Question

我在背景图片上粘贴了随机生成的条形码。

此条形码已随机旋转，倾斜和缩放。

然后，将此条形码随机放置在背景图像上。

我试图找出实际条形码的坐标，忽略扩展的黑色面具。

我是矩阵和图像处理的初学者，所以任何帮助，尤其是数学方面的帮助，都会受到赞赏。

这是我使用pdf417gen库生成条形码以及条形码的坐标。

import numpy as np
import os
import random
import sys

from pdf417gen import encode, render_image
from PIL import Image

def generate_barcode(self):
    barcode = encode("random text data", columns=5, security_level=5)
    scale = 5
    ratio = 3
    padding = 5

    barcode_image = render_image(barcode, scale=scale, ratio=ratio, padding=padding)
    barcode_coords = np.array([
        [(barcode_image.width - padding) / float(barcode_image.width), (barcode_image.height - padding) / float(barcode_image.height)],
        [padding / float(barcode_image.width), (barcode_image.height - padding) / float(barcode_image.height)],
        [padding / float(barcode_image.width), padding / float(barcode_image.height)],
        [(barcode_image.width - padding) / float(barcode_image.width), padding / float(barcode_image.height)]
    ])

    return (barcode_coords, barcode_image)

一旦我有条形码的图像和坐标，我会执行以下操作。

转换条形码的图像
尝试将坐标与图片的转换匹配
将图像粘贴到背景图像上
然后使用坐标

红色轮廓应勾勒出条形码的图像。

我在这里转换条形码图像并将其粘贴到背景图像上。

def composite_images(self, background_image, barcode_coords, barcode_image):
    coords = barcode_coords
    barcode = barcode_image

    # instantiating the transformation variables
    scale = random.randrange(4, 50) / 100.0
    size = int( min(background_image.size) * scale) # background_image.size returns (width, height)
    barcode = barcode.resize((int(size * 2.625), size)) # width:height ratio is 2.625:1
    rotation = random.randrange(0, 360)
    xstretch = random.randrange(0, 100) / 100.0
    ystretch = random.randrange(0, 100) / 100.0
    xshear = random.randrange(0, 100) / 100.0
    yshear = random.randrange(0, 100) / 100.0

    # set affine transform on the barcode coordinates
    affine_transform = get_affine_transform(rotation, xstretch, ystretch, xshear, yshear)
    coords = transform_coords(coords, affine_transform, True)
    expand_mask = transform_coords(np.array([  # shifts expand mask based on transformation
        [0.0, 0.0],
        [float(size * 2.625), 0.0],
        [float(size * 2.625), float(size)],
        [0.0, float(size)]
    ]), mat, False)

    minx = min(expand_mask[:,0])
    maxx = max(expand_mask[:,0])
    miny = min(expand_mask[:,1])
    maxy = max(expand_mask[:,1])

    mat_inv = np.linalg.inv(np.array([  # the inverse matrix
        [mat[0,0], mat[0,1], -minx],
        [mat[1,0], mat[1,1], -miny],
        [0,0,1.0]
    ]))
    image_matrix = (mat_inv[0,0], mat_inv[0,1], mat_inv[0,2], 
        mat_inv[1,0], mat_inv[1,1], mat_inv[1,2])
    new_size = (int(maxx-minx), int(maxy-miny))

    # set affine transform on the barcode image using data from coordinates affine transformation
    barcode = barcode.transform(new_size, method=Image.AFFINE, data=image_matrix)

    # paste the barcode image onto a random position on background image
    region_x = random.randrange(0, background_image.width - size)
    region_y = random.randrange(0, background_image.height - size)
    background_image.paste(barcode, (region_x, region_y))

    coords *= scale
    coords += [region_x / float(background_image.width), region_y / float(background_image.height)]

    return(coords, background_image)

def get_affine_transform(self, rotation, xstretch, ystretch, xshear, yshear):
    theta = -(rotation / 180.0) * np.pi
    return np.array([
        [np.cos(theta) * xstretch, -np.sin(theta) * xshear],
        [np.sin(theta) * ystretch, np.cos(theta) * yshear]
    ])

def transform_coords(self, coords, affine_transform, center):
    if center:
        coords -= (.5, .5) # center on origin
    coords = np.dot(coords, affine_transform.T)
    if center:
        coords += (.5, .5) # reset centering
    return coords

现在我使用composite_images()返回的坐标和图像（带粘贴的条形码）绘制红色轮廓。

def draw_red_outline(self, box_coords, image):
    outline = box_coords * [image.width, image.height]
    outline = outline.astype(int)
    outline = tuple(map(tuple, outline))

    draw = ImageDraw.Draw(image)
    draw.poly(outline, outline=(255,0,0,0))
    del draw

    image.show()

我不确定我的数学出错了。

Answer 1

要获取转换点的坐标，您可以执行以下操作：

获得转换矩阵后：

transformed_img = cv2.warpPerspective(source_img, m, image_shape)

您将其应用于图片：

transformed_img = cv2.warpPerspective(source_img, m, image_shape)

并且变换后的图像包含您想要计算的坐标和一些黑色区域的结果。

所以，解决4点中的每一点＆＃39;坐标（如果没有0坐标）如下：

point = np.array([w, h]) #width and hight of the source point (before transform)
homg_point = [point[0], point[1], 1] # homogeneous coords
transf_homg_point = m.dot(homg_point) # transform
transf_homg_point /= transf_homg_point1[2] # scale
transf_point = transf_homg_point[:2] # remove Cartesian coords
print(transf_point) #check the result

tranformation coordinates

保存已转换粘贴图像的坐标

1 个答案: