File size: 7,914 Bytes
b5b608e 0f8229d b5b608e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 | from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Any
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
class HTRProcessor:
model_input_names = ["pixel_values"]
@classmethod
def register_for_auto_class(cls, auto_class="AutoProcessor"):
"""Compatibility with transformers AutoProcessor (no-op for custom processor)."""
pass
def __init__(
self,
characters: list[str],
image_height: int = 64,
image_max_width: int = 3072,
width_stride: int = 32,
resample: str = "bilinear",
) -> None:
self.characters = characters
self.image_height = int(image_height)
self.image_max_width = int(image_max_width)
self.width_stride = int(width_stride)
self.resample = resample
self.id_to_char = {idx + 1: char for idx, char in enumerate(self.characters)}
@staticmethod
def _resolve_file(
path_or_repo_id: str, filename: str, local_files_only: bool
) -> str:
candidate = Path(path_or_repo_id) / filename
if candidate.exists():
return str(candidate)
return hf_hub_download(
repo_id=path_or_repo_id,
filename=filename,
local_files_only=local_files_only,
)
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: str,
local_files_only: bool = False,
**_: dict[str, Any],
) -> "HTRProcessor":
cfg_path = cls._resolve_file(
pretrained_model_name_or_path, "preprocessor_config.json", local_files_only
)
with open(cfg_path, "r", encoding="utf-8") as f:
cfg = json.load(f)
vocab_filename = cfg.get("vocab_file", "alphabet.json")
vocab_path = cls._resolve_file(
pretrained_model_name_or_path, vocab_filename, local_files_only
)
with open(vocab_path, "r", encoding="utf-8") as f:
vocab_data = json.load(f)
if isinstance(vocab_data, dict) and "characters" in vocab_data:
characters = vocab_data["characters"]
elif isinstance(vocab_data, list):
characters = vocab_data
else:
raise ValueError(
"Unsupported vocab file format. Expected list or {'characters': [...]} ."
)
return cls(
characters=characters,
image_height=cfg.get("image_height", 64),
image_max_width=cfg.get("image_max_width", 3072),
width_stride=cfg.get("width_stride", 32),
resample=cfg.get("resample", "bilinear"),
)
def save_pretrained(self, save_directory: str) -> None:
os.makedirs(save_directory, exist_ok=True)
vocab_path = os.path.join(save_directory, "alphabet.json")
with open(vocab_path, "w", encoding="utf-8") as f:
json.dump({"characters": self.characters}, f, ensure_ascii=False, indent=2)
preprocessor_cfg = {
"processor_class": self.__class__.__name__,
"vocab_file": "alphabet.json",
"image_height": self.image_height,
"image_max_width": self.image_max_width,
"width_stride": self.width_stride,
"resample": self.resample,
}
with open(
os.path.join(save_directory, "preprocessor_config.json"),
"w",
encoding="utf-8",
) as f:
json.dump(preprocessor_cfg, f, ensure_ascii=False, indent=2)
def _load_pil(self, image: str | Image.Image | np.ndarray) -> Image.Image:
if isinstance(image, Image.Image):
return image.convert("L")
if isinstance(image, np.ndarray):
if image.ndim == 2:
return Image.fromarray(image).convert("L")
if image.ndim == 3:
return Image.fromarray(image).convert("L")
raise ValueError(f"Unsupported ndarray shape: {image.shape}")
if isinstance(image, str):
return Image.open(image).convert("L")
raise TypeError(f"Unsupported image input type: {type(image)}")
def _preprocess_image(self, image: str | Image.Image | np.ndarray) -> np.ndarray:
img = self._load_pil(image)
w, h = img.size
if h <= 0:
raise ValueError("Input image has invalid height.")
scale = self.image_height / float(h)
new_w = max(1, int(w * scale))
resample_map = {
"nearest": Image.Resampling.NEAREST,
"bilinear": Image.Resampling.BILINEAR,
"bicubic": Image.Resampling.BICUBIC,
"lanczos": Image.Resampling.LANCZOS,
}
pil_resample = resample_map.get(
self.resample.lower(), Image.Resampling.BILINEAR
)
img = img.resize((new_w, self.image_height), resample=pil_resample)
arr = np.array(img)
if new_w > self.image_max_width:
arr = arr[:, : self.image_max_width]
new_w = self.image_max_width
if new_w % self.width_stride != 0:
aligned_w = ((new_w // self.width_stride) + 1) * self.width_stride
pad_width = aligned_w - new_w
arr = np.pad(
arr,
((0, 0), (0, pad_width)),
mode="constant",
constant_values=0,
)
new_w = aligned_w
arr = arr.astype(np.float32) / 255.0
if arr.ndim == 2:
arr = np.expand_dims(arr, axis=-1)
return arr.transpose(2, 0, 1).astype(np.float32)
def __call__(
self,
images: str | Image.Image | np.ndarray | list[str | Image.Image | np.ndarray],
return_tensors: str = "pt",
**_: dict[str, Any],
) -> dict[str, Any]:
batch_images = images if isinstance(images, list) else [images]
pixel_values = np.stack(
[self._preprocess_image(img) for img in batch_images], axis=0
)
if return_tensors == "pt":
return {"pixel_values": torch.from_numpy(pixel_values)}
if return_tensors == "np":
return {"pixel_values": pixel_values}
raise ValueError("Supported return_tensors values are 'pt' and 'np'.")
@staticmethod
def _ctc_greedy_decode(
logits_tnc: np.ndarray, blank_idx: int = 0
) -> list[list[int]]:
preds = np.argmax(logits_tnc, axis=2)
_, batch_size, _ = logits_tnc.shape
decoded: list[list[int]] = []
for n in range(batch_size):
seq = preds[:, n]
chars: list[int] = []
prev = blank_idx
for idx in seq:
token = int(idx)
if token != blank_idx and token != prev:
chars.append(token)
prev = token
decoded.append(chars)
return decoded
def batch_decode(
self,
logits: torch.Tensor | np.ndarray,
blank_idx: int = 0,
logit_layout: str = "ntc",
) -> list[str]:
logits_np = (
logits.detach().cpu().numpy()
if isinstance(logits, torch.Tensor)
else logits
)
if logits_np.ndim != 3:
raise ValueError(f"Expected logits rank 3, got shape {logits_np.shape}.")
if logit_layout == "ntc":
logits_tnc = np.transpose(logits_np, (1, 0, 2))
elif logit_layout == "tnc":
logits_tnc = logits_np
else:
raise ValueError("logit_layout must be 'ntc' or 'tnc'.")
decoded_ids = self._ctc_greedy_decode(logits_tnc, blank_idx=blank_idx)
return [
"".join(
self.id_to_char.get(token, "")
for token in seq
if token in self.id_to_char
)
for seq in decoded_ids
]
|