[OpenCV]如何将轮廓固定为矩形？

Question

备注

我是OpenCV(或计算机视觉)的新手，所以告诉我搜索查询会很有帮助！

我想问的是

我想写一个从图片中提取名片的程序。我能够提取一个粗略的轮廓，但反射的光变成了噪声，我无法提取准确的轮廓。请告诉我你的想法。

图像(原始数据)

raw data

输出

output data(rough outline)

代码

import math
import itertools
from glob import glob

import cv2
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

%matplotlib inline

def read_images():
    "read image data from data directory"
    names = glob('data/*.jpg')
    names.sort()
    return map(lambda name: cv2.imread(name), names)

def blur(img):
    "apply blur"
    return cv2.GaussianBlur(img, (25, 25), 0)

def show_images(images, column, color_type=cv2.COLOR_BGR2RGB):
    "plot images with matplotlib"
    plt.figure(figsize=(10,10), dpi=150)
    for n, img in zip(range(len(images)), images):
        p = plt.subplot(math.ceil(len(images) / column), column, n + 1)
        p.axis('off')
        if color_type is None:
            p.imshow(img)
        else:
            p.imshow(cv2.cvtColor(img, color_type))
    plt.show()

def detect_background_color(img):
    "detect background color"
    # Assume that the perimeter is all background
    height, width, *_ = img.shape
    background_colors = np.concatenate([
        img[5:height-5, 5],       img[5, 5:width-5],
        img[5:height-5, width-5], img[height-5, 5:width-5]
    ])
    background_colors = background_colors.astype(np.float32)

    # Assume that the background color is only one.
    K = 2
    iter_flg = cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER
    _, labels, centers = cv2.kmeans(
        background_colors, K, None, (iter_flg, 10, 1.0), 10,
        cv2.KMEANS_RANDOM_CENTERS)

    cnt1 = len(labels[labels==0])
    cnt2 = len(labels[labels==1])
    return centers[0] if cnt1 > cnt2 else centers[1]

def scale(img):
    bg = detect_background_color(img)
    return np.fix(np.sqrt(np.sum(np.square(img - bg), axis=2)) / 1.732).astype(np.uint8)

def binarize(img):
    th, bit = cv2.threshold(img, 40, 255, cv2.THRESH_BINARY)
    return bit

binarized = [binarize(scale(blur(img))) for img in read_images()]
show_images(binarized, 4, None)

Answer 1

看起来你需要应用形态学，尝试cv2.erode，然后cv2扩展操作。第一次将删除小于侵蚀内核大小的区域，第二次将恢复较大斑点的初始大小。您需要为这两个操作应用相同大小的内核。morphology。

还要检查这个：medium article