From ac766ed5ec375a384da5c454103aef055aa9344a Mon Sep 17 00:00:00 2001
From: Samo Penic <samo.penic@gmail.com>
Date: Fri, 16 Nov 2018 20:41:33 +0000
Subject: [PATCH] recognition is a bit more robust....
---
sid_process.py | 101 +++++++++++++++++++++++++++++++++++---------------
1 files changed, 71 insertions(+), 30 deletions(-)
diff --git a/sid_process.py b/sid_process.py
index 90d9b33..48326c0 100644
--- a/sid_process.py
+++ b/sid_process.py
@@ -1,9 +1,8 @@
import cv2
import numpy as np
-from skimage import morphology,img_as_ubyte
+from skimage import morphology, img_as_ubyte
from sklearn import svm
from sklearn.externals import joblib
-
"""
@@ -61,48 +60,90 @@
return np.ones((x, y), np.uint8)
-def getSID(image, classifier):
- image=255-image
- image=img_as_ubyte(image>100)
+def segment_by_contours(image, sorted_ctrs, classifier):
+ 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])
+ return sid_no
+
+
+def segment_by_sid_len(image,sid_len, classifier):
+ sid_no=""
+ #find biggest block of pixels
+
+ image1=cv2.morphologyEx(image,cv2.MORPH_DILATE, kernel(5,25), iterations=3)
+ cv2.imwrite("sidblock1.png",image1)
+ im2, ctrs, hier = cv2.findContours(
+ image1.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+ )
+ sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.contourArea(ctr)) #get bigges contour
+ x, y, w, h = cv2.boundingRect(sorted_ctrs[-1])
+ image=image[y:y+h,x+25:x+w-25]
+ cv2.imwrite("sidblock2.png",image)
+ imgHeight, imgWidth = image.shape[0:2]
+ numWidth=int(imgWidth/(sid_len))
+ for i in range(0,sid_len):
+ num=image[:,i*numWidth:(i+1)*numWidth]
+ num = img_as_ubyte(num < 128)
+ num = cv2.resize(num, (32, 32))
+
+ # cv2.rectangle(image,(x,y),( x + w, y + h ),(0,255,0),2)
+ cv2.imwrite("sid_no_{}.png".format(i), num)
+ sid_no = sid_no + str(classifier.predict(num.reshape(1, -1) / 255.0)[0])
+ return sid_no
+
+
+def getSID(image, classifier, sid_mask):
+ 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)
+ 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, 3), iterations=4)
# Again noise removal after closing
- image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(8,8), iterations=1)
+ #image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(8, 8), iterations=1)
+ image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(3, 3), iterations=1)
+
# Skeletonization
- image = img_as_ubyte(morphology.thin(image>128))
- cv2.imwrite("enSID1.png",image)
+ 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
# Making lines stronger
image = cv2.morphologyEx(image, cv2.MORPH_DILATE, kernel(5, 5), iterations=1)
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernel(10, 10))
# Thining again
- image = img_as_ubyte(morphology.skeletonize(image>0.5))
+ 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)
+ cv2.imwrite("enhancedSID.png",image)
+ 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])
+ sid_no = ""
+ #sid_len = len(sid_mask)
+ #sid_no = segment_by_sid_len(image, sid_len, classifier)
+ #if sid_mask is not None:
+ print(len(sid_mask),len(sorted_ctrs))
+ #if len(sid_mask)==len(sorted_ctrs):
+ sid_no=segment_by_contours(image,sorted_ctrs[1:],classifier)
print(sid_no)
- return image
+ if(len(sid_no)!=len(sid_mask)):
+ print("Ooops have to find another way")
+ sid_no=segment_by_sid_len(image,len(sid_mask),classifier)
+ return sid_no
--
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