def extract_image_data(data, languages=None): """Extract text from a binary string of data.""" tessdata_prefix = get_config('TESSDATA_PREFIX') if tessdata_prefix is None: raise IngestorException("TESSDATA_PREFIX is not set, OCR won't work.") languages = get_languages_iso3(languages) text = Cache.get_ocr(data, languages) if text is not None: return text try: img = Image.open(StringIO(data)) except DecompressionBombWarning as dce: log.debug("Image too large: %", dce) return None except IOError as ioe: log.info("Unknown image format: %r", ioe) return None # TODO: play with contrast and sharpening the images. extractor = Tesseract(tessdata_prefix, lang=languages) extractor.set_image(img) extractor.set_page_seg_mode(PageSegMode.PSM_AUTO_OSD) text = extractor.get_text() or '' text = text.decode(encoding="UTF-8") # extractor.clear() log.debug('OCR done: %s, %s characters extracted', languages, len(text)) Cache.set_ocr(data, languages, text) return text
def ocr_text(img): '''Perform OCR on the image.''' tr = Tesseract(lang='eng') tr.clear() pil_image = pil.Image.fromarray(img) tr.set_image(pil_image) utf8_text = tr.get_text() return utf8_text
def ocr_text(img): tr = Tesseract(lang='eng') tr.clear() pil_image = pil.Image.fromarray(img) # Turn off OCR word dictionaries tr.set_variable('load_system_dawg', "F") tr.set_variable('load_freq_dawg', "F") tr.set_variable('-psm', "7") # treat image as single line tr.set_variable('tessedit_char_whitelist', "ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789") tr.set_image(pil_image) utf8_text = tr.get_text() return unicode(utf8_text)
def index(request): #from tesserwrap import Tesseract #from PIL import Image img = Image.open("/home/df/projects/django/nuspyp/tesseracttest/test.png") tr = Tesseract() tr.ocr_image(img) img2 = dog( filename='/home/df/projects/django/nuspyp/tesseracttest/source.pdf') single_image = img2.sequence[0] tr.ocr_image(single_image) return HttpResponse(tr.get_text())
class PaperDetection: def __init__(self): cwd = os.path.dirname(os.path.realpath(__file__)) os.environ["TESSDATA_PREFIX"] = cwd self.tr = Tesseract(lang="deu") self.gs = goslate.Goslate() self.trained_paper = False self.paper_row_nw = None self.paper_row_se = None self.paper_col_nw = None self.paper_col_se = None self.paper_hist = None self.paper = None self.words = None self.translations = [] self.pointed_locations = deque(maxlen=20) def draw_paper_rect(self, frame): rows, cols, _ = frame.shape self.paper_row_nw = rows / 5 self.paper_row_se = 4 * rows / 5 self.paper_col_nw = 2 * cols / 5 self.paper_col_se = 3 * cols / 5 cv2.rectangle( frame, (self.paper_col_nw, self.paper_row_nw), (self.paper_col_se, self.paper_row_se), (0, 255, 0), 1 ) black = np.zeros(frame.shape, dtype=frame.dtype) frame_final = np.vstack([frame, black]) return frame_final def train_paper(self, frame): self.set_paper_hist(frame) self.trained_paper = True def get_paper(self, frame): paper_masked = image_analysis.apply_hist_mask(frame, self.paper_hist) contours = image_analysis.contours(paper_masked) max_contour = image_analysis.max_contour(contours) paper = image_analysis.contour_interior(frame, max_contour) return paper def set_paper(self, frame): self.paper = self.get_paper(frame) def paper_copy(self): paper = self.paper.copy() return paper def set_ocr_text(self, frame): paper = self.get_paper(frame) thresh = image_analysis.gray_threshold(paper, 100) paper_img = Image.fromarray(thresh) self.tr.set_image(paper_img) self.tr.get_text() self.words = self.tr.get_words() for w in self.words: translation = self.translate(w.value) self.translations.append(translation) def set_paper_hist(self, frame): hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) roi = hsv[self.paper_row_nw : self.paper_row_se, self.paper_col_nw : self.paper_col_se] self.paper_hist = cv2.calcHist([roi], [0, 1], None, [180, 256], [0, 180, 0, 256]) cv2.normalize(self.paper_hist, self.paper_hist, 0, 255, cv2.NORM_MINMAX) def get_word_at_point(self, point): for i, w in enumerate(self.words): x_nw, y_nw, x_se, y_sw = w.box x, y = point if x > x_nw and x < x_se and y > y_nw and y < y_sw: return self.translations[i] def get_word_index(self, point): for i, w in enumerate(self.words): x_nw, y_nw, x_se, y_sw = w.box x, y = point if x > x_nw and x < x_se and y > y_nw and y < y_sw: return i def translate(self, word): translated_word = self.gs.translate(word, "en", source_language="de") return translated_word def update_pointed_locations(self, point): index = self.get_word_index(point) if index != None: self.pointed_locations.append(index) def get_most_common_word(self): index = self.most_common_location() if index != None: word = self.translations[index].encode("ascii", errors="backslashreplace") return word def most_common_location(self): values = set(self.pointed_locations) index = None maxi = 0 for i in values: num = self.pointed_locations.count(i) if num > maxi: index = i frequency = float(self.pointed_locations.count(index)) / float(self.pointed_locations.maxlen) if frequency > 0.25: return index else: return None
class PaperDetection: def __init__(self): cwd = os.path.dirname(os.path.realpath(__file__)) os.environ['TESSDATA_PREFIX'] = cwd self.tr = Tesseract(lang='deu') self.gs = goslate.Goslate() self.trained_paper = False self.paper_row_nw = None self.paper_row_se = None self.paper_col_nw = None self.paper_col_se = None self.paper_hist = None self.paper = None self.words = None self.translations = [] self.pointed_locations = deque(maxlen=20) def draw_paper_rect(self, frame): rows,cols,_ = frame.shape self.paper_row_nw = rows/5 self.paper_row_se = 4*rows/5 self.paper_col_nw = 2*cols/5 self.paper_col_se = 3*cols/5 cv2.rectangle(frame,(self.paper_col_nw,self.paper_row_nw),(self.paper_col_se,self.paper_row_se), (0,255,0),1) black = np.zeros(frame.shape, dtype=frame.dtype) frame_final = np.vstack([frame, black]) return frame_final def train_paper(self, frame): self.set_paper_hist(frame) self.trained_paper = True def get_paper(self, frame): paper_masked = image_analysis.apply_hist_mask(frame, self.paper_hist) contours = image_analysis.contours(paper_masked) max_contour = image_analysis.max_contour(contours) paper = image_analysis.contour_interior(frame, max_contour) return paper def set_paper(self, frame): self.paper = self.get_paper(frame) def paper_copy(self): paper = self.paper.copy() return paper def set_ocr_text(self, frame): paper = self.get_paper(frame) thresh = image_analysis.gray_threshold(paper, 100) paper_img = Image.fromarray(thresh) self.tr.set_image(paper_img) self.tr.get_text() self.words = self.tr.get_words() for w in self.words: translation = self.translate(w.value) self.translations.append(translation) def set_paper_hist(self, frame): hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) roi = hsv[self.paper_row_nw:self.paper_row_se, self.paper_col_nw:self.paper_col_se] self.paper_hist = cv2.calcHist([roi], [0, 1], None, [180, 256], [0, 180, 0, 256]) cv2.normalize(self.paper_hist, self.paper_hist, 0, 255, cv2.NORM_MINMAX) def get_word_at_point(self, point): for i, w in enumerate(self.words): x_nw,y_nw,x_se,y_sw = w.box x,y = point if x > x_nw and x < x_se and y > y_nw and y < y_sw: return self.translations[i] def get_word_index(self, point): for i, w in enumerate(self.words): x_nw,y_nw,x_se,y_sw = w.box x,y = point if x > x_nw and x < x_se and y > y_nw and y < y_sw: return i def translate(self, word): translated_word = self.gs.translate(word,'en',source_language='de') return translated_word def update_pointed_locations(self, point): index = self.get_word_index(point) if index != None: self.pointed_locations.append(index) def get_most_common_word(self): index = self.most_common_location() if index != None: word = self.translations[index].encode('ascii', errors='backslashreplace') return word def most_common_location(self): values = set(self.pointed_locations) index = None maxi = 0 for i in values: num = self.pointed_locations.count(i) if num > maxi: index = i frequency = float(self.pointed_locations.count(index))/float(self.pointed_locations.maxlen) if frequency > 0.25: return index else: return None