def main():
parser = argparse.ArgumentParser(usage='%(prog)s [options] imageurl', description='Cappy, the little captcha solver.')
parser.version = "0.1"
parser.add_argument("imageurl", metavar="imageurl", type=str, help="the url to POST the captcha to")
parser.add_argument("-o", "--output", type=str, default="/tmp/captcha.png", help="where to save the temporary captcha file (default: /tmp/captcha.png)")
parser.add_argument("-oe", "--outputedited", type=str, default="/tmp/captcha-edited.png", help="where to save the temporary captcha file (default: /tmp/captcha-edited.png)")
parser.add_argument("-c", "--count", type=int, default=200, help="total number of captchas to try (default: 200)")
parser.add_argument("-v", action="version")
args = parser.parse_args()
imageurl = args.imageurl
output = args.output
outputedited = args.outputedited
totalcount = args.count
with PyTessBaseAPI(psm=PSM.SINGLE_WORD, oem=OEM.TESSERACT_ONLY) as api:
api.SetVariable("tessedit_char_whitelist", "abcdefghijklmnopqrstuvwxyz")
res = requests.get(imageurl, stream=True).raw
with open(output, "wb") as out_file:
shutil.copyfileobj(res, out_file)
del res
image = None
count = 0
starttime = time.time()
while count < totalcount:
image = cv2.imread(output, cv2.IMREAD_GRAYSCALE)
image = cv2.resize(image, None, fx=10, fy=10, interpolation=cv2.INTER_LINEAR)
image = cv2.GaussianBlur(image, (5,5), 0)
# image = cv2.bilateralFilter(image, 9, 75, 75)
# image = cv2.blur(image, (5,5))
# image = cv2.medianBlur(image, 9)
ret, image = cv2.threshold(image, 185, 255, cv2.THRESH_BINARY)
cv2.imwrite(outputedited, image)
api.SetImageFile(outputedited)
captcha = api.GetUTF8Text().replace(" ", "").rstrip().lower()
count += 1
elapsedtime = time.time() - starttime
print("Finished")
print("Time: " + str(round(elapsedtime)) + " seconds")
print("Total solves per 30 seconds: " + str(round((count/elapsedtime) * 30)))
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 get_blob_ocr_result(image: np.ndarray, rect: Rect, ppi: int = 0):
text = ''
confidence = 0.0
with PyTessBaseAPI(psm=PSM.SINGLE_LINE) as api:
# only read numbers (doesn't seem to work, known issue in v4.0)
#api.SetVariable('tessedit_char_whitelist', '0123456789')
api.SetImageBytes(*image)
api.SetRectangle(*astuple(rect))
if ppi != 0:
api.SetSourceResolution(ppi)
#api.Recognize()
text = api.GetUTF8Text()
confidence = api.AllWordConfidences()
if not len(confidence):
confidence = (0, )
return [text, confidence]
def tess(filename):
# frk = german fraktur
# psm=7 treats image as line of text, psm=10 for single char
with PyTessBaseAPI(lang='frk', psm=10) as api:
api.SetImageFile(filename)
actualLabel = filename[filename.rfind('_') + 1:-4]
predictLabel = api.GetUTF8Text().rstrip()
print(filename)
print('Actual:', actualLabel, 'Prediction:', predictLabel, \
'Confidence:', str(api.AllWordConfidences())) # 0 worst, 100 best
if actualLabel == predictLabel:
print('CORRECT ~~~\n')
return 1
else:
print('INCORRECT >:(\n')
return 0
def get_comp(input_name):
image = Image.open(input_name)
with PyTessBaseAPI(path=tesseract_path, lang='heb') as api:
image = preprocess_image(image)
api.SetImage(image)
boxes = api.GetComponentImages(RIL.TEXTLINE, True)
debug_log('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()
debug_log(u"Box[{0}]: x={x}, y={y}, w={w}, h={h}, "
"confidence: {1}, text: {2}".format(
