Python：图像分割作为分类的预处理 [英] Python: Image Segmentation as pre-process for Classification

问题描述

您建议使用哪种技术分割此图像中的字符，以准备输入与MNIST数据集一起使用的模型;因为他们一次只占一个角色。这个问题无论如何转变图像及其二值化都很重要。

What technique do you recommend to segment the characters in this image to be ready to fed a model like the ones use with MNIST dataset; because they take one character at a time. This question is regadless the importance of transforming the image and the binarization of it.

谢谢！

推荐答案

作为起点，我会尝试以下方法：

As a starting point i would try the following:

1. 使用OTSU阈值。


2. 比做一些形态操作以消除噪音并隔离每个数字。


3. 运行连接组件labling。


4. 如果分类得分较低，则将每个连接的组件提供给分类器以识别数字。


5. 最终验证您希望所有数字在线或多或少或多或少彼此保持一定距离。

1. Use OTSU threshold.
2. Than do some morphological operations to get rid of noise and to isolate each digit.
3. Run connected component labling.
4. Fed each connected component to your classifier to get recognize the digit if the classification score is low discard.
5. Final validation you expect all the digit to be more or less on line and in more or less some constant distance from each other.

以下是前4个阶段。现在，您需要添加识别软件以识别数字。

Here are the first 4 stages. Now you need to add your recognition software to recognize the digits.

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)

这篇关于Python：图像分割作为分类的预处理的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！