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app.py

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+import os
+import shutil
+import tempfile
+import cv2
+import numpy as np
+import gradio as gr
+import zipfile
+import patoolib
+import threading
+from paddleocr import PaddleOCR
+from PIL import Image
+
+def delayed_cleanup(path, delay=30):
+    def cleanup():
+        import time
+        time.sleep(delay)
+        try:
+            if os.path.isdir(path):
+                shutil.rmtree(path)
+            elif os.path.exists(path):
+                os.remove(path)
+        except Exception as e:
+            print(f"Cleanup failed: {e}")
+    threading.Thread(target=cleanup).start()
+
+ocr = PaddleOCR(use_angle_cls=True, lang='en', det_model_dir='models/det', rec_model_dir='models/rec', cls_model_dir='models/cls')
+
+def classify_background_color(avg_color, white_thresh=230, black_thresh=50, yellow_thresh=100):
+    r, g, b = avg_color
+    if r >= white_thresh and g >= white_thresh and b >= white_thresh:
+        return (255, 255, 255)
+    if r <= black_thresh and g <= black_thresh and b <= black_thresh:
+        return (0, 0, 0)
+    if r >= yellow_thresh and g >= yellow_thresh and b < yellow_thresh:
+        return (255, 255, 0)
+    return None
+
+def sample_border_color(image, box, padding=2):
+    h, w = image.shape[:2]
+    x_min, y_min, x_max, y_max = box
+    x_min = max(0, x_min - padding)
+    x_max = min(w-1, x_max + padding)
+    y_min = max(0, y_min - padding)
+    y_max = min(h-1, y_max + padding)
+
+    top = image[y_min:y_min+padding, x_min:x_max]
+    bottom = image[y_max-padding:y_max, x_min:x_max]
+    left = image[y_min:y_max, x_min:x_min+padding]
+    right = image[y_min:y_max, x_max-padding:x_max]
+
+    border_pixels = np.vstack((top.reshape(-1, 3), bottom.reshape(-1, 3),
+                               left.reshape(-1, 3), right.reshape(-1, 3)))
+    if border_pixels.size == 0:
+        return (255, 255, 255)
+    median_color = np.median(border_pixels, axis=0)
+    return tuple(map(int, median_color))
+
+def detect_text_boxes(image):
+    results = ocr.ocr(image, cls=True)
+    if not results or not results[0]:
+        return []
+    boxes = []
+    for line in results[0]:
+        box, (text, confidence) = line
+        if text.strip():
+            x_min = int(min(pt[0] for pt in box))
+            x_max = int(max(pt[0] for pt in box))
+            y_min = int(min(pt[1] for pt in box))
+            y_max = int(max(pt[1] for pt in box))
+            boxes.append(((x_min, y_min, x_max, y_max), text, confidence))
+    return boxes
+
+def remove_text_dynamic_fill(img_path, output_path):
+    image = cv2.imread(img_path)
+    if image is None:
+        return
+    if len(image.shape) == 2 or image.shape[2] == 1:
+        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+    else:
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+
+    boxes = detect_text_boxes(image)
+
+    for (bbox, text, confidence) in boxes:
+        if confidence < 0.4 or not text.strip():
+            continue
+        x_min, y_min, x_max, y_max = bbox
+        height = y_max - y_min
+        padding = 2 if height <= 30 else 4 if height <= 60 else 6
+        x_min_p = max(0, x_min - padding)
+        y_min_p = max(0, y_min - padding)
+        x_max_p = min(image.shape[1]-1, x_max + padding)
+        y_max_p = min(image.shape[0]-1, y_max + padding)
+        sample_crop = image[y_min_p:y_max_p, x_min_p:x_max_p]
+        avg_color = np.mean(sample_crop.reshape(-1, 3), axis=0)
+        fill_color = classify_background_color(avg_color)
+        if fill_color is None:
+            fill_color = sample_border_color(image, (x_min, y_min, x_max, y_max))
+        cv2.rectangle(image, (x_min_p, y_min_p), (x_max_p, y_max_p), fill_color, -1)
+
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    cv2.imwrite(output_path, image)
+
+def extract_comic_archive(archive_path, extract_to):
+    if archive_path.endswith(".cbz"):
+        with zipfile.ZipFile(archive_path, 'r') as zip_ref:
+            zip_ref.extractall(extract_to)
+    elif archive_path.endswith(".cbr"):
+        patoolib.extract_archive(archive_path, outdir=extract_to)
+
+def process_cbz_cbr(file):
+    temp_input = tempfile.mkdtemp()
+    temp_output = tempfile.mkdtemp()
+    extract_comic_archive(file.name, temp_input)
+    for root, _, files in os.walk(temp_input):
+        for fname in files:
+            if fname.lower().endswith((".jpg", ".jpeg", ".png")):
+                src = os.path.join(root, fname)
+                dst = os.path.join(temp_output, fname)
+                remove_text_dynamic_fill(src, dst)
+    zip_path = shutil.make_archive(temp_output, 'zip', temp_output)
+    delayed_cleanup(temp_input)
+    delayed_cleanup(temp_output)
+    delayed_cleanup(zip_path)
+    return zip_path
+
+demo = gr.Interface(
+    fn=process_cbz_cbr,
+    inputs=gr.File(file_types=[".cbz", ".cbr"], label="Upload Comic Archive (.cbz or .cbr)"),
+    outputs=gr.File(label="Download Cleaned Zip"),
+    title="Comic Text Cleaner (.cbz/.cbr)",
+    description="Upload a .cbz or .cbr file and get a zip of cleaned comic images (text removed using PaddleOCR)."
+)
+
+demo.launch()

requirements.txt

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+paddleocr==2.7.1
+paddlepaddle==2.5.2
+opencv-python
+gradio
+Pillow
+numpy
+pyunpack
+patool
+