def segments(self, show_int, pause_int):
img = self.img_gray
kernel1 = np.ones((2, 2), np.uint8)
kernel2 = np.ones((1, 1), np.uint8)
all_heights = []
def show_img(name, fname):
if int(show_int) != 0:
cv2.imshow(name, fname)
if int(pause_int) != 0:
cv2.waitKey(0)
show_img('Output0', img)
boxes_temp = np.zeros(img.shape[:2], np.uint8)
print "boxes generated"
binary = preprocessing.binary_img(img)
show_img('BinaryOutput', binary)
comp_labels, num = label(binary, return_num=True)
comp_labels = np.array(comp_labels)
allsegments = []
for component in range(1, (num)):
coordinates = {'x': 0, 'y': 0, 'h': 0, 'w': 0}
index = np.where(comp_labels == component)
Ymax = np.amax(index[0])
Ymin = np.amin(index[0])
Xmax = np.amax(index[1])
Xmin = np.amin(index[1])
cv2.rectangle(binary, (Xmin, Ymin), (Xmax, Ymax), (255, 0, 0), -1)
coordinates['x'] = Xmin
coordinates['y'] = Ymin
coordinates['w'] = (Xmax - Xmin)
coordinates['h'] = (Ymax - Ymin)
allsegments.append(coordinates)
show_img('WITHRECT', binary)
return allsegments
resultimagename = splitimagename[0] + "-result.jpg"
kernel1 = np.ones((2, 2), np.uint8)
kernel2 = np.ones((1, 1), np.uint8)
all_heights = []
img = cv2.imread(image, 0)
resultimage = cv2.imread(image)
# cv2.imshow('Output0',img)
words_temp = np.zeros(img.shape[:2], np.uint8)
boxes_temp = np.zeros(img.shape[:2], np.uint8)
binary = preprocessing.binary_img(img)
# cv2.imshow('Outputimg1',binary)
dilation = cv2.dilate(binary, kernel1, iterations=1)
# cv2.imshow('Outputimg2',dilation)
erosion = cv2.dilate(dilation, kernel1, iterations=1)
# cv2.imshow('Outputimg3',erosion)
edges = cv2.Canny(dilation, 50, 100)
# cv2.imshow('edges',edges)
dilation2 = cv2.dilate(edges, kernel1, iterations=1)
# cv2.imshow('Outputimg9999',dilation2)
inv9999 = 255 - dilation2
def init(self):
self.img_gray = cv2.cvtColor(self.img_rgb, cv2.COLOR_BGR2GRAY)
self.img_bin = preprocessing.binary_img(self.img_gray)
self.w, self.h = self.img_gray.shape[::-1]
import os, sys
from array import array
import cv2
import numpy as np
import json
import cv
import argparse
import shutil
import preprocessing
from scipy.stats import mode
img = cv2.imread("durga.jpg",0)
img_rgb = preprocessing.binary_img(img)
cv2.imshow("edges",img_rgb)
cv2.waitKey(0)
with open( str(bookdir+jsonfile) ) as f :
d = json.load( f )
allimages = []
allimages = d['book']['images']
#print allimages
f= open(fuzzysegbookdir+segbookfilename,'w')
"""str2="\n{\n \"imagepath\": "+"\""+fuzzysegbookdir+"\""+","
f.write(str2)
str3="\n \"template-imagepath\": "+"\""+tempimg+ "\""+","
f.write(str3)
str4= "\n \"segments\":[\n"
f.write(str4)"""
templateimg = cv2.imread(tempimg,0)
tempimg_bin = preprocessing.binary_img(templateimg)
w, h = templateimg.shape[::-1]
for i in range(len(allimages)):
nMatches = 0
str2="\n{\n \"imagepath\": "+"\""+fuzzysegbookdir+"\""+","
f.write(str2)
str3="\n \"template-imagepath\": "+"\""+tempimg+ "\""+","
f.write(str3)
str4= "\n \"segments\":[\n"
f.write(str4)
#print bookdir+allimages[i];
splitimagename=allimages[i].split(".")
resultimage=splitimagename[0]+"-result.jpg"
"""
***** this code is for preprocessing of both the source and template image and then apply template-match function.*****
def find_segments(self, show_int, pause_int, known_segments=None):
img = self.img_gray
kernel1 = np.ones((2, 2), np.uint8)
kernel2 = np.ones((1, 1), np.uint8)
all_heights = []
def show_img(name, fname):
if int(show_int) != 0:
cv2.imshow(name, fname)
if int(pause_int) != 0:
cv2.waitKey(0)
show_img('Output0', img)
boxes_temp = np.zeros(img.shape[:2], np.uint8)
print "boxes generated"
binary = preprocessing.binary_img(img)
show_img('BinaryOutput', binary)
dilation = cv2.dilate(binary, kernel1, iterations=1)
show_img('Dilation', dilation)
erosion = cv2.dilate(dilation, kernel1, iterations=1)
show_img('Erosion', erosion)
edges = cv2.Canny(dilation, 50, 100)
show_img('Edges', edges)
dilation2 = cv2.dilate(edges, kernel1, iterations=1)
show_img('Dilation9999', dilation2)
inv9999 = 255 - dilation2
show_img('inv9999', inv9999)
edges = cv2.dilate(edges, kernel1, iterations=1)
ret, thresh = cv2.threshold(erosion, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
x, y, w, h = cv2.boundingRect(c)
#annotate(boxes_temp,(255,255,255),-1)
if h > 10:
all_heights.append(h)
std_dev = np.std(all_heights)
mn = np.mean(all_heights)
md = np.median(all_heights)
for xx in contours:
cv2.drawContours(edges, [xx], -1, (255, 255, 255), -1)
show_img('edges2', edges)
ret, thresh = cv2.threshold(erosion, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
x, y, w, h = cv2.boundingRect(c)
#if (mn+(std_dev/2)<h):
cv2.rectangle(boxes_temp, (x, y), (x + w, y + h), (255, 0, 0), -1)
#annotate(img,(255,0,0),2)
show_img('boxes_temp', boxes_temp)
ret, thresh = cv2.threshold(boxes_temp, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
allsegments = []
for c in contours:
coordinates = DotDict({
'x': 0,
'y': 0,
'h': 0,
'w': 0,
'score': float(0.0)
})
x, y, w, h = cv2.boundingRect(c)
coordinates['x'] = x
coordinates['y'] = y
coordinates['w'] = w
coordinates['h'] = h
allsegments.append(ImgSegment(coordinates))
if known_segments is None:
known_segments = DisjointSegments()
disjoint_matches = known_segments.merge(allsegments)
# print "Disjoint Segments = " + json.dumps(disjoint_matches)
return disjoint_matches
