 = TransCompose(self.res6.outc, 32 * 4)
self.res7 = MultiResBlock(self.up7.outc*2, 32 * 4)
self.up8 = TransCompose(self.res7.outc, 32 * 2)
self.res8 = MultiResBlock(self.up8.outc*2, 32 * 2)
self.up9 = TransCompose(self.res8.outc, 32)
self.res9 = MultiResBlock(self.up9.outc*2, 32)
self.pool = nn.MaxPool2d(2)
self.outconv = nn.Conv2d(self.res9.outc, n_classes, kernel_size = 3, padding_mode = 'same')
def forward(self, x): def forward(self, x):
pass x = self.inconv(x)
res1 = self.res1(x)
pool1 = self.pool(res1)
res2 = self.res2(pool1)
pool2 = self.pool(res2)
class MultiResBlock(nn.Module): res3 = self.res3(pool2)
def __init__(self, in_channels, U, alpha = 1.67): pool3 = self.pool(res3)
super().__init__()
self.U = U
self.W = U*alpha
self.inc = in_channels
self.outc = int(self.W * 0.167) + int(self.W * 0.333) + int(self.W * 0.5)
def conv(self, in_channel, out_channel, kernel_size): res4 = self.res4(pool3)
return nn.Sequential( pool4 = self.pool(res4)
nn.Conv2d(in_channels = in_channel, out_channels = out_channel, kernel_size = kernel_size, stride = 1, padding_mode = 'same'),
nn.BatchNorm2d(out_channel),
nn.ReLU(inplace = True)
)
def forward(self, x): res5 = self.res5(pool4)
shortcut = nn.Sequential(
nn.Conv2d(in_channels = self.inc,out_channels = self.outc,kernel_size = 1,stride = 1, padding_mode = 'same'),
nn.BatchNorm2d(self.outc))(x)
conv3 = self.conv(self.inc, int(self.W * 0.167), 3)(x)
conv5 = self.conv(int(self.W * 0.167), int(self.W * 0.333), 3)(conv3)
conv7 = self.conv(int(self.W * 0.333), int(self.W * 0.5), 3)(conv5)
result = torch.cat([conv3, conv5, conv7], 2)
result = nn.BatchNorm2d(self.outc)(result)
result = torch.add(result, shortcut)
result = nn.Sequential(
nn.ReLU(inplace = True),
nn.BatchNorm2d(self.outc))(result)
return result
class ResPath(nn.Module):
def __init__(self, in_channels, out_channels, length):
super().__init__()
self.length = length
self.inc = in_channels
self.outc = out_channels
def unit(self, in_channels, out_channels, x):
shortcut = nn.Sequential(
nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 1, stride = 1,padding_mode = 'same'),
nn.BatchNorm2d(self.outc))(x)
conv = nn.Sequential(
nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 3, stride = 1,padding_mode = 'same'),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace = True))(x)
result = torch.add(conv, shortcut)
return nn.Sequential(nn.ReLU(inplace = True), nn.BatchNorm2d(out_channels))(result)
def forward(self, x): res1 = self.path1_9(res1)
x = self.unit(self.inc,self.outc, x) res2 = self.path2_8(res2)
for i in range(self.length -1): res3 = self.path3_7(res3)
x = self.unit(self.outc,self.outc,x) res4 = self.path4_6(res4)
return x
res6 = self.res6(torch.cat([res4, self.up6(res5)], 1))
res7 = self.res7(torch.cat([res3, self.up7(res6)], 1))
res8 = self.res8(torch.cat([res2, self.up8(res7)], 1))
res9 = self.res9(torch.cat([res1, self.up9(res8)], 1))
out = self.outconv(res9)
return out
mrnet/mrnet_parts.py
#!/usr/bin/env python #!/usr/bin/env python
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
\ No newline at end of file import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
class MultiResBlock(nn.Module):
def __init__(self, in_channels, U, alpha = 1.67):
super().__init__()
self.U = U
self.W = U * alpha
self.inc = in_channels
self.outc = int(self.W * 0.167) + int(self.W * 0.333) + int(self.W * 0.5)
def conv(self, in_channel, out_channel, kernel_size):
return nn.Sequential(
nn.Conv2d(in_channels = in_channel, out_channels = out_channel, kernel_size = kernel_size, stride = 1,padding = 1,
