Drop dependency on basicsr

modules/face_restoration_utils.py

@@ -17,6 +17,28 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
+def bgr_image_to_rgb_tensor(img: np.ndarray) -> torch.Tensor:
+    """Convert a BGR NumPy image in [0..1] range to a PyTorch RGB float32 tensor."""
+    assert img.shape[2] == 3, "image must be RGB"
+    if img.dtype == "float64":
+        img = img.astype("float32")
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    return torch.from_numpy(img.transpose(2, 0, 1)).float()
+
+
+def rgb_tensor_to_bgr_image(tensor: torch.Tensor, *, min_max=(0.0, 1.0)) -> np.ndarray:
+    """
+    Convert a PyTorch RGB tensor in range `min_max` to a BGR NumPy image in [0..1] range.
+    """
+    tensor = tensor.squeeze(0).float().detach().cpu().clamp_(*min_max)
+    tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0])
+    assert tensor.dim() == 3, "tensor must be RGB"
+    img_np = tensor.numpy().transpose(1, 2, 0)
+    if img_np.shape[2] == 1:  # gray image, no RGB/BGR required
+        return np.squeeze(img_np, axis=2)
+    return cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB)
+
+
 def create_face_helper(device) -> FaceRestoreHelper:
     from facexlib.detection import retinaface
     from facexlib.utils.face_restoration_helper import FaceRestoreHelper
@@ -43,7 +65,6 @@ def restore_with_face_helper(
 
     `restore_face` should take a cropped face image and return a restored face image.
     """
-    from basicsr.utils import img2tensor, tensor2img
     from torchvision.transforms.functional import normalize
     np_image = np_image[:, :, ::-1]
     original_resolution = np_image.shape[0:2]
@@ -56,23 +77,19 @@ def restore_with_face_helper(
         face_helper.align_warp_face()
         logger.debug("Found %d faces, restoring", len(face_helper.cropped_faces))
         for cropped_face in face_helper.cropped_faces:
-            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+            cropped_face_t = bgr_image_to_rgb_tensor(cropped_face / 255.0)
             normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
             cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
 
             try:
                 with torch.no_grad():
-                    restored_face = tensor2img(
+                    cropped_face_t = restore_face(cropped_face_t)
-                        restore_face(cropped_face_t),
-                        rgb2bgr=True,
-                        min_max=(-1, 1),
-                    )
                 devices.torch_gc()
             except Exception:
                 errors.report('Failed face-restoration inference', exc_info=True)
-                restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
 
-            restored_face = restored_face.astype('uint8')
+            restored_face = rgb_tensor_to_bgr_image(cropped_face_t, min_max=(-1, 1))
+            restored_face = (restored_face * 255.0).astype('uint8')
             face_helper.add_restored_face(restored_face)
 
         logger.debug("Merging restored faces into image")

requirements.txt

@@ -2,7 +2,6 @@ GitPython
 Pillow
 accelerate
 
-basicsr
 blendmodes
 clean-fid
 einops

requirements_versions.txt

@@ -1,7 +1,6 @@
 GitPython==3.1.32
 Pillow==9.5.0
 accelerate==0.21.0
-basicsr==1.4.2
 blendmodes==2022
 clean-fid==0.1.35
 einops==0.4.1