逆向透视变换的带透明通道的贴图数据增强

痛定思痛。 2022-10-16 00:51 183阅读 0赞

import numpy as np
    import cv2
    from math import *
    from PIL import Image
    import random
    
    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
    
    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)
        img = Image.open(pasteimgpath)
        img = np.array(img)
        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)
    
        img = Image.fromarray(img)
        img = img.convert('RGBA')
        img = np.array(img)
    
        result = cv2.warpPerspective(img, warpR, backsize)
        result = Image.fromarray(result)
        return result
    
    
    cc = 0
    for i in range(35):
        image = cv2.imread('/home/lhq/xg/{}.jpg'.format(i))
        try:
            image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
            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)
                        hua = np.array([List])
                        pts = np.array(List, dtype="float32")
                        im = np.zeros(shape=image.shape)
                        cv2.polylines(im,hua.astype(np.int32), True, (255,255,255),2)
                        # 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/mb3/{}.png'.format(random.randint(0,31)),warpR,size,backsize)
                        # transimg = getpaste('lena.png', warpR, size, backsize)
                        b, g, r, a = transimg.split()
                        image = Image.fromarray(image)
                        image.paste(transimg,(0,0),mask=a)
                        image = np.array(image)
    
                    except:
                        pass
                image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
                cv2.imshow('', image)
                cv2.waitKey(0)
        except:
            pass