padding_mode = 'same'), nn.BatchNorm2d(out_channel), nn.ReLU(inplace = True))
def forward(self, x):
shortcut = nn.Sequential(
nn.Conv2d(in_channels = self.inc, out_channels = self.outc, kernel_size = 1, stride = 1),
nn.BatchNorm2d(self.outc))(x)
conv3 = self.conv(self.inc, int(self.W * 0.167), 3)(x)
conv5 = self.conv(int(self.W * 0.167), int(self.W * 0.333), 3)(conv3)
conv7 = self.conv(int(self.W * 0.333), int(self.W * 0.5), 3)(conv5)
result = torch.cat([conv3, conv5, conv7], 1)
result = nn.BatchNorm2d(self.outc)(result)
result = torch.add(result, shortcut)
result = nn.Sequential(nn.ReLU(inplace = True), nn.BatchNorm2d(self.outc))(result)
return result
class TransCompose(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.inc = in_channels
self.outc = out_channels
def forward(self,x):
return nn.Sequential(
nn.ConvTranspose2d(in_channels = self.inc, out_channels = self.outc, kernel_size = 3, stride = 2),
nn.BatchNorm2d(self.outc)
)(x)
class ResPath(nn.Module):
def __init__(self, in_channels, out_channels, length):
super().__init__()
self.length = length
self.inc = in_channels
self.outc = out_channels
def unit(self, in_channels, out_channels, x):
shortcut = nn.Sequential(
nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 1, stride = 1),
nn.BatchNorm2d(self.outc))(x)
conv = nn.Sequential(
nn.Conv2d(in_channels = in_channels, out_channels = out_channels, kernel_size = 3, stride = 1, padding = 1),
nn.BatchNorm2d(out_channels), nn.ReLU(inplace = True))(x)
result = torch.add(conv, shortcut)
return nn.Sequential(nn.ReLU(inplace = True), nn.BatchNorm2d(out_channels))(result)
def forward(self, x):
x = self.unit(self.inc, self.outc, x)
for i in range(self.length - 1):
x = self.unit(self.outc, self.outc, x)
return x
mrnet/train.py
#!/usr/bin/env python #!/usr/bin/env python
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
\ No newline at end of file
import os
import logging
from tqdm import tqdm
import torch
import torch.nn as nn
from torch import optim
from torch.utils.data import DataLoader, random_split
from utils.dataset import BasicDataset
from utils.eval import eval_net
dir_img = 'data/train_imgs/'
dir_mask = 'data/train_masks/'
dir_checkpoint = 'checkpoint'
def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, val_percent = 0.1, save_cp = True, img_scale = 0.5):
dataset = BasicDataset(dir_img, dir_mask, img_scale)
n_val = int(len(dataset) * val_percent)
n_train = len(dataset) - n_val
train, val = random_split(dataset, [n_train, n_val])
train_loader = DataLoader(train, batch_size = batch_size, shuffle = True, num_workers = 8, pin_memory = True)
val_loader = DataLoader(val, batch_size = batch_size, shuffle = False, num_workers = 8, pin_memory = True)
optimizer = optim.Adam(net.parameters(), lr = lr)
criterion = nn.BCELoss()
for epoch in tqdm(range(epochs)):
net.train()
epoch_loss = 0
for batch in train_loader:
imgs = batch['image']
true_masks = batch['mask']
imgs = imgs.to(device = device, dtype = torch.float32)
mask_type = torch.float32 if net.n_classes == 1 else torch.long
true_masks = true_masks.to(device = device, dtype = mask_type)
masks_pred = net(imgs)
loss = criterion(masks_pred, true_masks)
epoch_loss += loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
val_score = eval_net(net, val_loader, device, n_val)
logging.info('Validation cross entropy: {}'.format(val_score))
try:
os.mkdir(dir_checkpoint)
logging.info('Created checkpoint directory')
except OSError:
pass
torch.save(net.state_dict(), dir_checkpoint + f'CP_epoch{epoch + 1}.pth')
torch.save(net.state_dict(), 'MODEL.pth')
resCalc.py
