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from collections import defaultdict
import gradio
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_styles
from sg3_torch_utils.ops import grid_sample_gradfix, bias_act, upfirdn2d
grid_sample_gradfix.enabled = False
bias_act.enabled = False
upfirdn2d.enabled = False
class GuidedDemo:
def __init__(self, face_anonymizer, cfg_face, multi_modal_truncation, truncation_value) -> None:
self.anonymizer = face_anonymizer
self.multi_modal_truncation = multi_modal_truncation
self.truncation_value = truncation_value
assert sum([x is not None for x in list(face_anonymizer.generators.values())]) == 1
self.generator = [x for x in list(face_anonymizer.generators.values()) if x is not None][0]
face_G_cfg = utils.load_config(cfg_face.anonymizer.face_G_cfg)
face_G_cfg.train.batch_size = 1
self.dl = instantiate(face_G_cfg.data.val.loader)
self.cache_dir = face_G_cfg.output_dir
self.precompute_edits()
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=self.truncation_value,
multi_modal_truncation=self.multi_modal_truncation, amp=True,
cache_id=cache_id,
all_styles=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)):
s = get_styles(
np.random.randint(0, 999999), self.generator,
None, truncation_value=self.truncation_value)
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 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)
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])
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])
class WebcamDemo:
def __init__(self, anonymizer) -> None:
self.anonymizer = anonymizer
with gradio.Row():
input_image = gradio.Image(type="pil", source="webcam", streaming=True)
output_image = gradio.Image(type="numpy", label="Output")
with gradio.Row():
truncation_value = gradio.Slider(0, 1, value=0, step=0.01)
truncation = gradio.Radio(["Multi-modal truncation", "Unimodal truncation"], value="Unimodal truncation")
with gradio.Row():
visualize_det = gradio.Checkbox(value=False, label="Show Detections")
track = gradio.Checkbox(value=False, label="Track detections (samples same latent variable per track)")
input_image.stream(
self.anonymize,
inputs=[input_image, visualize_det, truncation_value,truncation, track, gradio.Variable(False)],
outputs=[output_image])
self.track = True
def anonymize(self, img: Image, visualize_detection: bool, truncation_value, truncation_type, track, reset_track):
if reset_track:
self.anonymizer.reset_tracker()
mmt = truncation_type == "Multi-modal truncation"
img, cache_id = pil2torch(img)
img = tops.to_cuda(img)
self.anonymizer
if visualize_detection:
img = self.anonymizer.visualize_detection(img, cache_id=cache_id)
else:
img = self.anonymizer(
img,
truncation_value=truncation_value,
multi_modal_truncation=mmt,
amp=True,
cache_id=cache_id,
track=track)
img = utils.im2numpy(img)
return img
class ExampleDemo(WebcamDemo):
def __init__(self, anonymizer, source="webcam") -> None:
self.anonymizer = anonymizer
with gradio.Row():
input_image = gradio.Image(type="pil", source=source)
output_image = gradio.Image(type="numpy", label="Output")
with gradio.Row():
update_btn = gradio.Button("Update Anonymization").style(full_width=True)
resample = gradio.Button("Resample Latent Variables").style(full_width=True)
with gradio.Row():
truncation_value = gradio.Slider(0, 1, value=0, step=0.01)
truncation = gradio.Radio(["Multi-modal truncation", "Unimodal truncation"], value="Unimodal truncation")
visualize_det = gradio.Checkbox(value=False, label="Show Detections")
visualize_det.change(
self.anonymize,
inputs=[input_image, visualize_det, truncation_value, truncation, gradio.Variable(True), gradio.Variable(False)],
outputs=[output_image])
gradio.Examples(
["media/erling.jpg", "media/regjeringen.jpg"], inputs=[input_image]
)
update_btn.click(
self.anonymize,
inputs=[input_image, visualize_det, truncation_value, truncation, gradio.Variable(True), gradio.Variable(False)],
outputs=[output_image])
resample.click(
self.anonymize,
inputs=[input_image, visualize_det, truncation_value, truncation, gradio.Variable(True), gradio.Variable(True)],
outputs=[output_image])
input_image.change(
self.anonymize,
inputs=[input_image, visualize_det, truncation_value, truncation, gradio.Variable(False), gradio.Variable(True)],
outputs=[output_image])
self.track = False
self.truncation_value = truncation_value
class Information:
def __init__(self) -> None:
gradio.Markdown("## <center> Face Anonymization Architecture </center>")
gradio.Markdown("---")
gradio.Image(value="media/overall_architecture.png")
gradio.Markdown("## <center> Full-Body Anonymization Architecture </center>")
gradio.Markdown("---")
gradio.Image(value="media/full_body.png")
gradio.Markdown("### <center> Generative Adversarial Networks </center>")
gradio.Markdown("---")
gradio.Image(value="media/gan_architecture.png")
def pil2torch(img: Image.Image):
img = img.convert("RGB")
img = np.array(img)
img = np.rollaxis(img, 2)
return torch.from_numpy(img), None
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