Delete extract_img_pdf.py

extract_img_pdf.py

@@ -1,677 +0,0 @@
-#==================================================================================#
-#                    Find Contours from Image and Convert into PDF                 #
-#==================================================================================#
-import cv2, os
-import numpy as np
-from imutils.perspective import four_point_transform
-from PIL import Image 
-from unstructured.partition.pdf import partition_pdf
-import json, base64, io
-from flask import Flask, render_template, flash, redirect, url_for
-from dotenv import load_dotenv
-import pytesseract
-
-load_dotenv()
-
-app = Flask(__name__)
-app.secret_key = os.getenv("SECRET_KEY")
-pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
-poppler_path=r"C:\poppler-23.11.0\Library\bin"
-
-count = 0
-
-OUTPUT_FOLDER = "OUTPUTS"
-# os.makedirs(OUTPUT_FOLDER, exist_ok=True)
-IMAGE_FOLDER_PATH = os.path.join(OUTPUT_FOLDER, "SCANNED_IMAGE")
-# os.makedirs(IMAGE_FOLDER_PATH, exist_ok=True)
-
-PDF_FOLDER_PATH = os.path.join(OUTPUT_FOLDER, "SCANNED_PDF")
-# os.makedirs(PDF_FOLDER_PATH, exist_ok=True)
-JSON_FOLDER_PATH = os.path.join(OUTPUT_FOLDER, "EXTRACTED_JSON")
-
-for path in [OUTPUT_FOLDER, IMAGE_FOLDER_PATH, PDF_FOLDER_PATH, JSON_FOLDER_PATH]:
-    os.makedirs(path, exist_ok=True)
-
-# --- FUNCTION: Detect document contour ---
-def detect_document_contour(image):
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    blur = cv2.GaussianBlur(gray, (5, 5), 0)
-    _, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-
-    contours, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
-    contours = sorted(contours, key=cv2.contourArea, reverse=True)
-
-    for contour in contours:
-        area = cv2.contourArea(contour)
-        if area > 1000:
-            peri = cv2.arcLength(contour, True)
-            approx = cv2.approxPolyDP(contour, 0.02 * peri, True)
-            if len(approx) == 4:
-                return approx
-    return None
-
-# --- FUNCTION: Extract images from saved PDF ---
-def extract_images_from_pdf(pdf_path, output_json_path):
-    elements = partition_pdf(
-        filename=pdf_path,
-        
-        strategy="hi_res",
-        extract_image_block_types=["Image"],  # or ["Image", "Table"]
-        extract_image_block_to_payload=True,  # Set to True to get base64 in output
-    )
-    with open(output_json_path, "w") as f:
-        json.dump([element.to_dict() for element in elements], f, indent=4)
-
-    # Display extracted images
-    with open(output_json_path, 'r') as file:
-        file_elements = json.load(file)
-        
-    for i, element in enumerate(file_elements):
-        if "image_base64" in element["metadata"]:
-            image_data = base64.b64decode(element["metadata"]["image_base64"])
-            image = Image.open(io.BytesIO(image_data))
-            image.show(title=f"Extracted Image {i+1}")
-
-# --- Route: Home Page ---
-@app.route("/")
-def index():
-    return render_template("index.html")
-
-# --- Route: Scan Document ---
-@app.route("/scan")
-def scan_document():
-    global count
-
-    cap = cv2.VideoCapture(0 + cv2.CAP_DSHOW)
-    cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
-    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
-
-    scale = 0.5
-    contour = None
-
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            flash("Camera Error!", "error")
-            break
-
