huks0 · October 1, 2024 09:21 · huks0 · Apr 30, 2020 · nlosacco · Sep 3, 2020
diff --git a/cellrecognition.py b/cellrecognition.py
 import cv2
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import csv

 try:
    from PIL import Image
 except ImportError:
    import Image
 import pytesseract

 #read your file
 file=r'/Users/marius/Desktop/Masterarbeit/Medium/Medium.png'
 img = cv2.imread(file,0)
 img.shape

 #thresholding the image to a binary image
 thresh,img_bin = cv2.threshold(img,128,255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)

 #inverting the image 
 img_bin = 255-img_bin
 cv2.imwrite('/Users/marius/Desktop/cv_inverted.png',img_bin)
 #Plotting the image to see the output
 plotting = plt.imshow(img_bin,cmap='gray')
 plt.show()

 # countcol(width) of kernel as 100th of total width
 kernel_len = np.array(img).shape[1]//100
 # Defining a vertical kernel to detect all vertical lines of image 
 ver_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_len))
 # Defining a horizontal kernel to detect all horizontal lines of image
 hor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_len, 1))
 # A kernel of 2x2
 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))

 #Use vertical kernel to detect and save the vertical lines in a jpg
 image_1 = cv2.erode(img_bin, ver_kernel, iterations=3)
 vertical_lines = cv2.dilate(image_1, ver_kernel, iterations=3)
 cv2.imwrite("/Users/marius/Desktop/vertical.jpg",vertical_lines)
 #Plot the generated image
 plotting = plt.imshow(image_1,cmap='gray')
 plt.show()

 #Use horizontal kernel to detect and save the horizontal lines in a jpg
 image_2 = cv2.erode(img_bin, hor_kernel, iterations=3)
 horizontal_lines = cv2.dilate(image_2, hor_kernel, iterations=3)
 cv2.imwrite("/Users/marius/Desktop/horizontal.jpg",horizontal_lines)
 #Plot the generated image
 plotting = plt.imshow(image_2,cmap='gray')
 plt.show()

 # Combine horizontal and vertical lines in a new third image, with both having same weight.
 img_vh = cv2.addWeighted(vertical_lines, 0.5, horizontal_lines, 0.5, 0.0)
 #Eroding and thesholding the image
 img_vh = cv2.erode(~img_vh, kernel, iterations=2)
 thresh, img_vh = cv2.threshold(img_vh,128,255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
 cv2.imwrite("/Users/marius/Desktop/img_vh.jpg", img_vh)
 bitxor = cv2.bitwise_xor(img,img_vh)
 bitnot = cv2.bitwise_not(bitxor)
 #Plotting the generated image
 plotting = plt.imshow(bitnot,cmap='gray')
 plt.show()

 # Detect contours for following box detection
 contours, hierarchy = cv2.findContours(img_vh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

 def sort_contours(cnts, method="left-to-right"):
    # initialize the reverse flag and sort index
    reverse = False
    i = 0
    # handle if we need to sort in reverse
    if method == "right-to-left" or method == "bottom-to-top":
        reverse = True
    # handle if we are sorting against the y-coordinate rather than
    # the x-coordinate of the bounding box
    if method == "top-to-bottom" or method == "bottom-to-top":
        i = 1
    # construct the list of bounding boxes and sort them from top to
    # bottom
    boundingBoxes = [cv2.boundingRect(c) for c in cnts]
    (cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
    key=lambda b:b[1][i], reverse=reverse))
    # return the list of sorted contours and bounding boxes
    return (cnts, boundingBoxes)

 # Sort all the contours by top to bottom.
 contours, boundingBoxes = sort_contours(contours, method="top-to-bottom")

 #Creating a list of heights for all detected boxes
 heights = [boundingBoxes[i][3] for i in range(len(boundingBoxes))]

 #Get mean of heights
 mean = np.mean(heights)

 #Create list box to store all boxes in  
 box = []
 # Get position (x,y), width and height for every contour and show the contour on image
 for c in contours:
    x, y, w, h = cv2.boundingRect(c)
    if (w<1000 and h<500):
        image = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
        box.append([x,y,w,h])
        
 plotting = plt.imshow(image,cmap='gray')
 plt.show()

 #Creating two lists to define row and column in which cell is located
 row=[]
 column=[]
 j=0

 #Sorting the boxes to their respective row and column
 for i in range(len(box)):    
        
    if(i==0):
        column.append(box[i])
        previous=box[i]    
    
    else:
        if(box[i][1]<=previous[1]+mean/2):
            column.append(box[i])
            previous=box[i]            
            
            if(i==len(box)-1):
                row.append(column)        
            
        else:
            row.append(column)
            column=[]
            previous = box[i]
            column.append(box[i])
            
 print(column)
 print(row)

