feat: added alternative inpainting

secure_pixelation/__main__.py

@@ -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)

secure_pixelation/pixelation_process.py

@@ -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 = True):
+def pixelate(to_detect: str, generative_impaint: bool = True, debug_drawings: bool = False):
     raw_image = RawImage(to_detect)
 
     step_dir = raw_image.get_dir("steps")
@@ -84,9 +84,12 @@ 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")

secure_pixelation/simple_lama_bindings.py

@@ -1,5 +1,5 @@
 import os
-from typing import Optional
+from typing import Tuple
 
 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()