-        frame = cv2.rotate(frame, cv2.ROTATE_180)
-        display = frame.copy()
-        contour = detect_document_contour(display)
-
-        if contour is not None:
-            cv2.drawContours(display, [contour], -1, (0, 255, 0), 3)
-
-        resized = cv2.resize(display, (int(scale * display.shape[1]), int(scale * display.shape[0])))
-        cv2.imshow("📷 Scan Document - Press 's' to Save, ESC to Exit", resized)
-
-        key = cv2.waitKey(1) & 0xFF
-        if key == 27:  # ESC
-            break
-        elif key == ord('s') and contour is not None:
-            warped = four_point_transform(frame, contour.reshape(4, 2))
-            image_path = os.path.join(IMAGE_FOLDER_PATH, f"scanned_colored_{count}.jpg")
-            pdf_path = os.path.join(PDF_FOLDER_PATH, f"scanned_colored_{count}.pdf")
-            json_path = os.path.join(JSON_FOLDER_PATH, f"scanned_{count}.json")
-
-            cv2.imwrite(image_path, warped)
-            img = Image.open(image_path).convert("RGB")
-            img.save(pdf_path)
-            extract_images_from_pdf(pdf_path, json_path)
-
-            flash("✅ Document scanned and saved!", "success")
-            count += 1
-            break
-
-    cap.release()
-    cv2.destroyAllWindows()
-    return redirect(url_for("index"))
-
-
-# --- Run ---
-if __name__ == "__main__":
-     app.run(host="0.0.0.0", port=7860, debug=False)
-# while True:
-#     ret, frame = cap.read()
-#     if not ret:
-#         break
-
-#     frame = cv2.rotate(frame, cv2.ROTATE_180)
-#     display = frame.copy()
-
-#     contour = detect_document_contour(display)
-#     if contour is not None:
-#         cv2.drawContours(display, [contour], -1, (0, 255, 0), 3)
-
-#     cv2.imshow("Document Scanner", cv2.resize(display, (int(scale * display.shape[1]), int(scale * display.shape[0]))))
-
-#     key = cv2.waitKey(1) & 0xFF
-
-#     if key == 27:  # ESC to exit
-#         break
-
-#     elif key == ord('s') and contour is not None:
-#         warped = four_point_transform(frame, contour.reshape(4, 2))
-#         image_path = os.path.join(IMAGE_FOLDER_PATH, f"scanned_colored_{count}.jpg")
-#         pdf_path = os.path.join(PDF_FOLDER_PATH,f"scanned_colored_{count}.pdf")
-        
-#         # Save the Image
-#         cv2.imwrite(image_path, warped)
-#         print(f"[INFO] Saved: {image_path}")
-        
-#         # Convert to PDF
-#         img = Image.open(image_path)
-#         img_rgb = img.convert("RGB")
-#         img_rgb.save(pdf_path)
-#         print(f"[INFO] Converted to PDF: {pdf_path}")
-        
-#         # Extract and show embedded images from PDF
-#         print(f"[INFO] Extracting embedded images from PDF...")
-#         # extract_images_from_pdf(pdf_path, JSON_FOLDER_PATH)
-        
-#         count += 1
-# cap.release()
-# cv2.destroyAllWindows()
-
-
-''' Simple version Not a Flask APP '''
-# import cv2, os, json, base64, io
-# import numpy as np
-# from imutils.perspective import four_point_transform
-# from PIL import Image
-# from unstructured.partition.pdf import partition_pdf
-# import pytesseract
-
-# # --- PATH CONFIGURATION ---
-# pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
-# POPPLER_PATH = r"C:\poppler-23.11.0\Library\bin"
-
-# OUTPUT_FOLDER = "OUTPUTS"
-# IMAGE_FOLDER_PATH = os.path.join(OUTPUT_FOLDER, "SCANNED_IMAGE")
-# PDF_FOLDER_PATH = os.path.join(OUTPUT_FOLDER, "SCANNED_PDF")
-# JSON_OUTPUT_FOLDER = os.path.join(OUTPUT_FOLDER, "EXTRACTED_JSON")
