def extract_text_from_image(self, data):
"""Extract text from a binary string of data."""
tessdata = '/usr/share/tesseract-ocr/'
tessdata = self.manager.get_env('TESSDATA_PREFIX', tessdata)
languages = self.get_languages(self.result.languages)
key = sha1(data)
key.update(languages)
key = key.hexdigest()
text = self.manager.get_cache(key)
if text is not None:
return text
api = PyTessBaseAPI(lang=languages, path=tessdata)
try:
image = Image.open(StringIO(data))
# TODO: play with contrast and sharpening the images.
api.SetImage(image)
text = api.GetUTF8Text()
except DecompressionBombWarning as dce:
log.warning("Image too large: %r", dce)
return None
except IOError as ioe:
log.warning("Unknown image format: %r", ioe)
return None
finally:
api.Clear()
log.debug('[%s] OCR: %s, %s characters extracted', self.result,
languages, len(text))
self.manager.set_cache(key, text)
return text
def ocr(img, level):
"""Use tesseract OCR to detection images.
Args:
imagePath: File path of image.
level: Iteration level.
Returns:
An array with coordinate of boxes.
"""
result = []
with c_locale():
from tesserocr import PyTessBaseAPI
api = PyTessBaseAPI()
api.SetPageSegMode(PSM.AUTO_OSD)
# api.SetImageFile(imagePath)
api.SetImage(Image.fromarray(img))
blockIter = api.AnalyseLayout()
while blockIter.Next(level):
pt = blockIter.BlockType()
#result.append(blockIter.Baseline(level))
if pt in [1, 6]:
result.append(blockIter.BoundingBox(level) + (pt, ))
api.End()
return result
class Ocr:
def __init__(self):
self.api = None
def __enter__(self):
self.api = PyTessBaseAPI().__enter__()
self.api.SetVariable('tessedit_char_whitelist',
OCR_CHARACTER_WHITELIST)
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.api.__exit__(exc_type, exc_val, exc_tb)
def get_characters(self, image):
h, w = image.shape[:2]
if h < 1 or w < 1:
raise NoImageError()
img_pil = Image.fromarray(image)
self.api.SetImage(img_pil)
cell_text = self.api.GetUTF8Text().strip()
confidence = self.api.MeanTextConf()
return cell_text, confidence
def read_text_with_confidence(image,
lang='fast_ind',
path='/usr/share/tesseract-ocr/5/tessdata',
psm=4,
whitelist=''):
height, width = image.shape[:2]
if height <= 0 or width <= 0:
return '', 0
image_pil = Image.fromarray(image)
api = PyTessBaseAPI(lang=lang, psm=psm, path=path, oem=OEM.LSTM_ONLY)
try:
api.SetImage(image_pil)
if whitelist != '':
api.SetVariable('tessedit_char_whitelist', whitelist)
api.Recognize()
text = api.GetUTF8Text()
confidence = api.MeanTextConf()
except Exception:
print("[ERROR] Tesseract exception")
finally:
api.End()
return text, confidence
def get_text_bounding_boxes(image, psm=12):
bounding_boxes = []
height, width = image.shape[:2]
if height <= 0 or width <= 0:
return bounding_boxes
image_pil = Image.fromarray(image) # Load PIL image from numpy
api = PyTessBaseAPI(psm=psm, oem=OEM.LSTM_ONLY)
try:
# api.SetVariable('textord_tabfind_find_tables', 'true')
# api.SetVariable('textord_tablefind_recognize_tables', 'true')
api.SetImage(image_pil)
api.Recognize()
boxes = api.GetComponentImages(RIL.TEXTLINE, True)
for (im, box, _, _) in boxes:
x, y, w, h = box['x'], box['y'], box['w'], box['h']
bounding_boxes.append((x, y, w, h))
finally:
api.End()
return bounding_boxes
async def get_ocr_text(self, thresh_img):
# OCRにかけられるように画像変換
pil_img = Image.fromarray(thresh_img)
# OCR処理
api = PyTessBaseAPI(psm=PSM.AUTO, lang='jpn')
api.SetImage(pil_img)
# 空白文字と改行を除去して結果を返却
return api.GetUTF8Text().replace(' ', '').replace('\n', '')
def getWords(pages, letters_cache):
standard_words, split_words, letters = [], [], {'bid': [], 'letters': []}
prev_word = None
letter_detect = PyTessBaseAPI(psm=8, lang='eng')
letter_detect.SetVariable('tessedit_char_whitelist', ascii_uppercase)
bid = 0
for pg_num in pages:
page = doc[pg_num]
# get initial block bounding boxes
blocks = []
for block in page.getText("blocks"):
bbox = block[:4]
text = block[4].strip()
if len(text) != 1: # not a single letter
blocks.append({
'bid': bid,
'bbox': bbox,
'pg': page.number,
'text': text
})
bid += 1
elif not letters_cache:
# maps each bid to a corresponding dictionary letter
# this provides a heuristic for our search
sf, eps = 25 / 6, 1
pix = page.getPixmap(matrix=fitz.Matrix(sf, sf))
img = Image.open(io.BytesIO(pix.getPNGData()))
bbox = resize(bbox, sf, eps)
block_img = img.crop(bbox)
letter_detect.SetImage(block_img)
letter_detect.Recognize()
letter = letter_detect.AllWords()[0]
assert (len(letter) == 1)
letters['bid'].append(bid)
letters['letters'].append(letter.lower())
standard, split, prev_word, insert_word = groupBlocks(
blocks, prev_word, pg_num)
# last block from previous page (no spillover)
if insert_word:
add_word(standard, insert_word)
# clean up
standard_words.extend(standard)
split_words.extend(split)
# add the last word
if prev_word:
add_word(standard, prev_word)
# make sure all the blocks are properly formatted
for word in chain(standard_words, split_words):
test_word_format(word)
return standard_words, split_words, letters
def read_char(image, whitelist=None):
""" OCR a single character from an image. Useful for captchas."""
api = PyTessBaseAPI()
api.SetPageSegMode(10)
if whitelist is not None:
api.SetVariable("tessedit_char_whitelist", whitelist)
api.SetImage(image)
api.Recognize()
return api.GetUTF8Text().strip()
def getTextTest():
image = cv2.imread("okey-check6.jpg", 0)
api = PyTessBaseAPI()
api.SetImage(Image.fromarray(image))
# print api.GetUTF8Text()
image = api.GetThresholdedImage()
image.save("rus.new.exp6.tiff")
text = image_to_text(image, lang="rus")
# text = image_to_string(image, lang="rus")
print text
return receiptParser.getJson(text)
class OCR(object):
MAX_MODELS = 5
MIN_WIDTH = 10
MIN_HEIGHT = 10
def __init__(self):
# Tesseract language types:
_, self.supported = get_languages()
self.reset_engine('eng')
def language_list(self, languages):
models = [c for c in alpha3(languages) if c in self.supported]
if len(models) > self.MAX_MODELS:
log.warning("Too many models, limit: %s", self.MAX_MODELS)
models = models[:self.MAX_MODELS]
models.append('eng')
return '+'.join(sorted(set(models)))
def reset_engine(self, languages):
if hasattr(self, 'api'):
self.api.Clear()
self.api.End()
self.api = PyTessBaseAPI(lang=languages, oem=OEM.LSTM_ONLY)
def extract_text(self, data, languages=None, mode=PSM.AUTO_OSD):
"""Extract text from a binary string of data."""
languages = self.language_list(languages)
if languages != self.api.GetInitLanguagesAsString():
self.reset_engine(languages)
try:
image = Image.open(BytesIO(data))
# TODO: play with contrast and sharpening the images.
if image.width <= self.MIN_WIDTH:
return
if image.height <= self.MIN_HEIGHT:
return
if mode != self.api.GetPageSegMode():
self.api.SetPageSegMode(mode)
self.api.SetImage(image)
text = self.api.GetUTF8Text()
confidence = self.api.MeanTextConf()
log.info("%s chars (w: %s, h: %s, langs: %s, confidence: %s)",
len(text), image.width, image.height, languages,
confidence)
return text
except Exception as ex:
log.exception("Failed to OCR: %s", languages)
finally:
self.api.Clear()
def getText(rotation, image):
ImageFile.LOAD_TRUNCATED_IMAGES = True
image = Image.open(image)
image.save("loaded.jpg", dpi=(600, 600))
image = cv2.imread("loaded.jpg", 0)
if rotation != 0:
image = np.rot90(image, (360 - rotation) / 90)
# cv2.imwrite("rotated.jpg", image)
api = PyTessBaseAPI()
api.SetImage(Image.fromarray(image))
image = api.GetThresholdedImage()
text = image_to_text(image, lang="rus")
return receiptParser.getJson(text)
def getText(rotation, image):
ImageFile.LOAD_TRUNCATED_IMAGES = True
image = Image.open(image).convert("RGB")
b, g, r = image.split()
image = Image.merge("RGB", (r, g, b))
image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
if rotation != 0:
image = np.rot90(image, (360 - rotation) / 90)
api = PyTessBaseAPI()
api.SetImage(Image.fromarray(image))
image = api.GetThresholdedImage()
text = image_to_text(image, lang="rus")
# print text
return receiptParser.getJson(text)
def add_ocrinfo(tree, imgfile):
imgpil = Image.open(imgfile)
(orig_width, orig_height) = (imgpil.width, imgpil.height)
#root_width = tree[min(tree)]['width']
ratio = 1.0 * orig_width / config.width
#imgpil = imgpil.convert("RGB").resize(
# (orig_width * OCR_RATIO, orig_height * OCR_RATIO))
tesapi = PyTessBaseAPI(lang='eng')
tesapi.SetImage(imgpil)
tesapi.SetSourceResolution(config.ocr_resolution)
for nodeid in tree:
node = tree[nodeid]
if node['children'] and node['text'] == '':
node['ocr'] = ''
continue
x = max(node['x'] * ratio - 1, 0)
y = max(node['y'] * ratio - 1, 0)
x2 = min((node['x'] + node['width']) * ratio + 1, orig_width)
y2 = min((node['y'] + node['height']) * ratio + 1, orig_height)
width = int(x2 - x)
height = int(y2 - y)
if width > 3 and height > 3:
#tesapi.SetRectangle(int(x * OCR_RATIO), int(y * OCR_RATIO),
# int(width * OCR_RATIO), int(height * OCR_RATIO))
#print(int(x), int(y), int(width), int(height), orig_width, orig_height)
tesapi.SetRectangle(int(x), int(y), int(width), int(height))
ocr_text = tesapi.GetUTF8Text().strip().replace('\n', ' ')
if ocr_text.strip() == '':
x = min(x + width * 0.05, orig_width)
y = min(y + height * 0.05, orig_height)
width *= 0.9
height *= 0.9
tesapi.SetRectangle(int(x), int(y), int(width), int(height))
ocr_text = tesapi.GetUTF8Text().strip().replace('\n', ' ')
else:
ocr_text = ''
node['ocr'] = ocr_text
def read_word(image, whitelist=None, chars=None, spaces=False):
""" OCR a single word from an image. Useful for captchas.
Image should be pre-processed to remove noise etc. """
api = PyTessBaseAPI()
api.SetPageSegMode(8)
if whitelist is not None:
api.SetVariable("tessedit_char_whitelist", whitelist)
api.SetImage(image)
api.Recognize()
guess = api.GetUTF8Text()
if not spaces:
guess = ''.join([c for c in guess if c != " "])
guess = guess.strip()
if chars is not None and len(guess) != chars:
return guess, None
return guess, api.MeanTextConf()
def get_boxes(image_filename: str) -> list:
image = Image.open(image_filename)
width = image.width
height = image.height
max_width = width // 2
max_height = height // 2
api = PyTessBaseAPI(lang="jpn_vert")
# api.ReadConfigFile("tess.conf")
api.SetPageSegMode(PSM.SPARSE_TEXT_OSD)
api.SetImage(image)
api.Recognize(0)
ri = api.GetIterator()
level = RIL.WORD
boxes = []
for r in iterate_level(ri, level):
conf = r.Confidence(level)
text = r.GetUTF8Text(level)
left, top, right, bottom = r.BoundingBox(level)
# boxes = api.GetComponentImages(RIL.SYMBOL, True)
# for im, rect, _, _ in boxes:
# # im is a PIL image object
# # rect is a dict with x, y, w and h keys
# left, top, right, bottom = rect['x'], rect['y'], rect['w'], rect['h']
# api.SetRectangle(left, top, right, bottom)
# text = api.GetUTF8Text()
# conf = api.MeanTextConf()
print("'%s' \tConf: %.2f \tCoords: %d,%d,%d,%d" %
(text, conf, left, top, right, bottom))
box = {
'text': text,
'left': left,
'top': top,
'width': right - left,
'height': bottom - top
}
if should_ignore_box(conf, box, max_width, max_height):
continue
boxes.append(box)
api.End()
image.close()
return boxes
def read_box(self, crop, filtered, read_primes, text, table, old_read_primes, num):
cur_time = datetime.now().strftime(self.datetime_format)
#print("reading box")
api = None
try:
api = self.api.get(block=False)
#print("reading box")
except queue.Empty:
api = PyTessBaseAPI()
api.SetImage(Image.fromarray(filtered[crop[1]:crop[3], crop[0]:crop[2]]))
ocr_output = api.GetUTF8Text()
self.api.put(api)
#self.log.write("{}: Succeeded reading image x={} num_api={}\n".format(cur_time, crop[0], self.api.qsize()))
#self.log.flush()
sanitized = self.sanitize(ocr_output)
ocr_text = self.title_case(sanitized)
#self.log.write("{}: ocr text={}\n".format(cur_time, ocr_text))
text[crop[0] + crop[1]] = ocr_text
dict_text = self.dict_match(ocr_text)
self.update_table(dict_text, table, read_primes, old_read_primes)
class OcrWrapper(BaseImageToString):
_OPTIONS = ('tessedit_char_whitelist', '0123456789ABCDEF.-')
def __init__(self):
if sys.platform == 'win32':
self._ocr = PyTessBaseAPI(
path="C:\\Program Files\\Tesseract-OCR\\tessdata")
else:
self._ocr = PyTessBaseAPI()
self._ocr.SetVariable(self._OPTIONS[0], self._OPTIONS[1])
pass
def image_to_string(self, image: Image) -> str:
image.format = 'PNG'
self._ocr.SetImage(image)
raw_data = self._ocr.GetUTF8Text()
return raw_data
def end(self):
self._ocr.End()
def extract(self, page: Poppler.Page):
from tesserocr import PyTessBaseAPI # NOQA Stupid assert on LC_* == 'C'
ocr = PyTessBaseAPI(lang=settings.CAMPUSONLINE_BULLETIN_OCR_LANGUAGE)
text = page.text(QRectF()).strip()
if len(text) > settings.CAMPUSONLINE_BULLETIN_OCR_THRESHOLD:
self.clean = True
self.text = text
return
dpi = settings.CAMPUSONLINE_BULLETIN_OCR_DPI
buf = QBuffer()
buf.open(QIODevice.ReadWrite)
page.renderToImage(dpi, dpi).save(buf, "PNG")
bio = BytesIO()
bio.write(buf.data())
buf.close()
bio.seek(0)
img = Image.open(bio)
ocr.SetImage(img)
scanned = ocr.GetUTF8Text().strip()
img.close()
bio.close()
self.clean = False
self.text = scanned
class __impl:
def __init__(self, vs, imgOutput):
self.__vs = vs
self.__imgOutput = imgOutput
self.image = None
self.logger = Logger()
self.state = State()
self.tesseract = PyTessBaseAPI(psm=PSM.SINGLE_CHAR,
oem=OEM.LSTM_ONLY,
lang="digits")
self.filter = Filter()
self.signalThresholdY = 160
self.LAPPatternSesibility = 5
self.recordStamp = time.strftime(self.logger.timeFormat)
self.recordNum = 0
self.recordFolder = None
self.cntNum = 0
if (self.state.RecordImage):
root = 'record'
if not os.path.isdir(root):
os.mkdir(root)
self.recordFolder = os.path.join(root, self.recordStamp)
if not os.path.isdir(self.recordFolder):
os.mkdir(self.recordFolder)
def showImg(self, window, image):
if self.__imgOutput:
cv2.imshow(window, image)
def warmup(self):
time.sleep(2.0)
self.tesserOCR(np.zeros((1, 1, 3), np.uint8))
def tesserOCR(self, image):
self.tesseract.SetImage(Image.fromarray(image))
return self.tesseract.GetUTF8Text(
), self.tesseract.AllWordConfidences()
def dominantColor(self, img, clusters=2):
data = np.reshape(img, (-1, 3))
data = np.float32(data)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10,
1.0)
flags = cv2.KMEANS_RANDOM_CENTERS
_, _, centers = cv2.kmeans(data, 1, None, criteria, 10, flags)
return centers[0].astype(np.int32)
def analyzeRect(self, image, warped, box, x, y):
# find amount of color blue in warped area, assuming over X% is the lap signal
if (self.getAmountOfColor(warped, Colors.lower_blue_color,
Colors.upper_blue_color, True) > 0.1):
self.logger.info("Rundensignal")
self.state.setCurrentSignal(Signal.LAP)
return "Rundensignal"
def analyzeSquare(self, image, warped, box, x, y):
#dominantColor, percent, _ = self.dominantColor(warped, 3)
# dominantColor = self.dominantColor(
# cv2.cvtColor(warped, cv2.COLOR_BGR2HSV), 3)
""" color = 'k'
# find amount of color black in warped area, assuming over X% is a numeric signal
if ((dominantColor <= 70).all()):
color = 'Black'
elif ((dominantColor >= 180).all()):
color = 'White'
if (color): """
resizedWarp = cv2.resize(warped,
None,
fx=2.0,
fy=2.0,
interpolation=cv2.INTER_CUBIC)
# gray
optimized = cv2.cvtColor(resizedWarp, cv2.COLOR_BGR2GRAY)
# blur
optimized = cv2.GaussianBlur(optimized, (5, 5), 0)
# binary image
optimized = cv2.threshold(optimized, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# binary inversion if dominant color is black
""" if (color == 'Black'):
optimized = cv2.bitwise_not(optimized) """
# now check the frame (1px) of the image.. there shouldn't be any noise since its a clean signal background
h, w = optimized.shape[0:2]
clean = optimized[0, 0]
for iFrame in range(0, 2):
for iHeight in range(h):
if not (optimized[iHeight, iFrame] == clean) or not (
optimized[iHeight, w - 1 - iFrame] == clean):
return False
for iWidth in range(w):
# or not(optimized[h - iFrame, iWidth])
if not (optimized[iFrame, iWidth] == clean):
return False
# cv2.imwrite("records/opt/" + str(self.cntNum) + ".jpg", optimized)
output, confidence = self.tesserOCR(optimized)
# if the resulting text is below X% confidence threshold, we skip it
if not output or confidence[0] < 95:
return False
# clean up output from tesseract
output = output.replace('\n', '')
output = output.replace(' ', '')
if output.isdigit() and 0 < int(output) < 10:
""" self.showImg("opt " + str(self.cntNum),
np.hstack((resizedWarp, cv2.cvtColor(optimized, cv2.COLOR_GRAY2BGR)))) """
if y <= self.signalThresholdY:
self.logger.info('Stop Signal OCR: ' + output + ' X: ' +
str(x) + ' Y: ' + str(y) +
' Confidence: ' + str(confidence[0]) +
'%') # + ' DC: ' + str(dominantColor))
self.state.setStopSignal(int(output))
return 'S: ' + output
elif self.state.StopSignalNum != 0:
self.logger.info('Info Signal OCR: ' + output + ' X: ' +
str(x) + ' Y: ' + str(y) +
' Confidence: ' + str(confidence[0]) +
'%') # + ' DC: ' + str(dominantColor))
self.state.setCurrentSignal(Signal.UPPER, int(output))
return 'I: ' + output
def getAmountOfColor(self,
img,
lowerColor,
upperColor,
convert2hsv=True):
if (convert2hsv):
img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# create mask from color range
maskColor = cv2.inRange(img, lowerColor, upperColor)
# get ratio of active pixels
ratio_color = cv2.countNonZero(maskColor) / (img.size)
return ratio_color
# color picker for manual debugging
def pick_color(self, event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
pixel = self.image[y, x]
color = np.array([pixel[0], pixel[1], pixel[2]])
self.logger.info(pixel)
# capture frames from the camera
def capture(self, savedImg=None):
if (savedImg is not None):
image = savedImg
else:
image = self.__vs.read()
if (self.state.InvertCamera):
image = imutils.rotate(image, angle=180)
self.image = image
if (self.state.RecordImage):
self.recordNum += 1
cv2.imwrite(
os.path.join(self.recordFolder,
str(self.recordNum) + ".jpg"), image)
return
if (self.state.Approaching == Signal.UPPER
or self.state.Approaching == Signal.LOWER):
self.findNumberSignal(image)
elif (self.state.Approaching == Signal.LAP):
self.findLapSignal(image)
def findLapSignal(self, image):
contourImage = image.copy()
blur = cv2.GaussianBlur(image, (3, 3), 0)
hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)
self.image = hsv
mask = cv2.inRange(hsv, Colors.lower_blue_color,
Colors.upper_blue_color)
cnts = imutils.grab_contours(
cv2.findContours(mask.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE))
if len(cnts) > 0:
# transform all contours to rects
rects = [cv2.boundingRect(cnt) for cnt in cnts]
# now iterate all of the rects, trying to find an approximated sibiling shifted in Y-direction
for rect in rects:
(x, y, w, h) = rect
cv2.rectangle(contourImage, (x, y), (x + w, y + h),
(0, 0, 255), 2)
# try to match the pattern from a given rect in all rects
counterPart = [
counterRect for counterRect in rects
if (counterRect != rect and x - 5 <= counterRect[0] <=
x + 5 and 2 * -(h + 5) <= y - counterRect[1] <= 2 *
(h + 5) and w - 5 <= counterRect[2] <= w + 5)
and h - 5 <= counterRect[3] <= h + 5
]
if (counterPart):
(x, y, w, h) = counterPart[0]
cv2.rectangle(contourImage, (x, y), (x + w, y + h),
(0, 255, 0), 2)
self.logger.info('LAP Signal')
self.state.captureLapSignal()
break
self.showImg(
'contourImage',
np.hstack(
(hsv, contourImage, cv2.cvtColor(mask,
cv2.COLOR_GRAY2BGR))))
cv2.setMouseCallback('contourImage', self.pick_color)
def findNumberSignal(self, image):
image_height = np.size(image, 0)
image_width = np.size(image, 1)
contourImage = image.copy()
# focus only on the part of the image, where a signal could occur
# image = image[int(image.shape[0] * 0.2):int(image.shape[0] * 0.8), 0:int(image.shape[1]*0.666)]
mask = self.filter.autoCanny(image, 2, 3)
# get a list of contours in the mask, chaining to just endpoints
cnts = imutils.grab_contours(
cv2.findContours(mask.copy(), cv2.RETR_LIST,
cv2.CHAIN_APPROX_SIMPLE))
# only proceed if at least one contour was found
if len(cnts) > 0:
# loop contours
for self.cntNum, cnt in enumerate(cnts):
rect = cv2.minAreaRect(cnt)
_, _, angle = rect
# approximate shape
approx = cv2.approxPolyDP(cnt,
0.02 * cv2.arcLength(cnt, True),
True)
# the rectangle must not have a too big rotation (+/-10)
# and more than 3 connecting points
if len(approx) >= 3 and (-90 <= angle <= -80
or angle >= -10):
box = cv2.boxPoints(rect)
box = np.int0(box)
(x, y, w, h) = cv2.boundingRect(approx)
# limit viewing range
if (y <= image_height * 0.2 or x >= image_width * 0.8):
continue
if (w <= 5 or h <= 5):
continue
# we are in approaching mode, thus we only care for the lower signals <= threshold
if ((self.state.Approaching == Signal.UPPER
and y >= self.signalThresholdY)
and not self.state.Standalone):
continue
elif ((self.state.Approaching == Signal.LOWER
and y <= self.signalThresholdY)
and not self.state.Standalone):
continue
sideRatio = w / float(h)
absoluteSizeToImageRatio = (
100 / (image_width * image_height)) * (w * h)
# calculate area of the bounding rectangle
rArea = w * float(h)
# calculate area of the contour
cArea = cv2.contourArea(cnt)
if (cArea):
rectContAreaRatio = (100 / rArea) * cArea
else:
continue
# cv2.drawContours(contourImage, [box], 0, (255, 0, 0), 1)
result = None
# is the rectangle sideways, check for lap signal
# if (h*2 < w and y <= self.signalThresholdY and rectContAreaRatio >= 80):
#result = self.analyzeRect(image, four_point_transform(image, [box][0]), box, x, y)
# find all contours looking like a signal with minimum area (1%)
if absoluteSizeToImageRatio >= 0.01:
# is it approx a square, or standing rect? then check for info or stop signal
if 0.2 <= sideRatio <= 1.1:
# transform ROI
if (sideRatio <= 0.9):
coords, size, angle = rect
size = size[0] + 8, size[1] + 4
coords = coords[0] + 1, coords[1] + 1
rect = coords, size, angle
box = cv2.boxPoints(rect)
box = np.int0(box)
""" cv2.drawContours(
contourImage, [box], 0, (0, 255, 0), 1) """
warp = four_point_transform(image, [box][0])
result = self.analyzeSquare(
image, warp, box, x, y)
if (result):
if (self.__imgOutput):
color = None
if (y >= self.signalThresholdY):
color = (0, 0, 255)
else:
color = (255, 0, 0)
cv2.drawContours(contourImage, [box], 0, color,
1)
cv2.drawContours(contourImage, [cnt], -1,
color, 2)
""" M = cv2.moments(cnt)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
cv2.putText(contourImage, str(
self.cntNum), (cX - 30, cY - 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1) """
self.logger.debug(
"[" + str(self.cntNum) + "] SideRatio: " +
str(sideRatio) + " AreaRatio: " +
str(rectContAreaRatio) + " ContArea: " +
str(cArea) + " RectArea: " + str(rArea) +
" AbsSize: " +
str(absoluteSizeToImageRatio) +
" CntPoints: " + str(len(approx)) +
" Size: " + str(w) + "x" + str(h))
""" if (self.__imgOutput): # hsv img output
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
cv2.namedWindow('contourImage')
cv2.setMouseCallback('contourImage', self.pick_color)
# self.showImg("hsv", hsv) """
self.showImg(
"contourImage",
np.hstack((contourImage, cv2.cvtColor(mask,
cv2.COLOR_GRAY2BGR))))
class Analyzer(object):
TEXT_TYPES = set([
PT.FLOWING_TEXT, PT.HEADING_TEXT, PT.PULLOUT_TEXT, PT.VERTICAL_TEXT,
PT.CAPTION_TEXT
])
def __init__(self, lang=None):
super(Analyzer, self).__init__()
kwargs = {}
if lang is not None:
kwargs['lang'] = lang
self.api = PyTessBaseAPI(psm=PSM.AUTO_OSD, **kwargs)
def analyze_image(self, image):
page = Page()
self.api.SetImage(image)
self.api.Recognize()
iterator = self.api.GetIterator()
page.blocks = self.__decode_blocks(iterator, image)
page.size = Size(*image.size)
return page
def close(self):
self.api.End()
def __decode_blocks(self, iterator, image):
blocks = []
for tesseract_block in iterate_level(iterator, RIL.BLOCK):
block = Block()
block.bounding_box = BoundingBox.from_coordinates(
*tesseract_block.BoundingBox(RIL.BLOCK))
if not tesseract_block.GetUTF8Text(RIL.BLOCK).strip():
block.image = tesseract_block.GetImage(RIL.BLOCK, 0, image)
blocks.append(block)
continue
block.paragraphs = self.__decode_paragraphs(iterator)
blocks.append(block)
return blocks
def __decode_paragraphs(self, iterator):
paragraphs = []
for tesseract_paragraph in iterate_level(iterator, RIL.PARA):
paragraph = Paragraph()
paragraph.bounding_box = BoundingBox.from_coordinates(
*tesseract_paragraph.BoundingBox(RIL.PARA))
paragraph.lines = self.__decode_lines(iterator)
paragraphs.append(paragraph)
if iterator.IsAtFinalElement(RIL.BLOCK, RIL.PARA):
break
return paragraphs
def __decode_lines(self, iterator):
lines = []
for tesseract_line in iterate_level(iterator, RIL.TEXTLINE):
line = TextLine()
line.bounding_box = BoundingBox.from_coordinates(
*tesseract_line.BoundingBox(RIL.TEXTLINE))
line.words = self.__decode_words(iterator)
lines.append(line)
if iterator.IsAtFinalElement(RIL.PARA, RIL.TEXTLINE):
break
return lines
def __decode_words(self, iterator):
words = []
for tesseract_word in iterate_level(iterator, RIL.WORD):
font_attributes = tesseract_word.WordFontAttributes()
word = Word()
word.bounding_box = BoundingBox.from_coordinates(
*tesseract_word.BoundingBox(RIL.WORD))
word.confidence = float(tesseract_word.Confidence(
RIL.WORD)) / 100.0
word.text = tesseract_word.GetUTF8Text(RIL.WORD)
word.symbols = self.__decode_symbols(iterator)
font = Font()
font.bold = font_attributes['bold']
font.italic = font_attributes['italic']
font.underline = font_attributes['underlined']
font.monospace = font_attributes['monospace']
font.serif = font_attributes['serif']
font.pointsize = font_attributes['pointsize']
font.id = font_attributes['font_id']
for symbol in word.symbols:
symbol.font = font
words.append(word)
if iterator.IsAtFinalElement(RIL.TEXTLINE, RIL.WORD):
break
return words
def __decode_symbols(self, iterator):
symbols = []
for tesseract_symbol in iterate_level(iterator, RIL.SYMBOL):
symbol = Symbol()
symbol.bounding_box = BoundingBox.from_coordinates(
*tesseract_symbol.BoundingBox(RIL.SYMBOL))
symbol.confidence = float(tesseract_symbol.Confidence(
RIL.SYMBOL)) / 100.0
symbol.text = tesseract_symbol.GetUTF8Text(RIL.SYMBOL)
symbol.image = tesseract_symbol.GetBinaryImage(RIL.SYMBOL).convert(
'1', dither=Image.NONE)
symbols.append(symbol)
if iterator.IsAtFinalElement(RIL.WORD, RIL.SYMBOL):
break
return symbols
class TesseractOCR:
#private static TESSERACT_ENGINE_MODE = TessAPI1.TessOcrEngineMode.OEM_DEFAULT
#
# bpp - bits per pixel, represents the bit depth of the image, with 1 for
# binary bitmap, 8 for gray, and 24 for color RGB.
#
BBP = 8
DEFAULT_CONFIDENT_THRESHOLD = 60.0
MINIMUM_DESKEW_THRESHOLD = 0.05
def __init__(self, rgbaImage, dipCalculator, language):
self.mRgbaImage = rgbaImage
self.mDipCalculator = dipCalculator
self.mHandle = PyTessBaseAPI()
self.mOcrTextWrappers = []
self.mOcrBlockWrappers = []
self.mOcrLineWrappers = []
self.raWrappers = []
# self.mLanguage = language
self.mBufferedImageRgbaImage = Image.fromarray(self.mRgbaImage)
self.initOCR()
def baseInit(self, iteratorLevel):
width = 0
height = 0
channels = 1
if len(self.mRgbaImage.shape) == 2:
height, width = self.mRgbaImage.shape
else:
height, width, channels = self.mRgbaImage.shape
return self.baseInitIter(self.mRgbaImage, Rect(0, 0, width, height),
channels, iteratorLevel)
def baseInitIter(self, imageMat, rect, channels, iteratorLevel):
listdata = []
parentX = rect.x
parentY = rect.y
# subMat = imageMat[rect.y:rect.y+rect.height, rect.x:rect.width+rect.x]
#
# if(channels != 1):
# subMat = imageMat[rect.y:rect.y+rect.height, rect.x:rect.width+rect.x, 0:channels]
#tessAPI = PyTessBaseAPI()
#Convert to PIL image
imgPIL = Image.fromarray(imageMat)
self.mHandle.SetImage(imgPIL)
boxes = self.mHandle.GetComponentImages(iteratorLevel, True)
for i, (im, box, _, _) in enumerate(boxes):
wrapper = OCRTextWrapper.OCRTextWrapper()
self.mHandle.SetRectangle(box['x'], box['y'], box['w'], box['h'])
ocrResult = self.mHandle.GetUTF8Text()
wrapper.text = ocrResult
conf = self.mHandle.MeanTextConf()
wrapper.confidence = conf
self.mHandle.Recognize()
iterator = self.mHandle.GetIterator()
fontAttribute = iterator.WordFontAttributes()
wrapper.x = box['x'] + parentX
wrapper.y = box['y'] + parentY
wrapper.width = box['w']
wrapper.height = box['h']
wrapper.rect = Rect(wrapper.x, wrapper.y, wrapper.width,
wrapper.height)
# print(box)
#
if (fontAttribute != None):
wrapper.fontName = fontAttribute['font_name']
wrapper.bold = fontAttribute['bold']
wrapper.italic = fontAttribute['italic']
wrapper.underlined = fontAttribute['underlined']
wrapper.monospace = fontAttribute['monospace']
wrapper.serif = fontAttribute['serif']
wrapper.smallcaps = fontAttribute['smallcaps']
wrapper.fontSize = fontAttribute['pointsize']
wrapper.fontId = fontAttribute['font_id']
listdata.append(wrapper)
return listdata
def getBlockWithLocation(self, rect):
wrappers = []
for ocrTextWrapper in self.mOcrBlockWrappers:
bound = ocrTextWrapper.rect
if (RectUtil.contains(rect, bound)):
wrappers.append(OCRTextWrapper.OCRTextWrapper(ocrTextWrapper))
return wrappers
def getImage(self, rect):
x2 = rect.x + rect.width
y2 = rect.y + rect.height
mat = self.mRgbaImage[rect.y:y2, rect.x:x2]
return Image.fromarray(mat)
def getText(self, rect):
try:
self.mHandle.SetImage(self.mBufferedImageRgbaImage)
self.mHandle.SetRectangle(rect.x, rect.y, rect.width, rect.height)
text = self.mHandle.GetUTF8Text()
return text
except Exception as error:
print('Caught this error: ' + repr(error))
return ""
def getLineText(self, rect):
try:
self.mHandle.SetImage(self.mBufferedImageRgbaImage)
self.mHandle.SetRectangle(rect.x, rect.y, rect.width, rect.height)
text = self.mHandle.GetUTF8Text()
if (TextUtils.isEmpty(text)):
self.mHandle = PyTessBaseAPI(psm=PSM.SINGLE_LINE)
self.mHandle.SetImage(self.mBufferedImageRgbaImage)
self.mHandle.SetRectangle(rect.x, rect.y, rect.width,
rect.height)
text = self.mHandle.GetUTF8Text()
if (TextUtils.isEmpty(text)):
self.mHandle.SetImage(self.getImage(rect))
text = self.mHandle.GetUTF8Text()
self.mHandle = PyTessBaseAPI(psm=PSM.AUTO)
return text
except Exception as error:
print('Caught this error: ' + repr(error))
return ""
def getRectWordForLowConfidence(self, ocr):
try:
rect = ocr.bound()
self.mHandle = PyTessBaseAPI(psm=PSM.SINGLE_WORD)
self.mHandle.SetImage(self.mBufferedImageRgbaImage)
self.mHandle.SetRectangle(rect.x, rect.y, rect.width, rect.height)
ocr.text = self.mHandle.GetUTF8Text()
ocr.confidence = self.mHandle.MeanTextConf()
if (ocr.confidence <= Constants.TEXT_CONFIDENT_THRESHOLD):
self.mHandle.SetImage(self.getImage(rect))
ocr.text = self.mHandle.GetUTF8Text()
ocr.confidence = self.mHandle.MeanTextConf()
if (ocr.confidence <= Constants.TEXT_CONFIDENT_THRESHOLD):
return False
self.mHandle.Recognize()
iterator = self.mHandle.GetIterator()
fontAttribute = iterator.WordFontAttributes()
if (fontAttribute != None):
ocr.fontName = fontAttribute['font_name']
ocr.bold = fontAttribute['bold']
ocr.italic = fontAttribute['italic']
ocr.underlined = fontAttribute['underlined']
ocr.monospace = fontAttribute['monospace']
ocr.serif = fontAttribute['serif']
ocr.smallcaps = fontAttribute['smallcaps']
ocr.fontSize = fontAttribute['pointsize']
ocr.fontId = fontAttribute['font_id']
