def byThreshold(img):
grad = getGradient(img, useGradient=False)
im2, contours, hierarchy = cv2.findContours(grad, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 1)
return tl.concat_ver((img, grad))
def generateData(vis=False):
chars = string.ascii_uppercase + string.digits
# chars = string.digits
font = np.arange(0, 6, 1) #randint(0, 5)
# size = np.arange(2.5, 4, 0.5) #uniform(2.5, 3.5)
line_size = np.arange(1, 4, 1) #randint(1, 3)
blur = np.arange(0, 2, 1) #randint(0, 1)
kw = np.arange(1, 9, 2) #randint(3, 7)
kh = np.arange(1, 9, 2) #randint(3, 7)
generatedImgs = []
rows = []
for c in chars:
print("Generating - ", c)
row = []
for f in font:
for l in line_size:
# for b in blur:
for i in kw:
for j in kh:
img, _ = genSymbolImg(c, f, l, 1, i, j)
# _, croped = segmentSymbolsByContours(img)
resized = cv2.resize(img, (30, 40))
row.append(resized)
# cv2.imshow("W", resized)
# k = cv2.waitKey(0)
# if k == 27:
# exit()
generatedImgs.append(row)
rows.append(tl.concat_hor(row))
# cv2.imshow("ROW", tl.concat_hor(row))
# k = cv2.waitKey(0)
# if k == 27:
# break
print("Generation done")
generatedVisImg = tl.concat_ver(rows)
return generatedImgs, generatedVisImg
def segmentSymbolsByContours(img):
vis = cv2.cvtColor((255 - img), cv2.COLOR_GRAY2BGR)
gray = 255 - img
'''
Binarization
'''
ret, th = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
im2, contours, hierarchy = cv2.findContours(th, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
textRegions = []
for cnt in contours:
x, y, w, h = cv2.boundingRect(cnt)
if w * h > 10:
textRegions.append((x, y, x + w, y + h))
cv2.rectangle(vis, (x, y), (x + w, y + h), (0, 0, 255), 1)
break
bigImg = tl.concat_ver((vis, gray, th))
return bigImg, img[y:y + h, x:x + w]
else:
break
verticalSegments.append(0)
verticalSegments.append(imgBand.shape[1]-1)
verticalSegments = np.asarray(verticalSegments, dtype=np.int)
verticalSegments.sort()
# print verticalSegments
total, founded = dtl.getSegmentation1Accuracy(verticalSegments, maskCharBand)
indx = 1 if imagepath.find(categories[0]) != -1 else 0
totalPlates[indx] += 1
totalChars[indx] += total
foundedChars[indx] += founded
if total == founded:
fullPlates[indx] += 1
framenum += 1
fimg = tl.concat_ver((imgBand, thres, _horpDr))
#cv2.imshow("Result", cv2.resize(fimg,(0,0),fx=3,fy=3))
#cv2.waitKey(0)
cv2.imwrite("debug_imgs/Segmentation/"+str(framenum)+".jpg", fimg)
for i in range(len(categories)):
print ""
print "##########", categories[i - 1]
print "########## From %d characters founded %d not founded %d" % (totalChars[i], foundedChars[i], totalChars[i]-foundedChars[i])
print "########## From %d plates totally segmented %d not segmented %d" % (totalPlates[i], fullPlates[i], totalPlates[i]-fullPlates[i])
import cv2
import numpy as np
import tools as tl
img = cv2.imread("sudoku.png", 0)
ret, th = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
ret, th_inv = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
th_adap11 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2)
th_adap5 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 11, 2)
ret_o, th_o = cv2.threshold(img, 0, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
print(ret_o)
print(cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
row1 = tl.concat_hor((img, th, th_o))
row2 = tl.concat_hor((img, th_adap11, th_adap5))
final_img = tl.concat_ver((row1, row2))
cv2.imshow("Sudoku", final_img)
cv2.waitKey(0)
meanHeight = hsum / float(len(contours))
textRegions = []
for box in reversed(boxRegions):
if box[3] > meanHeight:
x, y, w, h = box
textRegions.append((x, y, x + w, y + h))
cv2.rectangle(vis, (x, y), (x + w, y + h), (0, 255, 0), 1)
textImg = []
for i in textRegions:
x1, y1, x2, y2 = i
textImg.append(img[y1:y2, x1:x2])
allTexts = tl.concat_ver(textImg)
row1 = tl.concat_hor((img, img_inv, filtered))
row2 = tl.concat_hor((th_o, closing, vis, allTexts))
# final = tl.concat_ver((row2))
cv2.imshow('myimg', cv2.resize(row2, (0, 0), fx=1, fy=1))
cv2.waitKey(0)
# for root, dirs, files in os.walk("../Data/"):
# for file in files:
# imagepath = os.path.join(root, file)
# if file.endswith('.jpg'):
# img = cv2.imread(imagepath)
import numpy as np