i, conf, ocrResult, **box))
def get_away_team_name(away_image):
away = away_image.crop(
(210 * RESIZE_FACTOR, 0, 270 * RESIZE_FACTOR, 25 * RESIZE_FACTOR))
away = pre_process.invert_if_neccessary(away)
# away.show()
with PyTessBaseAPI() as api:
api.SetImage(away)
text = api.GetUTF8Text().replace('\n', '').strip(' ')
# confidence = api.AllWordConfidences()
# Good only if the name is exactly 3 uppercase characters
if re.match("^[A-Za-z1-9]{3}$", text) is None:
return None # team name wasn't good
return text
def watch(self):
if not area_data_path.exists():
raise NotImplementedError()
with PyTessBaseAPI(lang="jpn") as api:
for i in range(10000):
time.sleep(1)
igrab = RectImageGrab()
image = igrab.grab()
api.SetImage(image)
txt = api.GetUTF8Text()
txt = "".join(txt.split())
self.ocr_result = txt
self._logger.info(f"OCR:{txt}")
if self.is_stopping is True:
break
return 1
def ocr_roi(rois):
"""
Function to do the OCR
Inputs: rois: A list of Images
Returns: A List of strings. One for each incoming Image
"""
# Initializing PyTessBaseApi again and again adds an unnecessary overhead.
# So initializing it just once
# PSM.SINGLE_LINE degrades performace
extracted_text = []
with PyTessBaseAPI(path=os.path.join(os.getcwd(), 'tessdata')) as api:
for roi in rois:
# Converting the OpenCV image to PIL image for tesseract
api.SetImage(Image.fromarray(roi))
extracted_text.append(api.GetUTF8Text())
return extracted_text
def rotate_to_upright(image):
with PyTessBaseAPI(psm=PSM.OSD_ONLY) as api:
api.SetImage(image)
os = api.DetectOS()
if os:
if os['orientation'] == Orientation.PAGE_RIGHT:
image = image.rotate(90, expand=True)
if os['orientation'] == Orientation.PAGE_LEFT:
image = image.rotate(270, expand=True)
if os['orientation'] == Orientation.PAGE_DOWN:
image = image.rotate(180, expand=True)
return image
def check_ocr(self):
image = self._open_image()
image_width = self.image_width
image_height = self.image_height
texts_id = self.TEXTSID
with PyTessBaseAPI() as api:
api.SetImage(image)
boxes = api.GetComponentImages(RIL.TEXTLINE, True)
boxes_out = np.empty([0, 4])
class_out = []
confi_out = []
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'])
# 这个是输出具体文字的方法，需要时去掉注释
ocrResult = api.GetUTF8Text()
conf = api.MeanTextConf()
ymin = float(box['y'] / image_height)
xmin = float(box['x'] / image_width)
ymax = float((box['y'] + box['h']) / image_height)
xmax = float((box['x'] + box['w']) / image_width)
bb = np.array([ymin, xmin, ymax, xmax])
bb = bb.reshape((1, 4))
# print("x:%s y:%s w:%s h:%s" % (box['x'], box['y'], box['w'], box['h']))
# print("w:%s h:%s" % (image_width, image_height))
# print("ymin:%s xmin:%s ymax:%s xmax:%s" % (ymin, xmin, ymax, xmax))
boxes_out = np.append(boxes_out, bb, axis=0)
class_out.append(texts_id)
if conf == 0:
conf = 99
conf = int(conf) / 100
confi_out.append(conf)
# print(ocrResult)
# print(conf)
# print(u"Box[{0}]: x={x}, y={y}, w={w}, h={h}, "
# "confidence: {1}, text: {2}".format(i, conf, ocrResult, **box))
out = []
out.append(boxes_out)
out.append(class_out)
out.append(confi_out)
return out
def execute2():
driver.save_screenshot('driver.png')
image_file = Image.open("driver.png") # open colour image
image_file= image_file.convert('L') # convert image to monochrome - this works
#image_file= image_file.convert('1') # convert image to black and white
image_file.save('result.png')
images = ['result.png' ]
with PyTessBaseAPI() as api:
for img in images:
api.SetImageFile(img)
pignore = (api.GetUTF8Text())
ignore= (api.AllWordConfidences())
pignore = str(pignore)
matchObj1 = re.search( r'Please Enter. (.*)', pignore, re.M|re.I)
if matchObj1:
game = matchObj1.group(1)
driver.find_element_by_id("adcopy_response").send_keys(game)
frame1 = driver.find_element_by_id("free_play_form_button")
driver.execute_script("$(arguments[0]).click();", frame1)
print ("Waiting 60 minutes for next claim")
time.sleep(3600)
brutal()
else:
matchObj2 = re.search( r'Please Enter, (.*)', pignore, re.M|re.I)
if matchObj2:
game2 = matchObj2.group(1)
driver.find_element_by_id("adcopy_response").send_keys(game2)
frame2 = driver.find_element_by_id("free_play_form_button")
driver.execute_script("$(arguments[0]).click();", frame2)
("Waiting 60 minutes for next claim")
time.sleep(3600)
brutal()
else:
matchObj3 = re.search( r'Ple ase Enter, (.*)', pignore, re.M|re.I)
if matchObj3:
game3 = matchObj3.group(1)
driver.find_element_by_id("adcopy_response").send_keys(game3)
frame3 = driver.find_element_by_id("free_play_form_button")
driver.execute_script("$(arguments[0]).click();", frame3)
("Waiting 60 minutes for next claim")
time.sleep(3600)
brutal()
else:
driver.execute_script("javascript:ACPuzzle.reload('')")
time.sleep(10)
execute2()
def resolve(path):
captcha_image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
captcha_image = cv2.resize(captcha_image,
None,
fx=6,
fy=6,
interpolation=cv2.INTER_LINEAR)
captcha_image = cv2.medianBlur(captcha_image, 9)
th, captcha_image = cv2.threshold(captcha_image, 65, 250,
cv2.THRESH_BINARY)
cv2.imwrite('captcha.png', captcha_image)
with PyTessBaseAPI() as api:
api.SetVariable('tessedit_char_whitelist',
'abcdefghijklmnopqrstuvwxyz1234567890')
api.SetImageFile('captcha.png')
return api.GetUTF8Text().replace(' ', '').rstrip().lower()
def readText(filename):
res = {}
image = Image.open(filename)
with PyTessBaseAPI() as api:
api.SetImage(image)
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()
if ocrResult != '':
## res[ocrResult] = (box['x'] + box['w']/2, box['y'] + box['h']/2)
res[ocrResult] = (box['x'], box['y'])
return res
def cyclethrough(self, index, filename, directory):
directory = askdirectory() # prompt user to select a folder
for filename in os.listdir(directory):
self.Stopper()
if filename.endswith(".JPG"):
img_read = cv2.imread(os.path.join(directory,filename))
area_lower_bound = 200 # originally 300
print filename # Doesn't know the full path
grayscale = cv2.cvtColor(img_read,
cv2.COLOR_BGR2GRAY) # potential improvement: using multiple color channels and combining results
block_size = 201
offset = 24
binar_adaptive = threshold_adaptive(grayscale, block_size=block_size, offset=offset)
# next, do noise removal
noisy = binar_adaptive.astype('uint8') * 255
im2, contours, hierarchy = cv2.findContours(noisy, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
large_contours = []
for cnt in contours:
if cv2.contourArea(cnt) > area_lower_bound:
large_contours.append(cnt)
black_bg = np.zeros((img_read.shape[0], img_read.shape[1]), dtype='uint8')
cv2.drawContours(black_bg, large_contours, -1, color=255, thickness=-1)
# Image.fromarray(black_bg).show() # black text on white background
combined = np.logical_and(255 - black_bg, 255 - noisy) # why are some tiny pixels left here?
img_for_tess = Image.fromarray(combined.astype('uint8') * 255)
with PyTessBaseAPI(psm=1) as api:
api.SetVariable("tessedit_char_whitelist",
"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0987654321-.:/()")
api.SetImage(img_for_tess)
self.DisplayImages(api.GetThresholdedImage(), os.path.join(directory, filename))
self.DisplayOCRText(api.GetUTF8Text())
continue
else:
continue
def findBoundingBoxesWord(fname):
""" Use OCR to find the bounding boxes of each word in a document"""
# This opens the converted pdf as an image file
image = Image.open(fname)
#This converts the original image to RGBA to allow for alpha channel
#composits (This allows for transparency in PIL)
img = image.convert("RGBA")
#This creates a new transparent image to composite with teh original
tmp = Image.new('RGBA', img.size, (0, 0, 0, 0))
#This creates the drawing object for the overlay
draw = ImageDraw.Draw(tmp)
with PyTessBaseAPI() as api:
api.SetImage(image)
# Interate over lines using OCR
#boxes = api.GetComponentImages(RIL.TEXTLINE, True)
# Iterate over words using OCR
boxes = api.GetComponentImages(RIL.WORD, 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()
#This calls the google translate function to translate a line of text for inclusion
#transText = g_translate(ocrResult)
#print transText
#This gets the OCR confidence
conf = api.MeanTextConf()
#scale the confidence to the opacity
opacity = opacityConversion(conf)
#draw = ImageDraw.Draw(image)
draw.rectangle(((box['x'], box['y']), ((box['x'] + box['w']),
(box['y'] + box['h']))),
fill=(244, 167, 66, opacity))
# This creates a composit image with the original image and the transparent overlay
img = Image.alpha_composite(img, tmp)
# This saves the new image
img.save(fname)
def textConfidence(fname):
with PyTessBaseAPI() as api:
#for image in images:
api.SetImageFile(fname)
text = api.GetUTF8Text()
#print api.AllWordConfidences()
print textstat.flesch_kincaid_grade(text)
print textstat.flesch_reading_ease(text)
print("90-100 : Very Easy")
print("80-89 : Easy")
print("70-79 : Fairly Easy")
print("60-69 : Standard")
print("50-59 : Fairly Difficult")
print("30-49 : Difficult")
print("0-29 : Very Confusing")
def process_image(img, page, refine_boxes):
try:
page.progress = ('Analysing layout', 0.0)
with PyTessBaseAPI(lang='ge', psm=3) as api:
api.SetVariable("hocr_char_boxes", "true")
api.SetImage(img)
api.Recognize()
hocr = api.GetHOCRText(0)
page.progress = ('Analysing layout', 1.0)
page_json = process_hocr(hocr, img, page)
if refine_boxes:
page_json = refine(img, page_json, page)
page_json_to_text(page_json, page)
return page_json
except:
page.progress = (f"Error processing page: {e}", -1)
return {}
def perform_ocr(img):
# First, preprocessor the image:
img = Image_Utils.preprocess_image(img)
# Next, convert image to python PIL (because pytesseract using PIL):
img_pil = Image.fromarray(img)
if not Ocr_Utils.OCR_API_HANDLE:
Ocr_Utils.OCR_API_HANDLE = PyTessBaseAPI(psm=PSM.SINGLE_COLUMN)
#Ocr_Utils.OCR_API_HANDLE = PyTessBaseAPI(psm=PSM.SINGLE_BLOCK)
Ocr_Utils.OCR_API_HANDLE.SetImage(img_pil)
text = Ocr_Utils.OCR_API_HANDLE.GetUTF8Text()
# Return extracted text:
return 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 run(is_invert_image=False, image_path=None, image_dir=None):
with PyTessBaseAPI(path=TESSDATA_PATH, lang='chi_sim') as api:
if image_dir != None:
for home, _, files in os.walk(image_dir):
for f in files:
if f == '.DS_Store':
continue
img = os.path.join(home, f)
print(img)
pretreatment_imgs = pretreatment(is_invert_image, img)
ocr(api, img)