# ocr.fontsize = self.getPreferenceFontSize(ocr)
self.mHandle = PyTessBaseAPI(psm=PSM.AUTO)
return True
except Exception as error:
print('Caught this error: ' + repr(error))
return False
def getWordsIn(self, rect):
wrappers = []
for ocrTextWrapper in self.mOcrTextWrappers:
bound = ocrTextWrapper.bound()
if (RectUtil.contains(rect, bound)):
wrappers.append(OCRTextWrapper.OCRTextWrapper(ocrTextWrapper))
return wrappers
def initOCR(self):
#
self.initText()
self.initBlock()
# self.initPara()
self.initLine()
#
def initBlock(self):
self.mOcrBlockWrappers = self.baseInit(RIL.BLOCK)
def initLine(self):
self.mOcrLineWrappers = self.baseInit(RIL.TEXTLINE)
invalidLineWrappers = []
# a line cannot contain another lines
for ocrLine in self.mOcrLineWrappers:
for otherOcrLine in self.mOcrLineWrappers:
if (ocrLine != otherOcrLine and RectUtil.contains(
ocrLine.bound(), otherOcrLine.bound())):
invalidLineWrappers.append(ocrLine)
self.mOcrLineWrappers = [
x for x in self.mOcrLineWrappers if x not in invalidLineWrappers
]
def initPara(self):
self.mOcrParaWrappers = self.baseInit(RIL.PARA)
def initText(self):
self.mOcrTextWrappers = self.baseInit(RIL.WORD)
def isOverlapText(self, rect, confident):
for ocrTextWrapper in self.mOcrTextWrappers:
bound = ocrTextWrapper.bound()
if (ocrTextWrapper.getConfidence() >= confident
and RectUtil.intersects(rect, bound)):
return True
return False
def reset(self):
self.mOcrTextWrappers = []
self.mOcrLineWrappers = []
self.initOCR()
# def rotateImage(bi) :
# iden = ImageDeskew(bi)
# imageSkewAngle = iden.getSkewAngle() # determine skew angle
# if imageSkewAngle > MINIMUM_DESKEW_THRESHOLD or imageSkewAngle < -MINIMUM_DESKEW_THRESHOLD :
# bi = ImageHelper.rotateImage(bi, -imageSkewAngle) # deskew
# return bi
def getPreferenceFontSize(self, ocrTextWrapper, parentHeight):
# TODO TODO
fontName = ocrTextWrapper.fontName
fontSize = ocrTextWrapper.fontSize
height = ocrTextWrapper.height * Constants.TEXT_BOX_AND_TEXT_HEIGHT_RATIO
# height = ocrTextWrapper.height
textHeight = int(
self.mDipCalculator.pxToHeightDip(min(parentHeight, height)))
# font = QFont(fontName, fontSize)
newFontSize = fontSize
if (self.getTextHeightUsingFontMetrics(ocrTextWrapper, fontName,
fontSize) == textHeight):
newFontSize = fontSize
elif (self.getTextHeightUsingFontMetrics(ocrTextWrapper, fontName,
fontSize) < textHeight):
while (self.getTextHeightUsingFontMetrics(ocrTextWrapper, fontName,
fontSize) < textHeight):
fontSize = fontSize + 1
newFontSize = fontSize
else:
while (self.getTextHeightUsingFontMetrics(ocrTextWrapper, fontName,
fontSize) > textHeight):
fontSize = fontSize - 1
newFontSize = fontSize
return newFontSize
def getTextHeightUsingFontMetrics(self, ocrTextWrapper, fontName,
fontSize):
# class SIZE(ctypes.Structure):
# _fields_ = [("cx", ctypes.c_long), ("cy", ctypes.c_long)]
# hdc = ctypes.windll.user32.GetDC(0)
# hfont = ctypes.windll.gdi32.CreateFontA(-fontSize, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, fontName)
# hfont_old = ctypes.windll.gdi32.SelectObject(hdc, hfont)
# size = SIZE(0, 0)
# ctypes.windll.gdi32.GetTextExtentPoint32A(hdc, text, len(text), ctypes.byref(size))
# ctypes.windll.gdi32.SelectObject(hdc, hfont_old)
# ctypes.windll.gdi32.DeleteObject(hfont)
# return size.cy
file = "fonts//" + fontName + ".ttf"
font = ImageFont.truetype(file, fontSize)
fontSize = font.getsize(ocrTextWrapper.text)
return fontSize[1]
def validCharacter(self, word):
return self.mHandle.IsValidCharacter(word)
#Don't have this method return TessAPI1.TessBaseAPIIsValidWord(mHandle, word) != 0
# return True
#TODO
# def getTextHeightUsingTextLayout(self,ocrTextWrapper, font) :
# frc = self.mGraphics.getFontRenderContext()
# loc = Point(0, 0)
# layout = TextLayout(ocrTextWrapper.text, font, frc)
# layout.draw(self.mGraphics, float(loc.x, loc.y))
# bounds = layout.getBounds()
# height = bounds.getHeight()
# return height
# def isValidTextUsingConfidentAndBoundaryCheck(self, ocrTextWrapper) :
# if (ocrTextWrapper.getConfidence() > Constants.TEXT_CONFIDENT_THRESHOLD + Constants.TEXT_CONFIDENT_THRESHOLD_SECONDARY_RANGE) :
# return True
#
# elif (ocrTextWrapper.getConfidence() <= Constants.TEXT_CONFIDENT_THRESHOLD) :
# return False
#
# return self.isValidTextUsingBoundaryCheck(ocrTextWrapper)
#
#
def getTextDimensions(self, text, fontName, fontSize):
file = "fonts//" + fontName + ".ttf"
try:
font = ImageFont.truetype(file, fontSize)
fontSize = font.getsize(text)
return fontSize
except OSError:
print(file)
def isValidTextUsingBoundaryCheck(self, ocrTextWrapper):
# confident between TextProcessor.TEXT_CONFIDENT_THRESHOLD and
# TextProcessor.TEXT_CONFIDENT_THRESHOLD +
# TextProcessor.TEXT_CONFIDENT_THRESHOLD_SECONDARY_RANGE
if (TextUtils.isEmpty(ocrTextWrapper.text)):
# We cannot calculate width of empty text
return True
# return True
# frc = mGraphics.getFontRenderContext()
# font = QFont(ocrTextWrapper.fontName,ocrTextWrapper.fontSize)
# loc = Point(0, 0)
# layout = TextLayout(ocrTextWrapper.text,font, frc)
# layout.draw(mGraphics, loc.getX(), loc.getY())
# bound = layout.getBounds()
width, height = self.getTextDimensions(ocrTextWrapper.text,
ocrTextWrapper.fontName,
ocrTextWrapper.fontSize)
fontRatio = float(height / width)
boundRatio = float(ocrTextWrapper.height / ocrTextWrapper.width)
fontArea = self.mDipCalculator.dipToHeightPx(
height) * self.mDipCalculator.dipToWidthPx(width)
boundArea = float(ocrTextWrapper.width * ocrTextWrapper.height)
#
# the different between dimensions of the text should be smaller than
# 10% of the max dimension.
# System.out.prln(" Ratio: " + fontRatio + ", " + boundRatio + ", "
# + Math.abs(boundRatio - fontRatio)
# / Math.max(boundRatio, fontRatio) + "," + fontArea + ", "
# + boundArea + ", " + Math.min(fontArea, boundArea)
# / Math.max(fontArea, boundArea))
# It the bound is square, it less likely that this text is correct
# TODO: This rule may not need it
# if (float(min(ocrTextWrapper.getWidth(),ocrTextWrapper.getHeight()) / max( ocrTextWrapper.getWidth(),
# ocrTextWrapper.getHeight())) > 0.95) :
# # if drawing text cannot create square, sorry -> invalid
# if (float(min(width, height) / max(width, height)) <= 0.95 and not validWord(ocrTextWrapper.text)) :
# return False
#
#
#
# print(self.mDipCalculator.dipToWidthPx(width), self.mDipCalculator.dipToHeightPx(height))
# print( ocrTextWrapper.width, ocrTextWrapper.height)
dimension = abs(boundRatio - fontRatio) / max(boundRatio, fontRatio)
# print(dimension)
dimensionCheck = abs(boundRatio - fontRatio) / max(
boundRatio, fontRatio
) <= Constants.TEXT_CONFIDENT_ACCEPTANCE_DIMENSION_RATIO_DIFFERENCE_THRESHOLD
areaCheckVal = min(fontArea, boundArea) / max(fontArea, boundArea)
# print(areaCheckVal)
# print(ocrTextWrapper.text)
areaCheck = min(fontArea, boundArea) / max(
fontArea,
boundArea) >= Constants.TEXT_AREA_ACCEPTANCE_DIFFERENCE_THRESHOLD
return dimensionCheck and areaCheck
def destroy(self):
self.mHandle.End
class TesserOCR(object):
def __init__(self,image=None,lang='chi_sim',psm=PSM.SINGLE_CHAR):
self.api = PyTessBaseAPI(lang=lang,psm=psm)
@staticmethod
def getinfo():
print(tesserocr.tesseract_version()) # print tesseract-ocr version
print(tesserocr.get_languages())
@staticmethod
def img2string(image):
pimg = opencv2pillow(image)
print(tesserocr.image_to_text(pimg))
@staticmethod
def analyzeRIL(image):
pimg = opencv2pillow(image)
with PyTessBaseAPI() as api:
api.SetImage(pimg)
boxes = api.GetComponentImages(RIL.TEXTLINE, True)
print('Found {} textline image components.'.format(len(boxes)))
for i, (im, box, _, _) in enumerate(boxes):
# im is a PIL image object
# box is a dict with x, y, w and h keys
api.SetRectangle(box['x'], box['y'], box['w'], box['h'])
ocrResult = api.GetUTF8Text()
conf = api.MeanTextConf()
print (u"Box[{0}]: x={x}, y={y}, w={w}, h={h}, "
"confidence: {1}, text: {2}").format(i, conf, ocrResult, **box)
def img2string_single(self,image):
pimg = opencv2pillow(image)
self.api.SetImage(pimg)
return self.api.GetUTF8Text()
@staticmethod
def detect(image):
pimg = opencv2pillow(image)
with PyTessBaseAPI(psm=PSM.AUTO_OSD) as api:
api.SetImage(pimg)
api.Recognize()
it = api.AnalyseLayout()
orientation, direction, order, deskew_angle = it.Orientation()
print("Orientation: {:d}".format(orientation))
print("WritingDirection: {:d}".format(direction))
print("TextlineOrder: {:d}".format(order))
print("Deskew angle: {:.4f}".format(deskew_angle))
@staticmethod
def segmentation(image):
pimg = opencv2pillow(image)
with PyTessBaseAPI(psm=PSM.OSD_ONLY) as api:
api.SetImage(pimg)
os = api.DetectOS()
print ("Orientation: {orientation}\nOrientation confidence: {oconfidence}\n"
"Script: {script}\nScript confidence: {sconfidence}").format(**os)
# this functions works with tesseract4+
@staticmethod
def analyzeLSTM(image):
pimg = opencv2pillow(image)
with PyTessBaseAPI(psm=PSM.OSD_ONLY, oem=OEM.LSTM_ONLY) as api:
api.SetImage(pimg)
os = api.DetectOrientationScript()
print ("Orientation: {orient_deg}\nOrientation confidence: {orient_conf}\n"
"Script: {script_name}\nScript confidence: {script_conf}").format(**os)
@staticmethod
def rect2string(image,rect):
with PyTessBaseAPI() as api:
api.SetImage(image)
api.SetVariable("save_blob_choices", "T")
api.SetRectangle(rect)#(37, 228, 548, 31)
api.Recognize()
ri = api.GetIterator()
level = RIL.SYMBOL
for r in iterate_level(ri, level):
symbol = r.GetUTF8Text(level) # r == ri
conf = r.Confidence(level)
if symbol:
print(u'symbol {}, conf: {}'.format(symbol, conf))
indent = False
ci = r.GetChoiceIterator()
for c in ci:
if indent:
print('\t\t ')
print('\t- ')
choice = c.GetUTF8Text() # c == ci
print(u'{} conf: {}'.format(choice, c.Confidence()))
indent = True
print('---------------------------------------------')
action_number = int(key) - 1
# get the tick number
(death_left, death_top, death_width, death_height) = death_location
tick_shot_pil = pyautogui.screenshot(region=(death_left + death_width,
death_top, tick_width,
tick_height))
tick_shot_cv = np.array(tick_shot_pil)
tick_shot_cv_gray = cv2.cvtColor(tick_shot_cv, cv2.COLOR_RGB2GRAY)
(_, tick_shot_cv_black) = cv2.threshold(tick_shot_cv_gray, 190, 255,
cv2.THRESH_BINARY_INV)
pil_cap_black_text_demoUI = Image.fromarray(tick_shot_cv_black)
tessocr_api.SetImage(pil_cap_black_text_demoUI)
text = tessocr_api.GetUTF8Text()
cur_tick_string_match = re.search('k[^\d]*(\d+)[^\d]*\/[^\d]*(\d+)', text)
if cur_tick_string_match:
cur_tick = int(cur_tick_string_match.group(1))
else:
print("skipping as didn't find tick")
continue
addActionDict(action_number, cur_tick)
processing_end_time = time.time()
time_budget = 0.4 - (processing_end_time - processing_start_time)
if time_budget > 0:
print(f'''hit budget, sleeping for {time_budget}''')
time.sleep(time_budget)
def run_ocr_in_chart(chart, pad=0, psm=PSM.SINGLE_LINE):
"""
Run OCR for all the boxes.