-
-# for path in [OUTPUT_FOLDER, IMAGE_FOLDER_PATH, PDF_FOLDER_PATH, JSON_OUTPUT_FOLDER]:
-#     os.makedirs(path, exist_ok=True)
-
-# # --- FUNCTION: Detect document contour ---
-# def detect_document_contour(image):
-#     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-#     blur = cv2.GaussianBlur(gray, (5, 5), 0)
-#     _, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
-#     contours, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
-#     contours = sorted(contours, key=cv2.contourArea, reverse=True)
-
-#     for contour in contours:
-#         area = cv2.contourArea(contour)
-#         if area > 1000:
-#             peri = cv2.arcLength(contour, True)
-#             approx = cv2.approxPolyDP(contour, 0.02 * peri, True)
-#             if len(approx) == 4:
-#                 return approx
-#     return None
-
-# # --- FUNCTION: Extract images from saved PDF ---
-# def extract_images_from_pdf(pdf_path, output_json_path):
-#     elements = partition_pdf(
-#         filename=pdf_path,
-#         poppler_path=POPPLER_PATH,
-#         strategy="hi_res",
-#         extract_image_block_types=["Image"],
-#         extract_image_block_to_payload=True,
-#     )
-    
-#     with open(output_json_path, "w") as f:
-#         json.dump([element.to_dict() for element in elements], f, indent=4)
-
-#     # Display extracted images
-#     with open(output_json_path, 'r') as file:
-#         file_elements = json.load(file)
-
-#     for i, element in enumerate(file_elements):
-#         if "image_base64" in element["metadata"]:
-#             image_data = base64.b64decode(element["metadata"]["image_base64"])
-#             image = Image.open(io.BytesIO(image_data))
-#             image.show(title=f"Extracted Image {i+1}")
-
-# # --- WEBCAM SCANNER START ---
-# # cap = cv2.VideoCapture(0 + cv2.CAP_DSHOW)
-# cap = cv2.VideoCapture("http://100.71.6.36:8080/video")
-# cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)
-# cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)
-
-# scale = 0.5
-# count = 0
-
-# while True:
-#     ret, frame = cap.read()
-#     if not ret:
-#         break
-
-#     frame = cv2.rotate(frame, cv2.ROTATE_180)
-#     display = frame.copy()
-
-#     contour = detect_document_contour(display)
-#     if contour is not None:
-#         cv2.drawContours(display, [contour], -1, (0, 255, 0), 3)
-
-#     cv2.imshow("Document Scanner", cv2.resize(display, (int(scale * display.shape[1]), int(scale * display.shape[0]))))
-
-#     key = cv2.waitKey(1) & 0xFF
-
-#     if key == 27:  # ESC to exit
-#         break
-
-#     elif key == ord('s') and contour is not None:
-#         warped = four_point_transform(frame, contour.reshape(4, 2))
-        
-#         image_path = os.path.join(IMAGE_FOLDER_PATH, f"scanned_colored_{count}.jpg")
-#         pdf_path = os.path.join(PDF_FOLDER_PATH, f"scanned_colored_{count}.pdf")
-#         json_path = os.path.join(JSON_OUTPUT_FOLDER, f"embedded_images_{count}.json")
-
-#         # Save Image
-#         cv2.imwrite(image_path, warped)
-#         print(f"[INFO] Saved image: {image_path}")
-
-#         # Convert to PDF
-#         img = Image.open(image_path)
-#         img_rgb = img.convert("RGB")
-#         img_rgb.save(pdf_path)
-#         print(f"[INFO] Converted to PDF: {pdf_path}")
-
-#         # Extract and show embedded images from PDF
-#         print(f"[INFO] Extracting embedded images from PDF...")
