Use resize_image where resizing is required

finetune/tag_images_by_wd14_tagger.py

@@ -11,7 +11,7 @@ from PIL import Image
 from tqdm import tqdm
 
 import library.train_util as train_util
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 
 setup_logging()
 import logging
@@ -42,10 +42,7 @@ def preprocess_image(image):
     pad_t = pad_y // 2
     image = np.pad(image, ((pad_t, pad_y - pad_t), (pad_l, pad_x - pad_l), (0, 0)), mode="constant", constant_values=255)
 
-    if size > IMAGE_SIZE:
+    image = resize_image(image, image.shape[0], image.shape[1], IMAGE_SIZE, IMAGE_SIZE)
-        image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), cv2.INTER_AREA)
-    else:
-        image = pil_resize(image, (IMAGE_SIZE, IMAGE_SIZE))
 
     image = image.astype(np.float32)
     return image

library/train_util.py

@@ -84,7 +84,7 @@ import library.model_util as model_util
 import library.huggingface_util as huggingface_util
 import library.sai_model_spec as sai_model_spec
 import library.deepspeed_utils as deepspeed_utils
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 
 setup_logging()
 import logging
@@ -1514,9 +1514,7 @@ class BaseDataset(torch.utils.data.Dataset):
         nh = int(height * scale + 0.5)
         nw = int(width * scale + 0.5)
         assert nh >= self.height and nw >= self.width, f"internal error. small scale {scale}, {width}*{height}"
-        interpolation = get_cv2_interpolation(subset.resize_interpolation if subset.resize_interpolation is not None else self.resize_interpolation)
+        image = resize_image(image, width, height, nw, nh, subset.resize_interpolation)
-        logger.info(f"Interpolation: {interpolation}")
-        image = cv2.resize(image, (nw, nh), interpolation=interpolation if interpolation is not None else cv2.INTER_AREA)
         face_cx = int(face_cx * scale + 0.5)
         face_cy = int(face_cy * scale + 0.5)
         height, width = nh, nw
@@ -2541,10 +2539,7 @@ class ControlNetDataset(BaseDataset):
                     cond_img.shape[0] == original_size_hw[0] and cond_img.shape[1] == original_size_hw[1]
                 ), f"size of conditioning image is not match / 画像サイズが合いません: {image_info.absolute_path}"
 
-                interpolation = get_cv2_interpolation(self.resize_interpolation)
+                cond_img = resize_image(cond_img, original_size_hw[1], original_size_hw[0], target_size_hw[1], target_size_hw[0], self.resize_interpolation)
-                cond_img = cv2.resize(
-                    cond_img, image_info.resized_size, interpolation=interpolation if interpolation is not None else cv2.INTER_AREA
-                )  # INTER_AREAでやりたいのでcv2でリサイズ
 
                 # TODO support random crop
                 # 現在サポートしているcropはrandomではなく中央のみ
@@ -2558,7 +2553,7 @@ class ControlNetDataset(BaseDataset):
                 # ), f"image size is small / 画像サイズが小さいようです: {image_info.absolute_path}"
                 # resize to target
                 if cond_img.shape[0] != target_size_hw[0] or cond_img.shape[1] != target_size_hw[1]:
-                    cond_img = pil_resize(cond_img, (int(target_size_hw[1]), int(target_size_hw[0])))
+                    cond_img = resize_image(cond_img, cond_img.shape[0], cond_img.shape[1], target_size_hw[1], target_size_hw[0], self.resize_interpolation)
 
             if flipped:
                 cond_img = cond_img[:, ::-1, :].copy()  # copy to avoid negative stride
@@ -2961,12 +2956,7 @@ def trim_and_resize_if_required(
     original_size = (image_width, image_height)  # size before resize
 
     if image_width != resized_size[0] or image_height != resized_size[1]:
-        # リサイズする
+        image = resize_image(image, image_width, image_height, resized_size[0], resized_size[1], resize_interpolation)
-        if image_width > resized_size[0] and image_height > resized_size[1]:
-            interpolation = get_cv2_interpolation(resize_interpolation)
-            image = cv2.resize(image, resized_size, interpolation=interpolation if interpolation is not None else cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
-        else:
-            image = pil_resize(image, resized_size)
 
     image_height, image_width = image.shape[0:2]
 
@@ -6566,28 +6556,3 @@ class LossRecorder:
             return 0
         return self.loss_total / losses
 
