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| 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 | |
| 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): | |
| self.client = vision.ImageAnnotatorClient() | |
| content = file_stream.read() | |
| image = vision.Image(content=content) | |
| response = self.client.safe_search_detection(image=image) | |
| safe = response.safe_search_annotation | |
| # Check the levels of adult, violence, racy, etc. content. | |
| if (safe.adult > vision.Likelihood.POSSIBLE or | |
| safe.violence > vision.Likelihood.POSSIBLE or | |
| safe.racy > vision.Likelihood.POSSIBLE): | |
| return True # The image violates safe search guidelines. | |
| return False # The image is considered safe. |