:param img:
:param boxes:
:param pad: padding before applying ocr
:param psm: PSM.SINGLE_WORD or PSM.SINGLE_LINE
:return:
"""
img = chart.image
# add a padding to the initial figure
fpad = 1
img = cv2.copyMakeBorder(img.copy(), fpad, fpad, fpad, fpad, cv2.BORDER_CONSTANT, value=(255, 255, 255))
fh, fw, _ = img.shape
api = PyTessBaseAPI(psm=psm, lang='eng')
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4, 4))
for tbox in chart.texts:
# adding a pad to original image. Some case in quartz corpus, the text touch the border.
x, y, w, h = ru.wrap_rect(u.ttoi(tbox.rect), (fh, fw), padx=pad, pady=pad)
x, y = x + fpad, y + fpad
if w * h == 0:
tbox.text = ''
continue
# crop region of interest
roi = img[y:y + h, x:x + w]
# to gray scale
roi_gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
#
roi_gray = cv2.resize(roi_gray, None, fx=3, fy=3, interpolation=cv2.INTER_CUBIC)
# binarization
_, roi_bw = cv2.threshold(roi_gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# removing noise from borders
roi_bw = 255 - clear_border(255-roi_bw)
# roi_gray = cv2.copyMakeBorder(roi_gray, 5, 5, 5, 5, cv2.BORDER_CONSTANT, value=255)
# when testing boxes from csv files
if tbox.num_comp == 0:
# Apply Contrast Limited Adaptive Histogram Equalization
roi_gray2 = clahe.apply(roi_gray)
_, roi_bw2 = cv2.threshold(roi_gray2, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
_, num_comp = morphology.label(roi_bw2, return_num=True, background=255)
tbox.regions.extend(range(num_comp))
pil_img = smp.toimage(roi_bw)
if SHOW:
pil_img.show()
max_conf = -np.inf
min_dist = np.inf
correct_text = ''
correct_angle = 0
u.log('---------------')
for angle in [0, -90, 90]:
rot_img = pil_img.rotate(angle, expand=1)
api.SetImage(rot_img)
conf = api.MeanTextConf()
text = api.GetUTF8Text().strip()
dist = abs(len(text.replace(' ', '')) - tbox.num_comp)
u.log('text: %s conf: %f dist: %d' % (text, conf, dist))
if conf > max_conf and dist <= min_dist:
max_conf = conf
correct_text = text
correct_angle = angle
min_dist = dist
tbox.text = post_process_text(lossy_unicode_to_ascii(correct_text))
tbox.text_conf = max_conf
tbox.text_dist = min_dist
tbox.text_angle = correct_angle
u.log('num comp %d' % tbox.num_comp)
u.log(u'** text: {} conf: {} angle: {}'.format(correct_text, max_conf, correct_angle))
api.End()
def capture_mrz(window: sg.Window,
camera_id: int) -> Tuple[List[str], Image.Image]:
"""
Capture the MRZ by using OCR and the camera footage.
:returns: MRZ lines in a list
"""
cap = cv2.VideoCapture(camera_id)
tess_api = PyTessBaseAPI(init=False, psm=PSM.SINGLE_BLOCK_VERT_TEXT)
tess_api.InitFull(
# https://github.com/DoubangoTelecom/ultimateMRZ-SDK/tree/master/assets/models
path="text_detection",
lang="mrz",
variables={
"load_system_dawg": "false",
"load_freq_dawg": "false",
"tessedit_char_whitelist": "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ<",
},
)
# mrz_list: List[List[str]] = []
pool = ThreadPool(processes=1)
ocr_running = False
while True:
_, frame = cap.read()
mrz = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
mrz = cv2.adaptiveThreshold(mrz, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 21, 10)
# mrz = cv2.adaptiveThreshold(mrz, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,3,2)
# mrz = cv2.GaussianBlur(mrz, (5,5), 0)
# _, mrz = cv2.threshold(mrz, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# mrz = cv2.GaussianBlur(mrz, (5,5), 0)
# mrz = cv2.medianBlur(mrz, 3)
frame_shown = copy.deepcopy(mrz)
width = 320
height = int(frame_shown.shape[0] * (320 / frame_shown.shape[1]))
frame_shown = cv2.resize(frame_shown, (width, height))
alpha = 0.8
frame_overlay = add_mrz_overlay(copy.deepcopy(frame_shown),
"<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<", 3,
0.9, False)
frame_overlay = add_mrz_overlay(
frame_overlay, "<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<", 2,
0.9, True)
cv2.addWeighted(frame_shown, alpha, frame_overlay, 1 - alpha, 0,
frame_shown)
imgbytes = cv2.imencode(".png", frame_shown)[1].tobytes()
window.write_event_value("-SHOW MRZ-", [imgbytes])
mrz = Image.fromarray(mrz)
if not ocr_running:
checked_frame = Image.fromarray(frame[:, :, ::-1])
tess_api.SetImage(mrz)
async_result = pool.apply_async(tess_api.GetUTF8Text)
ocr_running = True
if async_result.ready():
ocr_running = False
mrz_text = async_result.get()
result = parse_mrz_ocr(mrz_text)
if result is not None:
break
# if result and len(mrz_list) < 3:
# mrz_list.append(result)
# elif not result:
# mrz_list = []
# else:
# if all(x == mrz_list[0] for x in mrz_list):
# break
# When everything done, release the capture
cap.release()
# cv2.destroyAllWindows()
tess_api.End()
# return mrz_list[0]
window.write_event_value("-HIDE MRZ-", "")
return (result, checked_frame)
from tesserocr import PyTessBaseAPI
from PIL import Image
api = PyTessBaseAPI(lang='script/Thai')
api.SetImage(Image.open("sample/thaiid.jpeg"))
# api.SetImage(Image.open("sample/thaiid.jpeg"))
print(api.GetUTF8Text())
class ViewerWindow(Gtk.Window):
def __init__(self, filenames, kind, show, ml):
Gtk.Window.__init__(self)
self.ptx = 0
self.pty = 0
self.focus_id = -1
self.file_idx = 0
self.kind = kind
self.show_hidden = show
self.ml = ml
self.screen_hint = ''
self.in_hint_screen = False
self.colors = {}
self.memory = {}
self.elem_models = {}
self.filenames = filenames
self.tesapi = PyTessBaseAPI(lang='eng')
self.tesapi.SetVariable("tessedit_char_whitelist", WHITELIST)
self.init_ui()
self.load()
def init_ui(self):
self.connect("delete-event", Gtk.main_quit)
darea = Gtk.DrawingArea()
darea.connect("draw", self.on_draw)
darea.connect("motion-notify-event", self.move_over)
darea.connect("button-release-event", self.click_evt)
darea.connect("scroll-event", self.scroll_evt)
darea.connect("key-release-event", self.key_evt)
darea.set_events(Gdk.EventMask.POINTER_MOTION_MASK
| Gdk.EventMask.BUTTON_RELEASE_MASK
| Gdk.EventMask.BUTTON_PRESS_MASK
| Gdk.EventMask.SCROLL_MASK
| Gdk.EventMask.KEY_PRESS_MASK
| Gdk.EventMask.KEY_RELEASE_MASK)
darea.set_can_focus(True)
self.add(darea)
self.show_all()
def load(self, prev=False):
if self.file_idx == len(self.filenames):
Gtk.main_quit()
return
if prev:
self.file_idx -= 2
filename = self.filenames[self.file_idx]
(self.app, self.scr) = util.get_aux_info(filename)
if self.app not in self.memory:
self.memory[self.app] = {}
self.set_title(filename)
self.file_idx += 1
print("Loading %s" % filename)
self.pngfile = os.path.splitext(filename)[0] + '.png'
self.descname = os.path.splitext(filename)[0] + '.%s.txt' % self.kind
starttime = time.time()
self.tree = analyze.load_tree(filename)
hidden.find_hidden_ocr(self.tree)
hidden.mark_children_hidden_ocr(self.tree)
util.print_tree(self.tree, show_hidden=self.show_hidden)
if self.ml:
self.get_ml_rets()
else:
self.load_desc()
endtime = time.time()
print("Load time: %.3fs" % (endtime - starttime))
self.focus_id = -1
self.colors = {}
self.ptx = self.pty = 0
self.img = cairo.ImageSurface.create_from_png(self.pngfile)
print('Image:', self.img.get_width(), self.img.get_height())
root_item_id = min(self.tree)
root_node = self.tree[root_item_id]
print('Root node:', root_node['width'], root_node['height'])
self.scale = 1.0 * self.img.get_width() / config.width
#self.scale = analyze.find_closest(self.scale, analyze.SCALE_RATIOS)
print('Scale:', '%.3f' % self.scale, '->', '%.3f' % self.scale)
self.resize(self.img.get_width(), self.img.get_height())
self.mark_depth(self.tree)
for item_id in self.tree:
color_r = random.random() / 2
color_g = random.random() / 2
color_b = random.random() / 2
self.colors[item_id] = (color_r, color_g, color_b)
imgocr = Image.open(self.pngfile)
self.imgwidth = imgocr.width
self.imgheight = imgocr.height
#imgocr2 = imgocr.convert("RGB").resize(
# (imgocr.width * OCR_RATIO, imgocr.height * OCR_RATIO))
self.tesapi.SetImage(imgocr)
self.tesapi.SetSourceResolution(config.ocr_resolution)
self.dump_memory()
def remember(self, node, desc):
nodeid = node['id']
if not node['id']:
return
if node['id'] in self.memory[self.app]:
if desc != self.memory[self.app][nodeid]:
# multiple!
self.memory[self.app][nodeid] = 'MUL'
else:
self.memory[self.app][node['id']] = desc
def forget(self, node):
if node['id'] in self.memory[self.app]:
del self.memory[self.app][node['id']]
def get_elem_model(self, app):
elem_clas = elements.getmodel("../model/", "../guis/", app,
"../guis-extra/",
config.extra_element_scrs)
self.elem_models[app] = elem_clas
def get_ml_rets(self):
if self.app not in self.elem_models:
self.get_elem_model(self.app)
guess_descs = {}
guess_items = {} # type: Dict[str, List[int]]
guess_score = {}
elem_clas = self.elem_models[self.app]
elem_clas.set_imgfile(self.pngfile)
treeinfo = analyze.collect_treeinfo(self.tree)
for itemid in self.tree:
(guess_element,
score) = elem_clas.classify(self.scr, self.tree, itemid, None,
treeinfo)
if guess_element != 'NONE':
if tags.single(guess_element,
self.scr) and guess_element in guess_items:
old_item = guess_items[guess_element][0]
if guess_score[old_item] < score:
guess_items[guess_element] = [itemid]
guess_score[itemid] = score
del guess_descs[old_item]
guess_descs[itemid] = guess_element
else:
guess_descs[itemid] = guess_element
guess_score[itemid] = score
guess_items[guess_element] = (
guess_items.get(guess_element, []) + [itemid])
for nodeid in guess_descs:
self.tree[nodeid]['label'] = guess_descs[nodeid]
def load_desc(self):
if os.path.exists(self.descname):
with open(self.descname) as inf:
for line in inf.read().split('\n'):
if not line:
continue
(item_id, desc) = line.split(' ', 1)
item_id = int(item_id)
found = False
for nodeid in self.tree:
node = self.tree[nodeid]
if item_id in node['raw']:
if 'label' in node:
node['label'] += ' ' + desc
else:
node['label'] = desc
print(nodeid, '(', item_id, ')', '->', desc)
self.remember(node, desc)
found = True
break
if not found:
print("WARNING: %s (%s) is missing!" % (item_id, desc))
def mark_depth(self, tree):
for item_id in tree:
node = tree[item_id]
if 'depth' in node:
continue
self.mark_depth_node(tree, item_id, 0)
def mark_depth_node(self, tree, node_id, depth):
node = tree[node_id]
node['depth'] = depth
node['descs'] = []
for child in node['children']:
descs = self.mark_depth_node(tree, child, depth + 1)
node['descs'] += descs
return node['descs'] + [node_id]
def get_node_info(self, node):
(x, y, width, height, depth) = (node['x'], node['y'], node['width'],
node['height'], node['depth'])
x *= self.scale
y *= self.scale
width *= self.scale
height *= self.scale
width = min(width, self.imgwidth)
height = min(height, self.imgheight)
if x < 0:
width += x
x = 0
if y < 0:
height += y
y = 0
return (x, y, width, height, depth)
def find_containing_widget(self, px, py):
max_depth = 0
max_id = -1
for item_id in self.tree:
node = self.tree[item_id]
if self.ignore_node(node):
continue
if self.inside(node, px, py):
if node['depth'] > max_depth:
max_depth = node['depth']
max_id = item_id
return max_id
def inside(self, node, px, py):
(x, y, width, height, depth) = self.get_node_info(node)
return x <= px and x + width >= px and y <= py and y + height >= py
def ignore_node(self, node):
if node['class'].upper() == 'OPTION':
return True
if node.get('visible', '') == 'hidden':
return True
return False
def on_draw(self, wid, ctx):
ctx.select_font_face("Arial", cairo.FONT_SLANT_NORMAL,
cairo.FONT_WEIGHT_BOLD)
ctx.set_source_surface(self.img, 0, 0)
ctx.paint()
ctx.set_font_size(20)
ctx.set_line_width(5)
ctx.set_source_rgb(1.0, 0.0, 0.0)
max_click_id = -1
max_click_depth = 0
max_id = self.find_containing_widget(self.ptx, self.pty)
for item_id in self.tree:
node = self.tree[item_id]
depth = node['depth']
if max_id in node['descs'] and node['click']:
if depth > max_click_depth:
max_click_depth = depth
max_click_id = item_id
for item_id in self.tree:
node = self.tree[item_id]
if self.ignore_node(node):
continue
if item_id == max_id:
region_mode = False
else:
region_mode = True
(x, y, width, height, depth) = self.get_node_info(node)
if not self.inside(node, self.ptx, self.pty):
continue
self.show_widget(ctx, item_id, not region_mode, not region_mode)
if max_click_id != -1 and max_click_id != max_id:
self.show_widget(ctx, max_click_id, False, True)
if self.focus_id >= 0:
self.show_widget(ctx, self.focus_id, True, True, (1, 0, 0))
for itemid in self.tree:
node = self.tree[itemid]
if 'label' in node:
if itemid == self.focus_id:
color = (0, 1, 0)
else:
color = (0, 0, 1)
self.show_widget(ctx, itemid, True, False, (0, 0, 1))
self.show_desc(ctx, node, color)
#s.write_to_png('test.png')
#os.system("%s %s" % (config.picviewer_path, 'test.png'))
#report_time(start_time, "displayed")
def move_sibling(self, to_next):
leaf_list = []
any_list = []
for itemid in self.tree:
node = self.tree[itemid]
if not self.inside(node, self.clickx, self.clicky):
continue
if len(node['children']) == 0:
leaf_list.append(itemid)
any_list.append(itemid)
for i in range(len(leaf_list)):
if leaf_list[i] == self.focus_id:
if to_next:
idx = (i + 1) % len(leaf_list)
else:
idx = (i - 1) % len(leaf_list)
self.focus_id = leaf_list[idx]
return
if len(leaf_list) == 0:
for i in range(len(any_list)):
if any_list[i] == self.focus_id:
if to_next:
idx = (i + 1) % len(any_list)
else:
idx = (i - 1) % len(any_list)
self.focus_id = any_list[idx]
return
self.focus_id = any_list[0]
else:
self.focus_id = leaf_list[0]
def show_widget(self, ctx, item_id, fill, show_text, colors=None):
node = self.tree[item_id]
(x, y, width, height, depth) = self.get_node_info(node)
if colors is None:
color_r = self.colors[item_id][0]
color_g = self.colors[item_id][1]
color_b = self.colors[item_id][2]
else:
(color_r, color_g, color_b) = colors
ctx.rectangle(x, y, width, height)
if fill:
ctx.set_source_rgba(color_r, color_g, color_b, 0.3)
ctx.fill()
else:
ctx.set_source_rgba(color_r, color_g, color_b, 1)
ctx.set_line_width(5)
ctx.stroke()
if show_text:
max_char = int(width / ctx.text_extents("a")[2])
text = str(item_id)
if node['click']:
text = 'C' + text
if node['text']:
text = text + ':' + node['text'][:(max_char - 5)]
elif node['id']:
text += '#' + node['id'][:(max_char - 5)]
self.show_text(ctx, x + width / 2, y + height / 2, text, color_r,
color_g, color_b)
def show_desc(self, ctx, node, color=(0, 0, 1)):
desc = node['label']
(x, y, width, height, depth) = self.get_node_info(node)
self.show_text(ctx, x + width / 2, y + height / 2, desc, color[0],
color[1], color[2])
def show_text(self, ctx, x, y, text, color_r, color_g, color_b):
x_bearing, y_bearing, text_width, text_height = ctx.text_extents(
text)[:4]
ctx.move_to(x - text_width / 2, y + text_height / 2)
ctx.set_source_rgba(1, 1, 1, 1)
ctx.set_line_width(5)
ctx.text_path(text)
ctx.stroke()
ctx.move_to(x - text_width / 2, y + text_height / 2)
ctx.set_source_rgba(color_r, color_g, color_b, 1)
ctx.text_path(text)
ctx.fill()
def move_over(self, widget, evt):
self.ptx = evt.x
self.pty = evt.y
self.queue_draw()
def click_evt(self, widget, evt):
if self.in_hint_screen:
self.process_screen_hint_click(evt)
return
if evt.button == 3:
self.focus_id = -1
else:
self.clickx = evt.x
self.clicky = evt.y
self.focus_id = self.find_containing_widget(evt.x, evt.y)
self.queue_draw()
def scroll_evt(self, widget, evt):
if self.focus_id == -1:
return
scroll_up = evt.direction == Gdk.ScrollDirection.UP
if scroll_up:
self.focus_id = self.find_parent_widget(self.focus_id)
else:
self.focus_id = self.find_child_widget(self.focus_id)
self.queue_draw()
def find_parent_widget(self, wid):
for itemid in self.tree:
node = self.tree[itemid]
if self.ignore_node(node):
continue
if wid in node['children']:
return itemid
return wid
def find_child_widget(self, wid):
for itemid in self.tree[wid]['children']:
node = self.tree[itemid]
if self.ignore_node(node):
continue
if self.inside(node, self.clickx, self.clicky):
return itemid
return wid
def mark_direct(self):
enter = self.get_text('Please enter id_label', 'format: <id> <label>')
if enter is None:
return
if ' ' in enter:
nodeid, label = enter.split(' ')
else:
nodeid = enter
label = ''
nodeid = int(nodeid)
if nodeid not in self.tree:
print('missing node', nodeid)
return
node = self.tree[nodeid]
self.mark_node(node, label)
def mark_focused(self):
if self.focus_id < 0:
return
node = self.tree[self.focus_id]
label = self.get_text(
'Please enter label', 'label for %s: %s (%s) #%s' %
(self.focus_id, node['text'], node['desc'], node['id']))
if label is None:
return
if self.ml:
if label == '':
if 'label' not in self.tree[self.focus_id]:
return
self.generate_negative_hint(self.tree[self.focus_id]['label'])
del self.tree[self.focus_id]['label']
else:
self.generate_hint_for_widget(self.focus_id, label)
self.add_label(node, label)
else:
self.mark_node(node, label)
def generate_hint_for_widget(self, nodeid, label):
return self.generate_hint(label,
locator.get_locator(self.tree, nodeid))
def generate_negative_hint(self, label):
return self.generate_hint(label, 'notexist')
def generate_hint(self, label, hint):
print("@%s.%s %s" % (self.scr, label, hint))
def mark_node(self, node, label):
if label == '':
if 'label' in node:
del node['label']
self.forget(node)
else:
self.add_label(node, label)
self.remember(node, label)
self.save_labels()
def ocr_text(self):
node = self.tree[self.focus_id]
(x, y, width, height, _) = self.get_node_info(node)
print(x, y, width, height)
x = max(x - 1, 0)
y = max(y - 1, 0)
width = min(width + 2, self.imgwidth)
height = min(height + 2, self.imgheight)
#self.tesapi.SetRectangle(x * OCR_RATIO, y * OCR_RATIO,
# width * OCR_RATIO, height * OCR_RATIO)
self.tesapi.SetRectangle(x, y, width, height)
print("OCR ret:", self.tesapi.GetUTF8Text())
x = min(x + width * 0.05, self.imgwidth)
y = min(y + height * 0.05, self.imgheight)
width *= 0.9
height *= 0.9
self.tesapi.SetRectangle(x, y, width, height)
print("OCR ret:", self.tesapi.GetUTF8Text())
def save_region(self):
if self.focus_id == -1:
return
node = self.tree[self.focus_id]
(x, y, width, height, _) = self.get_node_info(node)
x = max(x - 1, 0)
y = max(y - 1, 0)
width = min(width + 2, self.imgwidth)
height = min(height + 2, self.imgheight)
regimg = cairo.ImageSurface(cairo.FORMAT_RGB24, int(width),
int(height))
ctx = cairo.Context(regimg)
ctx.set_source_surface(self.img, -x, -y)
ctx.paint()
regimg.write_to_png("/tmp/region.png")
def dump_memory(self):
for _id in self.memory[self.app]:
print('MEM %s -> %s' % (_id, self.memory[self.app][_id]))
def add_label(self, node, desc):
print('%s -> %s' % (util.describe_node(node, short=True), desc))
node['label'] = desc
def auto_label(self):
for nodeid in self.tree:
node = self.tree[nodeid]
if 'label' not in node and node['id'] in self.memory[self.app]:
if self.memory[self.app][node['id']] != 'MUL':
self.add_label(node, self.memory[self.app][node['id']])
