diff --git a/finetune/tag_images_by_wd14_tagger.py b/finetune/tag_images_by_wd14_tagger.py index cbc3d2d6..406f12f2 100644 --- a/finetune/tag_images_by_wd14_tagger.py +++ b/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 = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), cv2.INTER_AREA) - else: - image = pil_resize(image, (IMAGE_SIZE, IMAGE_SIZE)) + image = resize_image(image, image.shape[0], image.shape[1], IMAGE_SIZE, IMAGE_SIZE) image = image.astype(np.float32) return image diff --git a/library/train_util.py b/library/train_util.py index 94145cad..46219d4f 100644 --- a/library/train_util.py +++ b/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) - logger.info(f"Interpolation: {interpolation}") - image = cv2.resize(image, (nw, nh), interpolation=interpolation if interpolation is not None else cv2.INTER_AREA) + image = resize_image(image, width, height, nw, nh, subset.resize_interpolation) 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 = 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 + 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) # 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 - 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 = resize_image(image, image_width, image_height, resized_size[0], resized_size[1], resize_interpolation) 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"] diff --git a/library/utils.py b/library/utils.py index 07079c6d..9156864e 100644 --- a/library/utils.py +++ b/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 diff --git a/tools/detect_face_rotate.py b/tools/detect_face_rotate.py index d2a4d9cf..16fd7d0b 100644 --- a/tools/detect_face_rotate.py +++ b/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) - h = int(h * scale + .5) - if scale < 1.0: - face_img = cv2.resize(face_img, (w, h), interpolation=cv2.INTER_AREA) - else: - face_img = pil_resize(face_img, (w, h)) + rw = int(w * scale + .5) + rh = int(h * scale + .5) + face_img = resize_image(face_img, w, h, rw, rh) cx = int(cx * scale + .5) cy = int(cy * scale + .5) fw = int(fw * scale + .5) diff --git a/tools/resize_images_to_resolution.py b/tools/resize_images_to_resolution.py index 0f9e00b1..f5fbae2b 100644 --- a/tools/resize_images_to_resolution.py +++ b/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 - 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) + img = resize_image(img, img.shape[0], img.shape[1], new_height, new_width, 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'], - default='area', help='Interpolation method for resizing / リサイズ時の補完方法') + parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4', 'nearest', 'linear', 'box'], + 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')