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from transformers import ProcessorMixin
from PIL import Image
import torch
import torchvision.transforms as T
import numpy as np
import os
import json
class DFineProcessor(ProcessorMixin):
processor_class = "DFineProcessor"
def __init__(self, size=640):
self.size = size
def resize_with_aspect_ratio(self, image, size):
orig_w, orig_h = image.size
ratio = min(size / orig_w, size / orig_h)
new_w, new_h = int(orig_w * ratio), int(orig_h * ratio)
image = image.resize((new_w, new_h), Image.BILINEAR)
new_image = Image.new("RGB", (size, size))
pad_w, pad_h = (size - new_w) // 2, (size - new_h) // 2
new_image.paste(image, (pad_w, pad_h))
return new_image, ratio, pad_w, pad_h
def __call__(self, images, return_tensors="pt"):
if not isinstance(images, list):
images = [images]
processed_images = []
ratios = []
pad_ws = []
pad_hs = []
for image in images:
if isinstance(image, np.ndarray):
image = Image.fromarray(image[..., ::-1]) if image.shape[-1] == 3 else Image.fromarray(image)
if not isinstance(image, Image.Image):
raise ValueError("All inputs must be PIL images.")
resized_img, ratio, pad_w, pad_h = self.resize_with_aspect_ratio(image, self.size)
tensor_img = T.ToTensor()(resized_img)
processed_images.append(tensor_img)
ratios.append(ratio)
pad_ws.append(pad_w)
pad_hs.append(pad_h)
torch_imgs = torch.stack(processed_images)
ratios = torch.tensor(ratios)
pad_w = torch.tensor(pad_ws)
pad_h = torch.tensor(pad_hs)
orig_target_sizes = torch.tensor([[self.size, self.size]])
return {
"images": torch_imgs,
"orig_target_sizes": orig_target_sizes,
"ratio": ratios,
"pad_w": pad_w,
"pad_h": pad_h,
}
def save_pretrained(self, save_directory):
os.makedirs(save_directory, exist_ok=True)
with open(os.path.join(save_directory, "preprocessor_config.json"), "w") as f:
json.dump({"processor_class": self.__class__.__name__}, f)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
return cls()
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