...@@ -65,21 +65,18 @@ def get_subarea_info(img, mask): ...@@ -65,21 +65,18 @@ def get_subarea_info(img, mask):
back_value = img[np.where(real_bg_mask != 0)] back_value = img[np.where(real_bg_mask != 0)]
back_mean = np.mean(back_value) back_mean = np.mean(back_value)
info.append({'mean': area_mean,'back':back_mean,'size':area_size})
info.append({'id': i, 'size': area_size, 'area_mean': area_mean, 'back_mean': back_mean})
# endif # endif
df = pd.DataFrame(info) df = pd.DataFrame(info)
df['Intensity (a. u.)'] = df['area_mean'] - df['back_mean']
median = np.median(df['Intensity (a. u.)']) median = np.median(df['mean'])
b = 1.4826 b = 1.4826
mad = b * np.median(np.abs(df['Intensity (a. u.)'] - median)) mad = b * np.median(np.abs(df['mean'] - median))
lower_limit = median - (3 * mad) lower_limit = median - (3 * mad)
upper_limit = median + (3 * mad) upper_limit = median + (3 * mad)
#df = df[df['Intensity (a. u.)'] > lower_limit] df = df[df['mean'] >= lower_limit]
#df = df[df['Intensity (a. u.)'] < upper_limit] df = df[df['mean'] <= upper_limit]
df['value'] = df['mean']-df['back']
value = df['Intensity (a. u.)'].mean()
return value return (df['value'] * df['size']).sum() / df['size'].sum(),df.shape[0]
resUnet.py
#!/usr/bin/env python #!/usr/bin/env python
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
\ No newline at end of file from torch import nn
from torch.nn import functional as F
import torch
from torchvision import models
import torchvision
def conv3x3(in_, out):
return nn.Conv2d(in_, out, 3, padding = 1)
class ConvRelu(nn.Module):
def __init__(self, in_, out):
super().__init__()
self.conv = conv3x3(in_, out)
self.activation = nn.ReLU(inplace = True)
def forward(self, x):
x = self.conv(x)
x = self.activation(x)
return x
class DecoderBlock(nn.Module):
def __init__(self, in_channels, middle_channels, out_channels):
super().__init__()
self.block = nn.Sequential(ConvRelu(in_channels, middle_channels),
nn.ConvTranspose2d(middle_channels, out_channels, kernel_size = 3, stride = 2, padding = 1,
output_padding = 1), nn.ReLU(inplace = True))
def forward(self, x):
return self.block(x)
class Interpolate(nn.Module):
def __init__(self, size = None, scale_factor = None, mode = 'nearest', align_corners = False):
super(Interpolate, self).__init__()
self.interp = nn.functional.interpolate
self.size = size
self.mode = mode
self.scale_factor = scale_factor
self.align_corners = align_corners
def forward(self, x):
x = self.interp(x, size = self.size, scale_factor = self.scale_factor, mode = self.mode,
align_corners = self.align_corners)
return x
class DecoderBlockV2(nn.Module):
def __init__(self, in_channels, middle_channels, out_channels, is_deconv = True):
super(DecoderBlockV2, self).__init__()
self.in_channels = in_channels
if is_deconv:
"""
Paramaters for Deconvolution were chosen to avoid artifacts, following
link https://distill.pub/2016/deconv-checkerboard/
"""
self.block = nn.Sequential(ConvRelu(in_channels, middle_channels),
nn.ConvTranspose2d(middle_channels, out_channels, kernel_size = 4, stride = 2, padding = 1),
nn.ReLU(inplace = True))
else:
self.block = nn.Sequential(Interpolate(scale_factor = 2, mode = 'bilinear'),
ConvRelu(in_channels, middle_channels), ConvRelu(middle_channels, out_channels), )
def forward(self, x):
return self.block(x)
class UNet11(nn.Module):
def __init__(self, num_filters = 32, pretrained = False):
"""
:param num_classes:
:param num_filters:
:param pretrained:
False - no pre-trained network is used
True - encoder is pre-trained with VGG11
"""
super().__init__()
