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....
---
aoiOcr.py | 4 ++--
sid_process.py | 47 +++++++++++++++++++++++++++++++++++++++--------
2 files changed, 41 insertions(+), 10 deletions(-)
diff --git a/aoiOcr.py b/aoiOcr.py
index 74e4fdb..02eef68 100644
--- a/aoiOcr.py
+++ b/aoiOcr.py
@@ -6,8 +6,8 @@
classifier = joblib.load("filename.joblib")
#p = Paper(filename="testpage300dpi_scan1.png")
-p=Paper(filename='sizif111.tif', sid_classifier=classifier, settings=settings)
-# p=Paper(filename='processed_scans/20141016095134535_0028.tif')
+#p=Paper(filename='sizif111.tif', sid_classifier=classifier, settings=settings)
+p=Paper(filename='processed_scans/20141016095134535_0006.tif', sid_classifier=classifier, settings=settings)
# print(p.QRData)
# print(p.errors)
diff --git a/sid_process.py b/sid_process.py
index 67c689a..48326c0 100644
--- a/sid_process.py
+++ b/sid_process.py
@@ -79,6 +79,32 @@
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)
@@ -89,7 +115,9 @@
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)
@@ -101,18 +129,21 @@
# Thining again
image = img_as_ubyte(morphology.skeletonize(image > 0.5))
image = cv2.morphologyEx(image, cv2.MORPH_DILATE, kernel(10, 10))
-
+ 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])
sid_no = ""
- sid_len = 0
- if sid_mask is not None:
- if len(sid_mask)==len(sorted_ctrs):
- sid_no=segment_by_contours(image,sorted_ctrs,classifier)
- else:
- print("Ooops have to find another way")
+ #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)
+ 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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