Line segmention and basic contours usage

Author: dishank-b

Character_Segmentation/data/img_2.jpg

@@ -1,41 +1,145 @@
 import cv2 
 import numpy as np
-import sys
-import time
+import matplotlib.pyplot as plt
+
+np.set_printoptions(threshold='nan')
+
+
+def closewindows():
+	k = cv2.waitKey(0)
+	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+'_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
+
 
 #-------------Thresholding Image--------------#
 
 src_img = cv2.imread('./data/img_2.jpg', 1)
+# copy = src_img.copy()
+# 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("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,101,2)
-# ret3,thr_img = cv2.threshold(gud_img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+# gud_img = cv2.adaptiveThreshold(grey_img,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV,1001,2)
+gud_img = cv2.adaptiveThreshold(grey_img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY_INV,1001,2)
 
 kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
-closing = cv2.morphologyEx(gud_img, cv2.MORPH_CLOSE, kernel, iterations = 2) # To remove "pepper-noise"
+noise_remove = cv2.erode(gud_img,kernel,iterations = 2)
 
+
+# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(3,3))
 kernel1 = np.array([[1,0,1],[0,1,0],[1,0,1]], dtype = np.uint8)
-final = cv2.erode(closing,kernel1,iterations = 1)
+final_thr = cv2.dilate(gud_img,kernel1,iterations = 2)
+
+
+
+# closing = cv2.morphologyEx(gud_img, cv2.MORPH_OPEN, kernel, iterations = 1) # To remove "pepper-noise"
+# final_thr = np.empty(final.shape, dtype=np.uint8)
+# cv2.bitwise_not(final1, final_thr)
+
+
+#-------------/Thresholding Image-------------#
+
+
+#-------------Line Detection------------------#
+
+count_x = np.zeros(shape= (height))
+for y in range(height):
+	for x in range(width):
+		if noise_remove[y][x] == 255 :
+			count_x[y] = count_x[y]+1
+	# print(count_x[y])
+
+line_list = 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])
+# plt.show()
+
+# k = cv2.waitKey(0)
+
+# for y in range(len(line_list)):
+# 	if :
+# 		main_list.append(line_list[y-1]+10)
+# 		main_list.append(line_list[y]-5)
+
+# main_list.append(line_list[-1]+10)
+
+# print(main_list)
+
+# for y in main_list:
+# 	src_img[y][:] = (0 , 255,0)
+
+
+
+# final_thr = cv2.erode(final_thr,kernel1,iterations = 1)
+
+
+#----------------------------------------------------------#
+ 
+
+
+
+
+
+
+#-------------------------------------------------------------#
+
+
+#-------------Character segmenting------------#
+
+chr_img = final_thr.copy()
+
+contr_img, contours, hierarchy = cv2.findContours(chr_img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+# print(len(contours))
+final_contr = np.zeros((final_thr.shape[0],final_thr.shape[1],3), dtype = np.uint8)
+cv2.drawContours(final_contr, contours, -1, (0,255,0), 3)
+
+for cnt in contours:
+	x,y,w,h = cv2.boundingRect(cnt)
+	cv2.rectangle(src_img,(x,y),(x+w,y+h),(0,255,0),2)
+
+#-------------/Character segmenting-----------# 
+
+
 
-# blur = cv2.bilateralFilter(erosion,9,75,75)
 
 
 #-------------Displaying Image----------------#
 
 cv2.namedWindow('Source Image', cv2.WINDOW_NORMAL)
 cv2.namedWindow('Threshold Image', cv2.WINDOW_NORMAL)
+cv2.namedWindow('Contour Image', cv2.WINDOW_NORMAL)
 
 cv2.imshow("Source Image", src_img)
-cv2.imshow("Threshold Image", final)
+cv2.imshow("Threshold Image", final_thr)
+cv2.imshow("Contour Image", final_contr)
+
+#-------------/Displaying Image---------------#
 
 
 #-------------Closing Windows-----------------#
 
-k = cv2.waitKey(0)
-if k & 0xFF == ord('s'):
-	comment = input("Comment:-\n ")
-	cv2.imwrite('./data/test_result/'+comment+'.jpg',final)
-	# cv2.imwrite('OKAY.jpg',final)
-	print("Completed")
-else:
-	cv2.destroyAllWindows()
+closewindows()