self.pool = nn.MaxPool2d(2, 2)
self.encoder = models.vgg11(pretrained = pretrained).features
self.relu = self.encoder[1]
self.conv1 = self.encoder[0]
self.conv2 = self.encoder[3]
self.conv3s = self.encoder[6]
self.conv3 = self.encoder[8]
self.conv4s = self.encoder[11]
self.conv4 = self.encoder[13]
self.conv5s = self.encoder[16]
self.conv5 = self.encoder[18]
self.center = DecoderBlock(num_filters * 8 * 2, num_filters * 8 * 2, num_filters * 8)
self.dec5 = DecoderBlock(num_filters * (16 + 8), num_filters * 8 * 2, num_filters * 8)
self.dec4 = DecoderBlock(num_filters * (16 + 8), num_filters * 8 * 2, num_filters * 4)
self.dec3 = DecoderBlock(num_filters * (8 + 4), num_filters * 4 * 2, num_filters * 2)
self.dec2 = DecoderBlock(num_filters * (4 + 2), num_filters * 2 * 2, num_filters)
self.dec1 = ConvRelu(num_filters * (2 + 1), num_filters)
self.final = nn.Conv2d(num_filters, 1, kernel_size = 1)
def forward(self, x):
conv1 = self.relu(self.conv1(x))
conv2 = self.relu(self.conv2(self.pool(conv1)))
conv3s = self.relu(self.conv3s(self.pool(conv2)))
conv3 = self.relu(self.conv3(conv3s))
conv4s = self.relu(self.conv4s(self.pool(conv3)))
conv4 = self.relu(self.conv4(conv4s))
conv5s = self.relu(self.conv5s(self.pool(conv4)))
conv5 = self.relu(self.conv5(conv5s))
center = self.center(self.pool(conv5))
dec5 = self.dec5(torch.cat([center, conv5], 1))
dec4 = self.dec4(torch.cat([dec5, conv4], 1))
dec3 = self.dec3(torch.cat([dec4, conv3], 1))
dec2 = self.dec2(torch.cat([dec3, conv2], 1))
dec1 = self.dec1(torch.cat([dec2, conv1], 1))
return self.final(dec1)
class UNet16(nn.Module):
def __init__(self, num_classes = 1, num_filters = 32, pretrained = False, is_deconv = False):
"""
:param num_classes:
:param num_filters:
:param pretrained:
False - no pre-trained network used
True - encoder pre-trained with VGG16
:is_deconv:
False: bilinear interpolation is used in decoder
True: deconvolution is used in decoder
"""
super().__init__()
self.n_classes = num_classes
self.pool = nn.MaxPool2d(2, 2)
self.encoder = torchvision.models.vgg16(pretrained = pretrained).features
self.relu = nn.ReLU(inplace = True)
self.conv1 = nn.Sequential(self.encoder[0], self.relu, self.encoder[2], self.relu)
self.conv2 = nn.Sequential(self.encoder[5], self.relu, self.encoder[7], self.relu)
self.conv3 = nn.Sequential(self.encoder[10], self.relu, self.encoder[12], self.relu, self.encoder[14],
self.relu)
self.conv4 = nn.Sequential(self.encoder[17], self.relu, self.encoder[19], self.relu, self.encoder[21],
self.relu)
self.conv5 = nn.Sequential(self.encoder[24], self.relu, self.encoder[26], self.relu, self.encoder[28],
self.relu)
self.center = DecoderBlock(512, num_filters * 8 * 2, num_filters * 8)
self.dec5 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8)
self.dec4 = DecoderBlock(512 + num_filters * 8, num_filters * 8 * 2, num_filters * 8)
self.dec3 = DecoderBlock(256 + num_filters * 8, num_filters * 4 * 2, num_filters * 2)
self.dec2 = DecoderBlock(128 + num_filters * 2, num_filters * 2 * 2, num_filters)
self.dec1 = ConvRelu(64 + num_filters, num_filters)
self.final = nn.Conv2d(num_filters, num_classes, kernel_size = 1)
def forward(self, x):
conv1 = self.conv1(x)
conv2 = self.conv2(self.pool(conv1))
conv3 = self.conv3(self.pool(conv2))
conv4 = self.conv4(self.pool(conv3))
conv5 = self.conv5(self.pool(conv4))
center = self.center(self.pool(conv5))
dec5 = self.dec5(torch.cat([center, conv5], 1))
dec4 = self.dec4(torch.cat([dec5, conv4], 1))
dec3 = self.dec3(torch.cat([dec4, conv3], 1))
dec2 = self.dec2(torch.cat([dec3, conv2], 1))
dec1 = self.dec1(torch.cat([dec2, conv1], 1))