for pretreatment_img in pretreatment_imgs:
ocr(api, pretreatment_img)
if image_path != None:
pretreatment_imgs = pretreatment(is_invert_image, image_path)
ocr(api, image_path)
for pretreatment_img in pretreatment_imgs:
ocr(api, pretreatment_img)
def get_word_data(img):
image = Image.open(img, mode='r')
pdf = pdfpage.PDFPage('folder location', 1)
with PyTessBaseAPI() as api:
api.SetImage(image)
boxes = api.GetComponentImages(
RIL.WORD,
True) # option for TEXTLINE or SYMBOL (character) as well
for i, (im, box, _, _) in enumerate(boxes):
api.SetRectangle(box['x'], box['y'], box['w'], box['h'])
ocrResult = api.GetUTF8Text()
conf = api.MeanTextConf()
doc_word = word.Word(i, box['x'], box['y'], box['w'], box['h'],
conf, ocrResult)
# print ((u"Box[{0}]: x={x}, y={y}, w={w}, h={h}, ""confidence: {1}, text: {2}").format(i, conf, ocrResult, **box))
pdf.add_word(doc_word)
pdf.sort_dictionaries()
return pdf
def _get_word_block_list_from_image(
self,
image: Image,
find_type: int,
spec_box: dict = None) -> typing.List[WordBlock]:
word_list = list()
with PyTessBaseAPI(lang=self.lang) as api:
api.SetImage(image)
if spec_box:
ocr_result = api.GetUTF8Text()
return [WordBlock(box=spec_box, content=ocr_result)]
boxes = api.GetComponentImages(find_type, True)
for _, box, *_ in boxes:
api.SetRectangle(box['x'], box['y'], box['w'], box['h'])
ocr_result = api.GetUTF8Text()
word_list.append(WordBlock(box=box, content=ocr_result))
return word_list
def tesseract(path, filename, conf_dir, text_dir):
# OCR - use the Tesseract API through Cython and PyTesseract
with PyTessBaseAPI() as api:
pathFilename = path + "/" + filename
label_text = ""
ri = None
try:
# Set the image
api.SetImageFile(pathFilename)
# Run and verify the recognition process
label_text = api.GetUTF8Text()
label_text = label_text[:-1]
api.SetVariable("save_blob_choices", "T")
api.Recognize()
ri = api.GetIterator()
except:
return
conf_text = ""
# Iterate over each of the symbols of the file
level = RIL.SYMBOL
for r in iterate_level(ri, level):
try:
symbol = r.GetUTF8Text(level)
conf = 0.01 * r.Confidence(level)
# We only save non-break symbols
if (symbol not in ['\n', '\r', '\t', '\f']):
conf_text += symbol + "\t" + str(conf) + "\n"
except:
continue
if len(conf_text) > 0:
basename = filename[:-4]
# Write all the characters and their Confidence in the probabilities file
conf_pathFilename = conf_dir + "/" + basename + ".prob"
with open(conf_pathFilename, "w") as f:
f.write(conf_text.encode('utf-8'))
# Write the recognized text line in the text file
text_pathFilename = text_dir + "/" + basename + ".txt"
with open(text_pathFilename, "w") as f:
f.write(label_text.encode('utf-8'))
def ORIG_ocr_content_confidences_images():
fpath='/home/ub2/ARCHIVE/DOC_IMAGES/038c69d2-0e3e-43e4-8ea9-274b93d2144b.jpg'
fpath_out='/home/ub2/ARCHIVE/DOC_IMAGES/038c69d2-0e3e-43e4-8ea9-274b93d2144b_opencv.jpg'
fpath='/home/ub2/ARCHIVE/MDSCAN/completed/__ppm__-31.ppm'
fout='/home/ub2/ARCHIVE/MDSCAN/completed/__ppm__-31_opencv.pdf'
fpath='/home/ub2/ARCHIVE/DOC_IMAGES/1482885785_8855e56.jpg'
images = [fpath]
r={}
with PyTessBaseAPI() as api:
for img in images:
api.SetImageFile(img)
r['lines']=api.GetTextlines()
#r['images']=api.GetComponentImages()
#r['thresh']=api.GetThresholdedImage()
r['_words']=api.GetWords()
r['strips']=api.GetStrips()
r['text']=api.GetUTF8Text()
r['words']=api.AllWords()
r['word_conf']=api.AllWordConfidences()
word_conf_map=api.MapWordConfidences()
#r['word_conf_map']=api.MapWordConfidences()
#print api.GetUTF8Text()
#print api.AllWordConfidences()
