two step cli-based interact

UnetBasedMethod.py

 #!/usr/bin/env python
 # -*- coding:utf-8 -*-
-from multiprocessing import Pool
+import numpy as np
+import pandas as pd
+import torch
+from PIL import Image
+import cv2 as cv
+
+from unet import UNet
 from tqdm import tqdm
 import argparse
+import logging
 import os
-from utils.predict import predict
-from resCalc import draw_bar
+import re
+
+from utils.predict import predict_img
+from resCalc import draw_bar,save_img,calcRes,get_subarea_info
 
 
 def get_args():
@@ -31,7 +40,36 @@ if __name__ == '__main__':
             'dc .tif'), os.listdir('data/imgs/' + y))]
 
     if args.step == 1:
-        predict(path, args.load, args.scale, args.mask_threshold)
+        net = UNet(n_channels = 1, n_classes = 1)
+        logging.info("Loading model {}".format(args.module))
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        logging.info(f'Using device {device}')
+        net.to(device = device)
+        net.load_state_dict(torch.load(args.module, map_location = device))
+        logging.info("Model loaded !")
+
+        for i, fn in enumerate(tqdm(path)):
+            logging.info("\nPredicting image {} ...".format(fn[0] + '/' + fn[1]))
+            img = Image.open('data/imgs/' + fn[0] + '/' + fn[1])
+            mask = predict_img(net = net, full_img = img, scale_factor = args.scale, out_threshold = args.mask_threshold,
+                               device = device)
+
+            result = (mask * 255).astype(np.uint8)
+            save_img({'ori': img, 'mask': result}, fn[0], fn[1])
+            try:
+                os.makedirs('data/masks/'+fn[0])
+            except:
+                logging.info("path already exist")
+            cv.imwrite('data/masks/' + fn[0] + '/' + fn[1],result)
+            #calcRes(cv.cvtColor(np.asarray(img), cv.COLOR_RGB2BGR), result, fn[0], fn[1][:-4])
+    elif args.step == 2:
+        df = pd.DataFrame(columns = ('ug','iter', 'id', 'size', 'area_mean', 'back_mean'))
+        for i,fn in enumerate(tqdm(path)):
+            img = cv.imread('data/imgs/' + fn[0] + '/' + fn[1],0)
+            mask = cv.imread('data/masks/'+ fn[0] + '/' + fn[1],0)
+            calcRes(img, mask, fn[0], fn[1][:-4])
+
+
 
-    for exName in filter(lambda x: x != '.DS_Store', os.listdir('data/output')):
-        draw_bar(exName, os.listdir('data/output/' + exName + '/csv'))
+    #for exName in filter(lambda x: x != '.DS_Store', os.listdir('data/output')):
+    #    draw_bar(exName, os.listdir('data/output/' + exName + '/csv'))

resCalc.py

@@ -9,6 +9,7 @@ import logging
 import os
 import re
 
+
 def save_img(img_list, dir, name):
     num = len(img_list)
     plt.figure(figsize = (100, 20))
@@ -28,7 +29,7 @@ def save_img(img_list, dir, name):
 
 
 def calcRes(img, mask, dir = 'default_output', name = 'output'):
-    dic = get_subarea_infor(img, mask)
+    dic = get_subarea_info(img, mask)
     df = pd.DataFrame(dic)
     try:
         os.makedirs('data/output/' + dir + '/csv')
@@ -38,6 +39,17 @@ def calcRes(img, mask, dir = 'default_output', name = 'output'):
         df.to_csv('data/output/' + dir + '/csv/' + name + '.csv', index = False)
 
 
+def merge_df(dir):
+    df = pd.DataFrame(columns = ('ug', 'iter', 'id', 'size', 'area_mean', 'back_mean'))
+    for name in os.listdir(dir):
+        match_group = re.match('.*\s([dD]2[oO]|[lL][bB]|.*ug).*\s(.+)\.tif', name)
+        tmp = pd.read_csv('data/output/' + dir + '/csv/' + name)
+        tmp['ug'] = str.lower(match_group.group(1))[:-2] if str.lower(match_group.group(1)).endswith(
+            'ug') else str.lower(match_group.group(1))
+        tmp['iter'] = str.lower(match_group.group(2))
+        df = df.append(tmp, ignore_index = True, sort = True)
+
+
 def draw_bar(exName, names):
     df = pd.DataFrame(columns = ('class', 'perc', 'Label', 'Area', 'Mean', 'Std', 'BackMean', 'BackStd'))
     for name in names:
@@ -58,35 +70,58 @@ def draw_bar(exName, names):
     plt.show()
 
 
-def get_subarea_infor(img, mask):
-    area_num, labels, stats, centroids = cv.connectedComponentsWithStats(mask)
-    info = []
+# 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
 
+def get_subarea_info(img, mask):
+    area_num, labels, stats, centroids = cv.connectedComponentsWithStats(mask, connectivity = 8)
+    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)
+        area_value = img[group]
+        area_size = len(area_value)
+        area_mean = np.mean(area_value)
 