if self.n_classes > 1:
x_out = F.log_softmax(self.final(dec1), dim = 1)
else:
x_out = self.final(dec1)
return x_out
train.py
...@@ -4,112 +4,21 @@ import os ...@@ -4,112 +4,21 @@ import os
import sys import sys
import torch import torch
import torch.nn as nn
from torch import optim
from tqdm import tqdm
from utils.eval import eval_net import unet
import mrnet
from mrnet import MultiUnet
from unet import UNet from unet import UNet
from torch.utils.tensorboard import SummaryWriter
from utils.dataset import BasicDataset
from torch.utils.data import DataLoader, random_split
dir_img = 'data/train_imgs/' dir_img = 'data/train_imgs/'
dir_mask = 'data/train_masks/' dir_mask = 'data/train_masks/'
dir_checkpoint = 'checkpoints/' dir_checkpoint = 'checkpoints/'
def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, val_percent = 0.1, save_cp = True, img_scale = 0.5):
dataset = BasicDataset(dir_img, dir_mask, img_scale)
n_val = int(len(dataset) * val_percent)
n_train = len(dataset) - n_val
train, val = random_split(dataset, [n_train, n_val])
train_loader = DataLoader(train, batch_size = batch_size, shuffle = True, num_workers = 8, pin_memory = True)
val_loader = DataLoader(val, batch_size = batch_size, shuffle = False, num_workers = 8, pin_memory = True)
writer = SummaryWriter(comment = f'LR_{lr}_BS_{batch_size}_SCALE_{img_scale}')
global_step = 0
logging.info(f'''Starting training:
Epochs: {epochs}
Batch size: {batch_size}
Learning rate: {lr}
Training size: {n_train}
Validation size: {n_val}
Checkpoints: {save_cp}
Device: {device.type}
Images scaling: {img_scale}
''')
optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay = 1e-8)
#optimizer = optim.RMSprop(net.parameters(), lr = lr, weight_decay = 1e-8)
if net.n_classes > 1:
criterion = nn.CrossEntropyLoss()
else:
criterion = nn.BCEWithLogitsLoss()
for epoch in range(epochs):
net.train()
epoch_loss = 0
with tqdm(total = n_train, desc = f'Epoch {epoch + 1}/{epochs}', unit = 'img') as pbar:
for batch in train_loader:
imgs = batch['image']
true_masks = batch['mask']
assert imgs.shape[1] == net.n_channels, \
f'Network has been defined with {net.n_channels} input channels, ' \
f'but loaded images have {imgs.shape[1]} channels. Please check that ' \
'the images are loaded correctly.'
imgs = imgs.to(device = device, dtype = torch.float32)
mask_type = torch.float32 if net.n_classes == 1 else torch.long
true_masks = true_masks.to(device = device, dtype = mask_type)
masks_pred = net(imgs)
loss = criterion(masks_pred, true_masks)
epoch_loss += loss.item()
writer.add_scalar('Loss/train', loss.item(), global_step)
pbar.set_postfix(**{'loss (batch)': loss.item()})
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.update(imgs.shape[0])
global_step += 1
if global_step % (len(dataset) // (10 * batch_size)) == 0:
val_score = eval_net(net, val_loader, device, n_val)
if net.n_classes > 1:
logging.info('Validation cross entropy: {}'.format(val_score))
writer.add_scalar('Loss/test', val_score, global_step)
else:
logging.info('Validation Dice Coeff: {}'.format(val_score))
writer.add_scalar('Dice/test', val_score, global_step)
writer.add_images('images', imgs, global_step)
if net.n_classes == 1:
writer.add_images('masks/true', true_masks, global_step)
writer.add_images('masks/pred', torch.sigmoid(masks_pred) > 0.5, global_step)
if save_cp:
try:
os.mkdir(dir_checkpoint)
logging.info('Created checkpoint directory')
except OSError:
pass
torch.save(net.state_dict(), dir_checkpoint + f'CP_epoch{epoch + 1}.pth')
logging.info(f'Checkpoint {epoch + 1} saved !')
