diff --git a/main.py b/main.py
index 85cde4f..cd28954 100644
--- a/main.py
+++ b/main.py
@@ -1,18 +1,20 @@
 import glob
 import os
 
-from src.processing.loader import load_image, save_image
-from src.processing.linefinder import find_lines, preparation
+from src.processing.loader import load_image
+from src.processing.linefinder import find_lines
 from matplotlib import pyplot as plt
 
 from src.processing.receiptcutter import cut_receipt
 
 for root, dirs, files in os.walk("data"):
     for file in files:
-        if file.startswith("receipt-08"):
+        if file.startswith("receipt-12"):
             image = load_image("data/"+file)
             receipt = cut_receipt(image, draw_steps=True)
-            lines = find_lines(receipt)
+            lines = find_lines(receipt, draw_steps=True)
+            print("hh", len(lines))
+            #continue
             for line in lines:
                 plt.imshow(line, cmap="gray")
                 plt.show()
diff --git a/src/processing/linefinder.py b/src/processing/linefinder.py
index f1e1ea0..6123cb2 100644
--- a/src/processing/linefinder.py
+++ b/src/processing/linefinder.py
@@ -3,8 +3,12 @@ from scipy.ndimage import measurements
 
 from src.processing.imageprocessing import rgb2gray
 
+from matplotlib import pyplot as plt
 
-def find_lines(image):
+from src.utils.cmap_generator import rand_cmap
+
+
+def find_lines(image, draw_steps=False):
     gray, binary, magnitude = preparation(image)
     #backtrack = load_numpy("result/backtrack.npz")
     #if backtrack is None:
@@ -12,7 +16,7 @@ def find_lines(image):
         #save_numpy("result/backtrack.npz", backtrack)
     seams = calculate_seams(backtrack)
     labeled, ncomponents = group_empty_boxes(seams)
-    lines = generate_lines(labeled, ncomponents, gray)
+    lines = generate_lines(labeled, ncomponents, gray, draw_steps)
     return filter_lines(lines)
 
 
@@ -102,9 +106,14 @@ def group_empty_boxes(seams):
     return labeled, ncomponents
 
 
-def generate_lines(labeled, ncomponents, gray):
+def generate_lines(labeled, ncomponents, gray, draw_steps=False):
+    if draw_steps:
+        plt.imshow(labeled, cmap=rand_cmap(ncomponents+1, "hard"))
+        plt.title("Found line groups")
+        plt.show()
+
     groups = np.copy(labeled)
-    group_id = 1
+    group_id = 0
     entries = []
     pixelgroup = None
     in_top_mode = True
@@ -113,11 +122,12 @@ def generate_lines(labeled, ncomponents, gray):
 
     def submit_entry():
         nonlocal pixelgroup, group_id, entries
+        group_id += 1
         minps = np.min(pixelgroup, axis=0)
         maxps = np.max(pixelgroup, axis=0)
-        if pixel.shape[0] < 20 or maxps[1] - minps[1] < 5 or maxps[0] - minps[0] < 5:
-            return
         entry = gray[minps[1]:maxps[1] + 1, minps[0]:maxps[0] + 1]
+        if pixelgroup.shape[0] < 20 or maxps[1] - minps[1] < 5 or maxps[0] - minps[0] < 5:
+            return
         if pixelgroup.shape[0] / entry.shape[0] / entry.shape[1] < 0.10:
             return
         pixelgroup = np.subtract(pixelgroup, minps)
@@ -131,7 +141,6 @@ def generate_lines(labeled, ncomponents, gray):
         #white[pixelgroup[:, 1], pixelgroup[:, 0]] = entry[pixelgroup[:, 1], pixelgroup[:, 0]]
         entries.append(white)
         pixelgroup = None
-        group_id += 1
 
     for label in range(1, ncomponents+1):
         pixel = indices[labeled == label]
@@ -149,21 +158,30 @@ def generate_lines(labeled, ncomponents, gray):
             in_top_mode = False
         if pixelgroup is not None and minp[1] - 5 > np.max(pixelgroup, axis=0)[1]:
             submit_entry()
-        groups[labeled == label] = group_id
+        groups[labeled == label] = group_id+1
         if pixelgroup is None:
             pixelgroup = pixel[:, :]
         else:
             pixelgroup = np.concatenate((pixelgroup, pixel))
     submit_entry()
+    if draw_steps:
+        plt.imshow(groups, cmap=rand_cmap(group_id+1, "hard"))
+        plt.title("Combined and valid groups")
+        plt.show()
     return entries
 
 def filter_lines(lines):
     filtered = []
     for line in lines:
         cnt, vals = np.histogram(line, 256)
-        threshold = get_threshold(cnt)/256*1.13#*0.96
+        threshold = get_threshold(cnt)/256*0.96
         binary = (line > threshold).astype(np.int_)
         labeled, ncomponents = group_empty_boxes(binary)
-        if ncomponents > 2:
+        count = 0
+        for label in range(1, ncomponents+1):
+            pixcnt = np.sum(labeled == label)
+            if pixcnt > 10:
+                count += 1
+        if count > 2:
             filtered.append(line)
     return filtered
\ No newline at end of file
diff --git a/src/processing/receiptcutter.py b/src/processing/receiptcutter.py
index 4c6935a..c118248 100644
--- a/src/processing/receiptcutter.py
+++ b/src/processing/receiptcutter.py
@@ -3,6 +3,8 @@ from collections import defaultdict
 from skimage.transform import resize
 from scipy.ndimage import gaussian_filter
 
+from matplotlib import pyplot as plt
+
 from src.processing.imageprocessing import rgb2gray_value
 from scipy import signal
 import numpy as np
@@ -161,8 +163,8 @@ def draw_hough_lines(image, scale, results, references, shape, theta_res=5, widt
                 y = int(n - x * m)
                 if 0 < y < image.shape[0]:
                     draw_image[max(0, y - GREEN_WIDTH):y + GREEN_WIDTH, max(0, x - GREEN_WIDTH):x + GREEN_WIDTH] = np.array([255, 0, 0])
-    #plt.imshow(draw_image)
-    #plt.show()
+    plt.imshow(draw_image)
+    plt.show()
 
 
 def convert_to_lines(scale, results, references, shape, theta_res=5, width_res=5):
@@ -342,8 +344,8 @@ def draw_rectangle(image, corners):
             x = int(a[1] + (b[1] - a[1]) * i / 5000)
             y = int(a[0] + (b[0] - a[0]) * i / 5000)
             draw_image[max(0, y - 15):y + 10, max(0, x - 10):x + 10] = np.array([255, 0, 0])
-    #plt.imshow(draw_image)
-    #plt.show()
+    plt.imshow(draw_image)
+    plt.show()
 
 
 def crop_image(image, corners):