def ocr():
api = tesseract.TessBaseAPI()
api.Init(".", "eng", tesseract.OEM_DEFAULT)
api.SetPageSegMode(tesseract.PSM_AUTO)
mImgFile = "eurotext.jpg"
pixImage = tesseract.pixRead(mImgFile)
api.SetImage(pixImage)
text = api.GetUTF8Text()
conf = api.MeanTextConf()
print(text, len(text))
print("Cofidence Level: %d %%" % conf)
print("Confidences of All words")
header("Method 1", "*" * 10)
confOfText = api.AllWordConfidences()
print(confOfText)
print("Number of Words:")
print("counted by tesseract: %d" % len(confOfText))
print("counted by me: %d[%d]" % (countWords(text), countWords2(text)))
if len(confOfText) != countWords(text):
print("Why the words counted by tesseract are different from mine!!!!")
header("Method 2", "*" * 10)
confs = tesseract.AllWordConfidences(api)
print(confs, len(confs))
def getConfidence(mask, angle, me):
aDeg = math.degrees(angle)
rotated = rotate(mask, 90.0 - aDeg, False, (me[1], me[0]))
#rotated = rotate(mask, 90.0-adeg, False)
io.imsave("temp3_0.png", rotated)
#using the OCR
gifTempFile = "temp3_1.gif"
tifTempFile = "temp3_2.tif"
im = Image.open('temp3_0.png')
im.save(gifTempFile, "GIF")
original = Image.open(gifTempFile)
bg = original.resize(im.size, Image.NEAREST)
bg.save(tifTempFile)
pixImage = tesseract.pixRead(tifTempFile)
api.SetImage(pixImage)
outText = api.GetUTF8Text()
conf = api.MeanTextConf()
if showProcess:
wait = input("PRESS ENTER TO CONTINUE.")
if conf <= 10:
conf = 10
return conf
def ocr():
api = tesseract.TessBaseAPI()
api.Init(".","eng",tesseract.OEM_DEFAULT)
api.SetPageSegMode(tesseract.PSM_AUTO)
mImgFile = "eurotext.jpg"
pixImage=tesseract.pixRead(mImgFile)
api.SetImage(pixImage)
text=api.GetUTF8Text()
conf=api.MeanTextConf()
print(text,len(text))
print("Cofidence Level: %d %%"%conf)
print("Confidences of All words")
header("Method 1","*"*10)
confOfText=api.AllWordConfidences()
print(confOfText)
print("Number of Words:")
print("counted by tesseract: %d"%len(confOfText))
print("counted by me: %d[%d]"%(countWords(text), countWords2(text)))
if len(confOfText)!=countWords(text):
print("Why the words counted by tesseract are different from mine!!!!")
header("Method 2","*"*10)
confs=tesseract.AllWordConfidences(api)
print(confs, len(confs))
def gettext(imagepath):
api = tesseract.TessBaseAPI()
api.SetOutputName("outputName")
api.Init(".", "eng", tesseract.OEM_DEFAULT)
api.SetPageSegMode(tesseract.PSM_AUTO)
pixImage = tesseract.pixRead(imagepath)
api.SetImage(pixImage)
outText = api.GetUTF8Text()
print("OCR output:\n%s" % outText)
api.End()
return outText
def imageToText(self,imageLocation,imageTextLoc):
if not self.api:
self.api = tesseract.TessBaseAPI()
#self.api.SetOutputName("outputName")
self.api.Init("includes", "eng", tesseract.OEM_DEFAULT)
self.api.SetPageSegMode(tesseract.PSM_AUTO)
pixImage = tesseract.pixRead(imageLocation)
self.api.SetImage(pixImage)
outText = self.api.GetUTF8Text()
GObject.idle_add(self.addRowData, self.listStoreCounter, 2, "Saving Text to File")
textFile = open(imageTextLoc, 'w')
textFile.write(str(outText))
textFile.close()
GObject.idle_add(self.addRowData, self.listStoreCounter, 2, "Text Saved to File")
#self.api.End()
(outText,pixImage) = (None,None)
def imageToText(self, imageLocation, imageTextLoc):
if not self.api:
self.api = tesseract.TessBaseAPI()
#self.api.SetOutputName("outputName")
self.api.Init("includes", "eng", tesseract.OEM_DEFAULT)
self.api.SetPageSegMode(tesseract.PSM_AUTO)
pixImage = tesseract.pixRead(imageLocation)
