检测具有模糊边缘和不同背景的卡的边缘

Question

这是我的测试照片

我正在努力寻找卡片的边缘。但是，正如您所看到的，边缘有些模糊。

在这里找到一些建议：模糊边缘检测如何从python中的模糊图像中找到扭曲矩形的精确角点位置？，但没有一个能产生令人满意的边缘。

完整代码：

def auto_canny(image, sigma=0.5):
    v = np.median(image)
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))
    return cv2.Canny(image, lower, upper)

def add_contrast(img, contrast_level=8):
    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)

    l, a, b = cv2.split(lab)

    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(contrast_level, contrast_level))
    cl = clahe.apply(l)

    limg = cv2.merge((cl, a, b))

    final = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)

    return final

# ------------------------------------------ #
# FIND EDGES
# ------------------------------------------ #
img = add_contrast(img=img, contrast_level=8)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("gray", gray)

kernel_size = 5
blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)

edges = auto_canny(image=blur_gray) 

# Show images for testing
cv2.imshow('edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

您可以通过使用填充部分后台cv2.floodFill来改进解决方案。

在找到边之前增强对比度是一个不错的主意，但它似乎会创建一些瑕疵，使得查找边更加困难。

下面是一个代码示例：

import numpy as np
import cv2


def auto_canny(image, sigma=0.5):
    v = np.median(image)
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))
    return cv2.Canny(image, lower, upper)


img = cv2.imread('card.png')

h, w = img.shape[0], img.shape[1]

# Seed points for floodFill (use few points at each corner for improving robustness)
seedPoints = ((5, 5), (w-5, 5), (5, h-5), (w-5, h-5), 
              (w//2, 5), (w//2, h-5), (5, h//2), (w-5, h//2), 
              (w//4, 5), (w//4, h-5), (5, h//4),  (w-5, h//4),
              (w*3//4, 5), (w*3//4, h-5), (5, h*3//4),  (w-5, h*3//4))

# Fill parts of the background with black color
for seed in seedPoints:
    cv2.floodFill(img, None, seedPoint=seed, newVal=(0, 0, 0), loDiff=(2, 2, 2), upDiff=(2, 2, 2))

# ------------------------------------------ #
# FIND EDGES
# ------------------------------------------ #
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("gray", gray)

kernel_size = 5
blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)

# Second, process edge detection use Canny.
edges = auto_canny(image=blur_gray)

# Show images for testing
cv2.imshow('edges', edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

我知道这不是一个完整的解决方案，但我希望它能有所帮助...

Answer 2

这也不是一个完整的解决方案，但是如果红色部分有问题，您可以首先使用cv2修复这些部分。inpaint（）函数。然后，您可以应用其余的方法来查找卡片边缘。

# create an inpainting mask with "red-enough" pixels
mask = cv2.inRange(img_src_rgb, np.array([200,0,0]), np.array([255,50,50]))
# enlarge the mask to cover the borders
kernel = np.ones((3,3),np.uint8)
mask = cv2.dilate(mask,kernel,iterations = 1)
# inpaint the red parts using Navier-Stokes based approach
img_dst = cv2.inpaint(img_src, mask,50,cv2.INPAINT_NS)
cv2.imshow("no_red", img_dst)

结果图像如下。

编辑：现在我们知道你在问什么，下面是一个完整的解决方案。

修复后，您可以应用霍夫变换来找到图像中的强直线。

gray = cv2.cvtColor(img_dst, cv2.COLOR_RGB2GRAY)
edges = auto_canny(gray) # your auto_canny function, WITHOUT blur
lines = cv2.HoughLines(edges, 1, np.pi/90, 50)
for line in lines:
    rho,theta = line[0]
    a = np.cos(theta)
    b = np.sin(theta)
    x0 = a*rho
    y0 = b*rho
    x1 = int(x0 + 10000*(-b))
    y1 = int(y0 + 10000*(a))
    x2 = int(x0 - 10000*(-b))
    y2 = int(y0 - 10000*(a))
    cv2.line(img_dst,(x1,y1),(x2,y2),(0,255,0),1)

cv2.imwrite('linesDetected.jpg', img_dst)

同样，结果行如下。