import os, io, sys, inspect, statistics, json, cv2 from statistics import mean # from google.cloud import vision, storage from google.cloud import vision from google.cloud import vision_v1p3beta1 as vision_beta from PIL import Image, ImageDraw, ImageFont import colorsys from tqdm import tqdm from google.oauth2 import service_account ### LLaVA should only be installed if the user will actually use it. ### It requires the most recent pytorch/Python and can mess with older systems ''' @misc{li2021trocr, title={TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models}, author={Minghao Li and Tengchao Lv and Lei Cui and Yijuan Lu and Dinei Florencio and Cha Zhang and Zhoujun Li and Furu Wei}, year={2021}, eprint={2109.10282}, archivePrefix={arXiv}, primaryClass={cs.CL} } @inproceedings{baek2019character, title={Character Region Awareness for Text Detection}, author={Baek, Youngmin and Lee, Bado and Han, Dongyoon and Yun, Sangdoo and Lee, Hwalsuk}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={9365--9374}, year={2019} } ''' class OCREngine: BBOX_COLOR = "black" def __init__(self, logger, json_report, dir_home, is_hf, path, cfg, trOCR_model_version, trOCR_model, trOCR_processor, device): self.is_hf = is_hf self.logger = logger self.json_report = json_report self.path = path self.cfg = cfg self.do_use_trOCR = self.cfg['leafmachine']['project']['do_use_trOCR'] self.OCR_option = self.cfg['leafmachine']['project']['OCR_option'] self.double_OCR = self.cfg['leafmachine']['project']['double_OCR'] self.dir_home = dir_home # Initialize TrOCR components self.trOCR_model_version = trOCR_model_version self.trOCR_processor = trOCR_processor self.trOCR_model = trOCR_model self.device = device self.hand_cleaned_text = None self.hand_organized_text = None self.hand_bounds = None self.hand_bounds_word = None self.hand_bounds_flat = None self.hand_text_to_box_mapping = None self.hand_height = None self.hand_confidences = None self.hand_characters = None self.normal_cleaned_text = None self.normal_organized_text = None self.normal_bounds = None self.normal_bounds_word = None self.normal_text_to_box_mapping = None self.normal_bounds_flat = None self.normal_height = None self.normal_confidences = None self.normal_characters = None self.trOCR_texts = None self.trOCR_text_to_box_mapping = None self.trOCR_bounds_flat = None self.trOCR_height = None self.trOCR_confidences = None self.trOCR_characters = None self.set_client() self.init_craft() self.multimodal_prompt = """I need you to transcribe all of the text in this image. Place the transcribed text into a JSON dictionary with this form {"Transcription_Printed_Text": "text","Transcription_Handwritten_Text": "text"}""" self.init_llava() def set_client(self): if self.is_hf: self.client_beta = vision_beta.ImageAnnotatorClient(credentials=self.get_google_credentials()) self.client = vision.ImageAnnotatorClient(credentials=self.get_google_credentials()) else: self.client_beta = vision_beta.ImageAnnotatorClient(credentials=self.get_google_credentials()) self.client = vision.ImageAnnotatorClient(credentials=self.get_google_credentials()) def get_google_credentials(self): creds_json_str = os.getenv('GOOGLE_APPLICATION_CREDENTIALS') credentials = service_account.Credentials.from_service_account_info(json.loads(creds_json_str)) return credentials def init_craft(self): if 'CRAFT' in self.OCR_option: from craft_text_detector import load_craftnet_model, load_refinenet_model try: self.refine_net = load_refinenet_model(cuda=True) self.use_cuda = True except: self.refine_net = load_refinenet_model(cuda=False) self.use_cuda = False if self.use_cuda: self.craft_net = load_craftnet_model(weight_path=os.path.join(self.dir_home,'vouchervision','craft','craft_mlt_25k.pth'), cuda=True) else: self.craft_net = load_craftnet_model(weight_path=os.path.join(self.dir_home,'vouchervision','craft','craft_mlt_25k.pth'), cuda=False) def init_llava(self): if 'LLaVA' in self.OCR_option: from vouchervision.OCR_llava import OCRllava self.model_path = "liuhaotian/" + self.cfg['leafmachine']['project']['OCR_option_llava'] self.model_quant = self.cfg['leafmachine']['project']['OCR_option_llava_bit'] if self.json_report: self.json_report.set_text(text_main=f'Loading LLaVA model: {self.model_path} Quantization: {self.model_quant}') if self.model_quant == '4bit': use_4bit = True elif self.model_quant == 'full': use_4bit = False else: self.logger.info(f"Provided model quantization invlid. Using 4bit.") use_4bit = True self.Llava = OCRllava(self.logger, model_path=self.model_path, load_in_4bit=use_4bit, load_in_8bit=False) def init_gemini_vision(self): pass def init_gpt4_vision(self): pass def detect_text_craft(self): from craft_text_detector import read_image, get_prediction # Perform prediction using CRAFT image = read_image(self.path) link_threshold = 0.85 text_threshold = 0.4 low_text = 0.4 if self.use_cuda: self.prediction_result = get_prediction( image=image, craft_net=self.craft_net, refine_net=self.refine_net, text_threshold=text_threshold, link_threshold=link_threshold, low_text=low_text, cuda=True, long_size=1280 ) else: self.prediction_result = get_prediction( image=image, craft_net=self.craft_net, refine_net=self.refine_net, text_threshold=text_threshold, link_threshold=link_threshold, low_text=low_text, cuda=False, long_size=1280 ) # Initialize metadata structures bounds = [] bounds_word = [] # CRAFT gives bounds for text regions, not individual words text_to_box_mapping = [] bounds_flat = [] height_flat = [] confidences = [] # CRAFT does not provide confidences per character, so this might be uniformly set or estimated characters = [] # Simulating as CRAFT doesn't provide character-level details organized_text = "" total_b = len(self.prediction_result["boxes"]) i=0 # Process each detected text region for box in self.prediction_result["boxes"]: i+=1 if self.json_report: self.json_report.set_text(text_main=f'Locating text using CRAFT --- {i}/{total_b}') vertices = [{"x": int(vertex[0]), "y": int(vertex[1])} for vertex in box] # Simulate a mapping for the whole detected region as a word text_to_box_mapping.append({ "vertices": vertices, "text": "detected_text" # Placeholder, as CRAFT does not provide the text content directly }) # Assuming each box is a word for the sake of this example bounds_word.append({"vertices": vertices}) # For simplicity, we're not dividing text regions into characters as CRAFT doesn't provide this # Instead, we create a single large 'character' per detected region bounds.append({"vertices": vertices}) # Simulate flat bounds and height for each detected region x_positions = [vertex["x"] for vertex in vertices] y_positions = [vertex["y"] for vertex in vertices] min_x, max_x = min(x_positions), max(x_positions) min_y, max_y = min(y_positions), max(y_positions) avg_height = max_y - min_y height_flat.append(avg_height) # Assuming uniform confidence for all detected regions confidences.append(1.0) # Placeholder confidence # Adding dummy character for each box characters.append("X") # Placeholder character # Organize text as a single string (assuming each box is a word) # organized_text += "detected_text " # Placeholder text # Update class attributes with processed data self.normal_bounds = bounds self.normal_bounds_word = bounds_word self.normal_text_to_box_mapping = text_to_box_mapping self.normal_bounds_flat = bounds_flat # This would be similar to bounds if not processing characters individually self.normal_height = height_flat self.normal_confidences = confidences self.normal_characters = characters self.normal_organized_text = organized_text.strip() def detect_text_with_trOCR_using_google_bboxes(self, do_use_trOCR, logger): CONFIDENCES = 0.80 MAX_NEW_TOKENS = 50 self.OCR_JSON_to_file = {} ocr_parts = '' if not do_use_trOCR: if 'normal' in self.OCR_option: self.OCR_JSON_to_file['OCR_printed'] = self.normal_organized_text # logger.info(f"Google_OCR_Standard:\n{self.normal_organized_text}") # ocr_parts = ocr_parts + f"Google_OCR_Standard:\n{self.normal_organized_text}" ocr_parts = self.normal_organized_text if 'hand' in self.OCR_option: self.OCR_JSON_to_file['OCR_handwritten'] = self.hand_organized_text # logger.info(f"Google_OCR_Handwriting:\n{self.hand_organized_text}") # ocr_parts = ocr_parts + f"Google_OCR_Handwriting:\n{self.hand_organized_text}" ocr_parts = self.hand_organized_text # if self.OCR_option in ['both',]: # logger.info(f"Google_OCR_Standard:\n{self.normal_organized_text}\n\nGoogle_OCR_Handwriting:\n{self.hand_organized_text}") # return f"Google_OCR_Standard:\n{self.normal_organized_text}\n\nGoogle_OCR_Handwriting:\n{self.hand_organized_text}" return ocr_parts else: logger.info(f'Supplementing with trOCR') self.trOCR_texts = [] original_image = Image.open(self.path).convert("RGB") if 'normal' in self.OCR_option or 'CRAFT' in self.OCR_option: available_bounds = self.normal_bounds_word elif 'hand' in self.OCR_option: available_bounds = self.hand_bounds_word # elif self.OCR_option in ['both',]: # available_bounds = self.hand_bounds_word else: raise text_to_box_mapping = [] characters = [] height = [] confidences = [] total_b = len(available_bounds) i=0 for bound in tqdm(available_bounds, desc="Processing words using Google Vision bboxes"): i+=1 if self.json_report: self.json_report.set_text(text_main=f'Working on trOCR :construction: {i}/{total_b}') vertices = bound["vertices"] left = min([v["x"] for v in vertices]) top = min([v["y"] for v in vertices]) right = max([v["x"] for v in vertices]) bottom = max([v["y"] for v in vertices]) # Crop image based on Google's bounding box cropped_image = original_image.crop((left, top, right, bottom)) pixel_values = self.trOCR_processor(cropped_image, return_tensors="pt").pixel_values # Move pixel values to the appropriate device pixel_values = pixel_values.to(self.device) generated_ids = self.trOCR_model.generate(pixel_values, max_new_tokens=MAX_NEW_TOKENS) extracted_text = self.trOCR_processor.batch_decode(generated_ids, skip_special_tokens=True)[0] self.trOCR_texts.append(extracted_text) # For plotting word_length = max(vertex.get('x') for vertex in vertices) - min(vertex.get('x') for vertex in vertices) num_symbols = len(extracted_text) Yw = max(vertex.get('y') for vertex in vertices) Yo = Yw - min(vertex.get('y') for vertex in vertices) X = word_length / num_symbols if num_symbols > 0 else 0 H = int(X+(Yo*0.1)) height.append(H) map_dict = { "vertices": vertices, "text": extracted_text # Use the text extracted by trOCR } text_to_box_mapping.append(map_dict) characters.append(extracted_text) confidences.append(CONFIDENCES) median_height = statistics.median(height) if height else 0 median_heights = [median_height * 1.5] * len(characters) self.trOCR_texts = ' '.join(self.trOCR_texts) self.trOCR_text_to_box_mapping = text_to_box_mapping self.trOCR_bounds_flat = available_bounds self.trOCR_height = median_heights self.trOCR_confidences = confidences self.trOCR_characters = characters if 'normal' in self.OCR_option: self.OCR_JSON_to_file['OCR_printed'] = self.normal_organized_text self.OCR_JSON_to_file['OCR_trOCR'] = self.trOCR_texts # logger.info(f"Google_OCR_Standard:\n{self.normal_organized_text}\n\ntrOCR:\n{self.trOCR_texts}") # ocr_parts = ocr_parts + f"\nGoogle_OCR_Standard:\n{self.normal_organized_text}\n\ntrOCR:\n{self.trOCR_texts}" ocr_parts = self.trOCR_texts if 'hand' in self.OCR_option: self.OCR_JSON_to_file['OCR_handwritten'] = self.hand_organized_text self.OCR_JSON_to_file['OCR_trOCR'] = self.trOCR_texts # logger.info(f"Google_OCR_Handwriting:\n{self.hand_organized_text}\n\ntrOCR:\n{self.trOCR_texts}") # ocr_parts = ocr_parts + f"\nGoogle_OCR_Handwriting:\n{self.hand_organized_text}\n\ntrOCR:\n{self.trOCR_texts}" ocr_parts = self.trOCR_texts # if self.OCR_option in ['both',]: # self.OCR_JSON_to_file['OCR_printed'] = self.normal_organized_text # self.OCR_JSON_to_file['OCR_handwritten'] = self.hand_organized_text # self.OCR_JSON_to_file['OCR_trOCR'] = self.trOCR_texts # logger.info(f"Google_OCR_Standard:\n{self.normal_organized_text}\n\nGoogle_OCR_Handwriting:\n{self.hand_organized_text}\n\ntrOCR:\n{self.trOCR_texts}") # ocr_parts = ocr_parts + f"\nGoogle_OCR_Standard:\n{self.normal_organized_text}\n\nGoogle_OCR_Handwriting:\n{self.hand_organized_text}\n\ntrOCR:\n{self.trOCR_texts}" if 'CRAFT' in self.OCR_option: # self.OCR_JSON_to_file['OCR_printed'] = self.normal_organized_text self.OCR_JSON_to_file['OCR_CRAFT_trOCR'] = self.trOCR_texts # logger.info(f"CRAFT_trOCR:\n{self.trOCR_texts}") # ocr_parts = ocr_parts + f"\nCRAFT_trOCR:\n{self.trOCR_texts}" ocr_parts = self.trOCR_texts return ocr_parts @staticmethod def confidence_to_color(confidence): hue = (confidence - 0.5) * 120 / 0.5 r, g, b = colorsys.hls_to_rgb(hue/360, 0.5, 1) return (int(r*255), int(g*255), int(b*255)) def render_text_on_black_image(self, option): bounds_flat = getattr(self, f'{option}_bounds_flat', []) heights = getattr(self, f'{option}_height', []) confidences = getattr(self, f'{option}_confidences', []) characters = getattr(self, f'{option}_characters', []) original_image = Image.open(self.path) width, height = original_image.size black_image = Image.new("RGB", (width, height), "black") draw = ImageDraw.Draw(black_image) for bound, confidence, char_height, character in zip(bounds_flat, confidences, heights, characters): font_size = int(char_height) try: font = ImageFont.truetype("arial.ttf", font_size) except: font = ImageFont.load_default().font_variant(size=font_size) if option == 'trOCR': color = (0, 170, 255) else: color = OCREngine.confidence_to_color(confidence) position = (bound["vertices"][0]["x"], bound["vertices"][0]["y"] - char_height) draw.text(position, character, fill=color, font=font) return black_image def merge_images(self, image1, image2): width1, height1 = image1.size width2, height2 = image2.size merged_image = Image.new("RGB", (width1 + width2, max([height1, height2]))) merged_image.paste(image1, (0, 0)) merged_image.paste(image2, (width1, 0)) return merged_image def draw_boxes(self, option): bounds = getattr(self, f'{option}_bounds', []) bounds_word = getattr(self, f'{option}_bounds_word', []) confidences = getattr(self, f'{option}_confidences', []) draw = ImageDraw.Draw(self.image) width, height = self.image.size if min([width, height]) > 4000: line_width_thick = int((width + height) / 2 * 0.0025) # Adjust line width for character level line_width_thin = 1 else: line_width_thick = int((width + height) / 2 * 0.005) # Adjust line width for character level line_width_thin = 1 #int((width + height) / 2 * 0.001) for bound in bounds_word: draw.polygon( [ bound["vertices"][0]["x"], bound["vertices"][0]["y"], bound["vertices"][1]["x"], bound["vertices"][1]["y"], bound["vertices"][2]["x"], bound["vertices"][2]["y"], bound["vertices"][3]["x"], bound["vertices"][3]["y"], ], outline=OCREngine.BBOX_COLOR, width=line_width_thin ) # Draw a line segment at the bottom of each handwritten character for bound, confidence in zip(bounds, confidences): color = OCREngine.confidence_to_color(confidence) # Use the bottom two vertices of the bounding box for the line bottom_left = (bound["vertices"][3]["x"], bound["vertices"][3]["y"] + line_width_thick) bottom_right = (bound["vertices"][2]["x"], bound["vertices"][2]["y"] + line_width_thick) draw.line([bottom_left, bottom_right], fill=color, width=line_width_thick) return self.image def detect_text(self): with io.open(self.path, 'rb') as image_file: content = image_file.read() image = vision.Image(content=content) response = self.client.document_text_detection(image=image) texts = response.text_annotations if response.error.message: raise Exception( '{}\nFor more info on error messages, check: ' 'https://cloud.google.com/apis/design/errors'.format( response.error.message)) bounds = [] bounds_word = [] text_to_box_mapping = [] bounds_flat = [] height_flat = [] confidences = [] characters = [] organized_text = "" paragraph_count = 0 for text in texts[1:]: vertices = [{"x": vertex.x, "y": vertex.y} for vertex in text.bounding_poly.vertices] map_dict = { "vertices": vertices, "text": text.description } text_to_box_mapping.append(map_dict) for page in response.full_text_annotation.pages: for block in page.blocks: # paragraph_count += 1 # organized_text += f'OCR_paragraph_{paragraph_count}:\n' # Add paragraph label for paragraph in block.paragraphs: avg_H_list = [] for word in paragraph.words: Yw = max(vertex.y for vertex in word.bounding_box.vertices) # Calculate the width of the word and divide by the number of symbols word_length = max(vertex.x for vertex in word.bounding_box.vertices) - min(vertex.x for vertex in word.bounding_box.vertices) num_symbols = len(word.symbols) if num_symbols <= 3: H = int(Yw - min(vertex.y for vertex in word.bounding_box.vertices)) else: Yo = Yw - min(vertex.y for vertex in word.bounding_box.vertices) X = word_length / num_symbols if num_symbols > 0 else 0 H = int(X+(Yo*0.1)) avg_H_list.append(H) avg_H = int(mean(avg_H_list)) words_in_para = [] for word in paragraph.words: # Get word-level bounding box bound_word_dict = { "vertices": [ {"x": vertex.x, "y": vertex.y} for vertex in word.bounding_box.vertices ] } bounds_word.append(bound_word_dict) Y = max(vertex.y for vertex in word.bounding_box.vertices) word_x_start = min(vertex.x for vertex in word.bounding_box.vertices) word_x_end = max(vertex.x for vertex in word.bounding_box.vertices) num_symbols = len(word.symbols) symbol_width = (word_x_end - word_x_start) / num_symbols if num_symbols > 0 else 0 current_x_position = word_x_start characters_ind = [] for symbol in word.symbols: bound_dict = { "vertices": [ {"x": vertex.x, "y": vertex.y} for vertex in symbol.bounding_box.vertices ] } bounds.append(bound_dict) # Create flat bounds with adjusted x position bounds_flat_dict = { "vertices": [ {"x": current_x_position, "y": Y}, {"x": current_x_position + symbol_width, "y": Y} ] } bounds_flat.append(bounds_flat_dict) current_x_position += symbol_width height_flat.append(avg_H) confidences.append(round(symbol.confidence, 4)) characters_ind.append(symbol.text) characters.append(symbol.text) words_in_para.append(''.join(characters_ind)) paragraph_text = ' '.join(words_in_para) # Join words in paragraph organized_text += paragraph_text + ' ' #+ '\n' # median_height = statistics.median(height_flat) if height_flat else 0 # median_heights = [median_height] * len(characters) self.normal_cleaned_text = texts[0].description if texts else '' self.normal_organized_text = organized_text self.normal_bounds = bounds self.normal_bounds_word = bounds_word self.normal_text_to_box_mapping = text_to_box_mapping self.normal_bounds_flat = bounds_flat # self.normal_height = median_heights #height_flat self.normal_height = height_flat self.normal_confidences = confidences self.normal_characters = characters return self.normal_cleaned_text def detect_handwritten_ocr(self): with open(self.path, "rb") as image_file: content = image_file.read() image = vision_beta.Image(content=content) image_context = vision_beta.ImageContext(language_hints=["en-t-i0-handwrit"]) response = self.client_beta.document_text_detection(image=image, image_context=image_context) texts = response.text_annotations if response.error.message: raise Exception( "{}\nFor more info on error messages, check: " "https://cloud.google.com/apis/design/errors".format(response.error.message) ) bounds = [] bounds_word = [] bounds_flat = [] height_flat = [] confidences = [] characters = [] organized_text = "" paragraph_count = 0 text_to_box_mapping = [] for text in texts[1:]: vertices = [{"x": vertex.x, "y": vertex.y} for vertex in text.bounding_poly.vertices] map_dict = { "vertices": vertices, "text": text.description } text_to_box_mapping.append(map_dict) for page in response.full_text_annotation.pages: for block in page.blocks: # paragraph_count += 1 # organized_text += f'\nOCR_paragraph_{paragraph_count}:\n' # Add paragraph label for paragraph in block.paragraphs: avg_H_list = [] for word in paragraph.words: Yw = max(vertex.y for vertex in word.bounding_box.vertices) # Calculate the width of the word and divide by the number of symbols word_length = max(vertex.x for vertex in word.bounding_box.vertices) - min(vertex.x for vertex in word.bounding_box.vertices) num_symbols = len(word.symbols) if num_symbols <= 3: H = int(Yw - min(vertex.y for vertex in word.bounding_box.vertices)) else: Yo = Yw - min(vertex.y for vertex in word.bounding_box.vertices) X = word_length / num_symbols if num_symbols > 0 else 0 H = int(X+(Yo*0.1)) avg_H_list.append(H) avg_H = int(mean(avg_H_list)) words_in_para = [] for word in paragraph.words: # Get word-level bounding box bound_word_dict = { "vertices": [ {"x": vertex.x, "y": vertex.y} for vertex in word.bounding_box.vertices ] } bounds_word.append(bound_word_dict) Y = max(vertex.y for vertex in word.bounding_box.vertices) word_x_start = min(vertex.x for vertex in word.bounding_box.vertices) word_x_end = max(vertex.x for vertex in word.bounding_box.vertices) num_symbols = len(word.symbols) symbol_width = (word_x_end - word_x_start) / num_symbols if num_symbols > 0 else 0 current_x_position = word_x_start characters_ind = [] for symbol in word.symbols: bound_dict = { "vertices": [ {"x": vertex.x, "y": vertex.y} for vertex in symbol.bounding_box.vertices ] } bounds.append(bound_dict) # Create flat bounds with adjusted x position bounds_flat_dict = { "vertices": [ {"x": current_x_position, "y": Y}, {"x": current_x_position + symbol_width, "y": Y} ] } bounds_flat.append(bounds_flat_dict) current_x_position += symbol_width height_flat.append(avg_H) confidences.append(round(symbol.confidence, 4)) characters_ind.append(symbol.text) characters.append(symbol.text) words_in_para.append(''.join(characters_ind)) paragraph_text = ' '.join(words_in_para) # Join words in paragraph organized_text += paragraph_text + ' ' #+ '\n' # median_height = statistics.median(height_flat) if height_flat else 0 # median_heights = [median_height] * len(characters) self.hand_cleaned_text = response.text_annotations[0].description if response.text_annotations else '' self.hand_organized_text = organized_text self.hand_bounds = bounds self.hand_bounds_word = bounds_word self.hand_bounds_flat = bounds_flat self.hand_text_to_box_mapping = text_to_box_mapping # self.hand_height = median_heights #height_flat self.hand_height = height_flat self.hand_confidences = confidences self.hand_characters = characters return self.hand_cleaned_text def process_image(self, do_create_OCR_helper_image, logger): # Can stack options, so solitary if statements self.OCR = 'OCR:\n' if 'CRAFT' in self.OCR_option: self.do_use_trOCR = True self.detect_text_craft() ### Optionally add trOCR to the self.OCR for additional context if self.double_OCR: part_OCR = "\CRAFT trOCR:\n" + self.detect_text_with_trOCR_using_google_bboxes(self.do_use_trOCR, logger) self.OCR = self.OCR + part_OCR + part_OCR else: self.OCR = self.OCR + "\CRAFT trOCR:\n" + self.detect_text_with_trOCR_using_google_bboxes(self.do_use_trOCR, logger) # logger.info(f"CRAFT trOCR:\n{self.OCR}") if 'LLaVA' in self.OCR_option: # This option does not produce an OCR helper image if self.json_report: self.json_report.set_text(text_main=f'Working on LLaVA {self.Llava.model_path} transcription :construction:') image, json_output, direct_output, str_output, usage_report = self.Llava.transcribe_image(self.path, self.multimodal_prompt) self.logger.info(f"LLaVA Usage Report for Model {self.Llava.model_path}:\n{usage_report}") try: self.OCR_JSON_to_file['OCR_LLaVA'] = str_output except: self.OCR_JSON_to_file = {} self.OCR_JSON_to_file['OCR_LLaVA'] = str_output if self.double_OCR: self.OCR = self.OCR + f"\nLLaVA OCR:\n{str_output}" + f"\nLLaVA OCR:\n{str_output}" else: self.OCR = self.OCR + f"\nLLaVA OCR:\n{str_output}" # logger.info(f"LLaVA OCR:\n{self.OCR}") if 'normal' in self.OCR_option or 'hand' in self.OCR_option: if 'normal' in self.OCR_option: if self.double_OCR: part_OCR = self.OCR + "\nGoogle Printed OCR:\n" + self.detect_text() self.OCR = self.OCR + part_OCR + part_OCR else: self.OCR = self.OCR + "\nGoogle Printed OCR:\n" + self.detect_text() if 'hand' in self.OCR_option: if self.double_OCR: part_OCR = self.OCR + "\nGoogle Handwritten OCR:\n" + self.detect_handwritten_ocr() self.OCR = self.OCR + part_OCR + part_OCR else: self.OCR = self.OCR + "\nGoogle Handwritten OCR:\n" + self.detect_handwritten_ocr() # if self.OCR_option not in ['normal', 'hand', 'both']: # self.OCR_option = 'both' # self.detect_text() # self.detect_handwritten_ocr() ### Optionally add trOCR to the self.OCR for additional context if self.do_use_trOCR: if self.double_OCR: part_OCR = "\ntrOCR:\n" + self.detect_text_with_trOCR_using_google_bboxes(self.do_use_trOCR, logger) self.OCR = self.OCR + part_OCR + part_OCR else: self.OCR = self.OCR + "\ntrOCR:\n" + self.detect_text_with_trOCR_using_google_bboxes(self.do_use_trOCR, logger) # logger.info(f"OCR:\n{self.OCR}") else: # populate self.OCR_JSON_to_file = {} _ = self.detect_text_with_trOCR_using_google_bboxes(self.do_use_trOCR, logger) if