initial commit

Author: overcrash66

MultipleImageProcessing.py

@@ -0,0 +1,239 @@
+"""
+This script processes images by performing OCR (Optical Character Recognition) to extract text,
+translates the extracted text to a target language, and replaces the original text in the images
+with the translated text. The processed images are saved in a specified output folder.
+
+Usage:
+1. Ensure the 'ExportedImages' folder contains the images to be processed.
+2. Ensure the 'TranslatedImages' folder is empty or contains no conflicting filenames.
+3. Run the script and follow the prompts.
+
+Dependencies:
+- deep_translator
+- easyocr
+- concurrent.futures
+"""
+
+print("[INFO] Starting the image processing...")
+
+from PIL import Image, ImageDraw, ImageFont
+from deep_translator import GoogleTranslator
+import os
+import easyocr
+import warnings
+from concurrent.futures import ThreadPoolExecutor
+import time
+warnings.filterwarnings("ignore", category=RuntimeWarning, module="easyocr.utils")
+
+print("[INFO] Please make sure ExportedImages folder is empty!")
+print("[INFO] Please make sure TranslatedImages folder is empty!")
+print("[Warning] please make sure TranslatedImages folder is empty !")
+
+#add command line pause or ask user to press enter
+input("Press Enter to continue...")
+def process_image(filename, input_folder, output_folder, reader, translator):
+    print(f"[INFO] Processing {filename}...")
+
+    image_path = os.path.join(input_folder, filename)
+    extracted_text_boxes = perform_ocr(image_path, reader)
+
+    translated_texts = []
+    for box in extracted_text_boxes:
+        text = box[1]
+        try:
+            translated_texts.append(translator.translate(text))
+        except Exception as e:
+            print(f"[WARNING] Translation error for '{text}': {e}")
+            print(f"[WARNING] No translation found for: {text}")
+            translated_texts.append(None)
+
+    image = overlay_translated_text(image_path, translated_texts, extracted_text_boxes)
+
+    output_path = os.path.join(output_folder, filename)
+    image.save(output_path)
+    print(f"[INFO] Saved {filename} to {output_folder}.")
+
+print("[INFO] Loading the OCR and translation models...")
+source_lang = "en"
+target_lang = "fr"
+reader = easyocr.Reader([source_lang, target_lang], model_storage_directory='model')
+translator = GoogleTranslator(source="en", target="fr")
+
+def main():
+    input_folder = "ExportedImages"
+    output_folder = "TranslatedImages"
+    choice = input("Do you want to process images one by one or process multiple images? (Enter 1 for one by one or 2 for multiple files same time): ").strip().lower()
+    if choice not in ["1", "2"]:
+        print("[ERROR] Invalid choice. Please Enter 1 for one by one or 2 for multiple for multiple files same time.")
+        return
+
+    if choice == "1":
+        for filename in os.listdir(input_folder):
+            if filename.lower().endswith((".jpg", ".jpeg", ".png")):
+                process_image(filename, input_folder, output_folder, reader, translator)
+        return
+
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+    
+    if choice == "2":
+        num_workers = os.cpu_count() or 1
+        with ThreadPoolExecutor(max_workers=num_workers) as executor:
+            futures = [executor.submit(process_image, filename, input_folder, output_folder, reader, translator)
+                    for filename in os.listdir(input_folder)
+                    if filename.lower().endswith((".jpg", ".jpeg", ".png"))]
+
+            total_files = len(futures)
+            for i, future in enumerate(futures):
+                try:
+                    future.result()
+                except Exception as e:
+                    print(f"[ERROR] Failed to process {futures[i].filename}: {e}")
+                # Uncomment the following lines to show progress
+                # progress = (i + 1) / total_files * 100
+                # print(f"[INFO] Progress: {progress:.2f}%")
+
+def perform_ocr(image_path, reader):
+    # Perform OCR on the image
+    result = reader.readtext(image_path, width_ths = 0.8,  decoder = 'wordbeamsearch')
+
+    # Extract text and bounding boxes from the OCR result
+    extracted_text_boxes = [(entry[0], entry[1]) for entry in result if entry[2] > 0.4]
+
+    return extracted_text_boxes
+
+def get_font(image, text, width, height):
+
+    # Default values at start
+    font_size = None  # For font size
+    font = None  # For object truetype with correct font size
+    box = None  # For version 8.0.0
+    x = 0
+    y = 0
+
+    draw = ImageDraw.Draw(image)  # Create a draw object
+
+    # Test for different font sizes
