def extract_image_data(data, languages=None):
"""Extract text from a binary string of data."""
tessdata_prefix = get_config('TESSDATA_PREFIX')
if tessdata_prefix is None:
raise IngestorException("TESSDATA_PREFIX is not set, OCR won't work.")
languages = get_languages_iso3(languages)
text = Cache.get_ocr(data, languages)
if text is not None:
return text
try:
img = Image.open(StringIO(data))
except DecompressionBombWarning as dce:
log.debug("Image too large: %", dce)
return None
except IOError as ioe:
log.info("Unknown image format: %r", ioe)
return None
# TODO: play with contrast and sharpening the images.
extractor = Tesseract(tessdata_prefix, lang=languages)
extractor.set_image(img)
extractor.set_page_seg_mode(PageSegMode.PSM_AUTO_OSD)
text = extractor.get_text() or ''
text = text.decode(encoding="UTF-8")
# extractor.clear()
log.debug('OCR done: %s, %s characters extracted', languages, len(text))
Cache.set_ocr(data, languages, text)
return text
def extract_image_data(data, languages=None):
"""Extract text from a binary string of data."""
if TESSDATA_PREFIX is None:
raise ValueError('Env TESSDATA_PREFIX is not set, OCR will not work.')
key, text = get_cache(data)
if text is not None:
return text
try:
img = Image.open(StringIO(data))
except Exception as ex:
log.debug('Failed to parse image internally: %r', ex)
return ''
# TODO: play with contrast and sharpening the images.
try:
languages = _get_languages(languages)
extractor = Tesseract(TESSDATA_PREFIX, lang=languages)
extractor.set_page_seg_mode(PageSegMode.PSM_AUTO_OSD)
text = extractor.ocr_image(img)
log.debug('OCR done: %s, %s characters extracted', languages,
len(text))
set_cache(key, text)
return text
except Exception as ex:
log.exception(ex)
return ''
def parse_img():
im = Image.open("./temp.jpg") # the second one
im = im.filter(ImageFilter.MedianFilter())
enhancer = ImageEnhance.Contrast(im)
im = enhancer.enhance(2)
im = im.convert('1')
im.save('./temp2.jpg')
tr = Tesseract(os.environ["TESSDATA_PREFIX"],"eng")
text = tr.ocr_image(Image.open('./temp2.jpg'))
return redirect('http://mailsnail.tech/api/notify')
def index(request):
#from tesserwrap import Tesseract
#from PIL import Image
img = Image.open("/home/df/projects/django/nuspyp/tesseracttest/test.png")
tr = Tesseract()
tr.ocr_image(img)
img2 = dog(
filename='/home/df/projects/django/nuspyp/tesseracttest/source.pdf')
single_image = img2.sequence[0]
tr.ocr_image(single_image)
return HttpResponse(tr.get_text())
def __init__(self): cwd = os.path.dirname(os.path.realpath(__file__)) os.environ['TESSDATA_PREFIX'] = cwd self.tr = Tesseract(lang='deu') self.gs = goslate.Goslate() self.trained_paper = False self.paper_row_nw = None self.paper_row_se = None self.paper_col_nw = None self.paper_col_se = None self.paper_hist = None self.paper = None self.words = None self.translations = [] self.pointed_locations = deque(maxlen=20)
def extract_image_data(data, languages=None):
"""Extract text from a binary string of data."""
tessdata_prefix = get_config('TESSDATA_PREFIX')
if tessdata_prefix is None:
raise IngestorException("TESSDATA_PREFIX is not set, OCR won't work.")
languages = get_languages_iso3(languages)
text = Cache.get_ocr(data, languages)
if text is not None:
return text
img = Image.open(StringIO(data))
