python opencv 实现透视变换——将侧视图进行正投影

这个方法可以将倾斜拍摄的四边形图片投影成矩形，在图像处理工程里经常要用，之前写过一个C语言版本的，可以搜我博客：透视变换

但是python语言版本的比较少，根据网上一些资料总结了一下。

废话不多说，直接上效果图。

效果图

原图

运行demo用，加深理解

代码

代码1——自动找四边形角点，然后透视变化

思路：

二值化——滤波——膨胀——腐蚀——找最外边轮廓——拟合四边形——四个顶点映射——透视变换

#（基于透视的图像矫正）import cv2import mathimport numpy as npdef Img_Outline(input_dir):original_img = cv2.imread(input_dir)gray_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2GRAY)blurred = cv2.GaussianBlur(gray_img, (1, 1), 0) # 高斯模糊去噪（设定卷积核大小影响效果）_, RedThresh = cv2.threshold(blurred, 165, 255, cv2.THRESH_BINARY) # 设定阈值165（阈值影响开闭运算效果）kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))# 定义矩形结构元素closed = cv2.morphologyEx(RedThresh, cv2.MORPH_CLOSE, kernel) # 闭运算（链接块）opened = cv2.morphologyEx(closed, cv2.MORPH_OPEN, kernel) # 开运算（去噪点）return original_img, gray_img, RedThresh, closed, openeddef findContours_img(original_img, opened):contours, hierarchy = cv2.findContours(opened, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)#c = sorted(contours, key=cv2.contourArea, reverse=True)[1] # 计算最大轮廓的旋转包围盒#rect = cv2.minAreaRect(c)# 获取包围盒（中心点，宽高，旋转角度）#box = np.int0(cv2.boxPoints(rect)) # box#box[]#draw_img = cv2.drawContours(original_img.copy(), [box], -1, (0, 0, 255), 3)draw_img=original_img.copy()cv2.drawContours(draw_img, contours, -1, (255, 0, 0), 2)#拟合四边形cnt_len = cv2.arcLength(contours[0], True)box = cv2.approxPolyDP(contours[0], 0.02 * cnt_len, True)if len(box) == 4:cv2.drawContours(draw_img, [box], -1, (255, 255, 0), 3)'''box[0]: [[163 32]]右上box[1]: [[63 72]] 左上box[2]: [[150 215]]左下box[3]: [[268 144]]右下'''print("box[0]:", box[0])print("box[1]:", box[1])print("box[2]:", box[2])print("box[3]:", box[3])# for i in range(len(box)):#box_after[i]=box[3-i]box_after =[0]*4#排好序的角点输出，0号是左上角，顺时针输出box_after[0] = box[1]box_after[1] = box[0]box_after[2] = box[3]box_after[3] = box[2]print("box_after[0]:", box_after[0])print("box_after[1]:", box_after[1])print("box_after[2]:", box_after[2])print("box_after[3]:", box_after[3])return box_after,draw_img#return draw_imgdef Perspective_transform(box,original_img):# # 获取画框宽高(x=orignal_W,y=orignal_H)# orignal_W = math.ceil(np.sqrt((box[3][1] - box[2][1])**2 + (box[3][0] - box[2][0])**2))# orignal_H= math.ceil(np.sqrt((box[3][1] - box[0][1])**2 + (box[3][0] - box[0][0])**2))## # 原图中的四个顶点,与变换矩阵# pts1 = np.float32([box[0], box[1], box[2], box[3]])# pts2 = np.float32([[int(orignal_W+1),int(orignal_H+1)], [0, int(orignal_H+1)], [0, 0], [int(orignal_W+1), 0]])## # 生成透视变换矩阵；进行透视变换# M = cv2.getPerspectiveTransform(pts1, pts2)# result_img = cv2.warpPerspective(original_img, M, (int(orignal_W+3),int(orignal_H+1)))#ROTATED_SIZE_W = 600 # 透视变换后的表盘图像大小ROTATED_SIZE_H = 800 # 透视变换后的表盘图像大小# 原图中书本的四个角点(左上、右上、右下、左下),与变换后矩阵位置#pts1 = np.float32([[63, 72], [163, 32], [268, 144], [150, 215]])pts1 = np.float32([box[0], box[1], box[2], box[3]])# 变换后矩阵位置pts2 = np.float32([[0, 0], [ROTATED_SIZE_W, 0], [ROTATED_SIZE_W, ROTATED_SIZE_H], [0, ROTATED_SIZE_H], ])# 生成透视变换矩阵；进行透视变换M = cv2.getPerspectiveTransform(pts1, pts2)result_img = cv2.warpPerspective(original_img, M, (ROTATED_SIZE_W, ROTATED_SIZE_H))return result_imgif __name__=="__main__":input_dir = "./1.jpg"original_img, gray_img, RedThresh, closed, opened = Img_Outline(input_dir)box, draw_img = findContours_img(original_img,opened)#draw_img = findContours_img(original_img, opened)result_img = Perspective_transform(box,original_img)cv2.imshow("original", original_img)cv2.imshow("gray", gray_img)cv2.imshow("closed", closed)cv2.imshow("opened", opened)cv2.imshow("draw_img", draw_img)cv2.imshow("result_img", result_img)cv2.waitKey(0)cv2.destroyAllWindows()

代码2——手动输入四个角点，然后进行透视变换

'''box[0]: [[163 32]]右上box[1]: [[63 72]] 左上box[2]: [[150 215]]左下box[3]: [[268 144]]右下'''import cv2import numpy as npimport matplotlib.pyplot as pltimg = cv2.imread('1.jpg')ROTATED_SIZE = 600 #透视变换后的表盘图像大小CUT_SIZE= 0 #透视变换时四周裁剪长度W_cols, H_rows= img.shape[:2]print(H_rows, W_cols)# 原图中书本的四个角点(左上、右上、右下、左下),与变换后矩阵位置,排好序的角点输出，0号是左上角，顺时针输出pts1 = np.float32([[63, 72], [163, 32], [268, 144], [150, 215]])#变换后矩阵位置pts2 = np.float32([[0, 0],[ROTATED_SIZE,0],[ROTATED_SIZE, ROTATED_SIZE],[0,ROTATED_SIZE],])# 生成透视变换矩阵；进行透视变换M = cv2.getPerspectiveTransform(pts1, pts2)dst = cv2.warpPerspective(img, M, (ROTATED_SIZE,ROTATED_SIZE))cv2.imshow("original_img",img)cv2.imshow("result",dst)cv2.waitKey(0)cv2.destroyAllWindows()

参考文章;

1、Python-Opencv基于透视变换的图像矫正

2、opencv 四边形拟合_谈谈OpenCV中的四边形