Initial (and hopefully last) commit

Buchstaben_extrahieren2.py

@@ -0,0 +1,49 @@
+import fitz  # PyMuPDF
+import cv2
+import numpy as np
+from PIL import Image
+
+def save_letter_images_from_pdf(pdf_path, output_folder):
+    # Open the PDF file
+    doc = fitz.open(pdf_path)
+    
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+        
+        # Get the text boxes
+        text_boxes = page.get_text("dict")["blocks"]
+        
+        for block in text_boxes:
+            if "lines" in block:  # Check if block contains lines
+                for line in block["lines"]:
+                    for span in line["spans"]:
+                        # Extract the bounding box of each letter
+                        rect = fitz.Rect(span["bbox"])
+                        
+                        # Crop the page to the letter's bounding box
+                        pix = page.get_pixmap(clip=rect)
+                        
+                        # Convert to a numpy array
+                        img = np.array(pix.samples).reshape(pix.height, pix.width, pix.n)
+                        
+                        # Convert color space from BGRA to grayscale
+                        if pix.n == 4:
+                            img = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)
+                        else:
+                            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+                        # Resize to 28x28 pixels
+                        img_resized = cv2.resize(img, (28, 28), interpolation=cv2.INTER_AREA)
+
+                        # Save the image
+                        img_pil = Image.fromarray(img_resized)
+                        img_pil.save(f"{output_folder}/letter_page{page_num + 1}_box{len(text_boxes)}.png")
+
+    # Close the document
+    doc.close()
+
+# Example usage
+pdf_path = "/home/mia/Schule/KISY/schrifterkennung/letters.pdf"
+output_folder = "/home/mia/Schule/KISY/schrifterkennung/out/"
+save_letter_images_from_pdf(pdf_path, output_folder)
+