NeuralFalcon commited on
Commit
673bd17
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1 Parent(s): 0561c20

Update app.py

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Files changed (1) hide show
  1. app.py +40 -4
app.py CHANGED
@@ -26,11 +26,26 @@ def remove_watermark_area(original_image, text_mask_path):
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  cleaned_image = cv2.GaussianBlur(inpainted_image, (3, 3), 0)
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  return cleaned_image
 
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  def remove_watermark(image_path,saved_path):
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- # Load the original image
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- image = cv2.imread(image_path)
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-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Define the area of the watermark (adjust this based on the watermark size)
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  height, width, _ = image.shape
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  watermark_width = 185 # Adjust based on your watermark size
@@ -65,6 +80,12 @@ def random_image_name():
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  return str(uuid.uuid4())[:8]
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  def process_files(image_files):
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  image_list = []
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  if len(image_files) == 1:
@@ -83,14 +104,29 @@ def process_files(image_files):
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  zip_path = make_zip(image_list)
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  return zip_path,None
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  if not os.path.exists("./temp"):
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  os.mkdir("./temp")
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- demo = gr.Interface(
 
 
 
 
 
 
 
 
 
 
 
 
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  process_files,
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  [gr.File(type='filepath', file_count='multiple')],
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  [gr.File(),gr.Image()],
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  cache_examples=True
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  )
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  demo.launch(debug=True)
 
 
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  cleaned_image = cv2.GaussianBlur(inpainted_image, (3, 3), 0)
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  return cleaned_image
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+ from PIL import Image
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  def remove_watermark(image_path,saved_path):
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+ if isinstance(image_path, str) and os.path.isfile(image_path):
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+ # Load the image using OpenCV
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+ image = cv2.imread(image_path)
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+ elif isinstance(image_path, np.ndarray):
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+ # Directly use OpenCV image (NumPy array)
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+ image = image_path
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+ if len(image_path.shape) == 3 and image_path.shape[2] == 3:
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+ # Assuming it's in RGB format; convert to BGR
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+ image = cv2.cvtColor(image_path, cv2.COLOR_RGB2BGR)
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+ else:
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+ # Otherwise, assume it's already in BGR format
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+ image = image_path
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+ else:
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+ raise TypeError("Invalid image_path format")
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+ print(type(image))
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+ cv2.imwrite("test.jpg",image)
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+ image=cv2.resize(image,(1280,1280))
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  # Define the area of the watermark (adjust this based on the watermark size)
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  height, width, _ = image.shape
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  watermark_width = 185 # Adjust based on your watermark size
 
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  return str(uuid.uuid4())[:8]
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+ def process_file(pil_image):
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+ saved_path = f"./temp/{random_image_name()}.jpg"
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+ remove_watermark(pil_image, saved_path)
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+ return saved_path, saved_path
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+
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+
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  def process_files(image_files):
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  image_list = []
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  if len(image_files) == 1:
 
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  zip_path = make_zip(image_list)
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  return zip_path,None
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+
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  if not os.path.exists("./temp"):
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  os.mkdir("./temp")
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+
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+ meta_examples = ["./images/1.jpg", "./images/2.jpg", "./images/3.jpg", "./images/4.jpg", "./images/5.jpg", "./images/6.jpg"]
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+
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+ gradio_input=[gr.Image()]
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+ gradio_Output=[gr.File(),gr.Image()]
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+ gradio_interface = gr.Interface(fn=process_file, inputs=gradio_input,outputs=gradio_Output ,
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+ title="Meta Watermark Remover",
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+ examples=meta_examples)
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+ # gradio_interface.launch(debug=True)
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+
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+
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+
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+ gradio_multiple_images = gr.Interface(
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  process_files,
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  [gr.File(type='filepath', file_count='multiple')],
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  [gr.File(),gr.Image()],
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  cache_examples=True
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  )
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+ demo = gr.TabbedInterface([gradio_interface, gradio_multiple_images], ["Meta Watermark Remover","Bluk Meta Watermark Remover"],title="Meta Watermark Remover")
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  demo.launch(debug=True)
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+