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compare: Hazel:5baefdcc6ff85f3c9749043a07e2239476029e1e
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3 changed files with 5 additions and 8 deletions
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4
secure_pixelation/__main__.py
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@ -6,5 +6,5 @@ def cli():
print(f"Running secure_pixelation")
pixelate("assets/human_detection/test.png", generative_impaint=True)
pixelate("assets/human_detection/humans.png", generative_impaint=False)
pixelate("assets/human_detection/rev1.png", generative_impaint=False)
# pixelate("assets/human_detection/humans.png", generative_impaint=False)
# pixelate("assets/human_detection/rev1.png", generative_impaint=False)

5
secure_pixelation/pixelation_process.py
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@ -62,7 +62,7 @@ def pixelate_regions(raw_image: RawImage, image: Optional[np.ndarray] = None, pi
return image
def pixelate(to_detect: str, generative_impaint: bool = True, debug_drawings: bool = False):
def pixelate(to_detect: str, generative_impaint: bool = True, debug_drawings: bool = True):
raw_image = RawImage(to_detect)
step_dir = raw_image.get_dir("steps")
@ -84,12 +84,9 @@ def pixelate(to_detect: str, generative_impaint: bool = True, debug_drawings: bo
if generative_impaint:
step_2 = do_generative_impaint(raw_image, image=step_1)
step_2_alt = quick_impaint(raw_image, image=step_1)
else:
step_2 = quick_impaint(raw_image, image=step_1)
step_2_alt = do_generative_impaint(raw_image, image=step_1)
write_image(step_2, "step_2")
write_image(step_2_alt, "step_2_alt")
step_3 = pixelate_regions(raw_image, image=step_2)
write_image(step_3, "step_3")

4
secure_pixelation/simple_lama_bindings.py
View File

@ -1,5 +1,5 @@
import os
from typing import Tuple
from typing import Optional
import torch
import cv2
@ -11,7 +11,7 @@ from huggingface_hub import hf_hub_download
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
def prepare_img_and_mask(image: np.ndarray, mask: np.ndarray, device: torch.device, pad_out_to_modulo: int = 8, scale_factor: float = 1) -> Tuple[torch.Tensor, torch.Tensor]:
def prepare_img_and_mask(image: np.ndarray, mask: np.ndarray, device: torch.device, pad_out_to_modulo: int = 8, scale_factor: float = 1) -> tuple[torch.Tensor, torch.Tensor]:
def get_image(img: np.ndarray):
img = img.copy()