-#         extract_images_from_pdf(pdf_path, json_path)
-
-#         count += 1
-
-# cap.release()
-# cv2.destroyAllWindows()
-
-
-
-'''
-#==================================================================================#
-#                               Extract Images from PDF                            #
-#==================================================================================#
-from unstructured.partition.pdf import partition_pdf
-import pytesseract
-pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
-
-elements = partition_pdf(
-    filename=r"E:\Pratham\2025\Harsh Sir\Scratch Vision\images\page1.pdf",
-    poppler_path=r"C:\poppler-23.11.0\Library\bin",
-    strategy="hi_res",
-    extract_image_block_types=["Image"],  # or ["Image", "Table"]
-    extract_image_block_to_payload=True,  # Set to True to get base64 in output
-)
-
-import json, base64, io, os
-from PIL import Image
-
-# Save JSON output
-os.makedirs("output", exist_ok=True)
-with open("output/embedded-images-tables.json", "w") as f:
-    json.dump([element.to_dict() for element in elements], f, indent=4)
-
-
-def get_image_block_types(input_json_file_path: str):
-    with open(input_json_file_path, 'r') as file:
-        file_elements = json.load(file)
-
-    for element in file_elements:
-        if "image_base64" in element["metadata"]:
-            image_data = base64.b64decode(element["metadata"]["image_base64"])
-            image = Image.open(io.BytesIO(image_data))
-            image.show()
-
-# Example usage:
-get_image_block_types("output/embedded-images-tables.json")'''
-
-# from unstructured_client import UnstructuredClient
-# from unstructured_client.models import operations, shared
-# from unstructured.staging.base import elements_from_dicts, elements_to_json
-
-# import os
-# import base64
-# from PIL import Image
-# import io
-
-# if __name__ == "__main__":
-#     client = UnstructuredClient(
-#         api_key_auth=os.getenv("UNSTRUCTURED_API_KEY")
-#     )
-
-#     # Path to your PDF file
-#     local_input_filepath = "your-pdf-file.pdf"
-#     local_output_filepath = "output.json"
-
-#     with open(local_input_filepath, "rb") as f:
-#         files = shared.Files(
-#             content=f.read(),
-#             file_name=local_input_filepath
-#         )
-
-#     request = operations.PartitionRequest(
-#         shared.PartitionParameters(
-#             files=files,
-#             split_pdf_page=True,
-#             split_pdf_allow_failed=True,
-#             split_pdf_concurrency_level=15,
-#             # Extract Base64-encoded images and tables
-#             extract_image_block_types=["Image", "Table"]
-#         )
-#     )
-
-#     try:
-#         result = client.general.partition(request=request)
-
-#         for element in result.elements:
-#             if "image_base64" in element["metadata"]:
-#                 # Decode and display the image
-#                 image_data = base64.b64decode(element["metadata"]["image_base64"])
-#                 image = Image.open(io.BytesIO(image_data))
-#                 image.show()  # This will open the image
-        
-#         # Save results as JSON
-#         dict_elements = elements_from_dicts(element_dicts=result.elements)
-#         elements_to_json(
-#             elements=dict_elements,
-#             indent=2,
-#             filename=local_output_filepath
-#         )
-#     except Exception as e:
-#         print(e)
-
-# -------------------------------------------------------------------------------------- #
-
-# # STEP 1
-# # import libraries
-# import fitz  # PyMuPDF
-# import io
-# from PIL import Image
-
-# # STEP 2
-# # file path you want to extract images from
-# file = r"E:\Pratham\2025\Harsh Sir\Scratch Vision\images/page1_orig.pdf"
-
-# # open the file
-# pdf_file = fitz.open(file)
-
-# # STEP 3
-# # iterate over PDF pages
-# for page_index in range(len(pdf_file)):
-
-#     # get the page itself
-#     page = pdf_file.load_page(page_index)  # load the page
-#     image_list = page.get_images(full=True)  # get images on the page
-
-#     # printing number of images found in this page
-#     if image_list:
-#         print(f"[+] Found a total of {len(image_list)} images on page {page_index}")
-#     else:
-#         print("[!] No images found on page", page_index)
-    
-#     for image_index, img in enumerate(image_list, start=1):
-#         # get the XREF of the image
-#         xref = img[0]
-
-#         # extract the image bytes
-#         base_image = pdf_file.extract_image(xref)
-#         image_bytes = base_image["image"]
-
-#         # get the image extension
-#         image_ext = base_image["ext"]
