correct_skew.py

# import the necessary packages
import numpy as np
import argparse
import cv2
 
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
	help="path to input image file")
args = vars(ap.parse_args())
 
# load the image from disk
image = cv2.imread(args["image"])

# convert the image to grayscale and flip the foreground and background to ensure foreground is now "white" and the background is "black"
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bitwise_not(gray)
 
# threshold the image, setting all foreground pixels to 255 and all background pixels to 0
thresh = cv2.threshold(gray, 0, 255,
	cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]

# grab the (x, y) coordinates of all pixel values that are greater than zero, then use these coordinates to compute a rotated bounding box that contains all coordinates
coords = np.column_stack(np.where(thresh > 0))
angle = cv2.minAreaRect(coords)[-1]
 
# the `cv2.minAreaRect` function returns values in the range [-90, 0); as the rectangle rotates clockwise the returned angle trends to 0 -- in this special case we need to add 90 degrees to the angle
if angle < -45:
	angle = -(90 + angle)
 
# otherwise, just take the inverse of the angle to make it positive
else:
	angle = -angle

# rotate the image to deskew it
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image, M, (w, h),
	flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)

 
# show the output image
print("[INFO] angle: {:.3f}".format(angle))
cv2.imshow("Input", image)
cv2.imshow("Rotated", rotated)
k = cv2.waitKey(0) & 0xFF
if k == 27:
    cv2.destroyAllWindows()
elif k == ord('s'):
    cv2.imwrite('images/image.jpg', rotated)
    cv2.destroyAllWindows()