这是一个有趣的问题，我尝试了许多方法来纠正文档图像的方向，但都有不同的例外。
我分享一种基于文本导向的方法。对于文本区域检测，我使用的是输入图像的梯度图。在
所有其他实现细节都在代码中注释。在
请注意，只有当图像中的所有文本都具有相同的方向时，此操作才有效。#Document image orientation correction
#This approach is based on text orientation
#Assumption: Document image contains all text in same orientation
import cv2
import numpy as np
debug = True
#Display image
def display(img, frameName="OpenCV Image"):
if not debug:
return
h, w = img.shape[0:2]
neww = 800
newh = int(neww*(h/w))
img = cv2.resize(img, (neww, newh))
cv2.imshow(frameName, img)
cv2.waitKey(0)
#rotate the image with given theta value
def rotate(img, theta):
rows, cols = img.shape[0], img.shape[1]
image_center = (cols/2, rows/2)
M = cv2.getRotationMatrix2D(image_center,theta,1)
abs_cos = abs(M[0,0])
abs_sin = abs(M[0,1])
bound_w = int(rows * abs_sin + cols * abs_cos)
bound_h = int(rows * abs_cos + cols * abs_sin)
M[0, 2] += bound_w/2 - image_center[0]
M[1, 2] += bound_h/2 - image_center[1]
# rotate orignal image to show transformation
rotated = cv2.warpAffine(img,M,(bound_w,bound_h),borderValue=(255,255,255))
return rotated
def slope(x1, y1, x2, y2):
if x1 == x2:
return 0
slope = (y2-y1)/(x2-x1)
theta = np.rad2deg(np.arctan(slope))
return theta
def main(filePath):
img = cv2.imread(filePath)
textImg = img.copy()
small = cv2.cvtColor(textImg, cv2.COLOR_BGR2GRAY)
#find the gradient map
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
grad = cv2.morphologyEx(small, cv2.MORPH_GRADIENT, kernel)
display(grad)
#Binarize the gradient image
_, bw = cv2.threshold(grad, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
display(bw)
#connect horizontally oriented regions
#kernal value (9,1) can be changed to improved the text detection
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1))
connected = cv2.morphologyEx(bw, cv2.MORPH_CLOSE, kernel)
display(connected)
# using RETR_EXTERNAL instead of RETR_CCOMP
_ , contours, hierarchy = cv2.findContours(connected.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
mask = np.zeros(bw.shape, dtype=np.uint8)
#display(mask)
#cumulative theta value
cummTheta = 0
#number of detected text regions
ct = 0
for idx in range(len(contours)):
x, y, w, h = cv2.boundingRect(contours[idx])
mask[y:y+h, x:x+w] = 0
#fill the contour
cv2.drawContours(mask, contours, idx, (255, 255, 255), -1)
#display(mask)
#ratio of non-zero pixels in the filled region
r = float(cv2.countNonZero(mask[y:y+h, x:x+w])) / (w * h)
#assume at least 45% of the area is filled if it contains text
if r > 0.45 and w > 8 and h > 8:
#cv2.rectangle(textImg, (x1, y), (x+w-1, y+h-1), (0, 255, 0), 2)
rect = cv2.minAreaRect(contours[idx])
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(textImg,[box],0,(0,0,255),2)
#we can filter theta as outlier based on other theta values
#this will help in excluding the rare text region with different orientation from ususla value
theta = slope(box[0][0], box[0][1], box[1][0], box[1][1])
cummTheta += theta
ct +=1
#print("Theta", theta)
#find the average of all cumulative theta value
orientation = cummTheta/ct
print("Image orientation in degress: ", orientation)
finalImage = rotate(img, orientation)
display(textImg, "Detectd Text minimum bounding box")
display(finalImage, "Deskewed Image")
if __name__ == "__main__":
filePath = 'D:\data\img6.jpg'
main(filePath)
这是一张检测到文本区域的图像，从中我们可以看到一些文本区域丢失。文本方向检测在整个文档方向检测中起着至关重要的作用，因此，在文本检测算法中，根据文档类型进行一些小的调整，以使该方法更好地工作。在
这是正确定位的最终图像
请建议对此方法进行修改，使其更健壮。在