self.api.SetImage(pixImage)
outText = self.api.GetUTF8Text()
GObject.idle_add(self.addRowData, self.listStoreCounter, 2,
"Saving Text to File")
textFile = open(imageTextLoc, 'w')
textFile.write(str(outText))
textFile.close()
GObject.idle_add(self.addRowData, self.listStoreCounter, 2,
"Text Saved to File")
#self.api.End()
(outText, pixImage) = (None, None)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#from __future__ import print_function
import tesseract
import gc
import pprint
api = tesseract.TessBaseAPI()
api.SetOutputName("outputName");
#api.Init(".","eng")
api.Init(".","eng",tesseract.OEM_DEFAULT)
api.SetPageSegMode(tesseract.PSM_AUTO)
mImgFile = "eurotext.jpg"
print("Method 1: Leptonica->pixRead")
pixImage=tesseract.pixRead(mImgFile)
print("Type of pixiamge=",type(pixImage))
print("repr(pixiamge)=",repr(pixImage))
api.SetImage(pixImage)
outText=api.GetUTF8Text()
print(("OCR output:\n%s"%outText));
api.End()
outText=None
tesseract.pixDestroy(pixImage)
rotated = rotate(mask, 90.0 - adeg, False, (me[1], me[0]))
#rotated = rotate(mask, 90.0-adeg, False)
io.imsave("rotated3/" + str(i) + ".png", rotated)
#using the OCR
gifTempFile = "o1.gif"
tifTempFile = "o2.tif"
im = Image.open('rotated3/' + str(i) + '.png')
im.save(gifTempFile, "GIF")
original = Image.open(gifTempFile)
bg = original.resize(im.size, Image.NEAREST)
bg.save(tifTempFile)
pixImage = tesseract.pixRead(tifTempFile)
api.SetImage(pixImage)
outText = api.GetUTF8Text()
outText = outText.replace("\n", "")
outText = outText.replace("\t", "")
conf = api.MeanTextConf()
f.write(str(i) + "\t" + outText + "\t" + str(conf) + "\n")
f.close()
api.End()
'''
mask = mask * 255
io.imsave('bing.png', mask)
block_size = 40
api = tesseract.TessBaseAPI()
api.Init(tesslangpath, "eng", tesseract.OEM_DEFAULT)
api.SetPageSegMode(tesseract.PSM_SINGLE_CHAR)
api.SetVariable("classify_enable_learning", "0")
api.SetVariable("classify_enable_adaptive_matcher", "0")
image_dict = OrderedDict()
segment_text_list = []
cluster_pattern_list = []
# classify
for fname in test_images:
test_image = ImageFile(fname)
test_classes, test_segments = ocr.ocr(test_image, show_steps=verbose)
if use_tesseract:
tesseract_image = tesseract.pixRead(fname)
tesseract_classes = []
cluster_list = []
for segment in test_segments:
cluster_segments = prim.get_cluster(segment, test_segments)
if len(cluster_segments) == 10:
add = True
for list_cluster in cluster_list:
add = add and not tesseract_utils.is_cluster_match(
cluster_segments, list_cluster)
if not add:
break
if add:
cluster_list.append(cluster_segments)
pattern = tesseract_utils.get_pattern(cluster_segments)
cluster_pattern_list.append([cluster_segments, pattern])
api = tesseract.TessBaseAPI()
api.Init(tesslangpath, "eng", tesseract.OEM_DEFAULT)
api.SetPageSegMode(tesseract.PSM_SINGLE_CHAR)
api.SetVariable("classify_enable_learning", "0")
api.SetVariable("classify_enable_adaptive_matcher", "0")
image_dict = OrderedDict()
segment_text_list = []
cluster_pattern_list = []
# classify
for fname in test_images:
test_image = ImageFile(fname)
test_classes, test_segments = ocr.ocr(test_image, show_steps=verbose)
if use_tesseract:
tesseract_image = tesseract.pixRead(fname)
tesseract_classes = []
cluster_list = []
for segment in test_segments:
cluster_segments = prim.get_cluster(segment, test_segments)
if len(cluster_segments) == 10:
add = True
for list_cluster in cluster_list:
add = add and not tesseract_utils.is_cluster_match(cluster_segments, list_cluster)
if not add:
break
if add:
cluster_list.append(cluster_segments)
pattern = tesseract_utils.get_pattern(cluster_segments)
cluster_pattern_list.append([cluster_segments, pattern])
whitelist = tesseract_utils.get_whitelist(segment, cluster_segments, pattern)
def process(image):
global tess_api
sz = 500
ratio = (sz + 0.0) / image.shape[1]
dim = (sz, int(image.shape[0] * ratio))
orig = image
image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
if Param.Value("bilateral"):
bila = cv2.bilateralFilter(image, 11, 17, 17)
else:
bila = image
gray = cv2.cvtColor(bila, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 25, 50)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
if Param.Value("dilate"):
canny = cv2.dilate(canny, kernel, iterations=Param.Value("dilate"))
if Param.Value("erode"):
canny = cv2.erode(canny, kernel, iterations=Param.Value("erode"))
goodCnts = []
badCnts = []
if Param.Value('contours'):
(cnts, hier) = cv2.findContours(canny.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# loop over our contours
for c in cnts:
if cv2.contourArea(c) < 1000:
continue
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.01 * peri, True)
# print len(approx)
good = False
# if our approximated contour has four points, then
# we can assume that we have found our screen
if len(approx) == 4:
d01 = np.sum((approx[0] - approx[1])**2)
d12 = np.sum((approx[1] - approx[2])**2)
wh_ratio = np.sqrt(np.divide(float(d01), d12))
if wh_ratio > 1.5 and wh_ratio < 3:
good = True
elif 1 / wh_ratio > 1.5 and 1 / wh_ratio < 3:
approx = np.roll(approx, 1, axis=0)
good = True
if good:
goodCnts.append(approx)
else:
badCnts.append(approx)
cv2.drawContours(image, goodCnts, -1, (0, 255, 0), 2)
if Param.Value('all_contours'):
cv2.drawContours(image, badCnts, -1, (128, 128, 0), 1)
warped = None
if Param.Value('lines_p') and len(goodCnts) > 0:
lines = cv2.HoughLinesP(canny,
1,
np.pi / 180,
Param.Value('lines_threshold'),
minLineLength=Param.Value('lines_minlength'),
maxLineGap=Param.Value('lines_maxgap'))
if lines is None:
lines = []
else:
lines = lines[0]
goodLines = []
goodBoxes = []
badLines = []
for line in lines:
line = np.array([[line[0], line[1]], [line[2], line[3]]])
length2 = np.sum((line[0] - line[1])**2)
ok = False
for cnt in goodCnts:
minLength2 = np.sum((cnt[0] - cnt[3])**2) * 2
if length2 < minLength2:
continue
d = [geoPointLineDist(point[0], line) for point in cnt]
if d[0] < 10 and d[3] < 10:
goodLines.append(line)
goodBoxes.append(cnt)
elif d[1] < 10 and d[2] < 10:
goodLines.append(line)
goodBoxes.append(np.roll(cnt, 2, axis=0))
elif Param.Value('show_bad_lines'):
badLines.append(line)
if Param.Value('show_bad_lines'):
for line in goodLines:
cv2.line(image, line[0], line[1], (0, 0, 255), 1)
for line in badLines:
cv2.line(image, line[0], line[1], (0, 0, 255), 1)
if len(goodLines) > 0:
g = np.array(goodLines)
longestIndex = np.argmax(
np.sum((g[:, 1:2, :] - g[:, 0:1, :])**2, axis=2))
longestLine = goodLines[longestIndex]
cv2.line(image, tuple(longestLine[0]), tuple(longestLine[1]),
(200, 200, 0), 2)
box = goodBoxes[longestIndex]
tl = np.array(geoClosestPoint(box[0][0], longestLine, False))
bl = np.array(geoClosestPoint(box[3][0], longestLine, False))
tr = box[1][0]
br = box[2][0]
h = bl - tl
tl = tl + h * 3.1
bl = tl + h
h = br - tr
tr = tr + h * 3.1
br = tr + h
w = tr - tl
tl = tl - w * 1.1
tr = tr + w * 0.6
w = br - bl
bl = bl - w * 1.1
