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coords.py
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coords.py
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import cv2
import numpy as np
import os
# from transform_example import transfer
from pyimagesearch.transform import four_point_transform
from connected_component import components
from image_regg import registration
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)
def box_extractionqw(img_for_box_extraction_path):
img = cv2.imread(img_for_box_extraction_path, 0) # Read the image
img2 = cv2.imread(img_for_box_extraction_path, 0)
img3 = cv2.imread(img_for_box_extraction_path, 0)
font = cv2.FONT_HERSHEY_COMPLEX
# img = cv2.blur(img,(10,10))
(thresh, img_bin) = cv2.threshold(img, 128, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU) # Thresholding the image
img_bin = 255-img_bin # Invert the image
# img = np.full((100,80,3), 12, np.uint8)
# threshold image
# ret, threshed_img = cv2.threshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),
# 127, 255, cv2.THRESH_BINARY)
img = cv2.blur(img,(20,20))
cv2.imwrite("Methodology/Image_bin.jpg",img_bin)
# Defining a kernel length
kernel_length = np.array(img).shape[1]//400
# A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.
verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, kernel_length))
# A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line from the image.
hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))
# A kernel of (3 X 3) ones.
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
# Morphological operation to detect verticle lines from an image
img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=3)
verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=3)
cv2.imwrite("Methodology/verticle_lines.jpg",verticle_lines_img)
# Morphological operation to detect horizontal lines from an image
img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=3)
horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=3)
cv2.imwrite("Methodology/horizontal_lines.jpg",horizontal_lines_img)
# Weighting parameters, this will decide the quantity of an image to be added to make a new image.
alpha = 0.50
beta = 1.0 - alpha
# This function helps to add two image with specific weight parameter to get a third image as summation of two image.
img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)
img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)
(thresh, img_final_bin) = cv2.threshold(img_final_bin, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# For Debugging
# Enable this line to see verticle and horizontal lines in the image which is used to find boxes
cv2.imwrite("Methodology/img_final_bin.jpg",img_final_bin)
# Find contours for image, which will detect all the boxes
contours, hierarchy = cv2.findContours(
img_final_bin, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# print(len(contours))
# Sort all the contours by top to bottom.
(contours, boundingBoxes) = sort_contours(contours, method="left-to-right")
x1=0
y1=0
# check=0
idx = 0
for c in contours:
# if check!=3:
# Returns the location and width,height for every contour
x, y, w, h = cv2.boundingRect(c)
# check+=1
# if ((x==x1)):
if(cv2.contourArea(c)>30000):
# if ((x>(x1+100))): #outer
if (w > 300 and h > 400 and((h<(6*w))and(h>(0.3*w)))):
if ((x<(x1+100))): #inner
idx += 1
itr=0
approx = cv2.approxPolyDP(c, 0.009 * cv2.arcLength(c, True), True)
# draws boundary of contours.
cv2.drawContours(img3, [approx], 0, (0, 0, 255), 5)
#print(len(approx[1]))
# Used to flatted the array containing
# the co-ordinates of the vertices.
nq = approx.ravel()
#print(nq)
iq = 0
for jq in nq :
if(iq % 2 == 0):
xq = nq[iq]
yq = nq[iq + 1]
# String containing the co-ordinates.
string = str(xq) + " " + str(yq)
cv2.putText(img3, string, (xq, yq),
font, 0.5, (255, 255, 0))
if(itr==1):
# xq=xq-16
# yq=yq-16
extTop=[xq,yq]
if(itr==2):
# xq=xq+16
# yq=yq-16
extRight=[xq,yq]
if(itr==3):
# xq=xq+16
# yq=yq+16
extBot=[xq,yq]
if(itr==0):
# xq=xq-16
# yq=yq+16
extLeft=[xq,yq]
itr = itr + 1
# print(xq, yq)
iq = iq + 1
# Showing the final image.
# cv2.imwrite('image2.png', img3)
coords=[extTop,extRight,extBot,extLeft]
# print(coords)
#ignore below part and don't erase it
sum0=coords[0][0]+coords[0][1]
sum1=coords[1][0]+coords[1][1]
sum2=coords[2][0]+coords[2][1]
sum3=coords[3][0]+coords[3][1]
dif0=coords[0][0]-coords[0][1]
dif1=coords[1][0]-coords[1][1]
dif2=coords[2][0]-coords[2][1]
dif3=coords[3][0]-coords[3][1]
smin=min(sum0,sum1,sum2,sum3)
smax=max(sum0,sum1,sum2,sum3)
dmin=min(dif0,dif1,dif2,dif3)
dmax=max(dif0,dif1,dif2,dif3)
if smin==sum0:
tl=[coords[0][0],coords[0][1]]
elif smin==sum1:
tl=[coords[1][0],coords[1][1]]
elif smin==sum2:
tl=[coords[2][0],coords[2][1]]
elif smin==sum3:
tl=[coords[3][0],coords[3][1]]
#print(tl)
if smax==sum0:
br=[coords[0][0],coords[0][1]]
elif smax==sum1:
br=[coords[1][0],coords[1][1]]
elif smax==sum2:
br=[coords[2][0],coords[2][1]]
elif smax==sum3:
br=[coords[3][0],coords[3][1]]
#print(br)
if dmin==dif0:
bl=[coords[0][0],coords[0][1]]
elif dmin==dif1:
bl=[coords[1][0],coords[1][1]]
elif dmin==dif2:
bl=[coords[2][0],coords[2][1]]
elif dmin==dif3:
bl=[coords[3][0],coords[3][1]]
#print(bl)
if dmax==dif0:
tr=[coords[0][0],coords[0][1]]
elif dmax==dif1:
tr=[coords[1][0],coords[1][1]]
elif dmax==dif2:
tr=[coords[2][0],coords[2][1]]
elif dmax==dif3:
tr=[coords[3][0],coords[3][1]]
#print(tr)
rect=[tl,tr,bl,br]
return rect
print (coords)
x1=x #inner
# p=box_extractionqw("cropped/rollno38/2.png")
# print(p)