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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") | |
| 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 | |
| class GuidedDemo: | |
| def __init__(self, face_anonymizer, cfg_face) -> None: | |
| self.anonymizer = face_anonymizer | |
| 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=0, | |
| multi_modal_truncation=True, 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=0) | |
| 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]) | |
| 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 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") | |
| visualize_det = gradio.Checkbox(value=False, label="Show Detections") | |
| input_image.stream(self.anonymize, [input_image, visualize_det], [output_image]) | |
| self.track = True | |
| 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, multi_modal_truncation=True, amp=True, | |
| cache_id=cache_id, track=self.track) | |
| img = utils.im2numpy(img) | |
| return img | |
| class ExampleDemo(WebcamDemo): | |
| def __init__(self, anonymizer) -> None: | |
| 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", "coco_val2017_000000001000.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 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) | |
| with gradio.Tab("Face Anonymization"): | |
| ExampleDemo(anonymizer_face) | |
| demo.launch() |