 #calculating maximum number of cells
 countcol = 0
 for i in range(len(row)):
    countcol = len(row[i])
    if countcol > countcol:
        countcol = countcol

 #Retrieving the center of each column
 center = [int(row[i][j][0]+row[i][j][2]/2) for j in range(len(row[i])) if row[0]]

 center=np.array(center)
 center.sort()
 print(center)
 #Regarding the distance to the columns center, the boxes are arranged in respective order

 finalboxes = []
 for i in range(len(row)):
    lis=[]
    for k in range(countcol):
        lis.append([])
    for j in range(len(row[i])):
        diff = abs(center-(row[i][j][0]+row[i][j][2]/4))
        minimum = min(diff)
        indexing = list(diff).index(minimum)
        lis[indexing].append(row[i][j])
    finalboxes.append(lis)


 #from every single image-based cell/box the strings are extracted via pytesseract and stored in a list
 outer=[]
 for i in range(len(finalboxes)):
    for j in range(len(finalboxes[i])):
        inner=''
        if(len(finalboxes[i][j])==0):
            outer.append(' ')
        else:
            for k in range(len(finalboxes[i][j])):
                y,x,w,h = finalboxes[i][j][k][0],finalboxes[i][j][k][1], finalboxes[i][j][k][2],finalboxes[i][j][k][3]
                finalimg = bitnot[x:x+h, y:y+w]
                kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 1))
                border = cv2.copyMakeBorder(finalimg,2,2,2,2, cv2.BORDER_CONSTANT,value=[255,255])
                resizing = cv2.resize(border, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
                dilation = cv2.dilate(resizing, kernel,iterations=1)
                erosion = cv2.erode(dilation, kernel,iterations=2)
                
                out = pytesseract.image_to_string(erosion)
                if(len(out)==0):
                    out = pytesseract.image_to_string(erosion, config='--psm 3')
                inner = inner +" "+ out
            outer.append(inner)

 #Creating a dataframe of the generated OCR list
 arr = np.array(outer)
 dataframe = pd.DataFrame(arr.reshape(len(row), countcol))
 print(dataframe)
 data = dataframe.style.set_properties(align="left")
 #Converting it in a excel-file
 data.to_excel("/Users/marius/Desktop/output.xlsx")
	import cv2
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import csv

	try:
	from PIL import Image
	except ImportError:
	import Image
	import pytesseract

	#read your file
	file=r'/Users/marius/Desktop/Masterarbeit/Medium/Medium.png'
	img = cv2.imread(file,0)
	img.shape

	#thresholding the image to a binary image
	thresh,img_bin = cv2.threshold(img,128,255,cv2.THRESH_BINARY \| cv2.THRESH_OTSU)

	#inverting the image
	img_bin = 255-img_bin
	cv2.imwrite('/Users/marius/Desktop/cv_inverted.png',img_bin)
	#Plotting the image to see the output
	plotting = plt.imshow(img_bin,cmap='gray')
	plt.show()

	# countcol(width) of kernel as 100th of total width
	kernel_len = np.array(img).shape[1]//100
	# Defining a vertical kernel to detect all vertical lines of image
	ver_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_len))
	# Defining a horizontal kernel to detect all horizontal lines of image
	hor_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_len, 1))
	# A kernel of 2x2
	kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))

	#Use vertical kernel to detect and save the vertical lines in a jpg
	image_1 = cv2.erode(img_bin, ver_kernel, iterations=3)
	vertical_lines = cv2.dilate(image_1, ver_kernel, iterations=3)
	cv2.imwrite("/Users/marius/Desktop/vertical.jpg",vertical_lines)
	#Plot the generated image
	plotting = plt.imshow(image_1,cmap='gray')
	plt.show()

	#Use horizontal kernel to detect and save the horizontal lines in a jpg
	image_2 = cv2.erode(img_bin, hor_kernel, iterations=3)
	horizontal_lines = cv2.dilate(image_2, hor_kernel, iterations=3)
	cv2.imwrite("/Users/marius/Desktop/horizontal.jpg",horizontal_lines)
	#Plot the generated image
	plotting = plt.imshow(image_2,cmap='gray')
	plt.show()

	# Combine horizontal and vertical lines in a new third image, with both having same weight.
	img_vh = cv2.addWeighted(vertical_lines, 0.5, horizontal_lines, 0.5, 0.0)
	#Eroding and thesholding the image
	img_vh = cv2.erode(~img_vh, kernel, iterations=2)
	thresh, img_vh = cv2.threshold(img_vh,128,255, cv2.THRESH_BINARY \| cv2.THRESH_OTSU)
	cv2.imwrite("/Users/marius/Desktop/img_vh.jpg", img_vh)
	bitxor = cv2.bitwise_xor(img,img_vh)
	bitnot = cv2.bitwise_not(bitxor)
	#Plotting the generated image
	plotting = plt.imshow(bitnot,cmap='gray')
	plt.show()

