ready for interactive method

CVBasedMethod.py

@@ -6,7 +6,7 @@ import argparse
 from cvBasedMethod.util import *
 from cvBasedMethod.kmeans import kmeans, kmeans_back
 from cvBasedMethod.threshold import threshold
-from predict import predict
+from utils.predict import predict
 
 
 def method_kmeans(imglist, core = 5):
@@ -32,7 +32,7 @@ def method_newThreshold(imglist, process = 8):
 def get_args():
     parser = argparse.ArgumentParser(description = 'Identify targets from background by KMeans or Threshold',
                                      formatter_class = argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument('-m', '--method', metavar = 'M', type = int, default = 3,
+    parser.add_argument('-m', '--method', metavar = 'M', type = int, default = 0,
                         help = '0 for KMeans; 1 for Threshold; 2 for newThreshold; 3 for Unet; 4 for further process',
                         dest = 'method')
     parser.add_argument('-c', '--core', metavar = 'C', type = int, default = 5, help = 'Num of cluster', dest = 'core')
@@ -60,7 +60,3 @@ if __name__ == '__main__':
         method_threshold(path, args.process)
     elif args.method == 2:
         method_newThreshold(path, args.process)
\ No newline at end of file
-    elif args.method == 3:
-        predict(path, args.load, args.scale, args.mask_threshold)
-        for exName in filter(lambda x: x != '.DS_Store', os.listdir('data/output')):
-            draw_bar(exName, os.listdir('data/output/' + exName + '/csv'))

UnetBasedMethod.py

 #!/usr/bin/env python
 # -*- coding:utf-8 -*-
+from multiprocessing import Pool
+from tqdm import tqdm
+import argparse
+import os
+from utils.predict import predict
+from resCalc import draw_bar
+
+
+def get_args():
+    parser = argparse.ArgumentParser(description = 'A simple toolkit designed by Ulden',
+                                     formatter_class = argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--step', '-s', type = int, default = 1,help = "step 1: recognize; step 2: barcharts")
+
+    # Unet para
+    parser.add_argument('--module', '-m', default = 'data/module/MODEL.pth', metavar = 'FILE',
+                        help = "Specify the file in which the model is stored")
+    parser.add_argument('--mask-threshold', '-t', type = float,
+                        help = "Minimum probability value to consider a mask pixel white", default = 0.5)
+    parser.add_argument('--scale', '-S', type = float, help = "Scale factor for the input images", default = 0.5)
+
+    return parser.parse_args()
+
+
+if __name__ == '__main__':
+    args = get_args()
+
+    path = [(y, x) for y in filter(lambda x: x != '.DS_Store', os.listdir('data/imgs')) for x in filter(
+        lambda x: x.endswith('.tif') and not x.endswith('dc.tif') and not x.endswith('DC.tif') and not x.endswith(
+            'dc .tif'), os.listdir('data/imgs/' + y))]
+
+    if args.step == 1:
+        predict(path, args.load, args.scale, args.mask_threshold)
+
+    for exName in filter(lambda x: x != '.DS_Store', os.listdir('data/output')):
+        draw_bar(exName, os.listdir('data/output/' + exName + '/csv'))

cvBasedMethod/kmeans.py

@@ -2,7 +2,8 @@
 # -*- coding:utf-8 -*-
 from sklearn.cluster import KMeans
 from cvBasedMethod.util import *
-
+import matplotlib.pyplot as plt
+from resCalc import save_img,calcRes
 
 def kmeans(pair, cluster_num = 5,filter='butter'):
     img_list = pre(pair,'clahe',filter)

cvBasedMethod/threshold.py

 #!/usr/bin/env python
 # -*- coding:utf-8 -*-
 from cvBasedMethod.util import *
+from resCalc import save_img, calcRes
 
 
 def threshold(pair,filter='butter'):

cvBasedMethod/util.py

@@ -2,12 +2,6 @@
 # -*- coding:utf-8 -*-
 import numpy as np
 import cv2 as cv
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import logging
-import os
-import re
 from cvBasedMethod.filters import fft_mask,butterworth
 
 def remove_scratch(img):
@@ -62,84 +56,4 @@ def pre(pair,even='clahe',filter='butter'):
 
     return {'ori': img, 'even': even, 'filtered': filtered, 'denoise': denoise, 'blr': blr}
 
