检测文本图像是否颠倒 [英] Detect if a text image is upside down

问题描述

我有几百张图像(扫描的文档)，其中大多数是歪斜的.我想使用Python使它们偏斜.
这是我使用的代码:

I have some hundreds of images (scanned documents), most of them are skewed. I wanted to de-skew them using Python.
Here is the code I used:

import numpy as np
import cv2

from skimage.transform import radon


filename = 'path_to_filename'
# Load file, converting to grayscale
img = cv2.imread(filename)
I = cv2.cvtColor(img, COLOR_BGR2GRAY)
h, w = I.shape
# If the resolution is high, resize the image to reduce processing time.
if (w > 640):
    I = cv2.resize(I, (640, int((h / w) * 640)))
I = I - np.mean(I)  # Demean; make the brightness extend above and below zero
# Do the radon transform
sinogram = radon(I)
# Find the RMS value of each row and find "busiest" rotation,
# where the transform is lined up perfectly with the alternating dark
# text and white lines
r = np.array([np.sqrt(np.mean(np.abs(line) ** 2)) for line in sinogram.transpose()])
rotation = np.argmax(r)
print('Rotation: {:.2f} degrees'.format(90 - rotation))

# Rotate and save with the original resolution
M = cv2.getRotationMatrix2D((w/2,h/2),90 - rotation,1)
dst = cv2.warpAffine(img,M,(w,h))
cv2.imwrite('rotated.jpg', dst)

此代码对大多数文档都适用，除了某些角度:(180和0)和(90和270)通常被检测为相同角度(即，在(180和0)和(180和0)之间没有区别. (90和270)).因此，我得到了很多颠倒的文档.

This code works well with most of the documents, except with some angles: (180 and 0) and (90 and 270) are often detected as the same angle (i.e it does not make difference between (180 and 0) and (90 and 270)). So I get a lot of upside-down documents.

这里是一个例子:

Here is an example:

我得到的结果图像与输入图像相同.

The resulted image that I get is the same as the input image.

是否有建议使用Opencv和Python检测图像是否颠倒了?
PS:我尝试使用EXIF数据检查方向，但没有找到任何解决方案.

Is there any suggestion to detect if an image is upside down using Opencv and Python?
PS: I tried to check the orientation using EXIF data, but it didn't lead to any solution.

可以使用Tesseract(Python的pytesseract)检测方向，但是只有在图像包含很多字符时才可以.
对于可能需要此功能的任何人:

It is possible to detect the orientation using Tesseract (pytesseract for Python), but it is only possible when the image contains a lot of characters.
For anyone who may need this:

import cv2
import pytesseract


print(pytesseract.image_to_osd(cv2.imread(file_name)))

如果文档包含足够的字符，则Tesseract可以检测方向.但是，当图像的线条很少时，Tesseract建议的定向角度通常是错误的.因此，这不是100％的解决方案.

If the document contains enough characters, it is possible for Tesseract to detect the orientation. However, when the image has few lines, the orientation angle suggested by Tesseract is usually wrong. So this can not be a 100% solution.

推荐答案

Python3/OpenCV4脚本对齐扫描的文档.

旋转文档并汇总行.当文档旋转0度和180度时，图像中会出现很多黑色像素:

Rotate the document and sum the rows. When the document has 0 and 180 degrees of rotation, there will be a lot of black pixels in the image:

使用得分保持方法.对每个图像进行评分，以使其与斑马图案相似.得分最高的图像具有正确的旋转度.您链接的图像偏离了0.5度.为了便于阅读，我省略了一些功能，完整的代码可以在此处找到.

Use a score keeping method. Score each image for it's likeness to a zebra pattern. The image with the best score has the correct rotation. The image you linked to was off by 0.5 degrees. I omitted some functions for readability, the full code can be found here.

# Rotate the image around in a circle
angle = 0
while angle <= 360:
    # Rotate the source image
    img = rotate(src, angle)    
    # Crop the center 1/3rd of the image (roi is filled with text)
    h,w = img.shape
    buffer = min(h, w) - int(min(h,w)/1.15)
    roi = img[int(h/2-buffer):int(h/2+buffer), int(w/2-buffer):int(w/2+buffer)]
    # Create background to draw transform on
    bg = np.zeros((buffer*2, buffer*2), np.uint8)
    # Compute the sums of the rows
    row_sums = sum_rows(roi)
    # High score --> Zebra stripes
    score = np.count_nonzero(row_sums)
    scores.append(score)
    # Image has best rotation
    if score <= min(scores):
        # Save the rotatied image
        print('found optimal rotation')
        best_rotation = img.copy()
    k = display_data(roi, row_sums, buffer)
    if k == 27: break
    # Increment angle and try again
    angle += .75
cv2.destroyAllWindows()

如何判断文档是否颠倒?填写从文档顶部到图像中第一个非黑色像素的区域.用黄色测量面积.面积最小的图像将是正面朝上的图像:

How to tell if the document is upside down? Fill in the area from the top of the document to the first non-black pixel in the image. Measure the area in yellow. The image that has the smallest area will be the one that is right-side-up:

# Find the area from the top of page to top of image
_, bg = area_to_top_of_text(best_rotation.copy())
right_side_up = sum(sum(bg))
# Flip image and try again
best_rotation_flipped = rotate(best_rotation, 180)
_, bg = area_to_top_of_text(best_rotation_flipped.copy())
upside_down = sum(sum(bg))
# Check which area is larger
if right_side_up < upside_down: aligned_image = best_rotation
else: aligned_image = best_rotation_flipped
# Save aligned image
cv2.imwrite('/home/stephen/Desktop/best_rotation.png', 255-aligned_image)
cv2.destroyAllWindows()

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