	# Detect contours for following box detection
	contours, hierarchy = cv2.findContours(img_vh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

	def sort_contours(cnts, method="left-to-right"):
	# initialize the reverse flag and sort index
	reverse = False
	i = 0
	# handle if we need to sort in reverse
	if method == "right-to-left" or method == "bottom-to-top":
	reverse = True
	# handle if we are sorting against the y-coordinate rather than
	# the x-coordinate of the bounding box
	if method == "top-to-bottom" or method == "bottom-to-top":
	i = 1
	# construct the list of bounding boxes and sort them from top to
	# bottom
	boundingBoxes = [cv2.boundingRect(c) for c in cnts]
	(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
	key=lambda b:b[1][i], reverse=reverse))
	# return the list of sorted contours and bounding boxes
	return (cnts, boundingBoxes)

	# Sort all the contours by top to bottom.
	contours, boundingBoxes = sort_contours(contours, method="top-to-bottom")

	#Creating a list of heights for all detected boxes
	heights = [boundingBoxes[i][3] for i in range(len(boundingBoxes))]

	#Get mean of heights
	mean = np.mean(heights)

	#Create list box to store all boxes in
	box = []
	# Get position (x,y), width and height for every contour and show the contour on image
	for c in contours:
	x, y, w, h = cv2.boundingRect(c)
	if (w<1000 and h<500):
	image = cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
	box.append([x,y,w,h])

	plotting = plt.imshow(image,cmap='gray')
	plt.show()

	#Creating two lists to define row and column in which cell is located
	row=[]
	column=[]
	j=0

	#Sorting the boxes to their respective row and column
	for i in range(len(box)):

	if(i==0):
	column.append(box[i])
	previous=box[i]

	else:
	if(box[i][1]<=previous[1]+mean/2):
	column.append(box[i])
	previous=box[i]

	if(i==len(box)-1):
	row.append(column)

	else:
	row.append(column)
	column=[]
	previous = box[i]
	column.append(box[i])

	print(column)
	print(row)

	#calculating maximum number of cells
	countcol = 0
	for i in range(len(row)):
	countcol = len(row[i])
	if countcol > countcol:
	countcol = countcol

	#Retrieving the center of each column
	center = [int(row[i][j][0]+row[i][j][2]/2) for j in range(len(row[i])) if row[0]]

	center=np.array(center)
	center.sort()
	print(center)
	#Regarding the distance to the columns center, the boxes are arranged in respective order

	finalboxes = []
	for i in range(len(row)):
	lis=[]
	for k in range(countcol):
	lis.append([])
	for j in range(len(row[i])):
	diff = abs(center-(row[i][j][0]+row[i][j][2]/4))
	minimum = min(diff)
	indexing = list(diff).index(minimum)
	lis[indexing].append(row[i][j])
	finalboxes.append(lis)


	#from every single image-based cell/box the strings are extracted via pytesseract and stored in a list
	outer=[]
	for i in range(len(finalboxes)):
	for j in range(len(finalboxes[i])):
	inner=''
	if(len(finalboxes[i][j])==0):
	outer.append(' ')
	else:
	for k in range(len(finalboxes[i][j])):
	y,x,w,h = finalboxes[i][j][k][0],finalboxes[i][j][k][1], finalboxes[i][j][k][2],finalboxes[i][j][k][3]
	finalimg = bitnot[x:x+h, y:y+w]
	kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 1))
	border = cv2.copyMakeBorder(finalimg,2,2,2,2, cv2.BORDER_CONSTANT,value=[255,255])
	resizing = cv2.resize(border, None, fx=2, fy=2, interpolation=cv2.INTER_CUBIC)
	dilation = cv2.dilate(resizing, kernel,iterations=1)
	erosion = cv2.erode(dilation, kernel,iterations=2)

	out = pytesseract.image_to_string(erosion)
	if(len(out)==0):
	out = pytesseract.image_to_string(erosion, config='--psm 3')
	inner = inner +" "+ out
	outer.append(inner)

	#Creating a dataframe of the generated OCR list
	arr = np.array(outer)
	dataframe = pd.DataFrame(arr.reshape(len(row), countcol))
	print(dataframe)
	data = dataframe.style.set_properties(align="left")
	#Converting it in a excel-file
	data.to_excel("/Users/marius/Desktop/output.xlsx")