使用 OpenCV 找到一定纵横比的旋转矩形？

Question

I am not having difficulty transforming a found box, it is the fact that I am not able to detect the box in the first place when it is at an angle.

Here is a sample image I want the largest ~1230:123 rectangle in the image the problem is the rectangle can be rotated.

Here is a picture of a rotated barcode that I am unable to detect:

The function I have been using to process uses contour area just looks for the largest rectangle.

What methods should I use to look for a rotated rectangle so that even when rotated I can detect it?

    #PYTHON 3.6 Snippet for Image Processing

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# compute the Scharr gradient magnitude representation of the images
# in both the x and y direction using OpenCV 2.4
ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
gradX = cv2.Sobel(gray, ddepth=ddepth, dx=1, dy=0, ksize=-1)
gradY = cv2.Sobel(gray, ddepth=ddepth, dx=0, dy=1, ksize=-1)
# subtract the y-gradient from the x-gradient
gradient = cv2.subtract(gradX, gradY)
gradient = cv2.convertScaleAbs(gradient)
# blur and threshold the image
blurred = cv2.blur(gradient, (8, 8))
(_, thresh) = cv2.threshold(blurred, 225, 255, cv2.THRESH_BINARY)
# construct a closing kernel and apply it to the thresholded image
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# perform a series of erosions and dilations
closed = cv2.erode(closed, None, iterations = 4)
closed = cv2.dilate(closed, None, iterations = 4)
# find the contours in the thresholded image, then sort the contours
# by their area, keeping only the largest one
cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
c = sorted(cnts, key = cv2.contourArea, reverse = True)[0]
# compute the rotated bounding box of the largest contour

rect = cv2.minAreaRect(c)

Answer 1

You don't need all the preprocessing (like Sobel, erode, dilate) for finding before executing findContours .

findContours works better when contours are full (filled with white color) instead of having just the edges.
I suppose you can keep the code from cv2.findContours to the end, and get the result you are looking for.

You may use the following stages:

• Apply binary threshold using Otsu's thresholding (just in case image is not a binary image).
• Execute cv2.findContours , and Find the contour with the maximum area.
• Use cv2.minAreaRect for finding the minimum area bounding rectangle.

Here is a code sample:

import numpy as np
import cv2

img = cv2.imread('img.png', cv2.IMREAD_GRAYSCALE)  # Read input image as gray-scale

ret, img = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)  # Apply threshold using Otsu's thresholding (just in case image is not a binary image).

# Find contours in img.
cnts = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2]  # [-2] indexing takes return value before last (due to OpenCV compatibility issues).

# Find the contour with the maximum area.
c = max(cnts, key=cv2.contourArea)

# Find the minimum area bounding rectangle
# https://stackoverflow.com/questions/18207181/opencv-python-draw-minarearect-rotatedrect-not-implemented
rect = cv2.minAreaRect(c)
box = cv2.boxPoints(rect)
box = np.int0(box)

# Convert image to BGR (just for drawing a green rectangle on it).
bgr_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)

cv2.drawContours(bgr_img, [box], 0, (0, 255, 0), 2)

# Show images for debugging
cv2.imshow('bgr_img', bgr_img)
cv2.waitKey()
cv2.destroyAllWindows()

---

Result:

Note: The largest contour seems to be a parallelogram and not a perfect rectangle.