+    for size in range(1, 500):
+
+        # Create new font
+        new_font = ImageFont.truetype("DejaVuSans-Bold.ttf", size=size)
+
+        # Calculate bbox for version 8.0.0
+        new_box = draw.textbbox((0, 0), text, font=new_font)
+
+        # Calculate width and height
+        new_w = new_box[2] - new_box[0]  # Bottom - Top
+        new_h = new_box[3] - new_box[1]  # Right - Left
+
+        # If too big then exit with previous values
+        if new_w > width or new_h > height:
+            break
+
+        # Set new current values as current values
+        font_size = size
+        font = new_font
+        box = new_box
+        w = new_w
+        h = new_h
+
+        # Calculate position (minus margins in box)
+        x = (width - w) // 2 - box[0]  # Minus left margin
+        y = (height - h) // 2 - box[1]  # Minus top margin
+
+    return font, x, y
+
+def adjust_color_brightness(color, strength):
+    r, g, b = color
+    r = max(0, min(255, r + strength))
+    g = max(0, min(255, g + strength))
+    b = max(0, min(255, b + strength))
+    return (r, g, b)
+
+def extract_background_color(image, x_min, y_min, x_max, y_max):
+    margin = 10
+    region = image.crop((
+        max(x_min - margin, 0),
+        max(y_min - margin, 0),
+        min(x_max + margin, image.width),
+        min(y_max + margin, image.height),
+    ))
+    edge_colors = region.getcolors(region.size[0] * region.size[1])
+    background_color = max(edge_colors, key=lambda x: x[0])[1]
+    return adjust_color_brightness(background_color, 40)
+
+def determine_text_color(background_color):
+    # Calculate the luminance of the background color
+    luminance = (
+        0.299 * background_color[0]
+        + 0.587 * background_color[1]
+        + 0.114 * background_color[2]
+    ) / 255
+
+    # Determine the text color based on the background luminance
+    if luminance > 0.5:
+        return "black"  # Use black text for light backgrounds
+    else:
+        return "white"  # Use white text for dark backgrounds
+
+def overlay_translated_text(image_path, translated_texts, text_boxes):
+    # Open the image
+    image = Image.open(image_path)
+    draw = ImageDraw.Draw(image)
+
+    # Load a font that supports French accent marks
+    font = ImageFont.truetype("DejaVuSans-Bold.ttf", size=20)
+    
+    # Replace each text box with translated text
+    for text_box, translated in zip(text_boxes, translated_texts):
+
+        if translated is None:
+            continue
+
+        # Set initial values
+        x_min, y_min = text_box[0][0][0], text_box[0][0][1]
+        x_max, y_max = text_box[0][0][0], text_box[0][0][1]
+
+        for coordinate in text_box[0]:
+
+            x, y = coordinate
+
+            if x < x_min:
+                x_min = x
+            elif x > x_max:
+                x_max = x
+            if y < y_min:
+                y_min = y
+            elif y > y_max:
+                y_max = y
+
+        # Find the most common color in the text region
+        background_color = extract_background_color(image, x_min, y_min, x_max, y_max)
+
+        # Draw a rectangle to cover the text region with the original background color
+        draw.rectangle(((x_min, y_min), (x_max, y_max)), fill=background_color)
+
+        # Calculate font size, box
+        font, x, y = get_font(image, translated, x_max - x_min, y_max - y_min)
+
+        # Draw the translated text within the box
+        draw.text(
+            (x_min + x, y_min + y),
+            translated,
+            fill=determine_text_color(background_color),
+            font=font,
+        )
+
+    return image
+
+start_time = time.time()
+
+if __name__ == "__main__":
+    main()
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    elapsed_minutes = elapsed_time / 60
+    print(f"[INFO] Image processing completed in {elapsed_minutes:.2f} minutes.")
+    print("[INFO] Please check the 'TranslatedImages' folder for the processed images.")
+    print("[INFO] Thank you for using the image processing script!")
+

README.md

@@ -14,6 +14,21 @@ This project utilizes optical character recognition (OCR) and translation to tra
 1. Clone this repository to your local machine.
 2. Install the required Python dependencies using `pip install pipenv && pipenv install`.
 
+or
+
+```
+py -3.10 -m venv venv
+venv\Scripts\activate
+pip install -r requirements.txt
+```
+
+If you like to use torch with cuda:
+
+```
+pip uninstall torch torchvision
+pip install torch==2.5.1+cu118 torchaudio==2.5.1+cu118 torchvision==0.20.1+cu118 --index-url https://download.pytorch.org/whl/cu118
+```
+
 ## Usage
 
 1. Place your input images in the `input` folder.