current

correct_fgr.py

@@ -9,7 +9,7 @@ from data/automatte/*/*.png to generate two types of corrected images:
 
 The foreground correction process:
 1. Load alpha (from automatte folder)
-2. Apply thresholding (white = anything >253, black otherwise)
+2. Apply thresholding (white = anything >230, black otherwise)
 3. Contract the alpha channel 1px (equivalent to Select > Modify > Contract in Photoshop)
 4. Invert selection to create a mask
 5. Apply minimum filter with 4px radius to the RGB image using the inverted selection mask
@@ -27,8 +27,7 @@ import numpy as np
 from pathlib import Path
 import argparse
 
-
-def apply_threshold(alpha_channel, threshold=253):
+def apply_threshold(alpha_channel, threshold=230):
     """Apply thresholding: white for values > threshold, black otherwise."""
     _, thresholded = cv2.threshold(alpha_channel, threshold, 255, cv2.THRESH_BINARY)
     return thresholded
@@ -142,7 +141,7 @@ def apply_boundary_blur(rgb_image, boundary_mask, blur_sigma=0.5):
 
 
 def process_foreground_correction(expanded_path, automatte_path, fgr_output_path, masked_output_path, 
-                                 threshold=253, contract_pixels=1, minimum_radius=4, blur_sigma=0.5):
+                                 threshold=230, contract_pixels=1, minimum_radius=2, blur_sigma=0.5):
     """
     Process a single image pair for foreground correction.
     
@@ -194,19 +193,19 @@ def process_foreground_correction(expanded_path, automatte_path, fgr_output_path
     final_rgb = apply_boundary_blur(filtered_rgb, boundary_mask, blur_sigma=blur_sigma)
     
     # # DO NOT COMMIT: save alpha and exit
-    # cv2.imwrite("debug_expanded.png", expanded_img)
-    # cv2.imwrite("debug_automatte.png", automatte_img)
-    # cv2.imwrite("debug_alpha.png", alpha)
-    # cv2.imwrite("debug_thresholded_alpha.png", thresholded_alpha)
-    # cv2.imwrite("debug_contracted_alpha.png", contracted_alpha)
-    # cv2.imwrite("debug_selection_mask.png", selection_mask)
-    # cv2.imwrite("debug_filtered_rgb.png", filtered_rgb)
-    # import sys; sys.exit(0)
+    # cv2.imwrite("debug/debug_expanded.png", expanded_img)
+    # cv2.imwrite("debug/debug_automatte.png", automatte_img)
+    # cv2.imwrite("debug/debug_alpha.png", alpha)
+    # cv2.imwrite("debug/debug_thresholded_alpha.png", thresholded_alpha)
+    # cv2.imwrite("debug/debug_contracted_alpha.png", contracted_alpha)
+    # cv2.imwrite("debug/debug_selection_mask.png", selection_mask)
+    # cv2.imwrite("debug/debug_filtered_rgb.png", filtered_rgb)
+    # cv2.imwrite("debug/debug_final_rgb.png", final_rgb)
     
     # Apply the original contracted alpha (before inversion) to the final RGB image
     # Convert to RGBA for masked output
     masked_rgba = cv2.cvtColor(final_rgb, cv2.COLOR_BGR2BGRA)
-    masked_rgba[:, :, 3] = contracted_alpha  # Use the contracted alpha (before inversion)
+    masked_rgba[:, :, 3] = alpha  # Use predicted alpha
     
     # Create output directories if they don't exist
     os.makedirs(os.path.dirname(fgr_output_path), exist_ok=True)
@@ -263,12 +262,12 @@ def find_matching_pairs():
 def main():
     """Main function to process all image pairs."""
     parser = argparse.ArgumentParser(description="Apply foreground correction to image pairs")
-    parser.add_argument("--threshold", type=int, default=253, 
-                       help="Threshold value for alpha binarization (default: 253)")
+    parser.add_argument("--threshold", type=int, default=230, 
+                       help="Threshold value for alpha binarization (default: 230)")
     parser.add_argument("--contract-pixels", type=int, default=1,
                        help="Number of pixels to contract alpha channel (default: 1)")
-    parser.add_argument("--minimum-radius", type=int, default=4,
-                       help="Radius for minimum filter operation (default: 4)")
+    parser.add_argument("--minimum-radius", type=int, default=3,
+                       help="Radius for minimum filter operation (default: 2)")
     parser.add_argument("--blur-sigma", type=float, default=0.5,
                        help="Gaussian blur sigma for boundary smoothing (default: 0.5)")
     parser.add_argument("--sample", type=str, default=None,