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import cv2
import numpy as np
import os
import zipfile
import uuid
import gradio as gr
import uuid
def remove_watermark_area(original_image, text_mask_path):
# Ensure the mask is binary
text_mask = cv2.imread(text_mask_path, cv2.IMREAD_GRAYSCALE)
_, binary_mask = cv2.threshold(text_mask, 1, 255, cv2.THRESH_BINARY)
# Resize the mask to match the size of the original image area
mask_resized = cv2.resize(binary_mask, (original_image.shape[1], original_image.shape[0]))
# Expand the mask to cover more area if needed
kernel = np.ones((5, 5), np.uint8)
expanded_mask = cv2.dilate(mask_resized, kernel, iterations=1)
# Inpainting using the mask
inpainted_image = cv2.inpaint(original_image, expanded_mask, inpaintRadius=5, flags=cv2.INPAINT_TELEA)
# Optionally apply post-processing to improve results
cleaned_image = cv2.GaussianBlur(inpainted_image, (3, 3), 0)
return cleaned_image
from PIL import Image
def remove_watermark(image_path,saved_path):
if isinstance(image_path, str) and os.path.isfile(image_path):
# Load the image using OpenCV
image = cv2.imread(image_path)
elif isinstance(image_path, np.ndarray):
# Directly use OpenCV image (NumPy array)
image = image_path
if len(image_path.shape) == 3 and image_path.shape[2] == 3:
# Assuming it's in RGB format; convert to BGR
image = cv2.cvtColor(image_path, cv2.COLOR_RGB2BGR)
else:
# Otherwise, assume it's already in BGR format
image = image_path
else:
raise TypeError("Invalid image_path format")
print(type(image))
cv2.imwrite("test.jpg",image)
image=cv2.resize(image,(1280,1280))
# Define the area of the watermark (adjust this based on the watermark size)
height, width, _ = image.shape
watermark_width = 185 # Adjust based on your watermark size
watermark_height = 185 # Adjust based on your watermark size
x_start = 50
y_start = height - watermark_height+17
x_end = watermark_width-17
y_end = height-50
# Extract the watermark area
watermark_area = image[y_start:y_end, x_start:x_end]
# cv2.imwrite('watermark_area.jpg', watermark_area)
# Create the mask for the watermark area
text_mask_path = 'watermark_mask.png'
cleaned_image = remove_watermark_area(watermark_area, text_mask_path)
# cv2.imwrite('cleaned_watermark.jpg', cleaned_image)
# Paste back the cleaned watermark on the original image
image[y_start:y_end, x_start:x_end] = cleaned_image
cv2.imwrite(saved_path, image)
return image
def make_zip(image_list):
zip_path = f"./temp/{uuid.uuid4().hex[:6]}.zip"
with zipfile.ZipFile(zip_path, 'w') as zipf:
for image in image_list:
zipf.write(image, os.path.basename(image))
return zip_path
def random_image_name():
"""Generate a random image name."""
return str(uuid.uuid4())[:8]
def process_file(pil_image):
saved_path = f"./temp/{random_image_name()}.jpg"
remove_watermark(pil_image, saved_path)
return saved_path, saved_path
def process_files(image_files):
image_list = []
if len(image_files) == 1:
# saved_path = os.path.basename(image_files[0])
# saved_path = f"./temp/{saved_path}"
saved_path = f"./temp/{random_image_name()}.jpg"
remove_watermark(image_files[0], saved_path)
return saved_path, saved_path
else:
for image_path in image_files:
# saved_path = os.path.basename(image_path)
# saved_path = f"./temp/{saved_path}"
saved_path = f"./temp/{random_image_name()}.jpg"
remove_watermark(image_path, saved_path)
image_list.append(saved_path)
zip_path = make_zip(image_list)
return zip_path,None
if not os.path.exists("./temp"):
os.mkdir("./temp")
meta_examples = ["./images/1.jpg", "./images/2.jpg", "./images/3.jpg", "./images/4.jpg", "./images/5.jpg", "./images/6.jpg"]
gradio_input=[gr.Image(label='Upload an Image')]
gradio_Output=[gr.File(label='Download Image'),gr.Image(label='Display Image')]
gradio_interface = gr.Interface(fn=process_file, inputs=gradio_input,outputs=gradio_Output ,
title="Meta Watermark Remover",
examples=meta_examples)
# gradio_interface.launch(debug=True)
gradio_multiple_images = gr.Interface(
process_files,
[gr.File(type='filepath', file_count='multiple',label='Upload Images')],
[gr.File(label='Download File'),gr.Image(label='Display Image')],
cache_examples=True
)
demo = gr.TabbedInterface([gradio_interface, gradio_multiple_images], ["Meta Watermark Remover","Bluk Meta Watermark Remover"],title="Meta Watermark Remover")
demo.launch(debug=True)
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