-def get_cv2_interpolation(interpolation: Optional[str]) -> Optional[int]:
-    """
-    Convert interpolation value to cv2 interpolation integer
-    """
-    if interpolation is None:
-        return None 
-
-    if interpolation == "lanczos":
-        return cv2.INTER_LANCZOS4
-    elif interpolation == "nearest":
-        return cv2.INTER_NEAREST
-    elif interpolation == "bilinear" or interpolation == "linear":
-        return cv2.INTER_LINEAR
-    elif interpolation == "bicubic" or interpolation == "cubic":
-        return cv2.INTER_CUBIC
-    elif interpolation == "area":
-        return cv2.INTER_AREA
-    else:
-        return None
-
-def validate_interpolation_fn(interpolation_str: str) -> bool:
-    """
-    Check if a interpolation function is supported
-    """
-    return interpolation_str in ["lanczos", "nearest", "bilinear", "linear", "bicubic", "cubic", "area"]

library/utils.py

@@ -16,7 +16,6 @@ from PIL import Image
 import numpy as np
 from safetensors.torch import load_file
 
-
 def fire_in_thread(f, *args, **kwargs):
     threading.Thread(target=f, args=args, kwargs=kwargs).start()
 
@@ -89,6 +88,8 @@ def setup_logging(args=None, log_level=None, reset=False):
         logger = logging.getLogger(__name__)
         logger.info(msg_init)
 
+setup_logging()
+logger = logging.getLogger(__name__)
 
 # endregion
 
@@ -377,7 +378,7 @@ def load_safetensors(
 # region Image utils
 
 
-def pil_resize(image, size, interpolation=Image.LANCZOS):
+def pil_resize(image, size, interpolation):
     has_alpha = image.shape[2] == 4 if len(image.shape) == 3 else False
 
     if has_alpha:
@@ -385,7 +386,7 @@ def pil_resize(image, size, interpolation=Image.LANCZOS):
     else:
         pil_image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
 
-    resized_pil = pil_image.resize(size, interpolation)
+    resized_pil = pil_image.resize(size, resample=interpolation)
 
     # Convert back to cv2 format
     if has_alpha:
@@ -396,6 +397,100 @@ def pil_resize(image, size, interpolation=Image.LANCZOS):
     return resized_cv2
 
 
+def resize_image(image: np.ndarray, width: int, height: int, resized_width: int, resized_height: int, resize_interpolation: Optional[str] = None):
+    """
+    Resize image with resize interpolation. Default interpolation to AREA if image is smaller, else LANCZOS
+
+    Args:
+        image: numpy.ndarray
+        width: int Original image width
+        height: int Original image height
+        resized_width: int Resized image width
+        resized_height: int Resized image height
+        resize_interpolation: Optional[str] Resize interpolation method "lanczos", "area", "bilinear", "bicubic", "nearest", "box"
+
+    Returns:
+        image
+    """
+    interpolation = get_cv2_interpolation(resize_interpolation)
+    resized_size = (resized_width, resized_height)
+    if width > resized_width and height > resized_width:
+        image = cv2.resize(image, resized_size, interpolation=interpolation if interpolation is not None else cv2.INTER_AREA)  # INTER_AREAでやりたいのでcv2でリサイズ
+        logger.debug(f"resize image using {resize_interpolation}")
+    else:
+        image = cv2.resize(image, resized_size, interpolation=interpolation if interpolation is not None else cv2.INTER_LANCZOS4)  # INTER_AREAでやりたいのでcv2でリサイズ
+        logger.debug(f"resize image using {resize_interpolation}")
+
+    return image
+
+
+def get_cv2_interpolation(interpolation: Optional[str]) -> Optional[int]:
+    """
+    Convert interpolation value to cv2 interpolation integer
+
+    https://docs.opencv.org/3.4/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121
+    """
+    if interpolation is None:
+        return None 
+
+    if interpolation == "lanczos" or interpolation == "lanczos4":
+        # Lanczos interpolation over 8x8 neighborhood 
+        return cv2.INTER_LANCZOS4
+    elif interpolation == "nearest":
+        # Bit exact nearest neighbor interpolation. This will produce same results as the nearest neighbor method in PIL, scikit-image or Matlab. 
+        return cv2.INTER_NEAREST_EXACT
+    elif interpolation == "bilinear" or interpolation == "linear":
+        # bilinear interpolation
+        return cv2.INTER_LINEAR
+    elif interpolation == "bicubic" or interpolation == "cubic":
+        # bicubic interpolation 
+        return cv2.INTER_CUBIC
+    elif interpolation == "area":
+        # resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method. 
+        return cv2.INTER_AREA
+    elif interpolation == "box":
+        # resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire'-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method. 
+        return cv2.INTER_AREA
+    else:
+        return None
+
+def get_pil_interpolation(interpolation: Optional[str]) -> Optional[Image.Resampling]:
+    """
+    Convert interpolation value to PIL interpolation
+
+    https://pillow.readthedocs.io/en/stable/handbook/concepts.html#concept-filters
+    """
+    if interpolation is None:
+        return None 
+
+    if interpolation == "lanczos":
+        return Image.Resampling.LANCZOS
+    elif interpolation == "nearest":
+        # Pick one nearest pixel from the input image. Ignore all other input pixels.
+        return Image.Resampling.NEAREST
+    elif interpolation == "bilinear" or interpolation == "linear":
+        # For resize calculate the output pixel value using linear interpolation on all pixels that may contribute to the output value. For other transformations linear interpolation over a 2x2 environment in the input image is used.
+        return Image.Resampling.BILINEAR
+    elif interpolation == "bicubic" or interpolation == "cubic":
+        # For resize calculate the output pixel value using cubic interpolation on all pixels that may contribute to the output value. For other transformations cubic interpolation over a 4x4 environment in the input image is used.
+        return Image.Resampling.BICUBIC
+    elif interpolation == "area":
+        # Image.Resampling.BOX may be more appropriate if upscaling 
+        # Area interpolation is related to cv2.INTER_AREA
+        # Produces a sharper image than Resampling.BILINEAR, doesn’t have dislocations on local level like with Resampling.BOX.
+        return Image.Resampling.HAMMING
+    elif interpolation == "box":
+        # Each pixel of source image contributes to one pixel of the destination image with identical weights. For upscaling is equivalent of Resampling.NEAREST.
+        return Image.Resampling.BOX
+    else:
+        return None
+
+def validate_interpolation_fn(interpolation_str: str) -> bool:
+    """
+    Check if a interpolation function is supported
+    """
+    return interpolation_str in ["lanczos", "nearest", "bilinear", "linear", "bicubic", "cubic", "area", "box"]
+
 # endregion
 