import tools as tl
import genSmallTextImg as gsti
img, text = gsti.genSmallTextImg()
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# blur = cv2.blur(img,(21,1))
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img_inv = 255 - img
filtered = cv2.blur(img_inv, (5, 5))
ret_o, th_o = cv2.threshold(filtered, 0, 255, cv2.THRESH_OTSU)
im2, countours, hier = cv2.findContours(th_o, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(vis, countours, -1, (255, 0, 0), 1)
hsum = 0
for cnt in countours:
(x, y, w, h) = cv2.boundingRect(cnt)
hsum += h
meanH = hsum / float(len(countours))
for cnt in countours:
(x, y, w, h) = cv2.boundingRect(cnt)
print(h)
if (h > meanH - meanH * 0.1):
cv2.rectangle(vis, (x, y), (x + w, y + h), (0, 0, 255), 1)
final = tl.concat_ver((img, vis))
cv2.imshow("Sudoku", final)
cv2.waitKey(0)
verP = np.sum(gray, axis=1) / 255
verPVis = tl.getDrawProjectionVer(vis, verP)
gradX = cv2.Sobel(gray, ddepth=cv2.CV_64F, dx=1, dy=0, ksize=3)
gradX = np.abs(gradX)
(minVal, maxVal) = (np.min(gradX), np.max(gradX))
if maxVal - minVal > 0:
gradX = (255 * (
(gradX - minVal) / float(maxVal - minVal))).astype("uint8")
else:
gradX = np.zeros(gray.shape, dtype="uint8")
verPX = np.sum(gradX, axis=1) / 255
verPXVis = tl.getDrawProjectionVer(vis, verPX)
row1 = tl.concat_hor((gray, verPVis))
row2 = tl.concat_hor((gradX, verPXVis))
bigImg = tl.concat_ver((row1, row2))
scale = 0.8
cv2.imshow("Window2", cv2.resize(bigImg, (0, 0),
fx=scale,
fy=scale))
cv2.moveWindow("Window2", 0, 0)
k = cv2.waitKey(0)
if k == 27:
exit()
elif k == ord('q'):
exit()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, th = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
print ret
kernel = np.ones((5, 5), dtype=np.uint8)
# erosion = cv2.erode(th, kernel, iterations = 1)
# dilation = cv2.dilate(erosion, kernel, iterations = 1)
closing = cv2.morphologyEx(th, cv2.MORPH_OPEN, kernel, iterations=2)
im2, countours, hier = cv2.findContours(closing, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
print len(countours)
vis = cv2.cvtColor(closing, cv2.COLOR_GRAY2BGR)
cv2.drawContours(vis, countours, -1, (0, 255, 0), -1)
for cnt in countours:
x, y, w, h = cv2.boundingRect(cnt)
cv2.rectangle(vis, (x, y), (x + w, y + h), (0, 0, 255), 1)
final_img = tl.concat_ver((img, th, vis))
scale = 0.9
cv2.namedWindow("Final", cv2.WINDOW_AUTOSIZE)
cv2.imshow("Final", cv2.resize(final_img, (0, 0), fx=scale, fy=scale))
cv2.waitKey(0)
break
verticalSegments.append(0)
verticalSegments.append(imgBand.shape[1] - 1)
verticalSegments = np.asarray(verticalSegments, dtype=np.int)
verticalSegments.sort()
# print verticalSegments
total, founded = dtl.getSegmentation1Accuracy(verticalSegments,
maskCharBand)
indx = 1 if imagepath.find(categories[0]) != -1 else 0
totalPlates[indx] += 1
totalChars[indx] += total
foundedChars[indx] += founded
if total == founded:
fullPlates[indx] += 1
framenum += 1
fimg = tl.concat_ver((imgBand, bright, equ, thres, _horpDr))
cv2.imwrite("debug_imgs/Segmentation/" + str(framenum) + ".jpg", fimg)
for i in range(len(categories)):
print ""
print "##########", categories[i - 1]
print "########## From %d characters founded %d not founded %d" % (
totalChars[i], foundedChars[i], totalChars[i] - foundedChars[i])
print "########## From %d plates totally segmented %d not segmented %d" % (
totalPlates[i], fullPlates[i], totalPlates[i] - fullPlates[i])
if box[3] > meanHeight:
x,y,w,h = box
textRegions.append((x,y, x+w,y+h))
cv2.rectangle(vis,(x,y),(x+w,y+h),(0,0,255),1)
'''
Extract all text regions
'''
regionImgs = []
for textRegion in textRegions:
x1,y1,x2,y2 = textRegion
regionImgs.append(img[y1:y2,x1:x2])
scale = 0.8
row1 = tl.concat_hor((vis, gradX, blur))
row2 = tl.concat_hor((th, opening))
allTexts = tl.concat_ver(regionImgs)
bigImg = tl.concat_ver((row1, row2))
cv2.imshow("Window1", cv2.resize(bigImg, (0,0), fx = scale, fy = scale))
cv2.imshow("Window2", cv2.resize(allTexts, (0,0), fx = 1.4, fy = 1.4))
k = cv2.waitKey(0)
if k == 27:
exit()
elif k == ord('q'):
exit()
