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import os
import sys
if "APP_PATH" in os.environ:
os.chdir(os.environ["APP_PATH"])
# fix sys.path for import
sys.path.append(os.getcwd())
import gradio as gr
import re
import string
import random
import os
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F
from torchvision import transforms
from watermark_anything.data.metrics import msg_predict_inference
from notebooks.inference_utils import (
load_model_from_checkpoint,
default_transform,
unnormalize_img,
create_random_mask,
plot_outputs,
msg2str,
torch_to_np,
multiwm_dbscan
)
# Device configuration
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Constants
proportion_masked = 0.5 # Proportion of image to be watermarked
epsilon = 1 # min distance between decoded messages in a cluster
min_samples = 500 # min number of pixels in a 256x256 image to form a cluster
# Color map for visualization
color_map = {
-1: [0, 0, 0], # Black for -1
0: [255, 0, 255], # ? for 0
1: [255, 0, 0], # Red for 1
2: [0, 255, 0], # Green for 2
3: [0, 0, 255], # Blue for 3
4: [255, 255, 0], # Yellow for 4
}
def load_wam():
# Load the model from the specified checkpoint
exp_dir = "checkpoints"
json_path = os.path.join(exp_dir, "params.json")
ckpt_path = os.path.join(exp_dir, 'checkpoint.pth')
wam = load_model_from_checkpoint(json_path, ckpt_path).to(device).eval()
return wam
def image_detect(img_pil: Image.Image) -> (torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor):
img_pt = default_transform(img_pil).unsqueeze(0).to(device) # [1, 3, H, W]
# Detect the watermark in the multi-watermarked image
preds = wam.detect(img_pt)["preds"] # [1, 33, 256, 256]
mask_preds = F.sigmoid(preds[:, 0, :, :]) # [1, 256, 256], predicted mask
mask_preds_res = F.interpolate(mask_preds.unsqueeze(1), size=(img_pt.shape[-2], img_pt.shape[-1]), mode="bilinear", align_corners=False) # [1, 1, H, W]
bit_preds = preds[:, 1:, :, :] # [1, 32, 256, 256], predicted bits
# positions has the cluster number at each pixel. can be upsaled back to the original size.
try:
centroids, positions = multiwm_dbscan(bit_preds, mask_preds, epsilon=epsilon, min_samples=min_samples)
centroids_pt = torch.stack(list(centroids.values()))
except (UnboundLocalError) as e:
print(f"Error while detecting watermark: {e}")
positions = None
centroids = None
centroids_pt = None
return img_pt, (mask_preds_res>0.5).float(), positions, centroids, centroids_pt
def image_embed(img_pil: Image.Image, wm_msgs: torch.Tensor, wm_masks: torch.Tensor) -> (torch.Tensor, torch.Tensor, torch.Tensor):
img_pt = default_transform(img_pil).unsqueeze(0).to(device) # [1, 3, H, W]
# Embed the watermark message into the image
# Mask to use. 1 values correspond to pixels where the watermark will be placed.
multi_wm_img = img_pt.clone()
for ii in range(len(wm_msgs)):
wm_msg, mask = wm_msgs[ii].unsqueeze(0), wm_masks[ii]
outputs = wam.embed(img_pt, wm_msg)
multi_wm_img = outputs['imgs_w'] * mask + multi_wm_img * (1 - mask)
torch.cuda.empty_cache()
return img_pt, multi_wm_img, wm_masks.sum(0)
def create_bounding_mask(img_size, boxes):
"""Create a binary mask from bounding boxes.