# TODO: play with contrast and sharpening the images.
extractor = Tesseract(tessdata_prefix, lang=languages)
extractor.set_page_seg_mode(PageSegMode.PSM_AUTO_OSD)
text = extractor.ocr_image(img)
log.debug('OCR done: %s, %s characters extracted', languages, len(text))
Cache.set_ocr(data, languages, text)
return text
def handleFrameForTaskB(self, frame, regionCoordinates):
try:
coordinates = list()
for point in regionCoordinates:
coordinates.append(
[point[0] * frame.shape[1], point[1] * frame.shape[0]])
coordinates = np.int0(coordinates)
frame = cv2.drawContours(frame, [coordinates], 0, (0, 255, 0), 2)
warped = four_point_transform(frame, coordinates)
shrunk = cv2.cvtColor(warped[:, int(warped.shape[1] / 10):],
cv2.COLOR_BGR2GRAY)
scale = 6
shrunk = cv2.resize(
shrunk, (shrunk.shape[1] * scale, shrunk.shape[0] * scale),
interpolation=cv2.INTER_CUBIC)
_, shrunk = cv2.threshold(shrunk, 100, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
shrunk = 255 - cv2.dilate(
255 - shrunk, np.ones((1, 1)), iterations=1)
num, features = cv2.connectedComponents(255 - shrunk)
plate = str()
corners = list()
for i in range(0, num):
area = np.sum((features == i))
if area > scale**2 * 2 * 25 and area < scale * 4 * 500:
rows = np.any(features == i, axis=1)
cols = np.any(features == i, axis=0)
rmin, rmax = np.where(rows)[0][[0, -1]]
cmin, cmax = np.where(cols)[0][[0, -1]]
corners.append([rmin, cmin, rmax, cmax])
corners = np.array(corners)
idx = np.argsort(corners[:, 1])
sorted_corners = corners[idx]
for corner in sorted_corners:
minx = corner[0] - 2
miny = corner[1] - 2
maxx = corner[2] + 2
maxy = corner[3] + 2
if minx < 0:
minx = 0
if miny < 0:
miny = 0
snip = features[minx:maxx, miny:maxy]
if snip.shape[1] > snip.shape[0]:
continue
snip = cv2.erode(snip.astype(np.uint8),
np.ones((5, 5)),
iterations=1)
im = Image.fromarray(np.uint8(snip))
tr = Tesseract(datadir="/usr/share/tessdata")
letter = tr.ocr_image(im).rstrip()
for l in letter:
if l.isalnum():
letter = l
plate += letter.capitalize()
alphs = "".join(itertools.takewhile(str.isalpha, plate))
nums = plate[len(alphs):]
if len(alphs) == 2:
plate = alphs[0] + "-" + alphs[1] + "-" + nums
elif len(alphs) == 5:
plate = alphs[:3] + "-" + alphs[3:] + "-" + nums
else:
diffs = list()
alphscorners = sorted_corners[:len(alphs)]
for i in range(len(alphscorners)):
if sorted_corners[i][1] == alphscorners[-1][1]:
break
diffs.append(sorted_corners[i + 1][1] -
sorted_corners[i][3])
cuts = np.array(diffs) > np.mean(diffs)
rev_cuts = cuts[::-1]
for i in range(len(cuts[::-1])):
if (rev_cuts[i] == 1):
alphs = alphs[:len(cuts) - i] + "-" + alphs[len(cuts) -
i:]
plate = alphs + "-" + nums
if len(plate) < 5:
return None
elif len(plate) > 11:
return None
elif plate.count("-") > 2:
return None
elif plate.count("-") < 2:
return None
else:
return plate
except Exception as exception:
return None
def ocr(info):
cv2.imwrite('../fig/info.jpg', info)
img = Image.open('../fig/info.jpg')
tr = Tesseract(datadir='../data', lang='eng')
text = tr.ocr_image(img)
print(text)
import cv2
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
import math
from tesserwrap import Tesseract
from PIL import Image
tr = Tesseract("/usr/local/share")
def auto_canny(image, sigma=0.33):
v = np.median(image)
lower = int(max(0, (1.0 - sigma) * v))
upper = int(min(255, (1.0 + sigma) * v))
edged = cv2.Canny(image, lower, upper)
return edged
img = cv2.imread("image.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blurred = cv2.GaussianBlur(gray, (3, 3), 0)
threshold = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 11, 2)
wide = cv2.Canny(threshold, 10, 200)
tight = cv2.Canny(threshold, 225, 250)
auto = auto_canny(threshold)
#cv2.imshow('my_image', img)
#cv2.imshow("Edges", np.hstack([wide, tight, auto]))
#cv2.imshow("Wide",wide)
#cv2.imshow("Tight",tight)
