import cv2
import numpy as np
def image_for_detection(raw_image):
#remove tiny noises by blurring
sm_image = cv2.GaussianBlur(raw_image,(5,5),0)
#binarize
ret, bw_image = cv2.threshold(sm_image,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
#dilate
kernel = np.ones((2,2),np.uint8)
bw_image = cv2.dilate(bw_image,kernel)
return bw_image
def image_for_extraction(raw_image):
raw_image = cv2.GaussianBlur(raw_image,(3,3),0)
ret,no_sm_bw_image = cv2.threshold(raw_image,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
return no_sm_bw_image
def getTransformationMatrix(img):
#input should be a binarized image - text white, bg black
#Find all white pixels
pts = np.empty([0,0])
pts = cv2.findNonZero(img)
#Get rotated rect of white pixels
rect = cv2.minAreaRect(pts)
# rect[0] has the center of rectangle, rect[1] has width and height, rect[2] has the angle
# To draw the rotated box and save the png image, uncomment below
drawrect = img.copy()
drawrect = cv2.cvtColor(drawrect, cv2.COLOR_GRAY2BGR)
box = cv2.boxPoints(rect)
box = np.int0(box) # box now has four vertices of rotated rectangle
cv2.drawContours(drawrect,[box],0,(0,0,255),10)
cv2.imwrite('rotated_rect.png', drawrect)
#Change rotation angle if the tilt is in another direction
rect = list(rect)
if (rect[1][0] < rect[1][1]): # rect.size.width > rect.size.height
temp = list(rect[1])
temp[0], temp[1] = temp[1], temp[0]
rect[1] = tuple(temp)
rect[2] = rect[2] + 90.0
#convert rect back to numpy/tuple
rect = np.asarray(rect)
#Rotate the image according to the found angle
rotated_image = np.empty([0,0])
M = cv2.getRotationMatrix2D(rect[0], rect[2], 1.0)
#img = cv2.warpAffine(img, M, (img.shape[1],img.shape[0]))
#returns the transformation matrix for this rotation
return M
def rotate(image, M):
return cv2.warpAffine(image, M, (image.shape[1],image.shape[0]))
