From e6bd5af6a8e306a1cdef63402a77a980a04ad6e1 Mon Sep 17 00:00:00 2001
From: Grafting Rayman <156515434+GraftingRayman@users.noreply.github.com>
Date: Fri, 17 Jan 2025 11:06:44 +0000
Subject: Add files via upload

---
 scripts/r_faceboost/swapper.py | 42 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 42 insertions(+)
 create mode 100644 scripts/r_faceboost/swapper.py

(limited to 'scripts/r_faceboost/swapper.py')

diff --git a/scripts/r_faceboost/swapper.py b/scripts/r_faceboost/swapper.py
new file mode 100644
index 0000000..e0467cf
--- /dev/null
+++ b/scripts/r_faceboost/swapper.py
@@ -0,0 +1,42 @@
+import cv2
+import numpy as np
+
+# The following code is almost entirely copied from INSwapper; the only change here is that we want to use Lanczos
+# interpolation for the warpAffine call. Now that the face has been restored, Lanczos represents a good compromise
+# whether the restored face needs to be upscaled or downscaled.
+def in_swap(img, bgr_fake, M):
+    target_img = img
+    IM = cv2.invertAffineTransform(M)
+    img_white = np.full((bgr_fake.shape[0], bgr_fake.shape[1]), 255, dtype=np.float32)
+
+    # Note the use of bicubic here; this is functionally the only change from the source code
+    bgr_fake = cv2.warpAffine(bgr_fake, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0, flags=cv2.INTER_CUBIC)
+
+    img_white = cv2.warpAffine(img_white, IM, (target_img.shape[1], target_img.shape[0]), borderValue=0.0)
+    img_white[img_white > 20] = 255
+    img_mask = img_white
+    mask_h_inds, mask_w_inds = np.where(img_mask == 255)
+    mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
+    mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
+    mask_size = int(np.sqrt(mask_h * mask_w))
+    k = max(mask_size // 10, 10)
+    # k = max(mask_size//20, 6)
+    # k = 6
+    kernel = np.ones((k, k), np.uint8)
+    img_mask = cv2.erode(img_mask, kernel, iterations=1)
+    kernel = np.ones((2, 2), np.uint8)
+    k = max(mask_size // 20, 5)
+    # k = 3
+    # k = 3
+    kernel_size = (k, k)
+    blur_size = tuple(2 * i + 1 for i in kernel_size)
+    img_mask = cv2.GaussianBlur(img_mask, blur_size, 0)
+    k = 5
+    kernel_size = (k, k)
+    blur_size = tuple(2 * i + 1 for i in kernel_size)
+    img_mask /= 255
+    # img_mask = fake_diff
+    img_mask = np.reshape(img_mask, [img_mask.shape[0], img_mask.shape[1], 1])
+    fake_merged = img_mask * bgr_fake + (1 - img_mask) * target_img.astype(np.float32)
+    fake_merged = fake_merged.astype(np.uint8)
+    return fake_merged
-- 
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