-def save_img(img_list, dir, name):
-    num = len(img_list)
-    plt.figure(figsize = (100, 20))
-    plt.suptitle(name)
 
-    for i, title in zip(range(num), img_list):
-        img = img_list[title]
-        plt.subplot(1, num, i + 1)
-        plt.title(title)
-        plt.imshow(img, 'gray')
-    try:
-        os.makedirs('data/output/' + dir + '/graph')
-    except:
-        logging.info('Existing dir: data/output/' + dir )
-    plt.savefig('data/output/' + dir + '/graph/' + name[:-4] + '.png')
-    plt.close()
-
-
-def calcRes(img, mask, dir = 'default_output', name = 'output'):
-    dic = get_subarea_infor(img, mask)
-    df = pd.DataFrame(dic)
-    try:
-        os.makedirs('data/output/' + dir + '/csv')
-    except:
-        logging.info('Existing dir: out/' + dir + '/csv')
-    if len(df)!=0:
-        df.to_csv('data/output/' + dir + '/csv/' + name + '.csv',index = False)
-
-
-def draw_bar(exName,names):
-    df = pd.DataFrame(columns = ('class', 'perc', 'Label', 'Area', 'Mean', 'Std', 'BackMean', 'BackStd'))
-    for name in names:
-        tmp = pd.read_csv('data/output/' + exName + '/csv/' + name)
-        match_group = re.match('.*\s([dD]2[oO]|[lL][bB]|.*ug).*\s(.+)\.csv', name)
-        tmp['perc'] = str.lower(match_group.group(1))[:-2] if str.lower(match_group.group(1)).endswith('ug') else str.lower(
-            match_group.group(1))
-        tmp['perc'].replace({'d2o':'0'},inplace = True)
-        tmp['class'] = str.lower(match_group.group(2))
-        df = df.append(tmp, ignore_index = True, sort = True)
-
-    df = df[df['Area']>19]
-    df['Pure'] = df['Mean'] - df['BackMean']
-
-    sns.set_style("darkgrid")
-    #sns.catplot(x = 'perc',y = 'Mean',hue = 'class',kind='bar',data = df)
-    sns.pairplot(df, vars = ['Area', 'Mean', 'perc','class'])
-    plt.show()
-
-
-def get_subarea_infor(img, mask):
-    area_num, labels, stats, centroids = cv.connectedComponentsWithStats(mask)
-    info = []
-
-    for i in filter(lambda x: x != 0, range(area_num)):
-        group = np.where(labels == i)
-        img_value = img[group]
-        area_tmp = len(group[0])
-        mean_tmp = np.mean(img_value)
-        std_tmp = np.std(img_value)
-
-        pos = [(group[0][i], group[1][i]) for i in range(len(group[0]))]
-        res = np.zeros([200, 200], np.uint8)
-        for x in range(200):
-            for y in range(200):
-                if (x, y) in pos:
-                    res[x, y] = mask[x, y]
-                else:
-                    res[x, y] = 0
-
-        kernel = np.ones((17, 17), np.uint8)
-        mask_background = cv.erode(255 - res, kernel)
-        minimask = cv.bitwise_xor(mask_background, 255 - res)
-        realminimask = cv.bitwise_and(minimask, 255 - mask)
-
-        img_background = img[np.where(realminimask != 0)]
-        mean_value = np.mean(img_background)
-        std_value = np.std(img_background)
-
-        info.append({'Label': i, 'Area': area_tmp, 'Mean': mean_tmp, 'Std': std_tmp, 'BackMean': mean_value,
-            'BackStd': std_value})
-    return info
\ No newline at end of file

requirement.txt

@@ -4,3 +4,4 @@ tqdm
 matplotlib
 numpy
 opencv-python
+seaborn
\ No newline at end of file

