Question

我想加载图像并使用霍夫变换来捕获纸张内部的黑色边界区域，然后在盒子内部执行一些计数操作。当然，这要求我能够以相对较好的质量加载图像。

问题是，当我使用openCV的cv2.imread（）进行加载时，得到的图片非常淡化，无法快速处理。更糟糕的是，我无法使用cv2.imshow（）渲染图像，每次尝试看到它时，IDE都会挂起。因此，我必须使用matplotlib进行渲染并逐步查看图像。

我不知道其他用于图像处理的软件包（也许是枕头，但我不知道它是否可以完成我需要做的事情）。

我的原始图像是这样的：

img = cv2.imread("img1-min.jpg")

由于cv2.imshow（）方法导致窗口崩溃，我求助于matplotlib：

plt.imshow(img)
plt.title('my picture')
plt.show()

结果是：

之后：

gray = cv2.cvtColor (img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 75, 150)
lines = cv2.HoughLinesP(edges, 1, np.pi/180, 50, 50, 5)

if lines is not None:
    for line in lines:
        x1, y1, x2, y2 = line[0]
        cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 5)

plt.imshow(img)
plt.title('my picture')
plt.show()

输出为：

如您所见，非常混乱。我的直觉是，这是因为原始图像的加载方式。有什么方法可以改善加载过程，使其更容易应用霍夫线？

Answer 1

我相信cv2.imread()可以很好地加载图像，但是尺寸为2976x3838时是如此之大，您的IDE无法显示图像。我认为您错误地应用了cv2.HoughLinesP()。代替使用cv2.HoughLinesP()，这是一种检测行的替代方法

想法是先阈值，然后找到木板的边界框以创建遮罩。从此蒙版，我们执行透视变换以获得自顶向下的图像。这将使我们能够更好地检测线

一旦检测到木板，就可以提取ROI

然后我们只需检测垂直和水平线

结果

import cv2
import numpy as np

def perspective_transform(image, corners):
    def order_corner_points(corners):
        # Separate corners into individual points
        # Index 0 - top-right
        #       1 - top-left
        #       2 - bottom-left
        #       3 - bottom-right
        corners = [(corner[0][0], corner[0][1]) for corner in corners]
        top_r, top_l, bottom_l, bottom_r = corners[0], corners[1], corners[2], corners[3]
        return (top_l, top_r, bottom_r, bottom_l)

    # Order points in clockwise order
    ordered_corners = order_corner_points(corners)
    top_l, top_r, bottom_r, bottom_l = ordered_corners

    # Determine width of new image which is the max distance between 
    # (bottom right and bottom left) or (top right and top left) x-coordinates
    width_A = np.sqrt(((bottom_r[0] - bottom_l[0]) ** 2) + ((bottom_r[1] - bottom_l[1]) ** 2))
    width_B = np.sqrt(((top_r[0] - top_l[0]) ** 2) + ((top_r[1] - top_l[1]) ** 2))
    width = max(int(width_A), int(width_B))

    # Determine height of new image which is the max distance between 
    # (top right and bottom right) or (top left and bottom left) y-coordinates
    height_A = np.sqrt(((top_r[0] - bottom_r[0]) ** 2) + ((top_r[1] - bottom_r[1]) ** 2))
    height_B = np.sqrt(((top_l[0] - bottom_l[0]) ** 2) + ((top_l[1] - bottom_l[1]) ** 2))
    height = max(int(height_A), int(height_B))

    # Construct new points to obtain top-down view of image in 
    # top_r, top_l, bottom_l, bottom_r order
    dimensions = np.array([[0, 0], [width - 1, 0], [width - 1, height - 1], 
                    [0, height - 1]], dtype = "float32")

    # Convert to Numpy format
    ordered_corners = np.array(ordered_corners, dtype="float32")

    # Find perspective transform matrix
    matrix = cv2.getPerspectiveTransform(ordered_corners, dimensions)

    # Return the transformed image
    return cv2.warpPerspective(image, matrix, (width, height))

image = cv2.imread('1.jpg')
original = image.copy()
blur = cv2.bilateralFilter(image,9,75,75)
gray = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray,0,255, cv2.THRESH_OTSU + cv2.THRESH_BINARY_INV)[1]

cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

mask = np.zeros(image.shape, dtype=np.uint8)
for c in cnts:
    area = cv2.contourArea(c)
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.015 * peri, True)

    if area > 150000 and len(approx) == 4:
        cv2.drawContours(image,[c], 0, (36,255,12), 3)
        cv2.drawContours(mask,[c], 0, (255,255,255), -1)
        transformed = perspective_transform(original, approx)

mask = cv2.bitwise_and(mask, original)

# Remove horizontal lines
gray = cv2.cvtColor(transformed, cv2.COLOR_BGR2GRAY)
board_thresh = cv2.threshold(gray,0,255, cv2.THRESH_OTSU + cv2.THRESH_BINARY_INV)[1]
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (55,1))
detect_horizontal = cv2.morphologyEx(board_thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(transformed, [c], -1, (36,255,12), 9)
    pass

# Remove vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,55))
detect_vertical = cv2.morphologyEx(board_thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(transformed, [c], -1, (36,255,12), 9)

cv2.imwrite('thresh.png', thresh)
cv2.imwrite('image.png', image)
cv2.imwrite('mask.png', mask)
cv2.imwrite('transformed.png', transformed)
cv2.waitKey()

有没有一种方法可以使用OpenCV更好地加载图像

1 个答案: