检测文本之间的空间(OpenCV，Python) [英] Detect space between text (OpenCV, Python)

问题描述

我有以下代码(实际上，这是运行我正在处理的所有项目所需的代码的四分之一.)

I have the following code (which is in fact just 1 part of 4 needed to run all the project I am working on..):

#python classify.py --model models/svm.cpickle --image images/image.png

from __future__ import print_function
from sklearn.externals import joblib
from hog import HOG
import dataset
import argparse
import mahotas
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required = True,
    help = "path to where the model will be stored")
ap.add_argument("-i", "--image", required = True,
    help = "path to the image file")
args = vars(ap.parse_args())

model = joblib.load(args["model"])

hog = HOG(orientations = 18, pixelsPerCell = (10, 10),
    cellsPerBlock = (1, 1), transform = True)

image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

blurred = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(blurred, 30, 150)
(_, cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

cnts = sorted([(c, cv2.boundingRect(c)[0]) for c in cnts], key =
    lambda x: x[1])

for (c, _) in cnts:
    (x, y, w, h) = cv2.boundingRect(c)

    if w >= 7 and h >= 20:
        roi = gray[y:y + h, x:x + w]
        thresh = roi.copy()
        T = mahotas.thresholding.otsu(roi)
        thresh[thresh > T] = 255
        thresh = cv2.bitwise_not(thresh)

        thresh = dataset.deskew(thresh, 20)
        thresh = dataset.center_extent(thresh, (20, 20))

        cv2.imshow("thresh", thresh)

        hist = hog.describe(thresh)
        digit = model.predict([hist])[0]
        print("I think that number is: {}".format(digit))

        cv2.rectangle(image, (x, y), (x + w, y + h),
        (0, 255, 0), 1)
        cv2.putText(image, str(digit), (x - 10, y - 10),
        cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 255, 0), 2)
        cv2.imshow("image", image)
        cv2.waitKey(0)

此代码用于检测和识别图像中的手写数字.这是一个示例:

This code is detecting and recognizing handwriten digits from images. Here is an example:

假设我不在乎准确性识别.

Let's say I don't care about the accuracy recognition.

我的问题如下:如您所见，该程序将使用可以看到的所有数字并将其打印在控制台中.从控制台，我可以将它们保存在文本文件中，但我不能告诉程序数字之间没有空格.

My problem is the following: as you can see, the program take all the numbers he can see and print them in console. From console I can save them in a text file if I want BUT I can't tell the program that there is a space between the numbers.

我想要的是，如果我将数字打印在文本文件中，则应将它们与图像中的数字分开(对不起，但有点难以解释.). 数字不应该(即使在控制台中)一起打印，但在有空白的地方，也要打印空白.

What I want is that, if I print the numbers in a text file, they should be separated as in the image (sorry but it's a bit hard to explain..). The numbers should not be (even in console) printed all together but, where there is blank space, printed a blank area also.

看看第一个图像.前10位数字之后，图像中有空白，控制台中没有空白.

Take a look at the firs image. After the first 10 digits, there is a blank space in image which there isn't in console.

无论如何，这是完整代码的链接.有4个.py文件和3个文件夹.要执行该命令，请在文件夹中打开CMD，然后粘贴命令python classify.py --model models/svm.cpickle --image images/image.png，其中image.png是images文件夹中一个文件的名称.

Anyway, here is a link to full code. There are 4 .py files and 3 folders. To execute, open a CMD in the folder and paste the command python classify.py --model models/svm.cpickle --image images/image.png where image.png is the name of one file in images folder.

完整代码

先谢谢了.在我看来，所有这些工作都必须使用神经网络来完成，但我想首先尝试这种方式.我对此很陌生.

Thanks in advance. In my opinion all this work would have to be done using neural networks but I want to try it first this way. I'm pretty new to this.

推荐答案

使用此代码完成工作.它可以检测图像中的文本/数字区域.

Used this code to do the job. It detects region of text/digits in images.

import cv2

image = cv2.imread("C:\\Users\\Bob\\Desktop\\PyHw\\images\\test5.png")
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY) # grayscale
_,thresh = cv2.threshold(gray,150,255,cv2.THRESH_BINARY_INV) # threshold
kernel = cv2.getStructuringElement(cv2.MORPH_CROSS,(3,3))
dilated = cv2.dilate(thresh,kernel,iterations = 13) # dilate
_, contours, hierarchy = cv2.findContours(dilated,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE) # get contours


idx =0
# for each contour found, draw a rectangle around it on original image
for contour in contours:

    idx += 1

    # get rectangle bounding contour
    [x,y,w,h] = cv2.boundingRect(contour)

    # discard areas that are too large
    if h>300 and w>300:
        continue

    # discard areas that are too small
    if h<40 or w<40:
        continue

    # draw rectangle around contour on original image
    #cv2.rectangle(image,(x,y),(x+w,y+h),(255,0,255),2)

    roi = image[y:y + h, x:x + w]

    cv2.imwrite('C:\\Users\\Bob\\Desktop\\' + str(idx) + '.jpg', roi)

    cv2.imshow('img',roi)
    cv2.waitKey(0)

代码基于以下其他问题/答案:提取文本OpenCV

The code is based on this other question/answer: Extracting text OpenCV

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