Args:
img_size (tuple): Image size (height, width)
boxes (list): List of tuples (x1, y1, x2, y2) defining bounding boxes
Returns:
torch.Tensor: Binary mask tensor
"""
mask = torch.zeros(img_size)
for x1, y1, x2, y2 in boxes:
mask[y1:y2, x1:x2] = 1
return mask
def centroid_to_hex(centroid):
binary_int = 0
for bit in centroid:
binary_int = (binary_int << 1) | int(bit.item())
return format(binary_int, '08x')
# Load the model
wam = load_wam()
def detect_watermark(image):
if image is None:
return None, None, None, {"status": "error", "messages": [], "error": "No image provided"}
img_pil = Image.fromarray(image).convert("RGB")
det_img, pred, positions, centroids, centroids_pt = image_detect(img_pil)
# Convert tensor images to numpy for display
detected_img = torch_to_np(det_img.detach())
pred_mask = torch_to_np(pred.detach().repeat(1, 3, 1, 1))
# Create cluster visualization
if positions is not None:
resize_ori = transforms.Resize(det_img.shape[-2:])
rgb_image = torch.zeros((3, positions.shape[-1], positions.shape[-2]), dtype=torch.uint8)
for value, color in color_map.items():
mask_ = positions == value
for channel, color_value in enumerate(color):
rgb_image[channel][mask_.squeeze()] = color_value
rgb_image = resize_ori(rgb_image.float()/255)
cluster_viz = rgb_image.permute(1, 2, 0).numpy()
# Create message output as JSON
messages = []
for key in centroids.keys():
centroid_hex = centroid_to_hex(centroids[key])
centroid_hex_array = "-".join([centroid_hex[i:i+4] for i in range(0, len(centroid_hex), 4)])
messages.append({
"id": int(key),
"message": centroid_hex_array,
"color": color_map[key]
})
message_json = {
"status": "success",
"messages": messages,
"count": len(messages)
}
else:
cluster_viz = np.zeros_like(detected_img)
message_json = {
"status": "no_detection",
"messages": [],
"count": 0
}
return pred_mask, cluster_viz, message_json
def embed_watermark(image, wm_num, wm_type, wm_str, wm_loc):
if image is None:
return None, None, {
"status": "failure",
"messages": "No image provided"
}
if wm_type == "input":
if not re.match(r"^([0-9A-F]{4}-[0-9A-F]{4}-){%d}[0-9A-F]{4}-[0-9A-F]{4}$" % (wm_num-1), wm_str):
tip = "-".join([f"FFFF-{_}{_}{_}{_}" for _ in range(wm_num)])
return None, None, {
"status": "failure",
"messages": f"Invalid type input. Please use {tip}"
}
if wm_loc == "bounding":
if ROI_coordinates['clicks'] != wm_num * 2:
return None, None, {
"status": "failure",
"messages": "Invalid location input. Please draw at least %d bounding ROI" % (wm_num)
}
img_pil = Image.fromarray(image).convert("RGB")
# Generate watermark messages based on type
wm_msgs = []
if wm_type == "random":
chars = '-'.join(''.join(random.choice(string.hexdigits) for _ in range(4)) for _ in range(wm_num * 2))
wm_str = chars.lower()
wm_hex = wm_str.replace("-", "")
for i in range(0, len(wm_hex), 8):
chunk = wm_hex[i:i+8]
binary = bin(int(chunk, 16))[2:].zfill(32)
wm_msgs.append([int(b) for b in binary])
# Define a 32-bit message to be embedded into the images
wm_msgs = torch.tensor(wm_msgs, dtype=torch.float32).to(device)
# Create mask based on location type
wm_masks = None
if wm_loc == "random":
img_pt = default_transform(img_pil).unsqueeze(0).to(device)
# To ensure at least `proportion_masked %` of the width is randomly usable,
# otherwise, it is easy to enter an infinite loop and fail to find a usable width.
mask_percentage = img_pil.height / img_pil.width * proportion_masked / wm_num
wm_masks = create_random_mask(img_pt, num_masks=wm_num, mask_percentage=mask_percentage)
elif wm_loc == "bounding" and sections:
wm_masks = torch.zeros((len(sections), 1, img_pil.height, img_pil.width), dtype=torch.float32).to(device)
for idx, ((x_start, y_start, x_end, y_end), _) in enumerate(sections):
left = min(x_start, x_end)
right = max(x_start, x_end)
top = min(y_start, y_end)
bottom = max(y_start, y_end)
wm_masks[idx, 0, top:bottom, left:right] = 1
img_pt, embed_img_pt, embed_mask_pt = image_embed(img_pil, wm_msgs, wm_masks)
# Convert to numpy for display
img_np = torch_to_np(embed_img_pt.detach())
mask_np = torch_to_np(embed_mask_pt.detach().expand(3, -1, -1))
message_json = {
"status": "success",
"messages": wm_str
}
return img_np, mask_np, message_json
# ROI means Region Of Interest. It is the region where the user clicks
# to specify the location of the watermark.
ROI_coordinates = {
'x_temp': 0,
'y_temp': 0,
'x_new': 0,
'y_new': 0,
'clicks': 0,
}
sections = []
def get_select_coordinates(img, evt: gr.SelectData, num):
if ROI_coordinates['clicks'] >= num * 2:
gr.Warning(f"Cant add more than {num} of Watermarks.")
