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# app.py
import os
import shutil
import tempfile
import cv2
import numpy as np
import gradio as gr
from paddleocr import PaddleOCR
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:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
elif 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
if height <= 30:
padding = 2
elif height <= 60:
padding = 4
else:
padding = 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 process_folder(input_files):
temp_output = tempfile.mkdtemp()
for file in input_files:
filename = os.path.basename(file.name)
output_path = os.path.join(temp_output, filename)
remove_text_dynamic_fill(file.name, output_path)
zip_path = shutil.make_archive(temp_output, 'zip', temp_output)
return zip_path
demo = gr.Interface(
fn=process_folder,
inputs=gr.File(file_types=[".jpg", ".jpeg", ".png"], file_count="multiple", label="Upload Comic Images"),
outputs=gr.File(label="Download Cleaned Zip"),
title="Comic Text Cleaner",
description="Upload comic images and get a zip of cleaned versions (text removed). Uses PaddleOCR for detection."
)
demo.launch()
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