else:
print('skip MUL id: %s' % node['id'])
self.save_labels()
def remove_all(self):
for nodeid in self.tree:
node = self.tree[nodeid]
if 'label' in node:
del node['label']
def process_screen_hint_click(self, evt):
click_id = self.find_containing_widget(evt.x, evt.y)
if click_id == -1:
print('Invalid widget selected')
return
hint = locator.get_locator(self.tree, click_id)
if hint is None:
print('Cannot generate hint for this widget')
return
hint = str(hint)
if evt.button == 3:
# negate
hint = 'not ' + hint
print('Widget hint: "%s"' % hint)
self.add_screen_hint(hint)
def add_screen_hint(self, hint):
if self.screen_hint == '':
self.screen_hint = hint
else:
self.screen_hint += ' && ' + hint
def hint_screen(self):
if not self.in_hint_screen:
label = self.get_text('Please enter screen name',
'screen name like "signin"')
if label is None:
return
self.screen_hint_label = label
self.in_hint_screen = True
self.screen_hint = ''
else:
self.in_hint_screen = False
print("%%%s %s" % (self.screen_hint_label, self.screen_hint))
def key_evt(self, widget, evt):
if evt.keyval == Gdk.KEY_space:
self.mark_focused()
elif evt.keyval == Gdk.KEY_Tab:
self.load()
elif evt.keyval == Gdk.KEY_Left:
self.move_sibling(to_next=True)
elif evt.keyval == Gdk.KEY_Right:
self.move_sibling(to_next=False)
elif evt.keyval == Gdk.KEY_v:
self.ocr_text()
elif evt.keyval == Gdk.KEY_a:
self.auto_label()
elif evt.keyval == Gdk.KEY_p:
self.load(prev=True)
elif evt.keyval == Gdk.KEY_l:
self.mark_direct()
elif evt.keyval == Gdk.KEY_r:
self.remove_all()
elif evt.keyval == Gdk.KEY_s:
self.save_region()
elif evt.keyval == Gdk.KEY_x:
self.hint_screen()
self.queue_draw()
def save_labels(self):
with open(self.descname, 'w') as outf:
for itemid in sorted(self.tree):
node = self.tree[itemid]
if 'label' in node:
outf.write("%s %s\n" % (itemid, node['label']))
def get_text(self, title, prompt):
#base this on a message dialog
dialog = Gtk.MessageDialog(self, 0, Gtk.MessageType.QUESTION,
Gtk.ButtonsType.OK_CANCEL, title)
dialog.format_secondary_text(prompt)
#create the text input field
entry = Gtk.Entry()
#allow the user to press enter to do ok
entry.connect("activate",
lambda entry: dialog.response(Gtk.ResponseType.OK))
#create a horizontal box to pack the entry and a label
hbox = Gtk.HBox()
hbox.pack_start(Gtk.Label("Label:"), False, 5, 5)
hbox.pack_end(entry, True, 0, 0)
#add it and show it
dialog.vbox.pack_end(hbox, True, True, 0)
dialog.show_all()
#go go go
response = dialog.run()
if response == Gtk.ResponseType.OK:
text = entry.get_text()
else:
text = None
dialog.destroy()
return text
class FeatureCollector(object):
def __init__(self, tree, imgfile):
self.tree = tree
self.collect_texts()
self.imgfile = imgfile
self.tesapi = PyTessBaseAPI(lang='eng')
self.set_tes_image()
def set_tes_image(self):
#imgpil = Image.fromarray(numpy.uint8(imgdata * 255))
imgpil = Image.open(self.imgfile)
(self.imgwidth, self.imgheight) = (imgpil.width, imgpil.height)
imgpil = imgpil.convert("RGB").resize(
(imgpil.width * OCR_RATIO, imgpil.height * OCR_RATIO))
self.tesapi.SetImage(imgpil)
def add_ctx_attr(self, ctx, data, attr_re, word_limit=1000):
if ctx not in self.point:
self.point[ctx] = ''
words = attr_re.findall(data)
if word_limit and len(words) > word_limit:
return
for word in words:
if self.point[ctx]:
self.point[ctx] += ' '
self.point[ctx] += '%s' % word.lower()
def add_ctx(self, ctx, node, attrs, attr_re=anything_re, word_limit=None):
for attr in attrs:
self.add_ctx_attr(ctx, node[attr], attr_re, word_limit)
def collect_texts(self):
for nodeid in self.tree:
node = self.tree[nodeid]
if 'fulltext' not in node:
self.collect_text(node)
def collect_text(self, node):
""" Collect text from node and all its children """
if 'fulltext' in node:
return node['fulltext']
cls = node['class']
if cls == 'View':
text = node['desc']
else:
text = node['text']
for child in node['children']:
text = text.strip() + ' ' + self.collect_text(self.tree[child])
node['fulltext'] = text
return text
def prepare_neighbour(self):
node = self.tree[self.nodeid]
self.point['neighbour_ctx'] = ''
self.point['adj_ctx'] = ''
neighbour_count = 0
for other in self.tree:
if self.tree[other]['parent'] == node[
'parent'] and other != self.nodeid:
self.add_ctx_attr('neighbour_ctx',
self.collect_text(self.tree[other]), text_re)
self.add_ctx('neighbour_ctx', self.tree[other], ['id'], id_re)
if self.tree[other]['class'] == node['class']:
neighbour_count += 1
# left sibling
if (self.tree[other]['parent'] == node['parent']
and other != self.nodeid
and self.tree[other]['childid'] < node['childid']
and self.tree[other]['childid'] > node['childid'] - 2):
self.add_ctx_attr('adj_ctx',
self.collect_text(self.tree[other]), text_re)
self.add_ctx('adj_ctx', self.tree[other], ['id'], id_re)
self.point['neighbour_count'] = neighbour_count
def ctx_append(self, ctx, kind, clz, detail):
ret = ctx
ret += ' ' + kind + clz
regex = word_re
for part in regex.findall(detail):
ret += ' ' + kind + part
return ret
def collect_subtree_info(self, node, root):
if ignore_node(node):
return {'ctx': '', 'text': '', 'count': 0}
ctx = ''
count = 1
clz = node['class']
if clz == 'View':
text = node['desc'][:30]
else:
text = node['text'][:30]
desc = node['desc'][:30]
ctx += clz + ' '
ctx += node['id'] + ' '
ctx += text + ' '
ctx += desc + ' '
ctx += gen_ngram(text) + ' '
ctx += gen_ngram(desc) + ' '
if root is not None:
if node['width'] > 0.6 * config.width:
ctx = self.ctx_append(ctx, "WIDE", clz, node['id'])
if node['height'] > 0.6 * config.height:
ctx = self.ctx_append(ctx, "TALL", clz, node['id'])
if node['y'] + node['height'] < root['y'] + 0.3 * root['height']:
ctx = self.ctx_append(ctx, "TOP", clz, node['id'])
if node['x'] + node['width'] < root['x'] + 0.3 * root['width']:
ctx = self.ctx_append(ctx, "LEFT", clz, node['id'])
if node['y'] > root['y'] + 0.7 * root['height']:
ctx = self.ctx_append(ctx, "BOTTOM", clz, node['id'])
if node['x'] > root['x'] + 0.7 * root['width']:
ctx = self.ctx_append(ctx, "RIGHT", clz, node['id'])
for child in node['children']:
child_info = self.collect_subtree_info(
self.tree[child], root if root is not None else node)
ctx = ctx.strip() + ' ' + child_info['ctx']
count += child_info['count']
text = text.strip() + ' ' + child_info['text']
return {'ctx': ctx, 'text': text, 'count': count}
def prepare_children(self):
node = self.tree[self.nodeid]
#self.add_ctx_attr('node_subtree_text', self.collect_text(node), text_re, 10)
subtree_info = self.collect_subtree_info(node, None)
self.point['subtree'] = subtree_info['ctx']
self.point['node_subtree_text'] = subtree_info['text']
self.point['node_childs'] = subtree_info['count']
def prepare_ancestor(self):
node = self.tree[self.nodeid]
parent = node['parent']
self.point['parent_ctx'] = ''
parent_click = parent_scroll = parent_manychild = False
parent_depth = 0
while parent != 0 and parent != -1 and parent_depth < PARENT_DEPTH_LIMIT:
self.add_ctx('parent_ctx', self.tree[parent], ['class', 'id'],
id_re)
parent_click |= self.tree[parent]['click']
parent_scroll |= self.tree[parent]['scroll']
parent_manychild |= len(self.tree[parent]['children']) > 1
parent = self.tree[parent]['parent']
parent_depth += 1
self.point['parent_prop'] = [
parent_click, parent_scroll, parent_manychild
]
def prepare_self(self):
node = self.tree[self.nodeid]
# AUX info
self.point['id'] = self.nodeid
self.point['str'] = util.describe_node(node, None)
self.add_ctx('node_text', node, ['text'], text_re, 10)
self.add_ctx('node_ctx', node, ['desc'], text_re)
self.add_ctx('node_ctx', node, ['id'], id_re)
self.add_ctx('node_class', node, ['class'], id_re)
if 'Recycler' in node['class'] or 'ListView' in node['class']:
self.point['node_class'] += " ListContainer"
self.point['node_x'] = node['x']
self.point['node_y'] = node['y']
self.point['node_w'] = node['width']
self.point['node_h'] = node['height']
def prepare_point(self, nodeid, app, scr, caseid, imgdata, treeinfo, path):
"""Convert a node in the tree into a data point for ML"""
self.nodeid = nodeid
self.point = {}
# AUX info
self.point['app'] = app
self.point['scr'] = scr
self.point['case'] = caseid
self.prepare_self()
self.prepare_neighbour()
self.prepare_ancestor()
self.prepare_global(path, treeinfo)
self.prepare_img(imgdata)
self.prepare_ocr()
self.prepare_children()
return self.point
def prepare_global(self, path, treeinfo):
node = self.tree[self.nodeid]
has_dupid = False
is_itemlike = False
is_listlike = False
for _id in node['raw']:
if _id in treeinfo['dupid']:
has_dupid = True
break
for _id in node['raw']:
if _id in treeinfo['itemlike']:
is_itemlike = True
break
for _id in node['raw']:
if _id in treeinfo['listlike']:
is_listlike = True
break
self.point['node_prop'] = [
node['click'], node['scroll'],
len(node['children']) > 1, has_dupid, is_itemlike, is_listlike
]
self.point['path'] = path
def prepare_img(self, imgdata):
node = self.tree[self.nodeid]
# your widget should be inside the screenshot
# not always!
self.min_x = max(node['x'], 0)
self.min_y = max(node['y'], 0)
self.max_x = min(node['x'] + node['width'], self.imgwidth)
self.max_y = min(node['y'] + node['height'], self.imgheight)
self.empty = self.max_x <= self.min_x or self.max_y <= self.min_y
self.point['empty'] = self.empty
def prepare_ocr(self):
node = self.tree[self.nodeid]
if config.region_use_ocr and not self.empty:
if 'ocr' in node:
ocr_text = node['ocr']
else:
self.tesapi.SetRectangle(self.min_x * OCR_RATIO,
self.min_y * OCR_RATIO,
(self.max_x - self.min_x) * OCR_RATIO,
(self.max_y - self.min_y) * OCR_RATIO)
try:
ocr_text = self.tesapi.GetUTF8Text()
except:
logger.warning("tessearact fail to recognize")
ocr_text = ''
ocr_text = ocr_text.strip().replace('\n', ' ')
else:
ocr_text = 'dummy'
self.point['node_ocr'] = ocr_text
#if point['node_text'].strip() == '':
# point['node_text'] = ocr_text
logger.debug("%s VS %s" % (ocr_text, node['text']))
(missing, found, other) = (node['ocr_missing'], node['ocr_found'],
node['ocr_other'])
self.point['ocr_missing'] = missing
self.point['ocr_found'] = found
self.point['ocr_other'] = other
self.point['ocr_ratio'] = (1.0 * missing / (missing + other)
if missing + other > 0 else 0.0)
self.point['ocr_visible'] = node['visible']
class VariationParser:
def __init__(self):
self.tesseract = PyTessBaseAPI(path='./', psm=PSM.SINGLE_LINE)
self.item_db = json.load(open('en-us-var.json', 'rb'))
self.items: Set[str] = set()
self.active_section = 0
self.section_name = None
self.for_sale = False
self._tesseract_cache = {}
self._item_cache = {}
def annotate_frame(self, frame: numpy.ndarray) -> numpy.ndarray:
"""Parses various parts of a frame for catalog items and annotates it."""
