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import os
os.system("pip install --upgrade pip")
os.system("pip install ftfy regex tqdm")
os.system("pip install git+https://github.com/openai/CLIP.git")
os.system("pip install git+https://github.com/facebookresearch/detectron2@96c752ce821a3340e27edd51c28a00665dd32a30#subdirectory=projects/DensePose")
os.system("pip install git+https://github.com/hukkelas/DSFD-Pytorch-Inference")
import gradio
import numpy as np
import torch
from PIL import Image
from dp2 import utils
from tops.config import instantiate
import tops
import gradio.inputs
cfg_body = utils.load_config("configs/anonymizers/FB_cse.py")
anonymizer_body = instantiate(cfg_body.anonymizer, load_cache=False)
anonymizer_body.initialize_tracker(fps=1)
cfg_face = utils.load_config("configs/anonymizers/face.py")
anonymizer_face = instantiate(cfg_face.anonymizer, load_cache=False)
anonymizer_face.initialize_tracker(fps=1)
class ExampleDemo:
def __init__(self, anonymizer, multi_modal_truncation=False) -> None:
self.multi_modal_truncation = multi_modal_truncation
self.anonymizer = anonymizer
with gradio.Row():
input_image = gradio.Image(type="pil", label="Upload your image or try the example below!")
output_image = gradio.Image(type="numpy", label="Output")
with gradio.Row():
update_btn = gradio.Button("Update Anonymization").style(full_width=True)
visualize_det = gradio.Checkbox(value=False, label="Show Detections")
visualize_det.change(self.anonymize, inputs=[input_image, visualize_det], outputs=[output_image])
gradio.Examples(
["erling.jpg", "g7-summit-leaders-distraction.jpg"], inputs=[input_image]
)
update_btn.click(self.anonymize, inputs=[input_image, visualize_det], outputs=[output_image])
input_image.change(self.anonymize, inputs=[input_image, visualize_det], outputs=[output_image])
self.track = False
def anonymize(self, img: Image, visualize_detection: bool):
img, cache_id = pil2torch(img)
img = tops.to_cuda(img)
if visualize_detection:
img = self.anonymizer.visualize_detection(img, cache_id=cache_id)
else:
img = self.anonymizer(
img, truncation_value=0 if self.multi_modal_truncation else 1, multi_modal_truncation=self.multi_modal_truncation, amp=True,
cache_id=cache_id, track=self.track)
img = utils.im2numpy(img)
return img
def pil2torch(img: Image.Image):
img = img.convert("RGB")
img = np.array(img)
img = np.rollaxis(img, 2)
return torch.from_numpy(img), None
with gradio.Blocks() as demo:
gradio.Markdown("# <center> DeepPrivacy2 - Realistic Image Anonymization </center>")
gradio.Markdown("### <center> Håkon Hukkelås, Rudolf Mester, Frank Lindseth </center>")
with gradio.Tab("Full-Body Anonymization"):
ExampleDemo(anonymizer_body, multi_modal_truncation=True)
with gradio.Tab("Face Anonymization"):
ExampleDemo(anonymizer_face, multi_modal_truncation=False)
demo.launch() |