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attribute_guided_demo.py
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attribute_guided_demo.py
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from collections import defaultdict
import gradio
import click
import numpy as np
import torch
import cv2
from PIL import Image
from dp2 import utils
from tops.config import instantiate
import tops
import gradio.inputs
from stylemc import get_and_cache_direction, get_stylesW, init_affine_modules
class GuidedDemo:
def __init__(self, anonymizer, cfg_face) -> None:
self.anonymizer = anonymizer
assert sum([x is not None for x in list(anonymizer.generators.values())]) == 1
self.generator = [x for x in list(anonymizer.generators.values()) if x is not None][0]
cfg = list(anonymizer.generator_cfgs.values())[0]
cfg.train.batch_size = 1
self.dl = instantiate(cfg.data.val.loader)
self.cache_dir = cfg.output_dir
self.precompute_edits()
self.is_first = True
def precompute_edits(self):
self.precomputed_edits = set()
for edit in self.precomputed_edits:
get_and_cache_direction(self.cache_dir, self.dl, self.generator, edit)
if self.cache_dir.joinpath("stylemc_cache").is_dir():
for path in self.cache_dir.joinpath("stylemc_cache").iterdir():
text_prompt = path.stem.replace("_", " ")
self.precomputed_edits.add(text_prompt)
print(text_prompt)
self.edits = defaultdict(defaultdict)
def anonymize(self, img, show_boxes: bool, current_box_idx: int, current_styles, current_boxes, update_identity, edits, cache_id=None):
if not isinstance(img, torch.Tensor):
img, cache_id = pil2torch(img)
img = tops.to_cuda(img)
current_box_idx = current_box_idx % len(current_boxes)
edited_styles = [s.clone() for s in current_styles]
for face_idx, face_edits in edits.items():
for prompt, strength in face_edits.items():
direction = get_and_cache_direction(self.cache_dir, self.dl, self.generator, prompt)
edited_styles[int(face_idx)] += direction * strength
update_identity[int(face_idx)] = True
assert img.dtype == torch.uint8
img = self.anonymizer(
img, truncation_value=0,
multi_modal_truncation=True, amp=True,
cache_id=cache_id,
all_styles=iter(edited_styles),
update_identity=update_identity)
update_identity = [True for i in range(len(update_identity))]
img = utils.im2numpy(img)
if show_boxes:
x0, y0, x1, y1 = [int(_) for _ in current_boxes[int(current_box_idx)]]
img = cv2.rectangle(img, (x0, y0), (x1, y1), (255, 0, 0), 1)
return img, update_identity
def update_image(self, img, show_boxes):
img, cache_id = pil2torch(img)
img = tops.to_cuda(img)
det = self.anonymizer.detector.forward_and_cache(img, cache_id, load_cache=True)[0]
current_styles = []
for i in range(len(det)):
# Need to do forward pass to register all affine modules.
batch = det.get_crop(i, img)
batch["condition"] = batch["img"].float()
if self.multi_modal_truncation:
w = self.generator.style_net.multi_modal_truncate(0, n=1)
else:
w = self.generator.style_net.get_truncated(1, n=1)
if self.is_first:
init_affine_modules(self.generator, batch)
self.is_first = False
s = get_stylesW(w)
current_styles.append(s)
update_identity = [True for i in range(len(det))]
current_boxes = np.array(det.boxes)
edits = defaultdict(defaultdict)
cur_face_idx = -1 % len(current_boxes)
img, update_identity = self.anonymize(img, show_boxes, cur_face_idx, current_styles, current_boxes, update_identity, edits, cache_id=cache_id)
return img, current_styles, current_boxes, update_identity, edits, cur_face_idx
def change_face(self, change, cur_face_idx, current_boxes, input_image, show_boxes, current_styles, update_identity, edits):
cur_face_idx = (cur_face_idx+change) % len(current_boxes)
img, update_identity = self.anonymize(input_image, show_boxes, cur_face_idx, current_styles, current_boxes, update_identity, edits)
return img, update_identity, cur_face_idx
def add_style(self, face_idx: int, prompt: str, strength: float, input_image, show_boxes, current_styles, current_boxes, update_identity, edits):
face_idx = face_idx % len(current_boxes)
edits[face_idx][prompt] = strength
img, update_identity = self.anonymize(input_image, show_boxes, face_idx, current_styles, current_boxes, update_identity, edits)
return img, update_identity, edits
def switch_mmt(self, value):
self.multi_modal_truncation = value
def setup_interface(self):
current_styles = gradio.State()
current_boxes = gradio.State(None)
update_identity = gradio.State([])
edits = gradio.State([])
with gradio.Row():
input_image = gradio.Image(
type="pil", label="Upload your image or try the example below!", source="webcam")
output_image = gradio.Image(type="numpy", label="Output")
with gradio.Row():
update_btn = gradio.Button("Update Anonymization").style(full_width=True)
mmt_btn = gradio.Checkbox(label="Multi Modal anonymization", value=True)
with gradio.Row():
show_boxes = gradio.Checkbox(value=True, label="Show Selected")
cur_face_idx = gradio.Number(value=-1,label="Current", interactive=False)
previous = gradio.Button("Previous Person")
next_ = gradio.Button("Next Person")
with gradio.Row():
text_prompt = gradio.Textbox(
placeholder=" | ".join(list(self.precomputed_edits)),
label="Text Prompt for Edit")
edit_strength = gradio.Slider(0, 5, step=.01)
add_btn = gradio.Button("Add Edit")
add_btn.click(self.add_style, inputs=[cur_face_idx, text_prompt, edit_strength, input_image, show_boxes, current_styles, current_boxes, update_identity, edits], outputs=[output_image, update_identity, edits])
mmt_btn.change(self.switch_mmt, inputs=[mmt_btn], outputs=[])
self.multi_modal_truncation = mmt_btn.value
update_btn.click(self.update_image, inputs=[input_image, show_boxes], outputs=[output_image, current_styles, current_boxes, update_identity, edits, cur_face_idx])
input_image.change(self.update_image, inputs=[input_image, show_boxes], outputs=[output_image, current_styles, current_boxes, update_identity, edits, cur_face_idx])
previous.click(self.change_face, inputs=[gradio.State(-1), cur_face_idx, current_boxes, input_image, show_boxes, current_styles, update_identity, edits], outputs=[output_image, update_identity, cur_face_idx])
next_.click(self.change_face, inputs=[gradio.State(1), cur_face_idx, current_boxes, input_image, show_boxes, current_styles, update_identity, edits], outputs=[output_image, update_identity, cur_face_idx])
show_boxes.change(self.anonymize, inputs=[input_image, show_boxes, cur_face_idx, current_styles, current_boxes, update_identity, edits], outputs=[output_image, update_identity])
def pil2torch(img: Image.Image):
img = img.convert("RGB")
img = np.array(img)
img = np.rollaxis(img, 2)
return torch.from_numpy(img), None
@click.command()
@click.argument("config_path")
def main(config_path):
cfg_face = utils.load_config(config_path)
anonymizer_face = instantiate(cfg_face.anonymizer, load_cache=False)
anonymizer_face.initialize_tracker(fps=1)
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("Text-Guided Anonymization"):
GuidedDemo(anonymizer_face, cfg_face).setup_interface()
demo.launch()
main()