 # TODO make inf_utils.py

tools/detect_face_rotate.py

@@ -15,7 +15,7 @@ import os
 from anime_face_detector import create_detector
 from tqdm import tqdm
 import numpy as np
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -170,12 +170,9 @@ def process(args):
             scale = max(cur_crop_width / w, cur_crop_height / h)
 
         if scale != 1.0:
-          w = int(w * scale + .5)
+          rw = int(w * scale + .5)
-          h = int(h * scale + .5)
+          rh = int(h * scale + .5)
-          if scale < 1.0:
+          face_img = resize_image(face_img, w, h, rw, rh)
-            face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA)
-          else:
-            face_img = pil_resize(face_img, (w, h))
           cx = int(cx * scale + .5)
           cy = int(cy * scale + .5)
           fw = int(fw * scale + .5)

tools/resize_images_to_resolution.py

@@ -6,7 +6,7 @@ import shutil
 import math
 from PIL import Image
 import numpy as np
-from library.utils import setup_logging, pil_resize
+from library.utils import setup_logging, resize_image
 setup_logging()
 import logging
 logger = logging.getLogger(__name__)
@@ -22,14 +22,6 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
   if not os.path.exists(dst_img_folder):
     os.makedirs(dst_img_folder)
 
-  # Select interpolation method
-  if interpolation == 'lanczos4':
-    pil_interpolation = Image.LANCZOS
-  elif interpolation == 'cubic':
-    pil_interpolation = Image.BICUBIC
-  else:
-    cv2_interpolation = cv2.INTER_AREA
-
   # Iterate through all files in src_img_folder
   img_exts = (".png", ".jpg", ".jpeg", ".webp", ".bmp")                   # copy from train_util.py
   for filename in os.listdir(src_img_folder):
@@ -63,11 +55,7 @@ def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divi
         new_height = int(img.shape[0] * math.sqrt(scale_factor))
         new_width = int(img.shape[1] * math.sqrt(scale_factor))
 
-        # Resize image
+        img = resize_image(img,  img.shape[0], img.shape[1], new_height, new_width, interpolation)
-        if cv2_interpolation:
-          img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation)
-        else:
-          img = pil_resize(img, (new_width, new_height), interpolation=pil_interpolation)
       else:
         new_height, new_width = img.shape[0:2]
 
@@ -113,8 +101,8 @@ def setup_parser() -> argparse.ArgumentParser:
                       help='Maximum resolution(s) in the format "512x512,384x384, etc, etc" / 最大画像サイズをカンマ区切りで指定 ("512x512,384x384, etc, etc" など)', default="512x512,384x384,256x256,128x128")
   parser.add_argument('--divisible_by', type=int,
                       help='Ensure new dimensions are divisible by this value / リサイズ後の画像のサイズをこの値で割り切れるようにします', default=1)
-  parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4'],
+  parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4', 'nearest', 'linear', 'box'],
-                      default='area', help='Interpolation method for resizing / リサイズ時の補完方法')
+                      default=None, help='Interpolation method for resizing. Default to area if smaller, lanczos if larger / サイズ変更の補間方法。小さい場合はデフォルトでエリア、大きい場合はランチョスになります。')
   parser.add_argument('--save_as_png', action='store_true', help='Save as png format / png形式で保存')
   parser.add_argument('--copy_associated_files', action='store_true',
                       help='Copy files with same base name to images (captions etc) / 画像と同じファイル名（拡張子を除く）のファイルもコピーする')