do_create_OCR_helper_image and ('LLaVA' not in self.OCR_option): self.image = Image.open(self.path) if 'normal' in self.OCR_option: image_with_boxes_normal = self.draw_boxes('normal') text_image_normal = self.render_text_on_black_image('normal') self.merged_image_normal = self.merge_images(image_with_boxes_normal, text_image_normal) if 'hand' in self.OCR_option: image_with_boxes_hand = self.draw_boxes('hand') text_image_hand = self.render_text_on_black_image('hand') self.merged_image_hand = self.merge_images(image_with_boxes_hand, text_image_hand) if self.do_use_trOCR: text_image_trOCR = self.render_text_on_black_image('trOCR') if 'CRAFT' in self.OCR_option: image_with_boxes_normal = self.draw_boxes('normal') self.merged_image_normal = self.merge_images(image_with_boxes_normal, text_image_trOCR) ### Merge final overlay image ### [original, normal bboxes, normal text] if 'CRAFT' in self.OCR_option or 'normal' in self.OCR_option: self.overlay_image = self.merge_images(Image.open(self.path), self.merged_image_normal) ### [original, hand bboxes, hand text] elif 'hand' in self.OCR_option: self.overlay_image = self.merge_images(Image.open(self.path), self.merged_image_hand) ### [original, normal bboxes, normal text, hand bboxes, hand text] else: self.overlay_image = self.merge_images(Image.open(self.path), self.merge_images(self.merged_image_normal, self.merged_image_hand)) if self.do_use_trOCR: if 'CRAFT' in self.OCR_option: heat_map_text = Image.fromarray(cv2.cvtColor(self.prediction_result["heatmaps"]["text_score_heatmap"], cv2.COLOR_BGR2RGB)) heat_map_link = Image.fromarray(cv2.cvtColor(self.prediction_result["heatmaps"]["link_score_heatmap"], cv2.COLOR_BGR2RGB)) self.overlay_image = self.merge_images(self.overlay_image, heat_map_text) self.overlay_image = self.merge_images(self.overlay_image, heat_map_link) else: self.overlay_image = self.merge_images(self.overlay_image, text_image_trOCR) else: self.merged_image_normal = None self.merged_image_hand = None self.overlay_image = Image.open(self.path) try: from craft_text_detector import empty_cuda_cache empty_cuda_cache() except: pass class SafetyCheck(): def __init__(self, is_hf) -> None: self.is_hf = is_hf self.set_client() def set_client(self): if self.is_hf: self.client = vision.ImageAnnotatorClient(credentials=self.get_google_credentials()) else: self.client = vision.ImageAnnotatorClient(credentials=self.get_google_credentials()) def get_google_credentials(self): creds_json_str = os.getenv('GOOGLE_APPLICATION_CREDENTIALS') credentials = service_account.Credentials.from_service_account_info(json.loads(creds_json_str)) return credentials def check_for_inappropriate_content(self, file_stream): LEVEL = 2 content = file_stream.read() image = vision.Image(content=content) response = self.client.safe_search_detection(image=image) safe = response.safe_search_annotation likelihood_name = ( "UNKNOWN", "VERY_UNLIKELY", "UNLIKELY", "POSSIBLE", "LIKELY", "VERY_LIKELY", ) print("Safe search:") print(f" adult*: {likelihood_name[safe.adult]}") print(f" medical*: {likelihood_name[safe.medical]}") print(f" spoofed: {likelihood_name[safe.spoof]}") print(f" violence*: {likelihood_name[safe.violence]}") print(f" racy: {likelihood_name[safe.racy]}") # Check the levels of adult, violence, racy, etc. content. if (safe.adult > LEVEL or safe.medical > LEVEL or # safe.spoof > LEVEL or safe.violence > LEVEL #or # safe.racy > LEVEL ): print("Found violation") return True # The image violates safe search guidelines. print("Found NO violation") return False # The image is considered safe.