# Detect whether we are in in Nook Shopping catalog.
if not numpy.array_equal(frame[500, 20], (182, 249, 255)):
text = 'Navigate to Nook catalog to start!'
opts = {
'org': (200, 70),
'fontFace': cv2.FONT_HERSHEY_PLAIN,
'lineType': cv2.LINE_AA,
'fontScale': 3
}
frame = cv2.putText(frame,
text,
color=(0, 0, 0),
thickness=7,
**opts)
return cv2.putText(frame,
text,
color=(100, 100, 255),
thickness=3,
**opts)
# Show controls on screen.
cv2.rectangle(frame, (0, 0), (300, 130), (106, 226, 240), -1)
for i, line in enumerate(TUTORIAL_LINES):
if line.startswith('F'):
line += ' (%s)' % ('ON' if self.for_sale else 'OFF')
frame = cv2.putText(frame, line, (30, 25 + i * 30), 0, 0.8, 0, 2)
# Show user the item count at the bottom.
count_text = 'Item count: %d' % len(self.items)
if not self.section_name:
count_text = 'Items saved to disk'
frame = cv2.putText(frame, count_text, (500, 700), 0, 1, 0)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
section = numpy.nonzero(gray[20, 250:] == 156)[0]
if section.any() and section[0] != self.active_section:
# Grab the new section name
x1, *_, x2 = 250 + section
section_region = 255 - gray[8:32, x1 + 5:x2 - 5]
self.section_name = self.image_to_text(section_region)
# Reset item selection on section change
self.active_section = section[0]
self.items = set()
elif not self.active_section:
return frame # Return early if not section is found.
item_name = None
variation_name = None
selected = self.get_selected_item(frame)
if not selected: # Quit early if not item is selected
return frame
price_region = gray[selected.y1:selected.y2, 1070:1220]
if self.for_sale and price_region.min() > 100:
# Skip items not for sale
p1, p2 = (selected.x1, selected.y1 + 20), (selected.x2,
selected.y1 + 20)
return cv2.line(frame, p1, p2, color=(0, 0, 255), thickness=2)
# Parse item name and display a rectangle around it.
item_name = self.image_to_text(gray[selected.slice])
frame = cv2.putText(frame, item_name, selected.p1, 0, 1, 0)
# Parse variation and draw rectangle around it if there is one.
variation = self.get_variation(gray)
if variation:
frame = cv2.rectangle(frame, variation.p1, variation.p2, 0)
variation_name = self.image_to_text(gray[variation.slice])
frame = cv2.putText(frame, variation_name, variation.p1, 1, 2, 0)
# Match the name and optional variation against database and register it.
full_name = self.resolve_name(item_name, variation_name)
if full_name:
self.items.add(full_name)
return frame
def get_selected_item(self, frame: numpy.ndarray) -> Optional[Rectangle]:
"""Returns the rectangle around the selected item name if there is one."""
# Search for the yellow selected region along the item list area.
select_region = numpy.nonzero(frame[140:640, 1052, 0] < 100)[0]
if not select_region.any():
return None
rect = Rectangle(x1=635, x2=1050)
# Find the top/bottom boundaries of the selected area
rect.y1 = 140 + select_region[0] + 8
rect.y2 = 140 + select_region[-1] - 4
if rect.y2 - rect.y1 < 35:
return None
# Detect to width of the name by collapsing along the x axis
# and finding the right-most dark pixel (text).
item_region = frame[rect.y1:rect.y2, rect.x1:rect.x2, 1]
detected_text = numpy.nonzero(item_region.min(axis=0) < 50)[0]
if not detected_text.any():
return None
rect.x2 = 635 + detected_text[-1] + 10
return rect
def get_variation(self, gray: numpy.ndarray) -> Optional[Rectangle]:
"""Returns the rectangle around the variation text if there is one."""
# There's a white box if the item has a variation.
if gray[650, 25] != 250:
return None
variation = Rectangle(x1=30, y1=628, y2=665)
# Find the width of tqhe variation box by horizontal floodfill.
variation.x2 = numpy.argmax(gray[variation.y1, :] < 250) - 15
return variation
def resolve_name(self, item: Optional[str],
variation: Optional[str]) -> Optional[str]:
"""Resolves an item and optional variation name against the item database."""
key = (item, variation)
if key not in self._item_cache:
item = best_match(item, self.item_db)
variation = best_match(variation, self.item_db.get(item))
if variation:
self._item_cache[key] = f'{item} [{variation}]'
elif item and not self.item_db[item]:
self._item_cache[key] = item
else:
self._item_cache[key] = None
return self._item_cache[key]
def image_to_text(self, text_area: numpy.ndarray) -> str:
"""Runs OCR over a given image and returns the parsed text."""
img_hash = str(cv2.img_hash.averageHash(text_area)[0])
if img_hash not in self._tesseract_cache:
image = Image.fromarray(text_area)
self.tesseract.SetImage(image)
text = self.tesseract.GetUTF8Text().strip()
self._tesseract_cache[img_hash] = text
return self._tesseract_cache[img_hash]
def save_items(self) -> None:
""""Saves the collected items to a text file on disk and clears list."""
if not self.items or not self.section_name:
return
date = datetime.datetime.now().strftime('%d-%m-%Y %H-%M-%S')
with open(f'{self.section_name} ({date}).txt', 'w') as fp:
fp.write('\n'.join(sorted(self.items)))
self.section_name = None
self.items = set()
class TT2Predictor:
"""holds the several trainer predictor instances and common operations """
def __init__(self, **kwargs):
self.trainers_predictors_list = []
self.text_predictors_list = [
("previous_level", (1212, 231, 1230, 280), "0123456789", "8"),
("main_level", (1203, 323, 1223, 399), "0123456789", "8"),
("next_level", (1212, 445, 1230, 493), "0123456789", "8"),
("sub_level", (1177, 625, 1203, 692), "0123456789/", "8"),
("gold", (1091, 283, 1126, 471),
"0123456789.abcdefghijklmnopqrstuvwxyz", "7"),
("current_dps_down_no_tab", (389, 562, 423, 709),
"0123456789.abcdefghijklmnopqrstuvwxyz", "8"),
("last_hero", (124, 109, 148, 430),
"0123456789.ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz",
"7")
]
self.api = PyTessBaseAPI()
self.api.Init()
print(tesserocr.tesseract_version())
print(tesserocr.get_languages())
self.global_image = None
self.status = CurrentStatus()
boss_trainer = TrainerPredictor(
"boss_active_predictor",
["boss_active", "boss_inactive", "no_boss"],
(1224, 555, 1248, 648), 12, 46, 255.0, [200, 30])
egg_trainer = TrainerPredictor("egg_active_predictor",
["egg_active", "egg_inactive"],
(741, 31, 761, 64), 10, 16, 255.0,
[200, 30])
gold_pet_trainer = TrainerPredictor(
"gold_pet_predictor",
["goldpet", "nopet", "normalpet", "partial pet"],
(624, 364, 734, 474), 40, 40, 255.0, [200, 30])
tab_predictor = TrainerPredictor("tab_predictor", [
"skills_tab", "heroes_tab", "equipment_tab", "pet_tab",
"relic_tab", "shop_tab", "no_tab"
], (51, 1, 59, 717), 2, 179, 255.0, [200, 30])
self.trainers_predictors_list.append(boss_trainer)
self.trainers_predictors_list.append(egg_trainer)
self.trainers_predictors_list.append(gold_pet_trainer)
self.trainers_predictors_list.append(tab_predictor)
for trainer in self.trainers_predictors_list:
pass
#trainer.crop_images()
#trainer.process_images()
#trainer.read_and_pickle()
#trainer.train_graph()
saved_classes_file = glo.DATA_FOLDER + "/dataforclassifier/TrainerPredictor_list.pickle"
save_pickle(saved_classes_file, self.trainers_predictors_list)
def parse_raw_image(self):
with open(glo.RAW_FULL_FILE, 'rb') as f:
image = Image.frombytes('RGBA', (1280, 720), f.read())
for class_predictor in self.trainers_predictors_list:
class_predictor.predict_crop(image)
self.global_image = image
image.save(glo.UNCLASSIFIED_GLOBAL_CAPTURES_FOLDER + "/fullcapture" +
time.strftime("%Y%m%d-%H%M%S-%f") +
".png") # save original capture copy
def parse_image_text(self, predict_map):
return_dict = {}
for text_predictor in self.text_predictors_list:
if text_predictor[0] in predict_map:
img = self.global_image.crop(text_predictor[1])
img = img.convert('L')
img = img.rotate(90, expand=True)
self.api.SetImage(img)
self.api.SetVariable("tessedit_char_whitelist",
text_predictor[2])
self.api.SetVariable("tessedit_pageseg_mode",
text_predictor[3])
self.api.SetVariable("language_model_penalty_non_dict_word",
"0")
self.api.SetVariable("doc_dict_enable", "0")
text_capture = self.api.GetUTF8Text().encode('utf-8').strip()
return_dict[text_predictor[0]] = text_capture
print("raw text capture ", text_predictor[0], ":",
text_capture)
self.api.Clear()
return return_dict
def predict_parsed_all(self):
pred_dict = {}
for class_predictor in self.trainers_predictors_list:
pred_dict[class_predictor.name] = class_predictor.predict_parsed()
return pred_dict
def predict_parsed(self, predict_map, predict_map_text, **kwargs):
pred_dict = {"transition_level": False}
# check if image is level trasitioning. trivial prediction.
if hasattr(kwargs, "empty_image") and kwargs["empty_image"] is False:
pass
else:
img = self.global_image.crop((0, 0, 100, 100)) # black corner
extrema = img.convert("L").getextrema()
if extrema[0] == extrema[1]: # only one color
print("warning level transitioning")
pred_dict["transition_level"] = True
else:
pass
for class_predictor in self.trainers_predictors_list:
if class_predictor.name in predict_map:
pred_dict[
class_predictor.name] = class_predictor.predict_parsed()
pred_dict_text = self.parse_image_text(predict_map_text)
pred_dict.update(pred_dict_text)
self.status.update_status(pred_dict, self.trainers_predictors_list)
return pred_dict
def predict(self):
self.parse_raw_image()
return self.predict_parsed_all()
def check_predict(self, pred_dict, predictor, classification):
for class_predictor in self.trainers_predictors_list:
if class_predictor.name == predictor:
return int(
pred_dict[predictor]
) == class_predictor.pred_classes.index(classification)