writer.close()
def get_args(): def get_args():
parser = argparse.ArgumentParser(description = 'Train the UNet on images and target masks', parser = argparse.ArgumentParser(description = 'Train the UNet on images and target masks',
formatter_class = argparse.ArgumentDefaultsHelpFormatter) formatter_class = argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-e', '--epochs', metavar = 'E', type = int, default = 5, help = 'Number of epochs', parser.add_argument('-e', '--epochs', metavar = 'E', type = int, default = 1, help = 'Number of epochs',
dest = 'epochs') dest = 'epochs')
parser.add_argument('-b', '--batch-size', metavar = 'B', type = int, nargs = '?', default = 1, help = 'Batch size', parser.add_argument('-b', '--batch-size', metavar = 'B', type = int, nargs = '?', default = 1, help = 'Batch size',
dest = 'batchsize') dest = 'batchsize')
...@@ -117,7 +26,7 @@ def get_args(): ...@@ -117,7 +26,7 @@ def get_args():
help = 'Learning rate', dest = 'lr') help = 'Learning rate', dest = 'lr')
parser.add_argument('-f', '--load', dest = 'load', type = str, default = False, parser.add_argument('-f', '--load', dest = 'load', type = str, default = False,
help = 'Load model from a .pth file') help = 'Load model from a .pth file')
parser.add_argument('-s', '--scale', dest = 'scale', type = float, default = 0.5, parser.add_argument('-s', '--scale', dest = 'scale', type = float, default = 2.56,
help = 'Downscaling factor of the images') help = 'Downscaling factor of the images')
parser.add_argument('-v', '--validation', dest = 'val', type = float, default = 10.0, parser.add_argument('-v', '--validation', dest = 'val', type = float, default = 10.0,
help = 'Percent of the data that is used as validation (0-100)') help = 'Percent of the data that is used as validation (0-100)')
...@@ -137,7 +46,10 @@ if __name__ == '__main__': ...@@ -137,7 +46,10 @@ if __name__ == '__main__':
# - For 1 class and background, use n_classes=1 # - For 1 class and background, use n_classes=1
# - For 2 classes, use n_classes=1 # - For 2 classes, use n_classes=1
# - For N > 2 classes, use n_classes=N # - For N > 2 classes, use n_classes=N
net = UNet(n_channels = 1, n_classes = 1)
#net = UNet(n_channels = 1, n_classes = 1)
net = MultiUnet(n_channels = 1, n_classes = 1)
logging.info(f'Network:\n' logging.info(f'Network:\n'
f'\t{net.n_channels} input channels\n' f'\t{net.n_channels} input channels\n'
f'\t{net.n_classes} output channels (classes)\n' f'\t{net.n_classes} output channels (classes)\n'
...@@ -152,7 +64,7 @@ if __name__ == '__main__': ...@@ -152,7 +64,7 @@ if __name__ == '__main__':
# cudnn.benchmark = True # cudnn.benchmark = True
try: try:
train_net(net = net, device=device, epochs = args.epochs, batch_size = args.batchsize, lr = args.lr, mrnet.train_net(net = net, device=device, epochs = args.epochs, batch_size = args.batchsize, lr = args.lr,
img_scale = args.scale, val_percent = args.val / 100) img_scale = args.scale, val_percent = args.val / 100)
except KeyboardInterrupt: except KeyboardInterrupt:
torch.save(net.state_dict(), 'INTERRUPTED.pth') torch.save(net.state_dict(), 'INTERRUPTED.pth')
......
