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,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,bottom_r order
    dimensions = np.array([[0,0],[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,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],(36,12),3)
        cv2.drawContours(mask,(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,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.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(transformed,-1,9)
    pass

# Remove vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(1,55))
detect_vertical = cv2.morphologyEx(board_thresh,vertical_kernel,iterations=2)
cnts = cv2.findContours(detect_vertical,9)

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