return (img, sections)
# update new coordinates
ROI_coordinates['clicks'] += 1
ROI_coordinates['x_temp'] = ROI_coordinates['x_new']
ROI_coordinates['y_temp'] = ROI_coordinates['y_new']
ROI_coordinates['x_new'] = evt.index[0]
ROI_coordinates['y_new'] = evt.index[1]
# compare start end coordinates
x_start = ROI_coordinates['x_new'] if (ROI_coordinates['x_new'] < ROI_coordinates['x_temp']) else ROI_coordinates['x_temp']
y_start = ROI_coordinates['y_new'] if (ROI_coordinates['y_new'] < ROI_coordinates['y_temp']) else ROI_coordinates['y_temp']
x_end = ROI_coordinates['x_new'] if (ROI_coordinates['x_new'] > ROI_coordinates['x_temp']) else ROI_coordinates['x_temp']
y_end = ROI_coordinates['y_new'] if (ROI_coordinates['y_new'] > ROI_coordinates['y_temp']) else ROI_coordinates['y_temp']
if ROI_coordinates['clicks'] % 2 == 0:
sections[len(sections) - 1] = ((x_start, y_start, x_end, y_end), f"Mask {len(sections)}")
# both start and end point get
return (img, sections)
else:
point_width = int(img.shape[0]*0.05)
sections.append(((ROI_coordinates['x_new'], ROI_coordinates['y_new'],
ROI_coordinates['x_new'] + point_width, ROI_coordinates['y_new'] + point_width),
f"Click second point for Mask {len(sections) + 1}"))
return (img, sections)
def del_select_coordinates(img, evt: gr.SelectData):
del sections[evt.index]
# recreate section names
for i in range(len(sections)):
sections[i] = (sections[i][0], f"Mask {i + 1}")
# last section clicking second point not complete
if ROI_coordinates['clicks'] % 2 != 0:
if len(sections) == evt.index:
# delete last section
ROI_coordinates['clicks'] -= 1
else:
# recreate last section name for second point
ROI_coordinates['clicks'] -= 2
sections[len(sections) - 1] = (sections[len(sections) - 1][0], f"Click second point for Mask {len(sections) + 1}")
else:
ROI_coordinates['clicks'] -= 2
return (img[0], sections)
with gr.Blocks(title="Watermark Anything Demo") as demo:
gr.Markdown("""
# Watermark Anything Demo
This app demonstrates watermark detection and embedding using the Watermark Anything model.
Find the project [here](https://github.com/facebookresearch/watermark-anything).
""")
with gr.Tabs():
with gr.TabItem("Embed Watermark"):
with gr.Row():
with gr.Column():
embedding_img = gr.Image(label="Input Image", type="numpy")
with gr.Column():
embedding_num = gr.Slider(1, 5, value=1, step=1, label="Number of Watermarks")
embedding_type = gr.Radio(["random", "input"], value="random", label="Type", info="Type of watermarks")
embedding_str = gr.Textbox(label="Watermark Text", visible=False, show_copy_button=True)
embedding_loc = gr.Radio(["random", "bounding"], value="random", label="Location", info="Location of watermarks")
@gr.render(inputs=embedding_loc)
def show_split(wm_loc):
if wm_loc == "bounding":
embedding_box = gr.AnnotatedImage(
label="ROI",
color_map={
"ROI of Watermark embedding": "#9987FF",
"Click second point for ROI": "#f44336"}
)
embedding_img.select(
fn=get_select_coordinates,
inputs=[embedding_img, embedding_num],
outputs=embedding_box)
embedding_box.select(
fn=del_select_coordinates,
inputs=embedding_box,
outputs=embedding_box
)
else:
embedding_img.select()
embedding_btn = gr.Button("Embed Watermark")
marked_msg = gr.JSON(label="Marked Messages")
with gr.Row():
marked_image = gr.Image(label="Watermarked Image")
marked_mask = gr.Image(label="Position of the watermark")
def visible_text_label(embedding_type, embedding_num):
if embedding_type == "input":
tip = "-".join([f"FFFF-{_}{_}{_}{_}" for _ in range(embedding_num)])
return gr.update(visible=True, label=f"Watermark Text (Format: {tip})")
else:
return gr.update(visible=False)
def check_embedding_str(embedding_str, embedding_num):
if not re.match(r"^([0-9A-F]{4}-[0-9A-F]{4}-){%d}[0-9A-F]{4}-[0-9A-F]{4}$" % (embedding_num-1), embedding_str):
tip = "-".join([f"FFFF-{_}{_}{_}{_}" for _ in range(embedding_num)])
gr.Warning(f"Invalid format. Please use {tip}", duration=0)
return gr.update(interactive=False)
else:
return gr.update(interactive=True)
embedding_num.change(
fn=visible_text_label,
inputs=[embedding_type, embedding_num],
outputs=[embedding_str]
)
embedding_type.change(
fn=visible_text_label,
inputs=[embedding_type, embedding_num],
outputs=[embedding_str]
)
embedding_str.change(
fn=check_embedding_str,
inputs=[embedding_str, embedding_num],
outputs=[embedding_btn]
)
embedding_btn.click(
fn=embed_watermark,
inputs=[embedding_img, embedding_num, embedding_type, embedding_str, embedding_loc],
outputs=[marked_image, marked_mask, marked_msg]
)
with gr.TabItem("Detect Watermark"):
with gr.Row():
with gr.Column():
detecting_img = gr.Image(label="Input Image", type="numpy")
with gr.Column():
detecting_btn = gr.Button("Detect Watermark")
predicted_messages = gr.JSON(label="Detected Messages")
with gr.Row():
predicted_mask = gr.Image(label="Predicted Watermark Position")
predicted_cluster = gr.Image(label="Watermark Clusters")
detecting_btn.click(
fn=detect_watermark,
inputs=[detecting_img],
outputs=[predicted_mask, predicted_cluster, predicted_messages]
)
demo.launch()
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