逆向透视变换贴图数据增强

短命女 2022-10-15 04:50 147阅读 0赞

import numpy as np
    import cv2
    
    def rad(x):
      return x * np.pi / 180
    
    def order_points(pts):
        # initialzie a list of coordinates that will be ordered
        # such that the first entry in the list is the top-left,
        # the second entry is the top-right, the third is the
        # bottom-right, and the fourth is the bottom-left
        rect = np.zeros((4, 2), dtype="float32")
        # the top-left point will have the smallest sum, whereas
        # the bottom-right point will have the largest sum
        s = pts.sum(axis=1)
        rect[0] = pts[np.argmin(s)]
        rect[2] = pts[np.argmax(s)]
        # now, compute the difference between the points, the
        # top-right point will have the smallest difference,
        # whereas the bottom-left will have the largest difference
        diff = np.diff(pts, axis=1)
        rect[1] = pts[np.argmin(diff)]
        rect[3] = pts[np.argmax(diff)]
        # return the ordered coordinates
        return rect
    
    
    def four_point_transform(image, pts):
        # obtain a consistent order of the points and unpack them
        # individually
        rect = order_points(pts)
        (tl, tr, br, bl) = rect
        # compute the width of the new image, which will be the
        # maximum distance between bottom-right and bottom-left
        # x-coordiates or the top-right and top-left x-coordinates
        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))
        # compute the height of the new image, which will be the
        # maximum distance between the top-right and bottom-right
        # y-coordinates or the top-left and bottom-left y-coordinates
        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))
        # now that we have the dimensions of the new image, construct
        # the set of destination points to obtain a "birds eye view",
        # (i.e. top-down view) of the image, again specifying points
        # in the top-left, top-right, bottom-right, and bottom-left
        # order
        dst = np.array([
            [0, 0],
            [maxWidth - 1, 0],
            [maxWidth - 1, maxHeight - 1],
            [0, maxHeight - 1]], dtype="float32")
        # compute the perspective transform matrix and then apply it
        M = cv2.getPerspectiveTransform(rect, dst)
        M = np.linalg.inv(M)
        # warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
        # return the warped image
        return M,(maxWidth, maxHeight)
    
    def split(L,n):
        List = []
        for j in range(0, len(L), n):
            b = L[j:j + n]
            List.append(b)
        return List
    
    
    from math import *
    
    
    def rota(img, degree):
        height, width = img.shape[:2]
        # 旋转后的尺寸
        heightNew = int(width * fabs(sin(radians(degree))) + height * fabs(cos(radians(degree))))
        widthNew = int(height * fabs(sin(radians(degree))) + width * fabs(cos(radians(degree))))
        matRotation = cv2.getRotationMatrix2D((width / 2, height / 2), degree, 1)
        matRotation[0, 2] += (widthNew - width) / 2
        matRotation[1, 2] += (heightNew - height) / 2
        imgRotation = cv2.warpAffine(img, matRotation, (widthNew, heightNew), borderValue=(255, 255, 255))
        return imgRotation
    
    def getpaste(pasteimgpath,warpR,size,backsize):
        img = cv2.imread(pasteimgpath)
        h,w,_ = img.shape
        if size[0] < size[1] and w > h:
            img = rota(img,90)
        if size[0] > size[1] and w < h:
            img = rota(img, 90)
        img = cv2.resize(img,size)
        result = cv2.warpPerspective(img, warpR, backsize)
        return result
    
    import random
    for i in range(35):
        image = cv2.imread('/home/lhq/xg/{}.jpg'.format(i))
        H,W,_ = image.shape
        backsize = (W, H)
        with open('/home/lhq/xg/{}.txt'.format(i)) as f:
            datas = f.read().strip().split(' ')
            datas = split(datas,8)
            for data in datas:
                try:
                    data = [int(x) for x in data]
                    List = split(data,2)
                    pts = np.array(List, dtype="float32")
                    # apply the four point tranform to obtain a "birds eye view" of
                    # the image
                    warpR,size = four_point_transform(image, pts)
                    transimg = getpaste('/home/lhq/mb/{}.jpg'.format(random.randint(0,31)),warpR,size,backsize)
                    index = np.where(transimg > 0)
                    image[index] = transimg[index]
                    cv2.imshow('',image)
                    cv2.waitKey(0)
                except:
                    pass