br = br + w * 0.6
tl = tl / ratio
tr = tr / ratio
bl = bl / ratio
br = br / ratio
width = int(np.sqrt(np.sum((tl - tr)**2)))
height = int(np.sqrt(np.sum((tl - bl)**2)))
dst = np.array([[0, 0], [width - 1, 0], [width - 1, height - 1],
[0, height - 1]],
dtype="float32")
rect = np.array([tl, tr, br, bl], dtype="float32")
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(orig, M, (width, height))
if Param.Value('lines'):
lines = cv2.HoughLines(canny, 1, np.pi / 180,
Param.Value('lines_threshold'))
for rho, theta in (lines[0] if lines is not None else []):
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0 = b * rho
x1 = int(
x0 + 1000 * (-b)
) # Here i have used int() instead of rounding the decimal value, so 3.8 --> 3
y1 = int(
y0 + 1000 * (a)
) # But if you want to round the number, then use np.around() function, then 3.8 --> 4.0
x2 = int(
x0 - 1000 * (-b)
) # But we need integers, so use int() function after that, ie int(np.around(x))
y2 = int(y0 - 1000 * (a))
cv2.line(image, (x1, y1), (x2, y2), (0, 128, 128), 2)
Param.DisplayAll(image)
cv2.imshow("Image", display([image, bila, gray, canny]))
if warped is not None:
if Param.Value('ocr'):
warped = cv2.bilateralFilter(warped, 11, 17, 17)
if tess_api is None:
tess_api = tesseract.TessBaseAPI()
tess_api.Init(".", "eng", tesseract.OEM_DEFAULT)
tess_api.SetVariable("tessedit_char_whitelist", "0123456789+>")
tess_api.SetPageSegMode(tesseract.PSM_AUTO)
cv2.imwrite("ocr.png", warped) # SetCvImage segfauls. F**k it.
pixImage = tesseract.pixRead("ocr.png")
tess_api.SetImage(pixImage)
outText = tess_api.GetUTF8Text()
for line in outText.split("\n"):
if len(line) > 40:
print line
esr = fixEsr(line)
if esr is None:
esr = fixEsr(re.sub(r'\d (\d)', '+ \\1', line))
print esr
if esr is not None:
cv2.putText(warped, esr, (10, 20),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 128),
2)
cv2.imshow("Image2", warped)
def process(image):
global tess_api
sz = 500
ratio = (sz + 0.0) / image.shape[1]
dim = (sz, int(image.shape[0] * ratio))
orig = image
image = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
if Param.Value("bilateral"):
bila = cv2.bilateralFilter(image, 11, 17, 17)
else:
bila = image
gray = cv2.cvtColor(bila, cv2.COLOR_BGR2GRAY)
canny = cv2.Canny(gray, 25, 50)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
if Param.Value("dilate"):
canny = cv2.dilate(canny, kernel, iterations = Param.Value("dilate"))
if Param.Value("erode"):
canny = cv2.erode(canny, kernel, iterations = Param.Value("erode"))
goodCnts = []
badCnts = []
if Param.Value('contours'):
(cnts, hier) = cv2.findContours(canny.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# loop over our contours
for c in cnts:
if cv2.contourArea(c) < 1000:
continue
# approximate the contour
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.01 * peri, True)
# print len(approx)
good = False
# if our approximated contour has four points, then
# we can assume that we have found our screen
if len(approx) == 4:
d01 = np.sum((approx[0]-approx[1])**2)
d12 = np.sum((approx[1]-approx[2])**2)
wh_ratio = np.sqrt(np.divide(float(d01), d12))
if wh_ratio > 1.5 and wh_ratio < 3:
good = True
elif 1/wh_ratio > 1.5 and 1/wh_ratio < 3:
approx = np.roll(approx, 1, axis=0)
good = True
if good:
goodCnts.append(approx)
else:
badCnts.append(approx)
cv2.drawContours(image, goodCnts, -1, (0, 255, 0), 2)
if Param.Value('all_contours'):
cv2.drawContours(image, badCnts, -1, (128, 128, 0), 1)
warped = None
