From d5c694ac711ca3b434bf16bd920b90d1a7e758c4 Mon Sep 17 00:00:00 2001
From: Samo Penic <samo.penic@gmail.com>
Date: Sat, 17 Nov 2018 09:57:31 +0000
Subject: [PATCH] Improving the robustness of all three algorithms.
---
sid_process.py | 126 ++++++++++++++++++++++++++++++++---------
1 files changed, 98 insertions(+), 28 deletions(-)
diff --git a/sid_process.py b/sid_process.py
index 48326c0..1f93d3c 100644
--- a/sid_process.py
+++ b/sid_process.py
@@ -1,8 +1,6 @@
import cv2
import numpy as np
from skimage import morphology, img_as_ubyte
-from sklearn import svm
-from sklearn.externals import joblib
"""
@@ -59,6 +57,13 @@
def kernel(x, y):
return np.ones((x, y), np.uint8)
+def sid_compare(sid_no, sid_mask):
+ for s,es in zip(sid_mask,sid_no):
+ if s!='x' and s!=es:
+ return False
+ return True
+
+
def segment_by_contours(image, sorted_ctrs, classifier):
sid_no = ""
@@ -79,23 +84,34 @@
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)
+def segment_by_sid_len(image, original_image, sid_mask, classifier):
+ sid_no = ""
+ sid_len = len(sid_mask)
+ if sid_mask[0] == "1":
+ move_left = 45
+ elif sid_mask[0] == "x":
+ move_left = 55
+ else:
+ move_left = 0
+ # Remove noise
+ image2 = cv2.morphologyEx(original_image, cv2.MORPH_OPEN, kernel(2, 2), iterations=7)
+ # find biggest block of pixels
+ image1 = cv2.morphologyEx(image2, cv2.MORPH_DILATE, kernel(5, 25), iterations=4)
+ image1=img_as_ubyte(image1>50)
+ 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
+ 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)
+ image = image[y : y + h, x + 25 - move_left : x + w - 40] #+25,-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]
+ 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))
@@ -104,18 +120,66 @@
sid_no = sid_no + str(classifier.predict(num.reshape(1, -1) / 255.0)[0])
return sid_no
+def segment_by_7segments(image,original_image,sid_mask,classifier):
+ block_image = cv2.morphologyEx(original_image, cv2.MORPH_CLOSE, kernel(2, 2), iterations=10)
+ block_image =img_as_ubyte(block_image<50)
+ cv2.imwrite("sid_3rd1.png", block_image)
+ template = cv2.imread("template-8.png", 0)
+ w, h = template.shape[::-1]
+ res = cv2.matchTemplate(block_image, template, cv2.TM_CCOEFF_NORMED)
+ loc = np.where(res >= 0.75)
+ cimg = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
+ loc_filtered_x=[]
+ loc_filtered_y=[]
+ for pt in zip(*loc[::-1]):
+ pt=(pt[0]-10,pt[1]-10)
+ loc_filtered_y.append(pt[1])
+ loc_filtered_x.append(pt[0])
+# points.append(pt)
+ #filter points
+ if(len(loc_filtered_x)==0):
+ return ""
+ loc_filtered_x, loc_filtered_y = zip(
+ *sorted(zip(loc_filtered_x, loc_filtered_y))
+ )
+ a = np.diff(loc_filtered_x) > int(w/2)
+ a = np.append(a, True)
+ loc_filtered_x = np.array(loc_filtered_x)
+ loc_filtered_y = np.array(loc_filtered_y)
+ points = [loc_filtered_y[a], loc_filtered_x[a]]
+ for pt in zip(*points[::-1]):
+ cv2.rectangle(cimg, pt, (pt[0] + w, pt[1] + h), (0, 255, 255), 2)
+ cv2.imwrite("sid_3rd2.png", cimg)
+
+ sid_no=""
+ for i,pt in enumerate(zip(*points[::-1])):
+ num=image[pt[1]:pt[1] + h, pt[0]:pt[0]+w]
+ #cv2.imwrite("sid_3no_{}.png".format(i), num)
+ num = img_as_ubyte(num < 128)
+ try:
+ num = cv2.resize(num, (32, 32))
+ except:
+ return ""
+ cv2.imwrite("sid_3no_{}.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):
+ sid_warn = []
+ sid_err=[]
image = 255 - image
+ image_original=image.copy()
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=3)
# 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)
+ # don't do too much noise removal.
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel(3, 3), iterations=1)
# Skeletonization
@@ -129,21 +193,27 @@
# 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)
+ 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 = 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(len(sid_mask), len(sorted_ctrs))
+ sid_no = segment_by_contours(
+ image, sorted_ctrs[1:], classifier
+ ) # we remove largest contour that surrounds whole image
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
+ if len(sid_no) != len(sid_mask) or not sid_compare(sid_no,sid_mask):
+ sid_warn.append("Trying second SID algorithm.")
+ sid_no = segment_by_7segments(image, image_original, sid_mask, classifier)
+ print(sid_no)
+ if(len(sid_no))!=len(sid_mask):
+ sid_no = segment_by_sid_len(image, image_original, sid_mask, classifier)
+ sid_warn.append("Trying third SID algorithm.")
+
+
+ if not sid_compare(sid_no, sid_mask):
+ sid_err=['Wrong SID!']
+
+ return (sid_no, sid_err, sid_warn)
--
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