Python：图像分割作为分类的预处理

Question

你推荐分段这个图像中的字符准备喂养一个像MNIST数据集一样使用的模型;因为他们一次只带一个角色。这个问题无论是改变图像及其二值化的重要性都是无与伦比的。

谢谢！

Answer 1

作为一个起点，我会尝试以下方法：

1. 使用OTSU阈值。
2. 比做一些形态学操作来摆脱噪音并隔离每个数字。
3. 运行连接组件labling。
4. 将每个连接的组件送到您的分类器，以便在分类得分低丢弃时识别数字。
5. 最终确认你期望所有的数字或多或少处于在线状态，并且或多或少地与彼此之间保持一定的距离。

以下是前4个阶段。现在您需要添加识别软件来识别数字。

import cv2 
import numpy as np 
from matplotlib import pyplot as plt 

# Params 
EPSSILON = 0.4 
MIN_AREA = 10 
BIG_AREA = 75 

# Read img 
img = cv2.imread('i.jpg',0) 

# Otzu threshold 
a,thI = cv2.threshold(img,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU) 

# Morpholgical 
se = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(1,1)) 
thIMor = cv2.morphologyEx(thI,cv2.MORPH_CLOSE,se) 

# Connected compoent labling 
stats = cv2.connectedComponentsWithStats(thIMor,connectivity=8) 

num_labels = stats[0] 
labels = stats[1] 
labelStats = stats[2] 

# We expect the conneccted compoennt of the numbers to be more or less with a constats ratio 
# So we find the medina ratio of all the comeonets because the majorty of connected compoent are numbers 
ratios = [] 
for label in range(num_labels): 

    connectedCompoentWidth = labelStats[label,cv2.CC_STAT_WIDTH] 
    connectedCompoentHeight = labelStats[label, cv2.CC_STAT_HEIGHT] 

    ratios.append(float(connectedCompoentWidth)/float(connectedCompoentHeight)) 

# Find median ratio 
medianRatio = np.median(np.asarray(ratios)) 

# Go over all the connected component again and filter out compoennt that are far from the ratio 
filterdI = np.zeros_like(thIMor) 
filterdI[labels!=0] = 255 
for label in range(num_labels): 

    # Ignore biggest label 
    if(label==1): 
     filterdI[labels == label] = 0 
     continue 

    connectedCompoentWidth = labelStats[label,cv2.CC_STAT_WIDTH] 
    connectedCompoentHeight = labelStats[label, cv2.CC_STAT_HEIGHT] 

    ratio = float(connectedCompoentWidth)/float(connectedCompoentHeight) 
    if ratio > medianRatio + EPSSILON or ratio < medianRatio - EPSSILON: 
     filterdI[labels==label] = 0 

    # Filter small or large compoennt 
    if labelStats[label,cv2.CC_STAT_AREA] < MIN_AREA or labelStats[label,cv2.CC_STAT_AREA] > BIG_AREA: 
     filterdI[labels == label] = 0 

plt.imshow(filterdI) 


# Now go over each of the left compoenet and run the number recognotion 
stats = cv2.connectedComponentsWithStats(filterdI,connectivity=8) 
num_labels = stats[0] 
labels = stats[1] 
labelStats = stats[2] 

for label in range(num_labels): 

    # Crop the bounding box around the component 
    left = labelStats[label,cv2.CC_STAT_LEFT] 
    top = labelStats[label, cv2.CC_STAT_TOP] 
    width = labelStats[label, cv2.CC_STAT_WIDTH] 
    height = labelStats[label, cv2.CC_STAT_HEIGHT] 
    candidateDigit = labels[top:top+height,left:left+width] 


    # plt.figure(label) 
    # plt.imshow(candidateDigit) 

Answer 2

我连接到Amitay答案。

为2： 我会用细化的形态操作（看thinning algorithm in opencv）

对于3： 而在OpenCV的3.0已经有一个叫做cv::connectedComponents）功能

希望它可以帮助