Code for counting dilation error instances

count_error_dilation.py

@@ -0,0 +1,54 @@
+'''
+Counts how many images would differ from textbook_dilation.
+'''
+
+import os, re, sys, enum
+from dilate import check_no_error_dilate
+from common import Study
+
+def error_count(study, strokes_loc, ground_truth_loc):
+    '''
+    Dilates all files.
+
+    study: The Study used, which determines naming scheme.
+    strokes_loc: Folder location for where the stroke files are.
+    ground_truth_loc: Folder location for where the ground truth files are.
+    output_loc: Folder location to save the dilated files in.
+    '''
+    num_errors = 0
+    i = 0
+    files = os.listdir(strokes_loc)
+    for file in files:
+        # Extract data from file name
+        if study == Study.Rau:
+            file_re = re.search('(\d+)-(\d+)', file)
+        else:
+            file_re = re.search('(\d+)-(\d+)-(\d+)', file)
+        if file_re == None: continue
+
+        participant_id = file_re.group(1)
+        file_id = file_re.group(2)
+        if study == Study.Yuanxia: time_pressure = file_re.group(3)
+
+        i += 1
+        print('\rProcessing file', i, 'of', len(files), end='')
+        sys.stdout.flush()
+
+        background_label = 149
+        foreground_label = 29
+        input_image = strokes_loc + '/' + file
+        ground_truth_image = ground_truth_loc + '/' + file_id + '-GT.png'
+
+        num_errors += int(check_no_error_dilate(input_image, ground_truth_image, foreground_label, background_label))
+
+    print('\nFound', num_errors, 'files with errors in', i, 'files total')
+
+print('Processing Rau\'s strokes')
+error_count(Study.Rau, './rau/strokes', './rau/ground_truth')
+
+print('\nProcessing Rau\'s points')
+error_count(Study.Rau, './rau/points', './rau/ground_truth')
+
+print('\nProcessing Yuanxia\'s points')
+error_count(Study.Yuanxia, './yuanxia/points', './yuanxia/ground_truth')
+print()

dilate.py

@@ -15,8 +15,6 @@ def no_error_dilate(input_image, ground_truth_image, output_image, dilation_radi
     'place' (foreground pixels won't be dilated if they're in the background
     and vice versa).
     
-    All non-zero labels will be dilated.
-    
     input_image: The label image to dilate.
     ground_truth_image: The binary ground truth image.
     output_image: Where to save the result.
@@ -65,7 +63,31 @@ def no_error_dilate(input_image, ground_truth_image, output_image, dilation_radi
         it.iternext()
 
     Image.fromarray(output).save(output_image)
+
+def check_no_error_dilate(input_image, ground_truth_image, foreground_label = 255, background_label = 128):
+    '''
+    Returns true if no_error_dilate would act differently than textbook_dilation.
+    
+    input_image: The label image to dilate.
+    ground_truth_image: The binary ground truth image.
+    '''
+    image = scipy.ndimage.imread(input_image)
+    ground_truth = np.greater(scipy.ndimage.imread(ground_truth_image), 0)
 
+    # Iterate over all pixels for dilation
+    it = np.nditer(image, flags=['multi_index'])
+    while not it.finished:
+        pixel = int(it[0])
+
+        if pixel == foreground_label and not ground_truth[it.multi_index]:
+            return True
+        elif pixel == background_label and ground_truth[it.multi_index]:
+            return True
+
+        it.iternext()
+
+    return False
+
 def textbook_dilation(input_image, output_image, dilation_radius):
     '''
     Dilates all pixels a given radius (which excludes the pixel in question, so