# api is automatically finalized when used in a with-statement (context manager).
# otherwise api.End() should be explicitly called when it's no longer needed.
def img_display(pil_image):
b = BytesIO()
pil_image.save(b, format='png')
data = b.getvalue()
r = '<img src="data:image/png;base64,' + base64.encodestring(data) + '"/>'
return BS(r).renderContents().replace('<','<').replace('>','>').replace('\\n','\n')
#return r
#ip_img = display.Image(data=data, format='png', embed=True)
#return ip_img._repr_png_()
df=pd.DataFrame(map(lambda s: dict(zip(['word','conf'],s)),word_conf_map))
df['images']=map(lambda i: r['_words'][i][0],range(len(r['_words'])))
df['image2']=df.images.map(lambda img: img_display(img))
h = df.sort_values(['conf','word'],ascending=[True,True]).head().ix[:,['conf','word','image2']].to_html(escape=True)
HTML(BS(h).renderContents().replace('<','<').replace('>','>').replace('\\n','\n'))
def detector(image):
# load the example image and convert it to grayscale
inputImage = cv2.imread(image)
gray = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
# Threshold the image
gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# Write the grayscale to temp file
filename = "{}.png".format(os.getpid())
cv2.imwrite(filename, gray)
with PyTessBaseAPI() as api:
# Send the new grayscale image into Tesseract
api.SetImageFile(filename)
# We will segment the grayscale by words
boxes = api.GetComponentImages(RIL.WORD, True)
# With each bouding box for the words
for i, (im, box, _, _) in enumerate(boxes):
# Grab the coordinates of the bounding box
api.SetRectangle(box['x'], box['y'], box['w'], box['h'])
# Turn the returned bounding box coordinates into an array of coordinates
coord = list(box.values())
# Load grayscale for cropping
cropper = Image.open(filename)
# Cropped image is saved into new variable
crop_image = cropper.crop(
(coord[0], coord[1], coord[0] + coord[2], coord[1] + coord[3]))
# Convert the new image into a numpy array
cropped = numpy.array(crop_image)
# Create the new file name for the word
word_file = "word_" + str(i) + ".png"
# Have OpenCV save the cropped image into the new file
cv2.imwrite(word_file, cropped)
# Remove the grayscale image
os.remove(filename)
def orcTitle(path):
# make first page into jpeg
page = convert_from_path(path, first_page=0, last_page = 1)[0]
page.save(temp_file, 'JPEG')
# use ocr to extract title
# image = Image.open(temp_file)
with PyTessBaseAPI() as api:
api.SetImageFile(temp_file)
api.Recognize() # required to get result from the next line
ri = api.GetIterator()
# loop through and find largest text size
level = RIL.TEXTLINE
maxSize = 0
for r in iterate_level(ri, level):
# extract line of text
text = r.GetUTF8Text(level)
# get line's font size
fontSize = r.WordFontAttributes()['pointsize']
# check to see if current max
# remove extra spaces/newlines/tabs (etc.) when testing min length req
if len(''.join(text.split())) > 1 and fontSize > maxSize:
maxSize = fontSize
# loop through again and concatenate largest words
ri = api.GetIterator()
