使用 OpenCV 从桌面游戏卡图像中提取艺术品

Question

我在 python 中编写了一个小脚本，我试图提取或裁剪扑克牌中仅代表艺术品的部分，删除所有其余部分。我一直在尝试各种阈值方法，但无法到达那里。另请注意，我不能简单地手动记录艺术品的位置，因为它并不总是处于相同的位置或大小，而是始终处于矩形形状，而其他一切都只是文本和边框。

from matplotlib import pyplot as plt
import cv2

img = cv2.imread(filename)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

ret,binary = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY)

binary = cv2.bitwise_not(binary)
kernel = np.ones((15, 15), np.uint8)

closing = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)

plt.imshow(closing),plt.show()

当前的输出是我能得到的最接近的东西。我可以在正确的方式上尝试进一步争论以在白色部分周围绘制一个矩形，但我认为这不是一种可持续的方法：

最后一点，请参阅下面的卡片，并非所有框架的大小或位置都完全相同，但总会有一件艺术品，周围只有文字和边框。它不必非常精确地切割，但很明显，艺术是卡片的一个“区域”，被包含一些文本的其他区域包围。我的目标是尽可能地捕捉艺术品的区域。

Answer 1

我使用霍夫线变换来检测图像的线性部分。所有线的交叉点用于构造所有可能的矩形，其中不包含其他交叉点。由于您正在寻找的卡片部分始终是这些矩形中最大的部分（至少在您提供的示例中），因此我只是选择了这些矩形中最大的一个作为获胜者。该脚本无需用户交互即可运行。

import cv2
import numpy as np
from collections import defaultdict

def segment_by_angle_kmeans(lines, k=2, **kwargs):
    #Groups lines based on angle with k-means.
    #Uses k-means on the coordinates of the angle on the unit circle 
    #to segment `k` angles inside `lines`.

    # Define criteria = (type, max_iter, epsilon)
    default_criteria_type = cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER
    criteria = kwargs.get('criteria', (default_criteria_type, 10, 1.0))
    flags = kwargs.get('flags', cv2.KMEANS_RANDOM_CENTERS)
    attempts = kwargs.get('attempts', 10)

    # returns angles in [0, pi] in radians
    angles = np.array([line[0][1] for line in lines])
    # multiply the angles by two and find coordinates of that angle
    pts = np.array([[np.cos(2*angle), np.sin(2*angle)]
                    for angle in angles], dtype=np.float32)

    # run kmeans on the coords
    labels, centers = cv2.kmeans(pts, k, None, criteria, attempts, flags)[1:]
    labels = labels.reshape(-1)  # transpose to row vec

    # segment lines based on their kmeans label
    segmented = defaultdict(list)
    for i, line in zip(range(len(lines)), lines):
        segmented[labels[i]].append(line)
    segmented = list(segmented.values())
    return segmented

def intersection(line1, line2):
    #Finds the intersection of two lines given in Hesse normal form.
    #Returns closest integer pixel locations.
    #See /sf/answers/26846921/

    rho1, theta1 = line1[0]
    rho2, theta2 = line2[0]

    A = np.array([
        [np.cos(theta1), np.sin(theta1)],
        [np.cos(theta2), np.sin(theta2)]
    ])
    b = np.array([[rho1], [rho2]])
    x0, y0 = np.linalg.solve(A, b)
    x0, y0 = int(np.round(x0)), int(np.round(y0))
    return [[x0, y0]]


def segmented_intersections(lines):
    #Finds the intersections between groups of lines.

    intersections = []
    for i, group in enumerate(lines[:-1]):
        for next_group in lines[i+1:]:
            for line1 in group:
                for line2 in next_group:
                    intersections.append(intersection(line1, line2)) 
    return intersections

def rect_from_crossings(crossings):
    #find all rectangles without other points inside
    rectangles = []

    # Search all possible rectangles
    for i in range(len(crossings)):
        x1= int(crossings[i][0][0])
        y1= int(crossings[i][0][1])

        for j in range(len(crossings)):
            x2= int(crossings[j][0][0])
            y2= int(crossings[j][0][1])

            #Search all points
            flag = 1
            for k in range(len(crossings)):
                x3= int(crossings[k][0][0])
                y3= int(crossings[k][0][1])

                #Dont count double (reverse rectangles)
                if (x1 > x2 or y1 > y2):
                    flag = 0
                #Dont count rectangles with points inside   
                elif ((((x3 >= x1) and (x2 >= x3))and (y3 > y1) and (y2 > y3) or ((x3 > x1) and (x2 > x3))and (y3 >= y1) and (y2 >= y3))):    
                    if(i!=k and j!=k):    
                        flag = 0

            if flag:
                rectangles.append([[x1,y1],[x2,y2]])

    return rectangles

if __name__ == '__main__':
    #img = cv2.imread('TAJFp.jpg')
    #img = cv2.imread('Bj2uu.jpg')
    img = cv2.imread('yi8db.png')

    width = int(img.shape[1])
    height = int(img.shape[0])

    scale = 380/width
    dim = (int(width*scale), int(height*scale))
    # resize image
    img = cv2.resize(img, dim, interpolation = cv2.INTER_AREA) 

    img2 = img.copy()
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray,(5,5),cv2.BORDER_DEFAULT)

    # Parameters of Canny and Hough may have to be tweaked to work for as many cards as possible
    edges = cv2.Canny(gray,10,45,apertureSize = 7)
    lines = cv2.HoughLines(edges,1,np.pi/90,160)

    segmented = segment_by_angle_kmeans(lines)
    crossings = segmented_intersections(segmented)
    rectangles = rect_from_crossings(crossings)

    #Find biggest remaining rectangle
    size = 0
    for i in range(len(rectangles)):
        x1 = rectangles[i][0][0]
        x2 = rectangles[i][1][0]
        y1 = rectangles[i][0][1]
        y2 = rectangles[i][1][1]

        if(size < (abs(x1-x2)*abs(y1-y2))):
            size = abs(x1-x2)*abs(y1-y2)
            x1_rect = x1
            x2_rect = x2
            y1_rect = y1
            y2_rect = y2

    cv2.rectangle(img2, (x1_rect,y1_rect), (x2_rect,y2_rect), (0,0,255), 2)
    roi = img[y1_rect:y2_rect, x1_rect:x2_rect]

    cv2.imshow("Output",roi)
    cv2.imwrite("Output.png", roi)
    cv2.waitKey()

这些是您提供的样本的结果：

查找线交叉点的代码可以在这里找到：find intersection point of Two Lines Drawing using houghlines opencv

您可以在此处阅读有关霍夫线的更多信息。