vouchervision/OCR_google_cloud_vision.py

import os, io, sys, inspect, statistics, json
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

# currentdir = os.path.dirname(os.path.abspath(
#     inspect.getfile(inspect.currentframe())))
# parentdir = os.path.dirname(currentdir)
# sys.path.append(parentdir)


'''
@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}
}
'''

class OCRGoogle:

    BBOX_COLOR = "black"

    def __init__(self, is_hf, path, cfg, trOCR_model_version, trOCR_model, trOCR_processor, device):
        self.is_hf = is_hf

        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']

        # 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()

        
    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 detect_text_with_trOCR_using_google_bboxes(self, do_use_trOCR, logger):
        CONFIDENCES = 0.80
        MAX_NEW_TOKENS = 50

        self.OCR_JSON_to_file = {}
        
        if not do_use_trOCR:
            if self.OCR_option in ['normal',]:
                self.OCR_JSON_to_file['OCR_printed'] = self.normal_organized_text
                logger.info(f"Google_OCR_Standard:\n{self.normal_organized_text}")
                return f"Google_OCR_Standard:\n{self.normal_organized_text}"
            
            if self.OCR_option in ['hand',]:
                self.OCR_JSON_to_file['OCR_handwritten'] = self.hand_organized_text
                logger.info(f"Google_OCR_Handwriting:\n{self.hand_organized_text}")
                return f"Google_OCR_Handwriting:\n{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}"

        else:
            logger.info(f'Supplementing with trOCR')

            self.trOCR_texts = []
            original_image = Image.open(self.path).convert("RGB")

            if self.OCR_option in ['normal',]:
                available_bounds = self.normal_bounds_word
            elif self.OCR_option in ['hand',]:
                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 = []
            for bound in tqdm(available_bounds, desc="Processing words using Google Vision bboxes"):
                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 self.OCR_option in ['normal',]:
                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}")
                return f"Google_OCR_Standard:\n{self.normal_organized_text}\n\ntrOCR:\n{self.trOCR_texts}"
            if self.OCR_option in ['hand',]:
                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}")
                return f"Google_OCR_Handwriting:\n{self.hand_organized_text}\n\ntrOCR:\n{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}")
                return f"Google_OCR_Standard:\n{self.normal_organized_text}\n\nGoogle_OCR_Handwriting:\n{self.hand_organized_text}\n\ntrOCR:\n{self.trOCR_texts}"
            else:
                raise             

    @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)
            font = ImageFont.load_default().font_variant(size=font_size)
            if option == 'trOCR':
                color = (0, 170, 255)
            else:
                color = OCRGoogle.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=OCRGoogle.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 = OCRGoogle.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


    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


    def process_image(self, do_create_OCR_helper_image, logger):
        if self.OCR_option in ['normal', 'both']:
            self.detect_text()
        if self.OCR_option in ['hand', 'both']:
            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
        self.OCR = self.detect_text_with_trOCR_using_google_bboxes(self.do_use_trOCR, logger)
        logger.info(f"OCR:\n{self.OCR}")

        if do_create_OCR_helper_image:
            self.image = Image.open(self.path)

            if self.OCR_option in ['normal', 'both']:
                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 self.OCR_option in ['hand', 'both']:
                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')

            ### Merge final overlay image
            ### [original, normal bboxes, normal text]
            if self.OCR_option in ['normal']:
                self.overlay_image = self.merge_images(Image.open(self.path), self.merged_image_normal)
            ### [original, hand bboxes, hand text]
            elif self.OCR_option in ['hand']:
                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:
                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)


'''
BBOX_COLOR = "black" # green cyan

def render_text_on_black_image(image_path, handwritten_char_bounds_flat, handwritten_char_confidences, handwritten_char_heights, characters):
    # Load the original image to get its dimensions
    original_image = Image.open(image_path)
    width, height = original_image.size

    # Create a black image of the same size
    black_image = Image.new("RGB", (width, height), "black")
    draw = ImageDraw.Draw(black_image)

    # Loop through each character
    for bound, confidence, char_height, character in zip(handwritten_char_bounds_flat, handwritten_char_confidences, handwritten_char_heights, characters):
        # Determine the font size based on the height of the character
        font_size = int(char_height)
        font = ImageFont.load_default().font_variant(size=font_size)

        # Color of the character
        color = confidence_to_color(confidence)

        # Position of the text (using the bottom-left corner of the bounding box)
        position = (bound["vertices"][0]["x"], bound["vertices"][0]["y"] - char_height)

        # Draw the character
        draw.text(position, character, fill=color, font=font)

    return black_image

def merge_images(image1, image2):
    # Assuming both images are of the same size
    width, height = image1.size
    merged_image = Image.new("RGB", (width * 2, height))
    merged_image.paste(image1, (0, 0))
    merged_image.paste(image2, (width, 0))
    return merged_image

def draw_boxes(image, bounds, color):
    if bounds:
        draw = ImageDraw.Draw(image)
        width, height = image.size
        line_width = int((width + height) / 2 * 0.001)  # This sets the line width as 0.5% of the average dimension

        for bound in bounds:
            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=color,
                width=line_width
            )
    return image

def detect_text(path):
    client = vision.ImageAnnotatorClient()
    with io.open(path, 'rb') as image_file:
        content = image_file.read()
    image = vision.Image(content=content)
    response = 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))

    # Extract bounding boxes
    bounds = []
    text_to_box_mapping = {}
    for text in texts[1:]:  # Skip the first entry, as it represents the entire detected text
        # Convert BoundingPoly to dictionary
        bound_dict = {
            "vertices": [
                {"x": vertex.x, "y": vertex.y} for vertex in text.bounding_poly.vertices
            ]
        }
        bounds.append(bound_dict)
        text_to_box_mapping[str(bound_dict)] = text.description

    if texts:
        # cleaned_text = texts[0].description.replace("\n", " ").replace("\t", " ").replace("|", " ")
        cleaned_text = texts[0].description
        return cleaned_text, bounds, text_to_box_mapping
    else:
        return '', None, None
    
def confidence_to_color(confidence):
    """Convert confidence level to a color ranging from red (low confidence) to green (high confidence)."""
    # Using HSL color space, where Hue varies from red to green
    hue = (confidence - 0.5) * 120 / 0.5  # Scale confidence to range 0-120 (red to green in HSL)
    r, g, b = colorsys.hls_to_rgb(hue/360, 0.5, 1)  # Convert to RGB
    return (int(r*255), int(g*255), int(b*255))

def overlay_boxes_on_image(path, typed_bounds, handwritten_char_bounds, handwritten_char_confidences, do_create_OCR_helper_image):
    if do_create_OCR_helper_image:
        image = Image.open(path)
        draw = ImageDraw.Draw(image)
        width, height = image.size
        line_width = int((width + height) / 2 * 0.005)  # Adjust line width for character level

        # Draw boxes for typed text
        for bound in typed_bounds:
            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=BBOX_COLOR,
                width=1
            )

        # Draw a line segment at the bottom of each handwritten character
        for bound, confidence in zip(handwritten_char_bounds, handwritten_char_confidences):
            color = 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)
            bottom_right = (bound["vertices"][2]["x"], bound["vertices"][2]["y"] + line_width)
            draw.line([bottom_left, bottom_right], fill=color, width=line_width)

        text_image = render_text_on_black_image(path, handwritten_char_bounds, handwritten_char_confidences)
        merged_image = merge_images(image, text_image)  # Assuming 'overlayed_image' is the image with lines


        return merged_image
    else:
        return Image.open(path)
    
def detect_handwritten_ocr(path):
    """Detects handwritten characters in a local image and returns their bounding boxes and confidence levels.

    Args:
    path: The path to the local file.

    Returns:
    A tuple of (text, bounding_boxes, confidences)
    """
    client = vision_beta.ImageAnnotatorClient()

    with open(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 = client.document_text_detection(image=image, image_context=image_context)

    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_flat = []
    height_flat = []
    confidences = []
    character = []
    for page in response.full_text_annotation.pages:
        for block in page.blocks:
            for paragraph in block.paragraphs:
                for word in paragraph.words:
                    # Get the bottom Y-location (max Y) for the whole word
                    Y = max(vertex.y for vertex in word.bounding_box.vertices)

                    # Get the height of the word's bounding box
                    H = Y - min(vertex.y for vertex in word.bounding_box.vertices)

                    for symbol in word.symbols:
                        # Collecting bounding box for each symbol
                        bound_dict = {
                            "vertices": [
                                {"x": vertex.x, "y": vertex.y} for vertex in symbol.bounding_box.vertices
                            ]
                        }
                        bounds.append(bound_dict)

                        # Bounds with same bottom y height
                        bounds_flat_dict = {
                            "vertices": [
                                {"x": vertex.x, "y": Y} for vertex in symbol.bounding_box.vertices
                            ]
                        }
                        bounds_flat.append(bounds_flat_dict)
                        
                        # Add the word's height
                        height_flat.append(H)

                        # Collecting confidence for each symbol
                        symbol_confidence = round(symbol.confidence, 4)
                        confidences.append(symbol_confidence)
                        character.append(symbol.text)

    cleaned_text = response.full_text_annotation.text

    return cleaned_text, bounds, bounds_flat, height_flat, confidences, character



def process_image(path, do_create_OCR_helper_image):
    typed_text, typed_bounds, _ = detect_text(path)
    handwritten_text, handwritten_bounds, _ = detect_handwritten_ocr(path)

    overlayed_image = overlay_boxes_on_image(path, typed_bounds, handwritten_bounds, do_create_OCR_helper_image)
    return typed_text, handwritten_text, overlayed_image

'''

# ''' Google Vision'''
# def detect_text(path):
#     """Detects text in the file located in the local filesystem."""
#     client = vision.ImageAnnotatorClient()

#     with io.open(path, 'rb') as image_file:
#         content = image_file.read()

#     image = vision.Image(content=content)

#     response = 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))

#     return texts[0].description if texts else ''