unet/__init__.py
from .unet_model import UNet from .unet_model import UNet
from .train import train_net
\ No newline at end of file
unet/train.py
#!/usr/bin/env python #!/usr/bin/env python
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
\ No newline at end of file import argparse
import logging
import os
import sys
import torch
import torch.nn as nn
from torch import optim
from tqdm import tqdm
from utils.eval import eval_net
from torch.utils.tensorboard import SummaryWriter
from utils.dataset import BasicDataset
from torch.utils.data import DataLoader, random_split
dir_img = 'data/train_imgs/'
dir_mask = 'data/train_masks/'
dir_checkpoint = 'checkpoints/'
def train_net(net, device, epochs = 5, batch_size = 1, lr = 0.1, val_percent = 0.1, save_cp = True, img_scale = 0.5):
dataset = BasicDataset(dir_img, dir_mask, img_scale)
n_val = int(len(dataset) * val_percent)
n_train = len(dataset) - n_val
train, val = random_split(dataset, [n_train, n_val])
train_loader = DataLoader(train, batch_size = batch_size, shuffle = True, num_workers = 8, pin_memory = True)
val_loader = DataLoader(val, batch_size = batch_size, shuffle = False, num_workers = 8, pin_memory = True)
writer = SummaryWriter(comment = f'LR_{lr}_BS_{batch_size}_SCALE_{img_scale}')
global_step = 0
logging.info(f'''Starting training:
Epochs: {epochs}
Batch size: {batch_size}
Learning rate: {lr}
Training size: {n_train}
Validation size: {n_val}
Checkpoints: {save_cp}
Device: {device.type}
Images scaling: {img_scale}
''')
# optimizer = optim.Adam(net.parameters(), lr=lr, weight_decay = 1e-8)
optimizer = optim.RMSprop(net.parameters(), lr = lr, weight_decay = 1e-8)
# criterion = nn.BCEWithLogitsLoss()
if net.n_classes > 1:
criterion = nn.CrossEntropyLoss()
else:
criterion = nn.BCEWithLogitsLoss()
for epoch in range(epochs):
net.train()
epoch_loss = 0
with tqdm(total = n_train, desc = f'Epoch {epoch + 1}/{epochs}', unit = 'img') as pbar:
for batch in train_loader:
imgs = batch['image']
true_masks = batch['mask']
# assert imgs.shape[1] == net.n_channels, \
# f'Network has been defined with {net.n_channels} input channels, ' \
# f'but loaded images have {imgs.shape[1]} channels. Please check that ' \
# 'the images are loaded correctly.'
imgs = imgs.to(device = device, dtype = torch.float32)
mask_type = torch.float32 if net.n_classes == 1 else torch.long
true_masks = true_masks.to(device = device, dtype = mask_type)
masks_pred = net(imgs)
loss = criterion(masks_pred, true_masks)
epoch_loss += loss.item()
writer.add_scalar('Loss/train', loss.item(), global_step)
pbar.set_postfix(**{'loss (batch)': loss.item()})
optimizer.zero_grad()
loss.backward()
optimizer.step()
pbar.update(imgs.shape[0])
global_step += 1 # if global_step % (len(dataset) // (10 * batch_size)) == 0:
val_score = eval_net(net, val_loader, device, n_val)
if net.n_classes > 1:
logging.info('Validation cross entropy: {}'.format(val_score))
writer.add_scalar('Loss/test', val_score, global_step)
else:
logging.info('Validation Dice Coeff: {}'.format(val_score))
writer.add_scalar('Dice/test', val_score, global_step)
writer.add_images('images', imgs, global_step)
if net.n_classes == 1:
writer.add_images('masks/true', true_masks, global_step)
writer.add_images('masks/pred', torch.sigmoid(masks_pred) > 0.5, global_step)
if save_cp and epoch % 4 == 0:
try:
os.mkdir(dir_checkpoint)
logging.info('Created checkpoint directory')
except OSError:
pass
torch.save(net.state_dict(), dir_checkpoint + f'CP_epoch{epoch + 1}.pth')
logging.info(f'Checkpoint {epoch + 1} saved !')
torch.save(net.state_dict(), 'MODEL.pth')
writer.close()
\ No newline at end of file
utils/dataset.py
...@@ -13,7 +13,7 @@ class BasicDataset(Dataset): ...@@ -13,7 +13,7 @@ class BasicDataset(Dataset):
self.imgs_dir = imgs_dir self.imgs_dir = imgs_dir
self.masks_dir = masks_dir self.masks_dir = masks_dir
self.scale = scale self.scale = scale
assert 0 < scale <= 1, 'Scale must be between 0 and 1' #assert 0 < scale <= 1, 'Scale must be between 0 and 1'
self.ids = [splitext(file)[0] for file in listdir(imgs_dir) self.ids = [splitext(file)[0] for file in listdir(imgs_dir)
if not file.startswith('.')] if not file.startswith('.')]
...@@ -25,7 +25,7 @@ class BasicDataset(Dataset): ...@@ -25,7 +25,7 @@ class BasicDataset(Dataset):
@classmethod @classmethod
def preprocess(cls, pil_img, scale): def preprocess(cls, pil_img, scale):
w, h = pil_img.size w, h = pil_img.size
newW, newH = int(scale * w), int(scale * h) newW, newH = 256,256 #int(scale * w), int(scale * h)
assert newW > 0 and newH > 0, 'Scale is too small' assert newW > 0 and newH > 0, 'Scale is too small'
pil_img = pil_img.resize((newW, newH)) pil_img = pil_img.resize((newW, newH))
......
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