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🔥 remove torch pinning

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kba 2025-11-26 19:23:49 +01:00
parent e503c1a0b7
commit 000af16a47
2 changed files with 8 additions and 12 deletions
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2
requirements-ocr.txt
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@ -1,2 +1,2 @@
torch <= 2.0.1
torch
transformers <= 4.30.2

18
src/eynollah/sbb_binarize.py
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@ -9,15 +9,13 @@ Tool to load model and binarize a given image.
import os
import logging
from pathlib import Path
from typing import Dict, Optional
from typing import Optional
import numpy as np
import cv2
from ocrd_utils import tf_disable_interactive_logs
from eynollah.model_zoo import EynollahModelZoo
from eynollah.model_zoo.types import AnyModel
tf_disable_interactive_logs()
import tensorflow as tf
from tensorflow.python.keras import backend as tensorflow_backend
@ -323,7 +321,7 @@ class SbbBinarizer:
image = cv2.imread(image_path)
img_last = 0
model_file, model = self.models
self.logger.info('Predicting %s with model %s [%s/%s]', image_path if image_path else '[image]', model_file)
self.logger.info('Predicting %s with model %s', image_path if image_path else '[image]', model_file)
res = self.predict(model, image, use_patches)
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
@ -338,7 +336,6 @@ class SbbBinarizer:
img_fin = (res[:, :] == 0) * 255
img_last = img_last + img_fin
kernel = np.ones((5, 5), np.uint8)
img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0) * 255
if output:
@ -348,13 +345,13 @@ class SbbBinarizer:
else:
ls_imgs = list(filter(is_image_filename, os.listdir(dir_in)))
self.logger.info("Found %d image files to binarize in %s", len(ls_imgs), dir_in)
for i, image_name in enumerate(ls_imgs):
image_stem = image_name.split('.')[0]
self.logger.info('Binarizing [%3d/%d] %s', i + 1, len(ls_imgs), image_name)
image = cv2.imread(os.path.join(dir_in,image_name) )
for i, image_path in enumerate(ls_imgs):
self.logger.info('Binarizing [%3d/%d] %s', i + 1, len(ls_imgs), image_path)
image_stem = image_path.split('.')[0]
image = cv2.imread(os.path.join(dir_in,image_path) )
img_last = 0
model_file, model = self.models
self.logger.info('Predicting %s with model %s [%s/%s]', image_path if image_path else '[image]', model_file)
self.logger.info('Predicting %s with model %s', image_path if image_path else '[image]', model_file)
res = self.predict(model, image, use_patches)
img_fin = np.zeros((res.shape[0], res.shape[1], 3))
@ -369,7 +366,6 @@ class SbbBinarizer:
img_fin = (res[:, :] == 0) * 255
img_last = img_last + img_fin
kernel = np.ones((5, 5), np.uint8)
img_last[:, :][img_last[:, :] > 0] = 255
img_last = (img_last[:, :] == 0) * 255