本文介紹了從opencv中的數獨中提取框的處理方法，對大家解決問題具有一定的參考價值，需要的朋友們下面隨著小編來一起學習吧！
問題描述
我已經使用 opencv 將數獨圖像轉換為數獨網格
現在我想從圖像中提取每個框，最好的方法是什么?
據我所知，我正在嘗試找到線的交點以找到每個框的角
類 SudokuSolverPlay:def __init__(自我，圖像):def __preProcess(self, img):""""返回灰度圖像""""def __maskSudoku(self, img):""""返回蒙版圖像""""def __dectactEdge(self, img):""""返回數獨格子""""def drawLines(src, dest, 迭代=1):minLineLength = 100src = cv2.convertScaleAbs(src)對于 _ 在范圍內(迭代):線 = cv2.HoughLinesP(image=src, rho=1, theta=np.pi/180,閾值=100，行=np.array([])，minLineLength=minLineLength, maxLineGap=100)a, b, c = 線條.形狀對于范圍內的 i (a):x1, y1, x2, y2 = 線[i][0][0], 線[i][0][1], 線[i][0][2], 線[i][0][3]cv2.line(dest, (x1, y1), (x2, y2),255, 1, cv2.LINE_AA)src = cv2.convertScaleAbs(dest)def findVerticalLines(img):imgX = cv2.GaussianBlur(img, (5, 5), 0)kernelx = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 10))imgY = cv2.Sobel(img, cv2.CV_64F, 1, 0)imgY = cv2.convertScaleAbs(imgY)cv2.normalize(imgY, imgY, 0, 255, cv2.NORM_MINMAX)imgY = cv2.morphologyEx(imgY, cv2.MORPH_CLOSE, kernelx, 迭代次數=1)返回 imgYdef findHorizo??ntalLines(img):""與上面相同，只是參數不同""img1 = np.zeros(img.shape)邊緣 = cv2.Canny(img, 50, 150, 孔徑 = 3)拉普拉斯算子 = cv2.拉普拉斯算子(邊，cv2.CV_64F)drawLines(拉普拉斯算子，img1，迭代=1)sby = findVerticalLines(img1)sbx = findHorizo??ntalLines(img1)返回圖片1def 解決數獨(自我):灰色 = self.__preProcess(self.__originalImg)蒙面 = self.__maskSudoku(灰色)網格 = self.__dectactGrid(蒙面)如果 __name__ == '__main__':colorImg = cv2.imread('sudoku1.jpg')求解器 = SudokuSolverPlay(colorImg)求解器.solveSudoku()這里的 findVerticalLines() 和 findHorizo??ntalLines() 無法正確識別水平線和垂直線
假設最小盒子尺寸為 20*20
line_min_width = 20尋找水平線
kernal_h = np.ones((1,line_min_width), np.uint8)img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernal_h)尋找垂直線
kernal_v = np.ones((line_min_width,1), np.uint8)img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernal_v)合并并添加膨脹層以縮小小間隙
img_bin_final=img_bin_h|img_bin_vfinal_kernel = np.ones((3,3), np.uint8)img_bin_final=cv2.dilate(img_bin_final,final_kernel,iterations=1)應用連通分量分析
ret, labels, stats,centroids = cv2.connectedComponentsWithStats(~img_bin_final, connected=8, ltype=cv2.CV_32S)可視化連接組件圖像
如您所見，我們檢測到一些文本也是框，我們可以通過簡單的過濾條件輕松刪除它們，這里我過濾的區域應至少為 1000 像素條件.
在檢測到的盒子上繪制矩形.
### 1 和 0 以及我們不需要的背景和殘差連接組件對于 stats[2:] 中的 x,y,w,h,area:# cv2.putText(image,'box',(x-10,y-10),cv2.FONT_HERSHEY_SIMPLEX, 1.0,(0,255,0), 2)如果面積＞1000:cv2.rectangle(圖像,(x,y),(x+w,y+h),(0,255,0),2)最終輸出圖片
此答案基于我使用 OpenCV 在圖像中查找復選框/表格的解決方案.您可以在我的 博客中找到詳細說明走向數據科學.希望這將使您更接近解決方案.
