OpenCV图像处理基础到进阶之高阶操作

一、模板与多个对象匹配

python 复制代码

import cv2
import numpy as np
#
img_rgb = cv2.imread('image.jpg')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('tem.jpg',0)
h,w = template.shape[:2]
# 使用模板匹配方法 cv2.matchTemplate 进行模板匹配
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
# 设定匹配阈值
threshold = 0.9
# 获取匹配结果中所有符合阈值的点的坐标
loc = np.where(res >= threshold)    # (符合条件的行，符合条件的列)
# 遍历所有匹配点
for pt in zip(*loc[::-1]):
    # 在原图上绘制匹配区域的矩形框
    cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 1)


# a = np.array([2,4,6,8,10])
# c=np.where(a > 5)
# print(c)

运行结果

二、图像透视变换

图像透视变换(Perspective Transformation)是计算机视觉中一种重要的几何变换技术，它可以将图像从一种视角投影到另一种视角，常用于矫正倾斜、改变观察角度等应用场景。下面列举一个简单的发票例子，下面是图片发票原图

代码实现：

python 复制代码

import numpy as np
import cv2
def cv_show(name, img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
def order_points(pts):
    # 一共4个坐标点
    rect = np.zeros((4, 2), dtype="float32")  # 用来存储排序之后的坐标位置
    # 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
    s = pts.sum(axis=1)  #对pts矩阵的每一行进行求和操作。（x+y）
    rect[0] = pts[np.argmin(s)]
    rect[2] = pts[np.argmax(s)]
    diff = np.diff(pts, axis=1)  #对pts矩阵的每一行进行求差操作。(y-x)
    rect[1] = pts[np.argmin(diff)]
    rect[3] = pts[np.argmax(diff)]
    return rect
def four_point_transform(image, pts):
    # 获取输入坐标点
    rect = order_points(pts)
    (tl, tr, br, bl) = rect
    # 计算输入的w和h值
    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))
    maxWidth = max(int(widthA), int(widthB))
    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))
    maxHeight = max(int(heightA), int(heightB))
    # 变换后对应坐标位置
    dst = np.array([[0, 0], [maxWidth - 1, 0],
                    [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype="float32")
    # 图像透视变换 cv2.getPerspectiveTransform(src, dst[, solveMethod]) → MP获得转换之间的关系
    #  src：变换前图像四边形顶点坐标
    # cv2.warpPerspective(src, MP, dsize[, dst[, flags[, borderMode[, borderValue]]]]) → dst
    # 参数说明：
    # src：原图
    # MP：透视变换矩阵，3行3列
    # dsize： 输出图像的大小，二元元组(width, height)
    M = cv2.getPerspectiveTransform(rect, dst)
    warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
    # 返回变换后结果
    return warped
def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
    dim = None
    (h, w) = image.shape[:2]
    if width is None and height is None:
        return image
    if width is None:
        r = height / float(h)
        dim = (int(w * r), height)
    else:
        r = width / float(w)
        dim = (width, int(h * r))
    resized = cv2.resize(image, dim, interpolation=inter)
    #参数interpolation指定了在图像大小调整过程中如何处理像素插值的方法。cv2.INTER_AREA具体意味着使用面积插值方法。
    return resized

# 读取输入
image = cv2.imread('fapiao.jpg')
cv_show('image', image)

# 图片过大，进行缩小处理
ratio = image.shape[0] / 500.0  # 计算缩小比率
orig = image.copy()
image = resize(orig, height=500)
cv_show('1',image)

# 轮廓检测
print("STEP 1: 轮廓检测")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)  # 读取灰度图

edged = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]  # 自动寻找阈值二值化
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2]
image_contours = cv2.drawContours(image.copy(), cnts, -1, (0, 0, 255), 1)
cv_show('image_contours', image_contours)

print("STEP 2: 获取最大轮廓")
screenCnt = sorted(cnts, key=cv2.contourArea, reverse=True)[0]  # 获取面积最大的轮廓

peri = cv2.arcLength(screenCnt, True)  # 计算轮廓周长
screenCnt = cv2.approxPolyDP(screenCnt, 0.05 * peri, True)  # 轮廓近似
print(screenCnt.shape)
image_contour = cv2.drawContours(image.copy(), [screenCnt], -1, (0, 255, 0), 2)

cv2.imshow("image_contour", image_contour)
cv2.waitKey(0)

# 透视变换
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)
cv2.imwrite('invoice_new.jpg', warped)
cv2.namedWindow('xx',cv2.WINDOW_NORMAL)
cv2.imshow("xx", warped)
cv2.waitKey(0)

# 二值处理
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(warped, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv_show('ref',ref)

kernel = np.ones((2, 2), np.uint8)  # 设置kenenel大小
ref_new = cv2.morphologyEx(ref, cv2.MORPH_CLOSE, kernel)  # 闭运算,先膨胀再腐蚀
ref_new=resize(ref_new.copy(),width=500)
cv_show('ref_new',ref_new)
rotated_image = cv2.rotate(ref_new, cv2.ROTATE_90_COUNTERCLOCKWISE)
cv2.imshow("result", rotated_image)
cv2.waitKey(0)

运行结果

以上就是本文的主要内容，后续还会更新更多有关opencv的知识，欢迎大家关注

OpenCV图像处理基础到进阶之高阶操作

一、模板与多个对象匹配

二、 图像透视变换

二、图像透视变换