feat: added some stuff

.gitignore

@@ -162,3 +162,5 @@ cython_debug/
 .venv
 assets/*
 *.pt
+
+big-lama

README.md

@@ -27,7 +27,16 @@ secure-pixelation
 This is the generative ai model to impaint the blacked out areas.
 
 ```
+# get the pretrained models
 mkdir -p ./big-lama
 wget https://huggingface.co/smartywu/big-lama/resolve/main/big-lama.zip
 unzip big-lama.zip -d ./big-lama
+rm big-lama.zip
+
+# get the code to run the models
+cd big-lama
+git clone https://github.com/advimman/lama.git
+pip install torch==2.2.0 torchvision==0.17.0
+cd lama
+pip install -r requirements.txt
 ```

secure_pixelation/__main__.py

@@ -5,4 +5,6 @@ from .pixelation_process import pixelate
 def cli():
     print(f"Running secure_pixelation")
 
-    pixelate("assets/human_detection/test.png")
+    pixelate("assets/human_detection/test.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

@@ -2,6 +2,8 @@ from __future__ import annotations
 
 from typing import Optional
 from pathlib import Path
+import subprocess
+import sys
 
 import cv2
 import numpy as np
@@ -18,14 +20,18 @@ def blackout(raw_image: RawImage) -> np.ndarray:
     return image
 
 
+def get_mask(raw_image: RawImage) -> np.ndarray:
+    mask = np.zeros(raw_image.image.shape[:2], dtype=np.uint8)
+    for (x, y, w, h) in raw_image.bounding_boxes:
+        mask[y:y+h, x:x+w] = 255
+
+    return mask
+
+
 def impaint(raw_image: RawImage, image: Optional[np.ndarray] = None) -> np.ndarray:
     image = image if image is not None else raw_image.get_image()
     
-    # Create a mask where blacked-out areas are marked as 255 (white)
+    mask = get_mask(raw_image)
-    mask = np.zeros(image.shape[:2], dtype=np.uint8)
-
-    for (x, y, w, h) in raw_image.bounding_boxes:
-        mask[y:y+h, x:x+w] = 255
 
     # Apply inpainting using the Telea method
     return cv2.inpaint(image, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)
@@ -33,15 +39,41 @@ def impaint(raw_image: RawImage, image: Optional[np.ndarray] = None) -> np.ndarr
 
 def do_generative_impaint(raw_image: RawImage, image: Optional[np.ndarray] = None) -> np.ndarray:
     image = image if image is not None else raw_image.get_image()
+    lama_dict = raw_image.get_dir("steps") / "lama"
+    lama_dict.mkdir(exist_ok=True)
+    lama_dict_in = lama_dict / "in"
+    lama_dict_in.mkdir(exist_ok=True)
+    lama_dict_out = lama_dict / "out"
+    lama_dict_out.mkdir(exist_ok=True)
     
-    # Create a mask where blacked-out areas are marked as 255 (white)
+    cv2.imwrite(str(lama_dict_in / "image.png"), raw_image.image)
-    mask = np.zeros(image.shape[:2], dtype=np.uint8)
+    mask = get_mask(raw_image)
+    cv2.imwrite(str(lama_dict_in / "mask.png"), mask)
 
-    for (x, y, w, h) in raw_image.bounding_boxes:
+    # Run LaMa inference (adjust path if needed)
-        mask[y:y+h, x:x+w] = 255
+    try:
+        subprocess.run([
+            sys.executable, "big-lama/lama/bin/predict.py",
+            "model.path=big-lama/big-lama",
+            f"indir={str(lama_dict_in)}",
+            f"outdir={str(lama_dict_out)}"
+        ], check=True)
+    except subprocess.CalledProcessError as e:
+        print(f"Error running LaMa: {e}")
+        return image  # fallback to original if it fails
 
-    # Apply inpainting using the Telea method
+    # Load inpainted result
-    return cv2.inpaint(image, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)
+    result_path = os.path.join(output_dir, "image.png")
+    if os.path.exists(result_path):
+        inpainted_image = cv2.imread(result_path)
+    else:
+        print("Inpainted result not found, returning original.")
+        inpainted_image = image
+
+    # Cleanup
+    shutil.rmtree(base_dir)
+
+    return inpainted_image