level = RIL.TEXTLINE
title_list = []
for r in iterate_level(ri, level):
text = r.GetUTF8Text(level)
fontSize = ri.WordFontAttributes()['pointsize']
if len(''.join(text.split())) > 1 and fontSize > maxSize - 15:
# add title words to list
title_list.extend(r.GetUTF8Text(level).split())
# concatenate them back together
title = ' '.join(title_list)
os.remove(temp_file)
return title
def find_word_attribute(image, tessdata_3_path):
#Reading image
raw_img = Image.open(image)
#Scaling image
img = scale_image(raw_img)
#Initializing parameters
word_arr = []
bold_arr = []
#Using TessBaseAPI to read the fond attribute
with PyTessBaseAPI(path=tessdata_3_path) as api:
api.SetImage(img)
api.Recognize(0)
#print(api.GetUTF8Text())
ri = api.GetIterator()
level = RIL.WORD
for r in iterate_level(ri, level):
bb = r.BoundingBox(level)
if bb != None:
word = r.GetUTF8Text(level)
#word_arr.append(word)
font_name = r.WordFontAttributes()
#attr.append(font_name)
if word != None and font_name != None:
word_arr.append(word)
bold_arr.append(font_name)
Lang_name = r.WordRecognitionLanguage()
bool_value = r.WordIsFromDictionary()
conf = r.Confidence(level)
df1 = pd.DataFrame(word_arr)
df2 = pd.DataFrame(bold_arr)
df = pd.concat([df1, df2], axis=1)
df.rename(columns={df.columns[0]: "Word"}, inplace=True)
return (df)
def show_frame(cap,imgLabel,imgText):
ret, frame = cap.read()
img = cv.cvtColor(frame, cv.COLOR_RGB2BGR)
im = Image.fromarray(img)
with PyTessBaseAPI() as api:
api.SetImage(im)
boxes = api.GetComponentImages(RIL.TEXTLINE, True)
ocrResult = api.GetUTF8Text()
#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']) #conf = api.MeanTextConf()
cv.rectangle(img,(box['x'],box['y']),(box['x']+box['w'],box['y']+box['h']),(255,0,0),1)
im = Image.fromarray(img)
imgtk = ImageTk.PhotoImage(image=im)
imgLabel.imgtk = imgtk
imgLabel.configure(image=imgtk)
imgText.configure(text=ocrResult)
imgLabel.after(10, lambda: show_frame(cap,imgLabel,imgText))
def Spaces(image=None):
PrintGood(
'This returns the number of spaces in a specific image or images')
if isinstance(image, list) == False:
image = PromptList('Which image/images to Scan: ', image)
for image in image:
image = Image.open(image)
with PyTessBaseAPI() as api:
api.SetImage(image)
boxes = api.GetComponentImages(RIL.TEXTLINE, True)
Spaces = 0
for i, (im, box, _, _) in enumerate(boxes):
im.save('saving{}.jpg'.format(i))
api.SetRectangle(box['x'], box['y'], box['w'], box['h'])
ocrResult = api.GetUTF8Text()
conf = api.MeanTextConf()
text = str(ocrResult).replace('\n', '').split(' ')
Spaces = len(text) + Spaces
return int(Spaces)
def symbolConfidenc(img):
word = ''
count = 0
insertSpace = 'false'
with PyTessBaseAPI() as api:
api.SetImageFile(img)
api.Recognize()
ri = api.GetIterator()
#levelTwo = RIL.TEXTLINE
level = RIL.WORD
for r in iterate_level(ri, level):
#space = r.GetUTF8Text(levelTwo)#gets whole line includes everything unlike RIL.SYMBOL
symbol = r.GetUTF8Text(level) # r == ri
conf = r.Confidence(level)
if conf > 50:
word = word + ' ' + symbol
return word