-
-#         # save the image
-#         image_name = f"image{page_index+1}_{image_index}.{image_ext}"
-#         with open(image_name, "wb") as image_file:
-#             image_file.write(image_bytes)
-#             print(f"[+] Image saved as {image_name}")
-            
-# -------------------------------------------------------------------------------------- #
-
-# from pdf2image import convert_from_path
-# import numpy as np
-# import cv2
-
-# def extract_grid_cells_from_pdf(pdf_path, prefix="sub"):
-#     # Convert PDF's first page to image
-#     pages = convert_from_path(
-#         pdf_path,
-#         dpi=300,
-#         poppler_path=r"C:\poppler-23.11.0\Library\bin"  
-#     )
-#     pil = pages[0]
-#     img = np.array(pil)[:, :, ::-1]  # RGB→BGR
-
-#     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-#     _, thresh = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY_INV)
-#     kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
-#     dil = cv2.dilate(thresh, kernel, iterations=2)
-
-#     cnts, _ = cv2.findContours(dil, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-#     cells = [cv2.boundingRect(c) for c in cnts if cv2.contourArea(c) > 1000]
-#     cells = sorted(cells, key=lambda r: (r[1], r[0]))
-
-#     for i, (x, y, w, h) in enumerate(cells):
-#         crop = img[y:y+h, x:x+w]
-#         cv2.imwrite(f"{prefix}_{i:02d}.png", crop)
-#         print("Saved", f"{prefix}_{i:02d}.png")
-
-# if __name__ == "__main__":
-#     extract_grid_cells_from_pdf(
-#       r"E:\Pratham\2025\Harsh Sir\Scratch Vision\images\page1_orig.pdf"
-#     )
-
-
-# import cv2
-# import layoutparser as lp
-# from pdf2image import convert_from_path
-# from reportlab.pdfgen import canvas
-# from reportlab.lib.pagesizes import letter
-# import numpy as np
-# import tempfile
-# import os
-
-# # 1️⃣ Setup LayoutParser model
-# model = lp.Detectron2LayoutModel(
-#     "lp://PrimaLayout/PrimaLayout/mask_rcnn_R_50_FPN_3x/config",
-#     label_map={0: "Text", 1: "Title", 2: "List", 3: "Table", 4: "Figure"}
-# )
-
-# # 2️⃣ Utility to crop and save a layout region
-# def crop_and_save(img, block, out_dir, idx):
-#     x1, y1, x2, y2 = map(int, block.block.x_1_y_2_x_2_y_2)
-#     cropped = img[y1:y2, x1:x2]
-#     path = os.path.join(out_dir, f"crop_{idx}.png")
-#     cv2.imwrite(path, cropped)
-#     return path
-
-# # 3️⃣ Convert cropped images into multi-page PDF
-# def imgs_to_pdf(img_paths, output_pdf):
-#     c = canvas.Canvas(output_pdf, pagesize=letter)
-#     w, h = letter
-#     for img in img_paths:
-#         c.drawImage(img, 0, 0, width=w, height=h)
-#         c.showPage()
-#     c.save()
-
-# # 4️⃣ If user input is a PDF or image folder
-# def process_document(pdf_path, output_pdf):
-#     imgs = convert_from_path(pdf_path)
-#     cropped_paths = []
-#     with tempfile.TemporaryDirectory() as tmp:
-#         for page_idx, pil_im in enumerate(imgs):
-#             img = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
-#             layout = model.detect(img)
-#             for idx, block in enumerate(layout):
-#                 path = crop_and_save(img, block, tmp, f"{page_idx}_{idx}")
-#                 cropped_paths.append(path)
-#         imgs_to_pdf(cropped_paths, output_pdf)
-
-# # 5️⃣ Real-time camera/video feed
-# def process_video(output_pdf, src=0, frame_limit=100):
-#     cap = cv2.VideoCapture(src)
-#     idx = 0
-#     cropped_paths = []
-#     with tempfile.TemporaryDirectory() as tmp:
-#         while idx < frame_limit:
-#             ret, img = cap.read()
-#             if not ret:
-#                 break
-#             layout = model.detect(img)
-#             for i, block in enumerate(layout):
-#                 path = crop_and_save(img, block, tmp, f"{idx}_{i}")
-#                 cropped_paths.append(path)
-#             idx += 1
-#         cap.release()
-#         imgs_to_pdf(cropped_paths, output_pdf)
-
-# if __name__ == "__main__":
-#     import argparse
-
-#     ap = argparse.ArgumentParser()
-#     ap.add_argument("--input", required=True,
-#                     help="path to PDF or 'cam' for camera")
-#     ap.add_argument("--output", required=True, help="output PDF path")
-#     ap.add_argument("--frames", type=int, default=50,
-#                     help="frames to scan if using camera")
-#     args = ap.parse_args()
-
-#     if args.input.lower().endswith(".pdf"):
-#         process_document(args.input, args.output)
-#     elif args.input.lower() == "cam":
-#         process_video(args.output, src=0, frame_limit=args.frames)
-#     else:
-#         print("Unsupported input. Use PDF path or 'cam'.")
