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import os.path
from functools import lru_cache
import cv2
import numpy as np
from huggingface_hub import HfApi, HfFileSystem, hf_hub_download
from imgutils.data import ImageTyping
from imgutils.utils import open_onnx_model
hf_client = HfApi()
hf_fs = HfFileSystem()
@lru_cache()
def _get_available_models():
for f in hf_fs.glob('deepghs/text_detection/*/end2end.onnx'):
yield os.path.relpath(f, 'deepghs/text_detection').split('/')[0]
_ALL_MODELS = list(_get_available_models())
_DEFAULT_MODEL = 'dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015'
@lru_cache()
def _get_onnx_session(model):
return open_onnx_model(hf_hub_download(
'deepghs/text_detection',
f'{model}/end2end.onnx'
))
def detect_text(image: ImageTyping, model: str = _DEFAULT_MODEL, threshold: float = 0.05):
origin_width, origin_height = width, height = image.size
align = 32
if width % align != 0:
width += (align - width % align)
if height % align != 0:
height += (align - height % align)
input_ = np.array(image).transpose((2, 0, 1)).astype(np.float32) / 255.0
# noinspection PyTypeChecker
input_ = np.pad(input_[None, ...], ((0, 0), (0, 0), (0, height - origin_height), (0, width - origin_width)))
def _normalize(data, mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711)):
mean, std = np.asarray(mean), np.asarray(std)
return (data - mean[None, :, None, None]) / std[None, :, None, None]
ort = _get_onnx_session(model)
input_ = _normalize(input_).astype(np.float32)
output_, = ort.run(['output'], {'input': input_})
heatmap = output_[0]
heatmap = heatmap[:origin_height, :origin_width]
cnts = cv2.findContours((heatmap * 255.0).astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
bboxes = []
for c in cnts:
x, y, w, h = cv2.boundingRect(c)
x0, y0 = x, y
x1, y1 = x + w, y + h
area = heatmap[y0:y1, x0:x1]
valid_area = area[area >= 1e-4]
score = valid_area.mean().item()
if score >= threshold:
bboxes.append(((x0, y0, x1, y1), 'text', score))
return bboxes
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