編碼愉快 :)
-- 編輯 1
進行連接組件可視化的代碼
def imshow_components(labels):### 創建一個 hsv 圖像，每個標簽都有一個唯一的色調值label_hue = np.uint8(179*labels/np.max(labels))###使飽和度和音量為255empty_channel = 255*np.ones_like(label_hue)label_img = cv2.merge([label_hue, empty_channel, empty_channel])### 將 hsv 圖像轉換為 BGR 圖像標記的_img = cv2.cvtColor(標記的_img，cv2.COLOR_HSV2BGR)標記的_img[label_hue==0] = 0### 返回用于可視化連接組件的彩色圖像返回labeled_imgi have converted sudoku image into sudoku grid using opencv
now i want to extract each box from image what is best way to do this?
as per my knowledge i am trying to find intersection points of lines to find corner of each box
class SudokuSolverPlay: def __init__(self, image): def __preProcess(self, img): """return grayscale image""" def __maskSudoku(self, img): """return masked image""" def __dectactEdge(self, img): """return sudoku grid""" def drawLines(src, dest, iteration=1): minLineLength = 100 src = cv2.convertScaleAbs(src) for _ in range(iteration): lines = cv2.HoughLinesP(image=src, rho=1, theta=np.pi / 180, threshold=100, lines=np.array([]), minLineLength=minLineLength, maxLineGap=100) a, b, c = lines.shape for i in range(a): x1, y1, x2, y2 = lines[i][0][0], lines[i][0][1], lines[i][0][2], lines[i][0][3] cv2.line(dest, (x1, y1), (x2, y2),255, 1, cv2.LINE_AA) src = cv2.convertScaleAbs(dest) def findVerticalLines(img): imgX = cv2.GaussianBlur(img, (5, 5), 0) kernelx = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 10)) imgY = cv2.Sobel(img, cv2.CV_64F, 1, 0) imgY = cv2.convertScaleAbs(imgY) cv2.normalize(imgY, imgY, 0, 255, cv2.NORM_MINMAX) imgY = cv2.morphologyEx(imgY, cv2.MORPH_CLOSE, kernelx, iterations=1) return imgY def findHorizontalLines(img): """same as above only args different""" img1 = np.zeros(img.shape) edges = cv2.Canny(img, 50, 150, apertureSize=3) laplacian = cv2.Laplacian(edges, cv2.CV_64F) drawLines(laplacian, img1, iteration=1) sby = findVerticalLines(img1) sbx = findHorizontalLines(img1) return img1 def solveSudoku(self): gray = self.__preProcess(self.__originalImg) masked = self.__maskSudoku(gray) grid = self.__dectactGrid(masked) if __name__ == '__main__': colorImg = cv2.imread('sudoku1.jpg') solver = SudokuSolverPlay(colorImg) solver.solveSudoku()here
findVerticalLines()andfindHorizontalLines()are not able to dictect horizontal and vertical lines properly- original image
- masked image
- Canny edge dictation
- hough line transform
- horizontal lines
- Vertical Lines
解決方案One way to solve is to do a morphological operation to find vertical and horizontal lines from the canny edge image, then do a connected component analysis to find the boxes. I have done a sample version below. You can finetune it further to make it better. I started with the masked image as input.
### reading input image gray_scale=cv2.imread('masked_image.jpg',0)Performing canny edge detection and adding a dilation layer
img_bin = cv2.Canny(gray_scale,50,110) dil_kernel = np.ones((3,3), np.uint8) img_bin=cv2.dilate(img_bin,dil_kernel,iterations=1)Now, the dilated binary image looks like this.
assuming minimum box size would be 20*20
line_min_width = 20finding horizontal lines
kernal_h = np.ones((1,line_min_width), np.uint8) img_bin_h = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernal_h)finding vertical lines
kernal_v = np.ones((line_min_width,1), np.uint8) img_bin_v = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernal_v)merging and adding a dilation layer to close small gaps
img_bin_final=img_bin_h|img_bin_v final_kernel = np.ones((3,3), np.uint8) img_bin_final=cv2.dilate(img_bin_final,final_kernel,iterations=1)applying connected component analysis
ret, labels, stats,centroids = cv2.connectedComponentsWithStats(~img_bin_final, connectivity=8, ltype=cv2.CV_32S)visualising Connected component image
AS you can see, we have detected some text also as boxes, we can easily remove them with simple filter conditions, Here I'm filtering with the area should be minimum 1000 pixels condition.
drawing rectangles on the detected boxes.
### 1 and 0 and the background and residue connected components whihc we do not require for x,y,w,h,area in stats[2:]: # cv2.putText(image,'box',(x-10,y-10),cv2.FONT_HERSHEY_SIMPLEX, 1.0,(0,255,0), 2) if area>1000: cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),2)final output image
This answer is based on my solution to find checkboxes/tables in an image using OpenCV. You can find a detailed explanation in my blog at Towards Data Science. Hope this will take you closer to a solution.
Happy coding :)
-- edit 1
code to do connected component visualisation
def imshow_components(labels): ### creating a hsv image, with a unique hue value for each label label_hue = np.uint8(179*labels/np.max(labels)) ### making saturation and volume to be 255 empty_channel = 255*np.ones_like(label_hue) labeled_img = cv2.merge([label_hue, empty_channel, empty_channel]) ### converting the hsv image to BGR image labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR) labeled_img[label_hue==0] = 0 ### returning the color image for visualising Connected Componenets return labeled_img這篇關于從opencv中的數獨中提取框的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網！
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