-
-# import cv2
-# from PIL import Image
-# import numpy as np
-
-# def get_contours(frame):
-#     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-#     # Threshold to binary
-#     _, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY_INV)
-#     # Find contours
-#     contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-#     return contours
-
-# def extract_regions(frame, contours):
-#     rois = []
-#     for cnt in contours:
-#         x, y, w, h = cv2.boundingRect(cnt)
-#         if w*h < 1000:  # skip small noise
-#             continue
-#         roi = frame[y:y+h, x:x+w]
-#         rois.append(roi)
-#     return rois
-
-# def save_rois_as_pdf(rois, output_path):
-#     pil_imgs = []
-#     for roi in rois:
-#         rgb = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
-#         pil = Image.fromarray(rgb)
-#         pil_imgs.append(pil)
-#     if pil_imgs:
-#         pil_imgs[0].save(output_path, save_all=True, append_images=pil_imgs[1:])
-#         print(f"Saved {len(pil_imgs)} regions to {output_path}")
-
-# def main():
-#     cap = cv2.VideoCapture(0)
-#     all_rois = []
-#     print("Press 'c' to capture and extract; 'q' to quit.")
-
-#     while True:
-#         ret, frame = cap.read()
-#         if not ret:
-#             break
-#         cv2.imshow("Live Feed", frame)
-
-#         key = cv2.waitKey(1) & 0xFF
-#         if key == ord('c'):
-#             contours = get_contours(frame)
-#             rois = extract_regions(frame, contours)
-#             all_rois.extend(rois)
-#             print(f"Captured {len(rois)} regions.")
-#         elif key == ord('q'):
-#             break
-
-#     cap.release()
-#     cv2.destroyAllWindows()
-
-#     if all_rois:
-#         save_rois_as_pdf(all_rois, "output_contours.pdf")
-#     else:
-#         print("No regions captured.")
-
-# if __name__ == "__main__":
-#     main()
-
-# import cv2
-# from PIL import Image
-# import numpy as np
-
-# def get_edge_contours(frame, low=50, high=150):
-#     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-#     blurred = cv2.GaussianBlur(gray, (5, 5), 1.0)
-#     edges = cv2.Canny(blurred, low, high)
-#     contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-#     return contours, edges
-
-# def extract_edge_rois(frame, contours, min_area=1000):
-#     rois = []
-#     for cnt in contours:
-#         x, y, w, h = cv2.boundingRect(cnt)
-#         if w * h < min_area:
-#             continue
-#         roi = frame[y:y+h, x:x+w]
-#         rois.append(roi)
-#     return rois
-
-# def save_rois_as_pdf(rois, output_path):
-#     pil_imgs = []
-#     for roi in rois:
-#         rgb = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
-#         pil_imgs.append(Image.fromarray(rgb))
-#     if pil_imgs:
-#         pil_imgs[0].save(output_path, save_all=True, append_images=pil_imgs[1:])
-#         print(f"✅ Saved {len(pil_imgs)} edge-region(s) to {output_path}")
-#     else:
-#         print("⚠️ No edge-based regions detected—PDF not created.")
-
-# def main():
-#     cap = cv2.VideoCapture(0)
-#     all_rois = []
-#     print("Press ‘c’ to capture current edge regions, ‘q’ to quit.")
-
-#     while True:
-#         ret, frame = cap.read()
-#         if not ret:
-#             break
-
-#         contours, edges = get_edge_contours(frame)
-#         cv2.imshow("Live Feed", frame)
-#         cv2.imshow("Edges", edges)
-
-#         key = cv2.waitKey(1) & 0xFF
-#         if key == ord('c'):
-#             rois = extract_edge_rois(frame, contours)
-#             all_rois.extend(rois)
-#             print(f"🔄 Captured {len(rois)} edge-region(s). Total: {len(all_rois)}")
-#         elif key == ord('q'):
-#             break
-
-#     cap.release()
-#     cv2.destroyAllWindows()
-
-#     if all_rois:
-#         save_rois_as_pdf(all_rois, "edge_contours.pdf")
-#     else:
-#         print("❌ No regions captured.")
-
-# if __name__ == "__main__":
-#     main()
-
-