resCalc.py

 #!/usr/bin/env python
 # -*- coding:utf-8 -*-
+import pandas as pd
+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+import seaborn as sns
+import logging
+import os
+import re
+
+def save_img(img_list, dir, name):
+    num = len(img_list)
+    plt.figure(figsize = (100, 20))
+    plt.suptitle(name)
+
+    for i, title in zip(range(num), img_list):
+        img = img_list[title]
+        plt.subplot(1, num, i + 1)
+        plt.title(title)
+        plt.imshow(img, 'gray')
+    try:
+        os.makedirs('data/output/' + dir + '/graph')
+    except:
+        logging.info('Existing dir: data/output/' + dir)
+    plt.savefig('data/output/' + dir + '/graph/' + name[:-4] + '.png')
+    plt.close()
+
+
+def calcRes(img, mask, dir = 'default_output', name = 'output'):
+    dic = get_subarea_infor(img, mask)
+    df = pd.DataFrame(dic)
+    try:
+        os.makedirs('data/output/' + dir + '/csv')
+    except:
+        logging.info('Existing dir: out/' + dir + '/csv')
+    if len(df) != 0:
+        df.to_csv('data/output/' + dir + '/csv/' + name + '.csv', index = False)
+
+
+def draw_bar(exName, names):
+    df = pd.DataFrame(columns = ('class', 'perc', 'Label', 'Area', 'Mean', 'Std', 'BackMean', 'BackStd'))
+    for name in names:
+        tmp = pd.read_csv('data/output/' + exName + '/csv/' + name)
+        match_group = re.match('.*\s([dD]2[oO]|[lL][bB]|.*ug).*\s(.+)\.csv', name)
+        tmp['perc'] = str.lower(match_group.group(1))[:-2] if str.lower(match_group.group(1)).endswith(
+            'ug') else str.lower(match_group.group(1))
+        tmp['perc'].replace({'d2o': '0'}, inplace = True)
+        tmp['class'] = str.lower(match_group.group(2))
+        df = df.append(tmp, ignore_index = True, sort = True)
+
+    df = df[df['Area'] > 19]
+    df['Pure'] = df['Mean'] - df['BackMean']
+
+    sns.set_style("darkgrid")
+    # sns.catplot(x = 'perc',y = 'Mean',hue = 'class',kind='bar',data = df)
+    sns.pairplot(df, vars = ['Area', 'Mean', 'perc', 'class'])
+    plt.show()
+
+
+def get_subarea_infor(img, mask):
+    area_num, labels, stats, centroids = cv.connectedComponentsWithStats(mask)
+    info = []
+
+    for i in filter(lambda x: x != 0, range(area_num)):
+        group = np.where(labels == i)
+        img_value = img[group]
+        area_tmp = len(group[0])
+        mean_tmp = np.mean(img_value)
+        std_tmp = np.std(img_value)
+
+        pos = [(group[0][i], group[1][i]) for i in range(len(group[0]))]
+        res = np.zeros([200, 200], np.uint8)
+        for x in range(200):
+            for y in range(200):
+                if (x, y) in pos:
+                    res[x, y] = mask[x, y]
+                else:
+                    res[x, y] = 0
+
+        kernel = np.ones((17, 17), np.uint8)
+        mask_background = cv.erode(255 - res, kernel)
+        minimask = cv.bitwise_xor(mask_background, 255 - res)
+        realminimask = cv.bitwise_and(minimask, 255 - mask)
+
+        img_background = img[np.where(realminimask != 0)]
+        mean_value = np.mean(img_background)
+        std_value = np.std(img_background)
+
+        info.append({'Label': i, 'Area': area_tmp, 'Mean': mean_tmp, 'Std': std_tmp, 'BackMean': mean_value,
+                     'BackStd': std_value})
+    return info
\ No newline at end of file

train.py

@@ -3,13 +3,12 @@ import logging
 import os
 import sys
 
-import numpy as np
 import torch
 import torch.nn as nn
 from torch import optim
 from tqdm import tqdm
 
-from eval import eval_net
+from utils.eval import eval_net
 from unet import UNet
 
 from torch.utils.tensorboard import SummaryWriter

utils/eval.py

@@ -2,7 +2,7 @@ import torch
 import torch.nn.functional as F
 from tqdm import tqdm
 
-from dice_loss import dice_coeff
+from utils.dice_loss import dice_coeff
 
 
 def eval_net(net, loader, device, n_val):