Demo with tesseract / paddle and finetuned yolo 12

.gitignore

@@ -0,0 +1,141 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+tox/
+nox/
+coverage
+coverage.*
+cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+hypothesis/
+pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+webassets-cache
+
+# Scrapy stuff:
+scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+pybuilder/
+target/
+
+# Jupyter Notebook
+ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+python-version
+
+# pipenv
+#Pipfile.lock
+
+# poetry
+#poetry.lock
+
+# pdm
+pdm.toml
+pdm-python
+pdm-build/
+
+# PEP 582
+__pypackages__/
+
+# Celery
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath
+*.sage.py
+
+# Environments
+env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder
+spyderproject
+spyproject
+
+# Rope
+ropeproject
+
+# mkdocs
+/site
+
+# mypy
+mypy_cache/
+dmypy.json
+dmypy.json
+
+# Pyre
+pyre/
+
+# pytype
+pytype/
+
+# Cython
+cython_debug/
+
+# PyCharm
+# .idea/

app.py

@@ -1,7 +1,144 @@
 import gradio as gr
+import numpy as np
+import cv2
+import os
+from ultralytics import YOLO
+from PIL import Image
+from src.table_ocr import TableEx   
+from src.datum_ocr import DatumOCR
+from src.categories import CATEGORIES as categories, generate_colors
 
-def greet(name):
-    return "Hello " + name + "!!"
+os.environ['TESSDATA_PREFIX'] = './tessdata'
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def load_model():
+    """Load the custom YOLO model using Ultralytics"""
+    # Load the model using the Ultralytics YOLO class
+    model = YOLO('src/yoloCADex.pt')
+    model.to('cpu')
+    return model
+
+
+def process_image(image):
+    """Process the uploaded image with the YOLO model and return the results"""
+    
+    # Check if image is valid
+    if image is None or not isinstance(image, Image.Image):
+        return None, {"error": "Invalid image input"}, None, None, None
+    
+    model = load_model()
+    category_colors = generate_colors(len(categories))
+    img_array = np.array(image)                 # Convert to format expected by the model
+    table_extractor = TableEx()                 # Initialize TableEx for table extraction
+    date_extractor = DatumOCR()                 # Initialize DatumOCR for OCR
+    results = model(img_array)                  # Run inference with CPU specified
+    img_with_boxes = img_array.copy()           # Create a copy of the image for drawing
+    detections = []                             # Initialize results table
+    
+    table_data = []                             # Storage for extracted table data and images
+    table_images = []
+    
+    gdnt_data = []                             # Storage for extracted table data and images
+    surface_data = []
+
+
+    # Process results
+    for result in results:
+        boxes = result.boxes
+        for box in boxes:
+            # Get box coordinates
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            
+            # Get confidence and class
+            conf = float(box.conf[0])
+            cls_id = int(box.cls[0])
+            
+            if cls_id < len(categories):
+                cls_name = categories[cls_id]
+                color = category_colors[cls_id]
+                
+                if cls_name == "table":
+                    table_region, extracted_info = table_extractor.extract_table_data(img_array, x1, y1, x2, y2)
+                    if table_region is not None:
+                        table_images.append(table_region)
+                    if extracted_info is not None:
+                        table_data.append(extracted_info)
+            else:
+                cls_name = f"Unknown ({cls_id})"
+                color = (255, 255, 255)  # White for unknown categories
+
+            label = f"{cls_name} {conf:.2f}"
+            
+            # Draw rectangle with category-specific color
+            cv2.rectangle(img_with_boxes, (x1, y1), (x2, y2), color, 2)
+            
+            # Create a filled rectangle for text background
+            text_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)[0]
+            cv2.rectangle(img_with_boxes, (x1, y1 - text_size[1] - 10), (x1 + text_size[0], y1), color, -1)
+            
+            # Add label with contrasting text color
+            # Choose black or white text based on background brightness
+            brightness = (color[0] + color[1] + color[2]) / 3
+            text_color = (0, 0, 0) if brightness > 127 else (255, 255, 255)
+            cv2.putText(img_with_boxes, label, (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, text_color, 2)
+            
+            # Store detection for table with color information
+            detections.append({
+                "category": cls_name,
+                "confidence": conf,
+                "position": (x1, y1, x2, y2),
+                "color": color
+            })
+    
+
+        # Extract GD&T and Datum OCR information
+        gdnt_info = date_extractor.read_rois(img_array, [4], boxes, 0)
+        if gdnt_info is not None:
+            gdnt_data.append(gdnt_info)
+
+        surface_info = date_extractor.read_rois(img_array, [3,6,8,9,10,11], boxes, 0)
+        if surface_info is not None:
+            surface_data.append(surface_info)
+
+    # If we have detected tables but no extracted images, handle that case
+    if len(table_data) > 0 and len(table_images) == 0:
+        table_images = [Image.fromarray(np.zeros((100, 100, 3), dtype=np.uint8))]
+    
+    # Return the detection result image, any extracted table image, and the JSON data
+    return (
+        Image.fromarray(img_with_boxes),  # Main detection image
+        table_images[0] if table_images else None,  # First table image or None
+        table_data[0] if len(table_data) == 1 else table_data,  # JSON data for gr.JSON
+        gdnt_data[0] if len(gdnt_data) == 1 else table_data,  # JSON data for gr.JSON
+        surface_data[0] if len(surface_data) == 1 else table_data  # JSON data for gr.JSON
+    )
+
+# Create Gradio interface
+with gr.Blocks(title="CAD 2d Drawing Data Extraction") as app:
+    gr.Markdown("# CAD 2d Drawing Data Extraction")
+    gr.Markdown("Upload an image to detect objects. Tables will be automatically extracted.")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_image = gr.Image(type="pil", label="Input Image")
+            gr.Markdown("## Extracted Table Region")
+            table_image = gr.Image()
+            gr.Markdown("### Extracted GD&T Data")
+            gdnt_json = gr.JSON(open=True)
+            gr.Markdown("### Extracted Surface Data")
+            surface_json = gr.JSON(open=True)
+        with gr.Column(scale=3):
+            submit_btn = gr.Button("Detect Objects", variant="primary")
+            gr.Markdown("## Detection Results")
+            output_image = gr.Image()
+            gr.Markdown("### Extracted Table Data")
+            table_json = gr.JSON(open=True)
+
+    submit_btn.click(
+        fn=process_image,
+        inputs=[input_image],
+        outputs=[output_image, table_image, table_json, gdnt_json, surface_json]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    app.launch()

packages.txt

@@ -0,0 +1 @@
+tesseract-ocr-all

requirements.txt

@@ -0,0 +1,14 @@
+torch
+ultralytics
+gradio
+supervision
+paddlepaddle==3.0.0rc1
+paddleocr==2.9.1
+opencv-python
+Pillow
+numpy
+pandas
+scipy
+pytesseract
+sentence_transformers
+html_to_json

src/categories.py

@@ -0,0 +1,24 @@
+import colorsys
+
+# Define the categories
+CATEGORIES = ["info", "table", "part", "surface-all", "GD&T", "zoom", "surface-trie", 
+              "3D", "edge", "surface-check", "corner", "surface-ball", "info-table"]
+
+
+
+# Generate distinct colors for each category
+def generate_colors(n):
+    colors = []
+    for i in range(n):
+        # Use HSV color space to generate evenly distributed distinct colors
+        hue = i / n
+        sat = 0.8 + (i % 3) * 0.1  # Vary saturation slightly
+        val = 0.8 + (i % 2) * 0.1  # Vary value slightly
+        
+        # Convert to RGB
+        rgb = colorsys.hsv_to_rgb(hue, sat, val)
+        
+        # Convert to BGR (for OpenCV) and scale to 0-255
+        bgr = (int(rgb[2] * 255), int(rgb[1] * 255), int(rgb[0] * 255))
+        colors.append(bgr)
+    return colors

src/datum_ocr.py

@@ -0,0 +1,213 @@
+import cv2
+import pytesseract
+from paddleocr import PaddleOCR 
+from scipy import ndimage
+import supervision as sv
+import numpy as np
+import math
+
+from src.categories import CATEGORIES as categories
+
+
+symbol_map = {
+    "⏤": 'Straightness',
+    "⏥": 'Flatness', 
+    "⌭": 'Cylindricity', 
+    "○": 'Circularity', 
+    "⌯": 'Symmetry', 
+    "⌖": 'Position', 
+    "◎": 'Concentricity', 
+    "⟂": 'Perpendicularity', 
+    "∥": 'Parallelism', 
+    "∠": 'Angularity', 
+    "⌓": 'Profile of a surface', 
+    "⌒": 'Profile of a line',
+    "⌰": 'Total run-out', 
+    "↗": 'Circular run-out'
+}
+feature_symbol_map = {
+    'Ⓕ': '(Free state)',
+    'Ⓛ': '(LMC)',
+    'Ⓜ': '(MMC)',
+    'Ⓟ': '(Projected tolerance zone)',
+    'Ⓢ': '(RFS)',
+    'Ⓣ': '(Tangent plane)',
+    'Ⓤ': '(Unequal bilateral)'
+}
+
+class DatumOCR:
+    def __init__(self):
+        self.ocr = PaddleOCR(use_angle_cls=True, lang='en', show_log=False, use_gpu=False)
+        
+
+    def crop_img(self, img: np.array, box: any, rotation: int = 0):
+        crop = sv.crop_image(image=img , xyxy=box.xyxy[0].detach().cpu().numpy())
+        crop = ndimage.rotate(crop, rotation)
+        return crop
+
+    def crop_by_id(self, img : np.array, id: int, boxes: any, rotation: int = 0):
+        boxes_of_interest = [self.crop_img(img, box, rotation) for box in boxes if box.cls.item() == id]
+        return boxes_of_interest
+
+    def split_contures(self, img : np.array):
+        # Preprocessing 
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
+
+        # Find contours
+        cnts, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+        contours = []
+        # Filter for rectangles and squares
+        for c in cnts:
+            peri = cv2.arcLength(c, True)
+            approx = cv2.approxPolyDP(c, 0.04 * peri, True)
+            area = cv2.contourArea(c)
+            if len(approx) == 4 and area > 200:
+                x, y, w, h = cv2.boundingRect(c)
+                contours.append((x, y, w, h))
+                #cv2.drawContours(image, [approx], -1, (0, 255, 0), 3)
+        contours.sort(key=lambda rect: rect[0])
+        return contours
+
+    def clense_lines(self, img: np.array, linesize : int = 10):
+        """ Input the full label of gd&t as img
+        i.e. 
+        _______________
+        | o | 0.2 | A |
+        '-------------' 
+        """
+        clensed = img.copy()
+        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+        thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
+
+        # Remove horizontal lines
+        horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (linesize,1))
+        remove_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
+        cnts = cv2.findContours(remove_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        cnts = cnts[0] if len(cnts) == 2 else cnts[1]
+        for c in cnts:
+            cv2.drawContours(clensed, [c], -1, (255,255,255), 2)
+
+        # Remove vertical lines
+        vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,linesize))
+        remove_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
+        cnts = cv2.findContours(remove_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        cnts = cnts[0] if len(cnts) == 2 else cnts[1]
+        for c in cnts:
+            cv2.drawContours(clensed, [c], -1, (255,255,255), 2)
+        return clensed
+
+
+    def read_contures(self, rect, clensed : np.array, math_recognition : bool = True):
+        """
+            Input: 
+            grouped_rectangles: list of rect coordinates with x,y,w,h
+            clensed : preprocessed image to read from 
+        """
+        pix = []
+        first = math_recognition # as if no math recognition it should always use paddle
+        text = []
+        
+        #reverse = lines[::-1].copy()
+        for i, rect in enumerate(rect):
+            x, y, w, h = rect
+            roi = clensed[y:y+h, x:x+w]
+            if first:        
+                custom_config = r'--oem 3 -l eng_gdt --psm 6'
+                first = False
+                gdt = self.ocr_gdt(roi, custom_config)
+            else:
+                if math_recognition: 
+                   custom_config = r'--oem 3 -l eng_math --psm 6'
+                   gdt = self.ocr_gdt(roi, custom_config)
+                else: 
+                    gdt = self.ocr_paddle(roi)
+            text.append(gdt)    
+            pix.append(roi)
+            
+        return text, pix
+
+    def ocr_gdt(self, img: np.array, custom_config: str, debug : bool = False):
+        gdt = []
+        text_ex = pytesseract.image_to_data(img, config=custom_config, output_type='data.frame')
+        text_ex = text_ex[text_ex.conf != -1]
+        if len(text_ex['text']) == 1:
+            item = text_ex['text'].item()
+            gdt.append(str(item))
+            if item in symbol_map:
+                gdt.append(symbol_map[item])
+            elif item in feature_symbol_map:
+                gdt.append(feature_symbol_map[item])     
+            if debug: 
+                print('gdt - ' + item)
+        else:
+            gdt.append('not readable')
+        return gdt
+
+    def ocr_paddle(self, roi, debug: bool = False):
+        gdt = []
+        ocr_res = self.ocr.ocr(roi, cls=False, det=False, rec=True)
+        for idx in range(len(ocr_res)):
+            res = ocr_res[idx]
+            if res is not None: 
+                for line in res:
+                    gdt.append(str(line[0]))
+                    if debug: 
+                        print('txt - ' + str(line[1][0]))    
+        return gdt
+
+    def read_rois(self, sv_image: np.array, classes_to_detect: list[int], boxes: any, rotation: int):
+        """
+        Split up the result regions and try to read them -> result is Returned as an 2D array of Strings and an array of images as np.array 
+        sv_image = the full image to analize
+        class_to_detect = 4 (GD&T) or 6 (surface)
+        boxes = resulting boxes from YOLO (mostly ~ results[0].boxes)
+        rotation = angle the image needs to be rotated
+        """
+        res = []
+        
+        for class_to_detect in classes_to_detect:
+            if class_to_detect == 4:
+                remove_table_structure = True
+            else:
+                remove_table_structure = False
+            boi = self.crop_by_id(sv_image, class_to_detect, boxes, rotation)
+            # clensed = clense_lines(sv_image)
+            #sv.plot_image(image=clensed)
+            for b in boi:
+                if min(b.shape) == 0:
+                    continue
+                lines = self.read_roi(b, remove_table_structure, rotation)
+                res.append(f"{categories[class_to_detect]} : {lines}")
+        return res
+
+    def read_roi(self, b: np.array, remove_table_structure: bool , rotation: int):
+        # turn 90 degree if wrong aligned
+        h, w, _ = b.shape
+        threshold = 1.1
+        if h > w*threshold:
+            rot = -90
+            if rotation == 180:
+                rot = rot + 180
+            b = ndimage.rotate(b, rot)
+
+        if(remove_table_structure):
+            rect = self.split_contures(b)
+            linesize = math.ceil(max(b.shape)*0.10)-1
+            clensed = self.clense_lines(b, linesize)
+        else : 
+            w, h, _ = b.shape
+            rect = [(0,0, h, w)]
+            clensed = b
+
+        #preprocessing 
+        kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]])
+        # Apply the sharpening kernel
+        sharpened_image = cv2.filter2D(clensed , -1, kernel)
+        # thresholding
+        _, thresh_img = cv2.threshold(sharpened_image, 128, 255, 0, cv2.THRESH_BINARY)
+        
+        #[print(c) for c in rect]
+
+        lines, pix = self.read_contures(rect, thresh_img, remove_table_structure)
+        return lines #, pix, thresh_img

src/table_ocr.py

@@ -0,0 +1,63 @@
+import pandas as pd
+import re # regex
+import numpy as np
+import PIL.Image as Image
+from paddleocr import PPStructure
+import html_to_json
+
+
+class TableEx:
+    def __init__(self):
+        self.table_engine = PPStructure(lang='en', layout=False, show_log=True, use_gpu=False, download_models=True, rec=True)
+
+    def extract_table_information(self, pil_image : np.array):
+        #img_byte_arr = toBytes(pil_image)
+        #table_engine = PPStructure(lang='en', recovery=True, ocr=True, show_log=True, mode='kie')
+        result = self.table_engine(pil_image)
+        try: 
+            extracted_tables = html_to_json.convert_tables(result[0]['res']['html'])
+            extracted_tables = self.remove_empty_elements(extracted_tables)
+        except Exception as e:
+            print('Structure extraction Failed, using fallback plain text.')
+            x = [x['text'] for x in result[0]['res']]
+            extracted_tables = ' '.join(x)
+        return extracted_tables
+    
+    def remove_empty_elements(self, nested_list):
+        """
+        Recursively removes empty elements from a nested list.
+        """
+        cleaned_list = []
+        for item in nested_list:
+            if isinstance(item, list):
+                # Recurse into sublists
+                cleaned_sublist = self.remove_empty_elements(item)
+                if cleaned_sublist:
+                    cleaned_list.append(cleaned_sublist)
+            elif item != '':
+                # Add non-empty items to the cleaned list
+                cleaned_list.append(item)
+        return cleaned_list
+    
+    def extract_table_data(self, img_array, x1, y1, x2, y2):
+    # Crop the detected table region
+        table_region = img_array[max(0, y1):min(img_array.shape[0], y2), 
+                                max(0, x1):min(img_array.shape[1], x2)]
+        
+        if table_region.size > 0 and table_region.shape[0] > 0 and table_region.shape[1] > 0:
+            try:
+                # Save the table image for display
+                table_images = Image.fromarray(table_region)
+                # Extract table data
+                extracted_info = self.extract_table_information(table_region)
+                # Store the extracted data with position info
+                table_data = extracted_info[0]
+
+            except Exception as e:
+                print(f"Error extracting table data: {e}")
+                table_data = {
+                    "region": f"({x1}, {y1}) to ({x2}, {y2})",
+                    "error": str(e),
+                    "data": None
+                }
+        return table_images, table_data

src/utils/imageFormater.py

@@ -0,0 +1,55 @@
+import PIL.Image
+import numpy as nd
+import cv2
+import io
+import base64
+from io import BytesIO
+
+def toPIL(array: nd.array) -> PIL.Image:
+    return PIL.Image.fromarray(array).convert('RGB')
+
+def toBytes(pilImage : PIL.Image) -> bytes:
+    img_byte_arr = io.BytesIO()
+    pilImage.save(img_byte_arr, format='JPEG')
+    return img_byte_arr.getvalue()
+
+def toBASE64(pilImage : PIL.Image) -> str:
+    buffered = BytesIO()
+    pilImage.save(buffered, format="JPEG")
+    img_str = base64.b64encode(buffered.getvalue())
+
+
+def redLines(bgr) -> nd.array:
+    # Convert the image to grayscale#
+    if len(bgr) == 0:
+        return bgr
+
+    cont = bgr.copy()
+    gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
+
+    # Threshold the image to create a binary mask
+    _, thresh = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY_INV)
+
+    # Set the line size (adjust as needed)
+    linesize = 25
+
+    # Remove horizontal lines
+    horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (linesize, 1))
+    remove_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
+    cnts = cv2.findContours(remove_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
+    for c in cnts:
+        cv2.drawContours(cont, [c], -1, (255, 0, 0), 2)  # Draw in red
+
+    # Remove vertical lines
+    vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, linesize))
+    remove_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
+    cnts = cv2.findContours(remove_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    cnts = cnts[0] if len(cnts) == 2 else cnts[1]
+    for c in cnts:
+        cv2.drawContours(cont, [c], -1, (255, 0, 0), 2)  # Draw in red
+    
+    return cont
+
+
+

src/utils/rotation.py

@@ -0,0 +1,65 @@
+from PIL import Image
+
+def predict(yolo_result : any
+                   ,img : Image = None) -> int:
+    """
+    pedicts the orientation based on the position of the largest table anotation and returns a degree the image should be rotated.
+    
+    input:
+    yolo_result : for one image
+    img : PIL Image, default None 
+          if None the image from result is taken
+    """
+    result = yolo_result
+    boxes = result.boxes  # Boxes object for bounding box outputs
+    # masks = result.masks  # Masks object for segmentation masks outputs
+    # keypoints = result.keypoints  # Keypoints object for pose outputs
+    # probs = result.probs  # Probs object for classification outputs
+    if img is None: 
+        img = result.plot()  # BGR-order numpy array
+        img = Image.fromarray(img[..., ::-1])  # RGB-order PIL image
+
+    box_with_max_volume = get_reference_table(boxes)
+    if box_with_max_volume == None:
+        return 0 #early return if nothing found.
+    
+    # Get the coordinates of the box with the largest volume
+    x1, y1, x2, y2 = box_with_max_volume.xyxy[0].tolist()
+
+    # Get the distances to the borders
+    dist_top = y1
+    dist_left = x1
+    dist_right = img.width - x2
+    dist_bottom = img.height - y2
+
+    # Determine the rotation angle based on the distances to the borders
+    if dist_top < dist_bottom and dist_left < dist_right: # top left corner
+        rotation_angle = 180  # Rotate by 180 degrees
+    elif dist_top < dist_bottom and dist_left >= dist_right: # top right corner
+        rotation_angle = -90  # Rotate by -90 degrees
+    elif dist_top > dist_bottom and dist_left < dist_right: # bottom left
+        rotation_angle = 90  
+    else:
+        rotation_angle = 0  # do nothing
+
+    return rotation_angle # , box_with_max_volume
+    # Rotate the original image by the calculated angle
+    # rotated_image = img.rotate(rotation_angle, expand=True)
+
+
+def get_reference_table(boxes):
+    """
+    Returns the reference table from result
+    """
+    # get all tables by class id
+    tables = [box for box in boxes if box.cls.item() == 1]
+    if len(tables) == 0:
+        return None
+    # detect box with the largest volume
+    box_with_max_volume = max(
+        tables,
+        key=lambda box: (box.xywhn[0][2].item() * box.xywhn[0][3].item())
+    )
+
+    # retun the biggest box
+    return box_with_max_volume

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+						<div class="SVELTE_HYDRATER contents" data-target="Pickle" data-props="{&quot;status&quot;:&quot;caution&quot;,&quot;imports&quot;:[{&quot;module&quot;:&quot;torch&quot;,&quot;name&quot;:&quot;HalfStorage&quot;,&quot;safety&quot;:&quot;innocuous&quot;},{&quot;module&quot;:&quot;torch.nn.modules.conv&quot;,&quot;name&quot;:&quot;Conv2d&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch._utils&quot;,&quot;name&quot;:&quot;_rebuild_tensor_v2&quot;,&quot;safety&quot;:&quot;innocuous&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.block&quot;,&quot;name&quot;:&quot;RepCSP&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.conv&quot;,&quot;name&quot;:&quot;Concat&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch&quot;,&quot;name&quot;:&quot;device&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.conv&quot;,&quot;name&quot;:&quot;RepConv&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.block&quot;,&quot;name&quot;:&quot;RepNCSPELAN4&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch&quot;,&quot;name&quot;:&quot;FloatStorage&quot;,&quot;safety&quot;:&quot;innocuous&quot;},{&quot;module&quot;:&quot;torch.nn.modules.loss&quot;,&quot;name&quot;:&quot;BCEWithLogitsLoss&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch.nn.modules.linear&quot;,&quot;name&quot;:&quot;Identity&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.block&quot;,&quot;name&quot;:&quot;SPPELAN&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.tasks&quot;,&quot;name&quot;:&quot;DetectionModel&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch.nn.modules.pooling&quot;,&quot;name&quot;:&quot;MaxPool2d&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.utils&quot;,&quot;name&quot;:&quot;IterableSimpleNamespace&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch&quot;,&quot;name&quot;:&quot;LongStorage&quot;,&quot;safety&quot;:&quot;innocuous&quot;},{&quot;module&quot;:&quot;__builtin__&quot;,&quot;name&quot;:&quot;set&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch.nn.modules.container&quot;,&quot;name&quot;:&quot;Sequential&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch.nn.modules.activation&quot;,&quot;name&quot;:&quot;SiLU&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.block&quot;,&quot;name&quot;:&quot;DFL&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.block&quot;,&quot;name&quot;:&quot;ADown&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.utils.loss&quot;,&quot;name&quot;:&quot;v8DetectionLoss&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.utils.tal&quot;,&quot;name&quot;:&quot;TaskAlignedAssigner&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch.nn.modules.batchnorm&quot;,&quot;name&quot;:&quot;BatchNorm2d&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.block&quot;,&quot;name&quot;:&quot;RepBottleneck&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;collections&quot;,&quot;name&quot;:&quot;OrderedDict&quot;,&quot;safety&quot;:&quot;innocuous&quot;},{&quot;module&quot;:&quot;torch._utils&quot;,&quot;name&quot;:&quot;_rebuild_parameter&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch.nn.modules.upsampling&quot;,&quot;name&quot;:&quot;Upsample&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.utils.loss&quot;,&quot;name&quot;:&quot;BboxLoss&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.head&quot;,&quot;name&quot;:&quot;Detect&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;ultralytics.nn.modules.conv&quot;,&quot;name&quot;:&quot;Conv&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch.nn.modules.container&quot;,&quot;name&quot;:&quot;ModuleList&quot;,&quot;safety&quot;:&quot;suspicious&quot;},{&quot;module&quot;:&quot;torch&quot;,&quot;name&quot;:&quot;Size&quot;,&quot;safety&quot;:&quot;suspicious&quot;}]}">
+
+<div class="sm:relative ">
+		<span class="peer" tabindex="0"><button title="Pickle imports detected" class="flex select-none items-center rounded-md border px-1 py-1 text-xs text-gray-400 hover:cursor-pointer hover:bg-gray-50 hover:text-gray-500 dark:border-gray-700 dark:hover:bg-gray-950 lg:py-0 "><svg class="text-[0.65rem] lg:mr-1 text-orange-500" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" viewBox="0 0 11 13" preserveAspectRatio="xMidYMid meet" fill="none"><path fill-rule="evenodd" clip-rule="evenodd" d="M3 4.5C3 3.67157 2.32843 3 1.5 3C0.671573 3 0 3.67157 0 4.5V11.5C0 12.3284 0.671573 13 1.5 13C2.32843 13 3 12.3284 3 11.5V4.5ZM7 4.5C7 3.67157 6.32843 3 5.5 3C4.67157 3 4 3.67157 4 4.5V11.5C4 12.3284 4.67157 13 5.5 13C6.32843 13 7 12.3284 7 11.5V4.5ZM9.5 3C10.3284 3 11 3.67157 11 4.5V7.5C11 8.32843 10.3284 9 9.5 9C8.67157 9 8 8.32843 8 7.5V4.5C8 3.67157 8.67157 3 9.5 3ZM11 11C11 10.1716 10.3284 9.5 9.5 9.5C8.67157 9.5 8 10.1716 8 11V11.5C8 12.3284 8.67157 13 9.5 13C10.3284 13 11 12.3284 11 11.5V11Z" fill="currentColor"></path><path d="M0 0.5C0 0.223858 0.223858 0 0.5 0H10.5C10.7761 0 11 0.223858 11 0.5V0.5C11 1.60457 10.1046 2.5 9 2.5H2C0.895431 2.5 0 1.60457 0 0.5V0.5Z" class="fill-current text-gray-300 dark:text-gray-400"></path></svg>
+				<div class="hidden lg:block">pickle</div></button></span>
+		<div role="tooltip" class="absolute left-5 right-5 z-40 mt-2 hidden max-h-96 max-w-full overflow-y-auto rounded-xl border px-4 pb-4 pt-3 text-base focus-within:block hover:block peer-focus-within:block sm:mt-0 sm:w-80 sm:max-w-none text-orange-500 border-orange-100 bg-linear-to-br from-orange-50 to-white dark:from-orange-500/10 dark:to-gray-900 dark:bg-gray-900 sm:left-0 sm:right-auto sm:top-6"><div class="mb-3 flex items-center"><h4 class="text-smd grow font-semibold">Detected Pickle imports (33)</h4>
+					<button class="grow-0 opacity-60 hover:opacity-100"><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1.1em" height="1.1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M24 9.4L22.6 8L16 14.6L9.4 8L8 9.4l6.6 6.6L8 22.6L9.4 24l6.6-6.6l6.6 6.6l1.4-1.4l-6.6-6.6L24 9.4z" fill="currentColor"></path></svg></button></div>
+
+				<ul class="mb-4 font-mono text-xs text-gray-600 dark:text-gray-300"><li><span>&quot;torch.HalfStorage&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.conv.Conv2d&quot;</span>,
+						</li><li><span>&quot;torch._utils._rebuild_tensor_v2&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.block.RepCSP&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.conv.Concat&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.device&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.conv.RepConv&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.block.RepNCSPELAN4&quot;</span>,
+						</li><li><span>&quot;torch.FloatStorage&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.loss.BCEWithLogitsLoss&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.linear.Identity&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.block.SPPELAN&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.tasks.DetectionModel&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.pooling.MaxPool2d&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.utils.IterableSimpleNamespace&quot;</span>,
+						</li><li><span>&quot;torch.LongStorage&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;__builtin__.set&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.container.Sequential&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.activation.SiLU&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.block.DFL&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.block.ADown&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.utils.loss.v8DetectionLoss&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.utils.tal.TaskAlignedAssigner&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.batchnorm.BatchNorm2d&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.block.RepBottleneck&quot;</span>,
+						</li><li><span>&quot;collections.OrderedDict&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch._utils._rebuild_parameter&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.upsampling.Upsample&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.utils.loss.BboxLoss&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.head.Detect&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;ultralytics.nn.modules.conv.Conv&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.nn.modules.container.ModuleList&quot;</span>,
+						</li><li><span class="text-orange-500">&quot;torch.Size&quot;</span>
+						</li></ul>
+				<p class="text-sm text-gray-700 dark:text-gray-300"><a class="flex cursor-pointer items-center underline" href="/docs/hub/security-pickle" target="_blank"><svg class="mr-1.5 text-gray-300 dark:text-gray-600" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24" fill="currentColor"><path d="M8.892 21.854a6.25 6.25 0 0 1-4.42-10.67l7.955-7.955a4.5 4.5 0 0 1 6.364 6.364l-6.895 6.894a2.816 2.816 0 0 1-3.89 0a2.75 2.75 0 0 1 .002-3.888l5.126-5.127a1 1 0 1 1 1.414 1.414l-5.126 5.127a.75.75 0 0 0 0 1.06a.768.768 0 0 0 1.06 0l6.895-6.894a2.503 2.503 0 0 0 0-3.535a2.56 2.56 0 0 0-3.536 0l-7.955 7.955a4.25 4.25 0 1 0 6.01 6.01l6.188-6.187a1 1 0 1 1 1.414 1.414l-6.187 6.186a6.206 6.206 0 0 1-4.42 1.832z"></path></svg>
+						How to fix it?</a></p></div></div></div></div>
+
+				<div class="flex items-center gap-x-3 dark:text-gray-300 sm:ml-auto"><div class="SVELTE_HYDRATER contents" data-target="LineWrapButton" data-props="{&quot;classNames&quot;:&quot;text-xs&quot;,&quot;lineSelectorClass&quot;:&quot;blob-line&quot;}">
+
+<button class="text-xs" type="button" title="Toggle Line Wrap"><svg class="opacity-50" width="1em" height="1em" viewBox="0 0 12 11" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M0.75 1.25H11.25M0.75 5H9C9.75 5 11.25 5.375 11.25 6.875C11.25 8.375 9.99975 8.75 9.375 8.75H6M6 8.75L7.5 7.25M6 8.75L7.5 10.25M0.75 8.75H3.75" stroke="currentColor" stroke-width="1.125" stroke-linecap="round" stroke-linejoin="round"></path></svg></button></div>
+					51.7 MB</div></div>
+
+			<div class="relative min-h-[100px] rounded-b-lg border border-t-0 leading-tight dark:border-gray-800 dark:bg-gray-925">
+				<div class="p-4 py-8 text-center">This file is stored with
+							<a class="underline" href="https://git-lfs.github.com/">Git LFS</a>
+							. It is too big to display, but you can still
+							<a download class="underline" href="/krockema/YoloCadExtract/resolve/main/yoloCADex.pt">download</a>
+							it.
+						</div>
+					<div class="border-t p-4"><h3 class="mb-2 font-semibold leading-relaxed">Git LFS Details</h3>
+						<ul class="break-words font-mono text-sm"><li><strong>SHA256:</strong>
+								90613d54a108ee9240a15add2e4432469b2026017f12aac14f49908a26a9bb7b</li>
+							<li><strong>Pointer size:</strong>
+								133 Bytes</li>
+							<li><strong>Size of remote file:</strong>
+								51.7 MB</li>
+							</ul>
+						<div class="mb-3 mt-2.5"><a class="flex items-center underline" href="/krockema/YoloCadExtract/raw/main/yoloCADex.pt" target="_blank"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M25.7 9.3l-7-7A.908.908 0 0 0 18 2H8a2.006 2.006 0 0 0-2 2v24a2.006 2.006 0 0 0 2 2h16a2.006 2.006 0 0 0 2-2V10a.908.908 0 0 0-.3-.7zM18 4.4l5.6 5.6H18zM24 28H8V4h8v6a2.006 2.006 0 0 0 2 2h6z" fill="currentColor"></path></svg>
+								Raw pointer file
+							</a></div>
+						<p class="text-sm text-gray-500">Git Large File Storage (LFS) replaces large files with text pointers inside Git, while storing the file
+							contents on a remote server.
+							<a class="underline" href="https://git-lfs.github.com/" target="_blank">More info</a>.
+						</p></div></div></section></div></main>
+
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