薄板样条插值(Thin plate splines)的实现与使用一只帅气的小菜鸡的博客-CSDN博客薄板样条插值

import cv2
import numpy as np
from PIL import Image

# 首先读入img
img_pil = Image.open('D:\\Postgraduate\\工作文件夹\\7-Table\\脚本\\img_0.jpg')
img = cv2.cvtColor(np.asarray(img_pil), cv2.COLOR_RGB2BGR)
label = open('D:\\Postgraduate\\工作文件夹\\7-Table\\脚本\\img_0.txt',
             'r', encoding='utf8')

lines = label.readlines()

for line in lines:
    points = []
    point_ = line.split(' ')[0:8]
    for i in range(len(point_)//2):
        points.append((int(point_[2*i]), int(point_[2*i+1])))

    # 找面积最小的矩形
    rect = cv2.minAreaRect(np.array(points))
    # 得到最小矩形的坐标
    box = cv2.boxPoints(rect)
    dest_w, dest_h = int(rect[1][0]), int(rect[1][1])
    # 标准化坐标到整数
    box = np.int0(box)

    x, y, w, h = cv2.boundingRect(np.array(points))
    roi_img = img[y:y+h, x:x+w]
    cv2.imshow('roi_img', roi_img)
    cv2.waitKey()
    # 画上绿色的圆圈
    # for i in range(len(points)):
    #     points[i] = (points[i][0]-x, points[i][1]-y)
    # for point in points:
    #     cv2.circle(roi_img, point, 1, (0, 255, 0), 2)
    # cv2.imshow('img', roi_img)
    # cv2.waitKey()

    # 实例化tps
    tps = cv2.createThinPlateSplineShapeTransformer()

    # 源点集合，处理为合适的格式
    sourceshape = np.array(points, np.int32)
    sourceshape = sourceshape.reshape(1, -1, 2)

    # opencv中匹配函数
    matches = []
    N = len(points)
    for i in range(0, N):
        matches.append(cv2.DMatch(i, i, 0))

    # 开始变动，获取目标点
    newpoints = []
    N = N//2
    dx = int(w/(N-1))
    for i in range(0, N):
        newpoints.append((dx*i, 2))
    for i in range(N-1, -1, -1):
        newpoints.append((dx*i, h-2))

    print(points, newpoints)
    targetshape = np.array(newpoints, np.int32)
    targetshape = targetshape.reshape(1, -1, 2)

    # 估计插值矩阵，并进行tps插值获取插值后的图像
    tps.estimateTransformation(targetshape, sourceshape, matches)
    roi_img_ = tps.warpImage(roi_img)

    # for point in newpoints:
    #     cv2.circle(roi_img_, (int(point[0]), int(point[1])), 1, (0, 0, 255), 2)

    cv2.namedWindow('img2', cv2.WINDOW_FREERATIO)

    cv2.imshow('img2', roi_img_)
    roi_img_ = cv2.resize(roi_img_, (max(dest_w, dest_h), min(dest_w, dest_h)), interpolation=cv2.INTER_NEAREST)
    cv2.imwrite('C:\\Users\\12116\\Desktop\\img_0_rotate.jpg', roi_img_)
    cv2.waitKey()