I want to isolate every character in the following image:

and it should create a rectangular bounding box around each character. My code is creating a circular bounding box. I need to supply these isolated character images to my trained model to predict the character. I haven't done image processing before which leads me to asking such a question.

This is the code I'm using:

# Standard imports

import cv2

import numpy as np;

from PIL import Image

params = cv2.SimpleBlobDetector_Params()

# Change thresholds

params.minThreshold = 10;

params.maxThreshold = 200;

#Filter by Color

params.filterByColor=False

params.blobColor=255

# Filter by Area.

params.filterByArea = False

params.minArea = 50

# Filter by Circularity

params.filterByCircularity = False

params.minCircularity = 0.0785

#

# # Filter by Convexity

params.filterByConvexity = False

params.minConvexity = 0.87

#

# # Filter by Inertia

params.filterByInertia = False

params.minInertiaRatio = 0.01

# Read image

im = cv2.imread("C:\\xx\\testimages\\bw_plate.jpg", cv2.IMREAD_GRAYSCALE)

cv2.threshold(im,200,255,cv2.THRESH_BINARY_INV,im)

# Set up the detector with default parameters.

detector = cv2.SimpleBlobDetector_create(params)

# Detect blobs.

keypoints = detector.detect(im)

# Draw detected blobs as red circles.

# cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS ensures the size of the circle corresponds to the size of blob

im_with_keypoints = cv2.drawKeypoints(im, keypoints, np.array([]), (0, 0, 255),

cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)

# Show keypoints

cv2.imshow("Keypoints", im_with_keypoints)

cv2.waitKey(0)

My output with the following code is:

Why is it not detecting 0 and 2 properly? Also how can I create separate jpeg files for every isolated character?

The C++ implementation of my project uses CblobResult class which did the segmentation. Is there any equivalent library in python?

This is what the final output must look like for every character after segmentation:

解决方案

After removing background noises you can input image like this:

Then you can get what you want using following code:

import cv2

img = cv2.imread('test4.jpg', 0)

cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU,img)

image, contours, hier = cv2.findContours(img, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)

contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])

cv2.imshow("contours", img)

cv2.waitKey(0)

d=0

for ctr in contours:

# Get bounding box

x, y, w, h = cv2.boundingRect(ctr)

# Getting ROI

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

cv2.imshow('character: %d'%d,roi)

cv2.imwrite('character_%d.png'%d, roi)

cv2.waitKey(0)

cv2.destroyAllWindows()

d+=1