From 762a5e258a90387922d6c6eb3ecc9a7ca7c96144 Mon Sep 17 00:00:00 2001
From: Samo Penic <samo.penic@gmail.com>
Date: Fri, 16 Nov 2018 18:53:49 +0000
Subject: [PATCH] refactored and debugged.
---
aoiOcr.py | 4 +
sid_process.py | 68 +++++++++++++++++++--------------
Ocr.py | 8 +++-
3 files changed, 48 insertions(+), 32 deletions(-)
diff --git a/Ocr.py b/Ocr.py
index 35c6729..970fb70 100644
--- a/Ocr.py
+++ b/Ocr.py
@@ -6,13 +6,14 @@
class Paper:
- def __init__(self, filename=None, sid_classifier=None):
+ def __init__(self, filename=None, sid_classifier=None, settings=None):
self.filename = filename
self.invalid = None
self.QRData = None
+ self.settings = settings
self.errors = []
self.warnings = []
- self.sid_classifier=sid_classifier
+ self.sid_classifier = sid_classifier
if filename is not None:
self.loadImage(filename)
self.runOcr()
@@ -216,11 +217,14 @@
def get_enhanced_sid(self):
if self.sid_classifier is None:
return "x"
+ if self.settings is not None:
+ sid_mask=self.settings.get("sid_mask", None)
es = getSID(
self.img[
int(0.045 * self.imgHeight) : int(0.085 * self.imgHeight),
int(0.7 * self.imgWidth) : int(0.99 * self.imgWidth),
],
self.sid_classifier,
+ sid_mask
)
return es
diff --git a/aoiOcr.py b/aoiOcr.py
index 72ed3b9..5a27df0 100644
--- a/aoiOcr.py
+++ b/aoiOcr.py
@@ -1,10 +1,12 @@
from Ocr import Paper
from sklearn.externals import joblib
+
+settings={'sid_mask':'11xx0xxx',}
classifier = joblib.load('filename.joblib')
#p=Paper(filename='testpage300dpi_scan1.png')
-p=Paper(filename='sizif111.tif', sid_classifier=classifier)
+p=Paper(filename='sizif111.tif', sid_classifier=classifier, settings=settings)
#p=Paper(filename='processed_scans/20141016095134535_0028.tif')
print(p.QRData)
diff --git a/sid_process.py b/sid_process.py
index 90d9b33..67c689a 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 +60,59 @@
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 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)
# 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)
+ 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 = 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")
print(sid_no)
- return image
+ return sid_no
--
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