Upload 2 files (#1)

- Upload 2 files (fe737d48cd1b75bfc59c968ea83caee0b4744260)

ToomBComicCleanerGradio.py

@@ -0,0 +1,120 @@
+# 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()

requirement.txt

@@ -0,0 +1,5 @@
+numpy
+opencv-python
+paddlepaddle       
+paddleocr
+gradio