better line segmentation algo

Author: dishank-b

Character_Segmentation/data/test_result/perfect line segmentation_adpthres.jpg

@@ -10,21 +10,59 @@ def closewindows():
 	if k & 0xFF == ord('s'):
 		comment = input("Comment:-\n ")
 		cv2.imwrite('./data/test_result/'+comment+'_thres'+'.jpg',final_thr)
-		cv2.imwrite('./data/test_result/'+comment+'_src'+'.jpg',src_img)
+		cv2.imwrite('./data/test_result/'+comment+'_adpthres'+'.jpg',gud_img)
 		cv2.imwrite('./data/test_result/'+comment+'_contr'+'.jpg',final_contr)
 		print("Completed")
 	elif k & 0xFF == int(27):
 		cv2.destroyAllWindows()
 	else:
 		closewindows()
 
-def line_array(array):
-	list_x = []
-	for y in range(len(array)):
-		if all(i >= 3 for i in array[y:y+9]) == True:
-			list_x.append(y-1)
-	return list_x
+def strtline(y, array):
+	count_ahead = 0
+	count_prev = 0
+	for i in array[y:y+20]:
+		if i > 3:
+			count_ahead+= 1  
+	for i in array[y-10:y]:
+		if i==0:
+			count_prev += 1  
+	return count_ahead, count_prev
+
+def endline(y, array):
+	count_ahead = 0
+	count_prev = 0
+	for i in array[y:y+10]:
+		if i==0:
+			count_ahead+= 1  
+	for i in array[y-20:y]:
+		if i >3:
+			count_prev += 1  
+	return count_ahead, count_prev
 
+def line_array(array):
+	list_x_upper = []
+	list_x_lower = []
+	for y in range(5, len(array)-5):
+		s_a, s_p = strtline(y, array)
+		e_a, e_p = endline(y, array)
+		if s_a>=16 and s_p>=7:
+			list_x_upper.append(y)
+			# noise_remove[y][:] = 255
+		if e_a>=5 and e_p>=16:
+			list_x_lower.append(y)
+			# noise_remove[y][:] = 255
+	return list_x_upper, list_x_lower
+
+# def refine_array(count_array,array_upper, array_lower):
+# 	for y in array_upper:
+# 		if all(i >= 3 for i in count_array[y:y+10]) == False:
+# 			array_upper.remove(y)
+# 	for y in array_lower:
+# 		if all(i >= 3 for i in count_array[y-10:y]) == False:
+# 			array_lower.remove(y)
+
+# 	return array_upper, array_lower
 
 #-------------Thresholding Image--------------#
 
@@ -33,27 +71,30 @@ def line_array(array):
 # src_img = cv2.resize(copy, dsize =(1500, 1000), interpolation = cv2.INTER_AREA)
 height = src_img.shape[0]
 width = src_img.shape[1]
-print("#----------------------------#")
-print("Image Info:-")
+print("#---------Image Info:--------#")
 print("Height =",height,"\nWidth =",width)
 print("#----------------------------#")
 
 grey_img = cv2.cvtColor(src_img, cv2.COLOR_BGR2GRAY)
 
 gud_img = cv2.adaptiveThreshold(grey_img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV,101,2)
 
+# edges = cv2.Canny(grey_img,100,200)
+
 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
 noise_remove = cv2.erode(gud_img,kernel,iterations = 2)
 
-kernel1 = np.array([[1,0,1],[0,1,0],[1,0,1]], dtype = np.uint8)
+
 
 opening = cv2.morphologyEx(gud_img, cv2.MORPH_OPEN, kernel, iterations = 2) # To remove "pepper-noise"
 kernel1 = np.array([[1,0,1],[0,1,0],[1,0,1]], dtype = np.uint8)
-final_thr = cv2.dilate(noise_remove,kernel1,iterations = 3)
+final_thr = cv2.dilate(opening,kernel1,iterations = 1)
 
 #-------------/Thresholding Image-------------#
 
 
+
+
 #-------------Line Detection------------------#
 
 count_x = np.zeros(shape= (height))
@@ -63,12 +104,28 @@ def line_array(array):
 			count_x[y] = count_x[y]+1
 	# print(count_x[y])
 
-line_list = line_array(count_x)
+# line_list_upper, line_list_lower = line_array(count_x)
 # print(line_list) 
 
-# t = np.arange(0,height, 1)
-# plt.plot(t, count_x[t])
-# plt.axis([0, height, 0, 350])
+upper_lines, lower_lines = line_array(count_x)
+
+
+
+if len(upper_lines)==len(lower_lines):
+	print("Oh Yeah!!!")
+
+for y in upper_lines:
+	noise_remove[y][:] = 255	
+
+for y in lower_lines:
+	noise_remove[y][:] = 255
+
+
+t = np.arange(0,height, 1)
+plt.plot(t, count_x[t])
+plt.axis([0, height, 0, 350])
+
+
 
 
 # for y in range(len(line_list)):
@@ -81,7 +138,7 @@ def line_array(array):
 # print(main_list)
 
 # for y in main_list:
-# 	src_img[y][:] = (0 , 255,0)
+	# src_img[y][:] = (0 , 255,0)
 
 
 
@@ -124,12 +181,14 @@ def line_array(array):
 cv2.namedWindow('Source Image', cv2.WINDOW_NORMAL)
 cv2.namedWindow('Threshold Image', cv2.WINDOW_NORMAL)
 cv2.namedWindow('Contour Image', cv2.WINDOW_NORMAL)
+cv2.namedWindow('noise_remove Image', cv2.WINDOW_NORMAL)
 
 cv2.imshow("Source Image", src_img)
 cv2.imshow("Threshold Image", final_thr)
 cv2.imshow("Contour Image", final_contr)
+cv2.imshow('noise_remove Image', noise_remove)
 
-# plt.show()
+plt.show()
 
 #-------------/Displaying Image---------------#