+        # Background 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
+        area_points = np.array([mask[x, y] if (x, y) in pos else 0 for x in range(200) for y in range(200)])
+        kernel = np.ones((15, 15), np.uint8)
+        bg_area_mask = cv.erode(area_points, kernel)
+        surround_bg_mask = cv.bitwise_xor(bg_area_mask, 255 - area_points)
+        real_bg_mask = cv.bitwise_and(surround_bg_mask, 255 - mask)
+
+        back_value = img[np.where(real_bg_mask != 0)]
+        back_mean = np.mean(back_value)
+
+        info.append({'id': i, 'size': area_size, 'area_mean': area_mean, 'back_mean': back_mean})
+    return info
\ No newline at end of file

server.py

+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+from flask import Flask, render_template, url_for
+
+app = Flask("Im")
+
+
+@app.route('/')
+def layout():
+    return render_template('layout.html')
+
+
+@app.route('/compute/')
+def filetree():
+    return 'filetree'
+
+
+if __name__ == '__main__':
+    app.run()

templates/layout.html

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <link rel="stylesheet" href="https://unpkg.com/purecss@1.0.0/build/pure-min.css"
+          integrity="sha384-CCTZv2q9I9m3UOxRLaJneXrrqKwUNOzZ6NGEUMwHtShDJ+nCoiXJCAgi05KfkLGY" crossorigin="anonymous">
+    <script src="https://apps.bdimg.com/libs/jquery/2.1.4/jquery.min.js"></script>
+    <script src="../static/echarts.js"></script>
+    <title>main</title>
+</head>
+<body>
+    <div class="pure-g">
+        <div class="pure-u-1-3">
+            {% include 'tree.html' %}
+        </div>
+        <div class="pure-u-1-3">
+        </div>
+        <div class="pure-u-1-3">
+        </div>
+    </div>
+    <script type="text/javascript">
+
+    </script>
+</body>
+</html>
\ No newline at end of file

templates/tree.html

+<ul id="browser" class="filetree treeview-famfamfam">
+    <li><span class="folder">Folder 1</span>
+        <ul>
+            <li><span class="folder">Item 1.1</span>
+                <ul>
+                    <li><span class="file">Item 1.1.1</span></li>
+                </ul>
+            </li>
+            <li><span class="folder">Folder 2</span>
+                <ul>
+                    <li><span class="folder">Subfolder 2.1</span>
+                        <ul id="folder21">
+                            <li><span class="file">File 2.1.1</span></li>
+                            <li><span class="file">File 2.1.2</span></li>
+                        </ul>
+                    </li>
+                    <li><span class="folder">Subfolder 2.2</span>
+                        <ul>
+                            <li><span class="file">File 2.2.1</span></li>
+                            <li><span class="file">File 2.2.2</span></li>
+                        </ul>
+                    </li>
+                </ul>
+            </li>
+            <li class="closed"><span class="folder">Folder 3 (closed at start)</span>
+                <ul>
+                    <li><span class="file">File 3.1</span></li>
+                </ul>
+            </li>
+            <li><span class="file">File 4</span></li>
+        </ul>
+    </li>
+</ul>
\ No newline at end of file

utils/predict.py

-import logging
-import numpy as np
 import torch
 import torch.nn.functional as F
-from PIL import Image
-import cv2 as cv
-from tqdm import tqdm
 from torchvision import transforms
 
-from unet import UNet
 from utils.dataset import BasicDataset
-from cvBasedMethod.util import save_img,calcRes
-
 
 def predict_img(net, full_img, device, scale_factor = 1, out_threshold = 0.5):
     net.eval()
@@ -30,27 +22,4 @@ def predict_img(net, full_img, device, scale_factor = 1, out_threshold = 0.5):
         probs = tf(probs.cpu())
         full_mask = probs.squeeze().cpu().numpy()
 
-    return full_mask > out_threshold
-
+    return full_mask > out_threshold
\ No newline at end of file
-
-def predict(file_names, model, scale, mask_threshold):
-    net = UNet(n_channels = 1, n_classes = 1)
-
-    logging.info("Loading model {}".format(model))
-
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    logging.info(f'Using device {device}')
-    net.to(device = device)
-    net.load_state_dict(torch.load(model, map_location = device))
-
-    logging.info("Model loaded !")
-
-    for i, fn in enumerate(tqdm(file_names)):
-        logging.info("\nPredicting image {} ...".format(fn[0]+'/'+fn[1]))
-        img = Image.open('data/imgs/' + fn[0] + '/' + fn[1])
-        mask = predict_img(net = net, full_img = img, scale_factor = scale, out_threshold = mask_threshold,
-                           device = device)
-
-        result = (mask * 255).astype(np.uint8)  # result.save(out_files[i])  # logging.info("Mask saved to {}".format(out_files[i]))
-        save_img({'ori':img,'mask':result},fn[0],fn[1])
-        calcRes(cv.cvtColor(np.asarray(img), cv.COLOR_RGB2BGR),result,fn[0],fn[1][:-4])
\ No newline at end of file