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python - 为什么我的图像在 OpenCV Python 中显示为灰色？

问题描述

我正在开发一个项目，该项目由我的代码识别数独拼图的图像然后解决它组成。我现在正在研究图像识别部分。它工作得很好，直到我意识到我一直在让整个程序在 y 轴上翻转。所以我换了

dimensions = np.array([[0, 0], [width, 0], [width, height], [0, height]], dtype = "float32")

和

dimensions = np.array([[width, 0], [0, 0], [0, height], [width, height]], dtype = "float32")

这似乎改变了一切，现在我运行它，我只得到一个灰色的图像。这是我的代码。请注意，我对 opencv 相当陌生。另外，我在我的代码中画线，当我运行它时，这些线仍然出现。只是没有出现实际图像。

#Imports
import cv2 as cv
import numpy as np
import math

#Load image
img = cv.imread('sudoku_test_image.jpeg')

#Transforms perspective
def perspectiveTransform(img, corners):
    def orderCornerPoints(corners):
        #Corners sperated into their own points
        #Index 0 = top-right
        #      1 = top-left
        #      2 = bottom-left
        #      3 = bottom-right

        #Corners to points
        corners = [(corner[0][0], corner[0][1]) for corner in corners]

        add = np.sum(corners)
        top_l = corners[np.argmin(add)]
        bottom_r = corners[np.argmax(add)]
        diff = np.diff(corners, 1)
        top_r = corners[np.argmin(diff)]
        bottom_l = corners[np.argmax(diff)]

        return (top_r, top_l, bottom_l, bottom_r)

    ordered_corners = orderCornerPoints(corners)
    top_r, top_l, bottom_l, bottom_r = ordered_corners

    #Find width of new image (Using distance formula)
    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))

    #Find height of new image (Using distance formula)
    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))

    #Make top down view
    #Order: top-right, top-left, bottom-left, bottom-right
    dimensions = np.array([[width, 0], [0, 0], [0, height], [width, height]], dtype = "float32")

    #Make ordered_corners var numpy format
    ordered_corners = np.array(ordered_corners, dtype = 'float32')

    #Transform the perspective
    m = cv.getPerspectiveTransform(ordered_corners, dimensions)

    return cv.warpPerspective(img, m, (width, height))

#Processes image (Grayscale, median blur, adaptive threshold)
def processImage(img):
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    blur = cv.medianBlur(gray, 3)
    thresh = cv.adaptiveThreshold(blur,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY_INV,11,3)
    return thresh

#Find and sort contours
img_processed = processImage(img)
cnts = cv.findContours(img_processed, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnts = sorted(cnts, key=cv.contourArea, reverse=True)

#Perform perspective transform
peri = cv.arcLength(cnts[0], True)
approx = cv.approxPolyDP(cnts[0], 0.01 * peri, True)
transformed = perspectiveTransform(img, approx)

#Draw lines
height = transformed.shape[0]
width = transformed.shape[1]

#for vertical lines
line_x = 0
x_increment_val = round((1/9) * width)

#for horizontal lines
line_y = 0
y_increment_val = round((1/9) * height)

#vertical lines
for i in range(10):
    cv.line(transformed, (line_x, 0), (line_x, height), (0, 0, 255), 1)
    line_x += x_increment_val

#horizontal lines
for i in range(10):
    cv.line(transformed, (0, line_y), (width, line_y), (0, 0, 255), 1)
    line_y += y_increment_val

#Show image
cv.imshow('Sudoku', transformed)
cv.waitKey(0)
cv.destroyAllWindows()

这是我的输入图像 我的输入图像

标签： pythonopencv

解决方案

似乎输入角点计算错误。在您的perspectiveTransform函数中，您有以下代码段，显然计算了数独谜题的四个角：

    # Corners to points
    corners = [(corner[0][0], corner[0][1]) for corner in corners]

    add = np.sum(corners)
    top_l = corners[np.argmin(add)]
    bottom_r = corners[np.argmax(add)]
    diff = np.diff(corners, 1)
    top_r = corners[np.argmin(diff)]
    bottom_l = corners[np.argmax(diff)]

    return (top_r, top_l, bottom_l, bottom_r)

检查(top_r, top_l, bottom_l, bottom_r)元组。那些坐标是错误的，我不知道你在计算之后在做什么，corners但是top_l,和计算肯定有问题。如果您像这样对元组进行硬编码：bottom_rtop_rbottom_l

ordered_corners = [(697, 99), (108, 121), (52, 730), (735, 730)] # orderCornerPoints(corners)

要通过拼图的实际角落（从右上角开始，逆时针），那么您的转换是正确的：

在此处输入图像描述

建议：当存在程序错误时，使用调试器，逐步调试，检查变量的实际值和中间计算。

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