OpenCV练习（2）图像校正

• 1、傅里叶变换 + 霍夫变换+ 直线 + 角度 + 旋转
• 2、边缘检测 + 霍夫变换 + 直线+角度 + 旋转
• 3、四点透视 + 角度 + 旋转
• 4、检测矩形轮廓 + 角度 + 旋转

1.目的

实现类似全能扫面王的图像校正功能

2. 基于轮廓提取和透射变换

基于轮廓提取和透射变换的矫正算法更适用于车牌、身份证、人民币、书本、 发票一类矩形形状而且边界明显的物体矫正。所校正的内容必须要有清晰的完整轮廓。

基本步骤：

1. 图片灰度化
2. 二值化/canny边缘检测等操作
3. 检测轮廓,并筛选出目标轮廓（通过横纵比或面积去除干扰轮廓)
4. 获取图像顶点
5. 透视变换

代码：

import cv2
import numpy as np

def contour_to_rect(contour):
    pts = contour.reshape(4, 2)
    print(pts)
    rect = np.zeros((4, 2), dtype = "float32")
    # top-left point has the smallest sum
    # bottom-right has the largest sum
    s = pts.sum(axis = 1)
#     print(s)
    rect[0] = pts[np.argmin(s)]
#     print(pts[np.argmin(s)])
    rect[2] = pts[np.argmax(s)]
    # compute the difference between the points:
    # the top-right will have the minumum difference
    # the bottom-left will have the maximum difference
    diff = np.diff(pts, axis = 1)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect

# approximate the contour by a more primitive polygon shape
def approximate_contour(contour):
    peri = cv2.arcLength(contour, True)
    return cv2.approxPolyDP(contour, 0.032 * peri, True)

# 获取顶点坐标
def get_receipt_contour(contours):
    # loop over the contours
    for c in contours:
        approx = approximate_contour(c)
        # if our approximated contour has four points, we can assume it is receipt's rectangle
        if len(approx) == 4:
            return approx


def wrap_perspective(img, rect):
    # unpack rectangle points: top left, top right, bottom right, bottom left
    (tl, tr, br, bl) = rect
    # compute the width of the new image
    widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    # compute the height of the new image
    heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    # take the maximum of the width and height values to reach
    # our final dimensions
    maxWidth = max(int(widthA), int(widthB))
    maxHeight = max(int(heightA), int(heightB))
    # destination points which will be used to map the screen to a "scanned" view
    dst = np.array([
        [0, 0],
        [maxWidth - 1, 0],
        [maxWidth - 1, maxHeight - 1],
        [0, maxHeight - 1]], dtype = "float32")
    # calculate the perspective transform matrix
    M = cv2.getPerspectiveTransform(rect, dst)
    # warp the perspective to grab the screen
    return cv2.warpPerspective(img, M, (maxWidth, maxHeight))


if __name__ == "__main__":
    img = cv2.imread("./images/1.png")
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    # 用高斯滤波处理原图像降噪
    blur = cv2.GaussianBlur(gray, (5, 5), 0)
	#canny边缘检测（仅针对这次的输入图片）
    edged = cv2.Canny(blur, 50, 150)
    contours, h = cv2.findContours(edged.copy(), mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
    # img_contours = cv2.drawContours(img.copy(), contours, -1, (0, 0, 255), 3)

    largest_contours = sorted(contours, key=cv2.contourArea, reverse=True)[:5]
    # img_largest_contours = cv2.drawContours( img.copy(), largest_contours, -1, (0, 0, 255), 3)

    receipt_contour = get_receipt_contour(largest_contours)
    # img_receipt_contour = cv2.drawContours(img.copy(), [receipt_contour], -1, (0, 0, 255), 3)

    ori_img = img.copy()
    for coor in contour_to_rect(receipt_contour):
        cv2.circle(ori_img, (int(coor[0]), int(coor[1])), 1, (0, 0, 255), 4)
	# 进行透视变换
    scanned = wrap_perspective(img.copy(), contour_to_rect(receipt_contour))

2.1 图像灰度化 

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

blur = cv2.GaussianBlur(gray, (5, 5), 0)（如果有噪声可以加个高斯滤波）

2.2 canny边缘检测

edged = cv2.Canny(blur, 50, 150) （三个参数分别输入图像，最小阈值，最大阈值）

下图中左为输出，右为边缘检测结果。看起来效果还行

左为所有轮廓绘制，右为面积前五的绘制。可以看到，书中长方形的框并不在检测中，这是因为在进行边缘检测时色差不大导致没检测出来，后续使用闭运算处理将连断出填充完。