if Param.Value('lines_p') and len(goodCnts) > 0:
lines = cv2.HoughLinesP(canny, 1, np.pi/180, Param.Value('lines_threshold'), minLineLength = Param.Value('lines_minlength'), maxLineGap = Param.Value('lines_maxgap'))
if lines is None:
lines = []
else:
lines = lines[0]
goodLines = []
goodBoxes = []
badLines = []
for line in lines:
line = np.array([[line[0], line[1]], [line[2], line[3]]])
length2 = np.sum((line[0]-line[1])**2)
ok = False
for cnt in goodCnts:
minLength2 = np.sum((cnt[0]-cnt[3])**2) * 2
if length2 < minLength2:
continue
d = [geoPointLineDist(point[0], line) for point in cnt]
if d[0] < 10 and d[3] < 10:
goodLines.append(line)
goodBoxes.append(cnt)
elif d[1] < 10 and d[2] < 10:
goodLines.append(line)
goodBoxes.append(np.roll(cnt, 2, axis=0))
elif Param.Value('show_bad_lines'):
badLines.append(line)
if Param.Value('show_bad_lines'):
for line in goodLines:
cv2.line(image, line[0], line[1], (0,0,255), 1)
for line in badLines:
cv2.line(image, line[0], line[1], (0,0,255), 1)
if len(goodLines) > 0:
g = np.array(goodLines)
longestIndex = np.argmax(np.sum((g[:,1:2,:] - g[:,0:1,:])**2, axis=2))
longestLine = goodLines[longestIndex]
cv2.line(image, tuple(longestLine[0]), tuple(longestLine[1]), (200, 200, 0), 2)
box = goodBoxes[longestIndex]
tl = np.array(geoClosestPoint(box[0][0], longestLine, False))
bl = np.array(geoClosestPoint(box[3][0], longestLine, False))
tr = box[1][0]
br = box[2][0]
h = bl - tl
tl = tl + h * 3.1
bl = tl + h
h = br - tr
tr = tr + h * 3.1
br = tr + h
w = tr - tl
tl = tl - w * 1.1
tr = tr + w * 0.6
w = br - bl
bl = bl - w * 1.1
br = br + w * 0.6
tl = tl/ratio
tr = tr/ratio
bl = bl/ratio
br = br/ratio
width = int(np.sqrt(np.sum((tl-tr)**2)))
height = int(np.sqrt(np.sum((tl-bl)**2)))
dst = np.array([
[0, 0],
[width - 1, 0],
[width - 1, height - 1],
[0, height - 1]], dtype = "float32")
rect = np.array([tl, tr, br, bl], dtype = "float32")
M = cv2.getPerspectiveTransform(rect, dst)
warped = cv2.warpPerspective(orig, M, (width, height))
if Param.Value('lines'):
lines = cv2.HoughLines(canny, 1, np.pi/180, Param.Value('lines_threshold'))
for rho,theta in (lines[0] if lines is not None else []):
a = np.cos(theta)
b = np.sin(theta)
x0 = a*rho
y0 = b*rho
x1 = int(x0 + 1000*(-b)) # Here i have used int() instead of rounding the decimal value, so 3.8 --> 3
y1 = int(y0 + 1000*(a)) # But if you want to round the number, then use np.around() function, then 3.8 --> 4.0
x2 = int(x0 - 1000*(-b)) # But we need integers, so use int() function after that, ie int(np.around(x))
y2 = int(y0 - 1000*(a))
cv2.line(image, (x1,y1), (x2,y2), (0,128,128), 2)
Param.DisplayAll(image)
cv2.imshow("Image", display([image, bila, gray, canny]))
if warped is not None:
if Param.Value('ocr'):
warped = cv2.bilateralFilter(warped, 11, 17, 17)
if tess_api is None:
tess_api = tesseract.TessBaseAPI()
tess_api.Init(".","eng",tesseract.OEM_DEFAULT)
tess_api.SetVariable("tessedit_char_whitelist", "0123456789+>")
tess_api.SetPageSegMode(tesseract.PSM_AUTO)
cv2.imwrite("ocr.png", warped) # SetCvImage segfauls. F**k it.
pixImage=tesseract.pixRead("ocr.png")
tess_api.SetImage(pixImage)
outText=tess_api.GetUTF8Text()
for line in outText.split("\n"):
if len(line) > 40:
print line
esr = fixEsr(line)
if esr is None:
esr = fixEsr(re.sub(r'\d (\d)', '+ \\1', line))
print esr
if esr is not None:
cv2.putText(warped, esr, (10, 20), cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 128), 2)
cv2.imshow("Image2", warped)
