Python 透视变换

变换前：

变换后：

import cv2
import matplotlib.pyplot as plt
import numpy as np


# 图像预处理
def detect_img(img):
    # BGR 转 RGB
    rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    # 获取图像尺寸
    H, W = rgb_img.shape[0:2]
    # 裁剪目标区域
    split_img = rgb_img[410:620, 1020:1330]
    # 图像尺寸重制
    new_img = cv2.resize(split_img, (W // 4, H // 4))
    # RGB 转 Gray
    gray = cv2.cvtColor(new_img, cv2.COLOR_RGB2GRAY)
    # 图像阈值分割
    ret, thresh = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY)
    return H, W, rgb_img, split_img, gray, new_img, thresh


# 图像轮廓提取
def img_cnt(thresh):
    # 图像开运算
    kernel = np.ones((11, 11), np.uint8)
    open_img = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
    # 图像轮廓查找
    cnt, _ = cv2.findContours(open_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    # 画出轮廓
    copy_img = new_img.copy()
    draw_img = cv2.drawContours(copy_img, cnt, 0, (0, 255, 0), 2)
    approx_img = new_img.copy()
    return open_img, cnt, copy_img, draw_img, approx_img


# 图像多边形拟合
def draw_ploy_4rect(cnt, approx_img):
    global R_up, R_down, L_down, L_up
    # 外接四边形
    count_cnt = len(cnt)
    for i in range(0, count_cnt):
        # 凸包检测
        hull_cnt = cv2.convexHull(cnt[i])
        Hull_img = cv2.drawContours(approx_img, [hull_cnt], -1, (255, 65, 65), 3)
        # cv2.imshow("hull", Hull_img)
        cnt_len = cv2.arcLength(hull_cnt, True)
        # 轮廓多边形拟合
        approx_cnt = cv2.approxPolyDP(hull_cnt, 0.01 * cnt_len, True)

        # cv2.drawContours(approx_img, [approx_cnt], 0, (255, 0, 255), 2)
        # 四边形轮廓筛选
        if len(approx_cnt) == 4:
            cv2.drawContours(approx_img, [], 0, (255, 0, 255), 2)
            # x, y, w, h = cv2.boundingRect(approx_cnt)
            # 获取轮廓坐标
            R_up = [approx_cnt[0][0][0], approx_cnt[0][0][1]]
            R_down = [approx_cnt[1][0][0], approx_cnt[1][0][1]]
            L_down = [approx_cnt[2][0][0] + 2, approx_cnt[2][0][1] + 5]
            L_up = [approx_cnt[3][0][0], approx_cnt[3][0][1]]
            # cv2.drawMarker(approx_img, L_down, (255, 255, 0), 3, cv2.MARKER_TILTED_CROSS, 10)

        # 获取轮廓质心
        M = cv2.moments(hull_cnt)
        # print(M)
        cx = int(M["m10"] / M["m00"])
        cy = int(M["m01"] / M["m00"])
        # print("cx:", cx, "cy:", cy)
        poly_img_4 = cv2.drawMarker(approx_img, (cx, cy), (255, 255, 0), 3, cv2.MARKER_TILTED_CROSS, 5)
        return poly_img_4, R_up, R_down, L_down, L_up


# 透视变换
def Perspective_Transform(L_up, R_up, R_down, L_down, rgb_img):
    # 设定坐标
    New_L_up = L_up
    New_L_down = [L_up[0], L_up[1] + 467]
    New_R_up = [L_up[0] + 467, L_up[1]]
    New_R_down = [L_up[0] + 467, L_up[1] + 467]

    input_points = np.float32([L_up, R_up, R_down, L_down])
    output_points = np.float32([New_L_up, New_R_up, New_R_down, New_L_down])
    # 创建变换矩阵
    mat = cv2.getPerspectiveTransform(input_points, output_points)
    # 执行透视变换
    out_img = cv2.warpPerspective(rgb_img.copy(), mat, (W, H))
    New_out_img = cv2.resize(out_img, (W // 4, H // 4))
    return New_out_img


if __name__ == "__main__":
    img = cv2.imread(r"E:\DeskTop\ppt_img.bmp")
    # 图像预处理
    H, W, rgb_img, split_img, gray, new_img, thresh = detect_img(img)
    # 图像轮廓提取
    open_img, cnt, copy_img, draw_img, approx_img = img_cnt(thresh)
    # 图像多边形拟合
    poly_img_4, R_up, R_down, L_down, L_up = draw_ploy_4rect(cnt, approx_img)
    # 图像透视变换
    New_out_img = Perspective_Transform(L_up, R_up, R_down, L_down, rgb_img)
    # 显示图像
    plt.subplot(241), plt.imshow(rgb_img, cmap=None), plt.title('org')
    plt.subplot(242), plt.imshow(split_img, cmap=None), plt.title('split_img')
    plt.subplot(243), plt.imshow(gray, cmap='gray'), plt.title('gray')
    plt.subplot(244), plt.imshow(open_img, cmap='gray'), plt.title('open_img')
    plt.subplot(245), plt.imshow(draw_img, cmap=None), plt.title('draw_img')
    plt.subplot(246), plt.imshow(poly_img_4, cmap=None), plt.title('poly_img_4')
    plt.subplot(247), plt.imshow(New_out_img, cmap=None), plt.title('New_out_img')
    plt.subplot(248), plt.imshow(rgb_img, cmap=None), plt.title('org')
    plt.show()

    # cv2.imshow("out_img", New_out_img)
    # cv2.waitKey(0)
    # cv2.destoryAllWindows()