From 02e0f7bc51acfa06e7299919b54b56a3c7eca02b Mon Sep 17 00:00:00 2001
From: Samo Penic <samo.penic@gmail.com>
Date: Fri, 16 Nov 2018 18:22:33 +0000
Subject: [PATCH] Initial version of sid recognition. Cutting the numerals is not perfect yet.
---
sid_process.py | 33 +++++++++++++++++++++++++++++----
1 files changed, 29 insertions(+), 4 deletions(-)
diff --git a/sid_process.py b/sid_process.py
index 210cfe7..90d9b33 100644
--- a/sid_process.py
+++ b/sid_process.py
@@ -1,6 +1,10 @@
import cv2
import numpy as np
from skimage import morphology,img_as_ubyte
+from sklearn import svm
+from sklearn.externals import joblib
+
+
"""
(1) The text is an array of chars (in row-major order) where
@@ -57,19 +61,18 @@
return np.ones((x, y), np.uint8)
-def enhanceSID(image):
+def getSID(image, classifier):
image=255-image
image=img_as_ubyte(image>100)
cv2.imwrite("enSID0.png", image)
# Remove noise
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(2,2), iterations=1)
# Closing. Connect non connected parts
- image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(5, 5), iterations=2)
+ image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(5, 3), iterations=4)
# Again noise removal after closing
+
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(8,8), iterations=1)
# Skeletonization
- ##For thinning I am using erosion
- ##image = cv2.erode(image,kernel(4,4),iterations = 40)
image = img_as_ubyte(morphology.thin(image>128))
cv2.imwrite("enSID1.png",image)
# Stub removal (might not be necessary if thinning instead of skeletonize is used above
@@ -80,4 +83,26 @@
# Thining again
image = img_as_ubyte(morphology.skeletonize(image>0.5))
image = cv2.morphologyEx(image, cv2.MORPH_DILATE, kernel(10, 10))
+
+ im2,ctrs, hier = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+ sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])
+
+ #classifier = joblib.load('filename.joblib')
+
+ sid_no=""
+ for i, ctr in enumerate(sorted_ctrs):
+ # Get bounding box
+ x, y, w, h = cv2.boundingRect(ctr)
+ # Getting ROI
+ if(w<h/2):
+ sid_no=sid_no+"1"
+ continue
+ roi = image[y:y+h, x:x+w]
+ roi = img_as_ubyte(roi < 128)
+ roi = cv2.resize(roi,(32,32))
+
+ #cv2.rectangle(image,(x,y),( x + w, y + h ),(0,255,0),2)
+ cv2.imwrite('sid_no_{}.png'.format(i), roi)
+ sid_no=sid_no+str(classifier.predict(roi.reshape(1,-1)/255.0)[0])
+ print(sid_no)
return image
--
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