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decorated with confidence value for cnnrnn ocr model

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vahidrezanezhad 2025-07-03 11:50:47 +02:00
parent 065f1f9a93
commit 59ea493803
1 changed files with 15 additions and 2 deletions
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17
src/eynollah/eynollah.py
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@ -5129,7 +5129,7 @@ class Eynollah_ocr:
self.b_s = int(batch_size)
else:
self.model_ocr_dir = dir_models + "/model_step_1225000_ocr"#"/model_step_125000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
self.model_ocr_dir = dir_models + "/model_step_900000_ocr"#"/model_step_25000_ocr"#"/model_step_1050000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
model_ocr = load_model(self.model_ocr_dir , compile=False)
self.prediction_model = tf.keras.models.Model(
@ -5487,7 +5487,7 @@ class Eynollah_ocr:
if not self.export_textline_images_and_text:
if w_scaled < 640:#1.5*image_width:
if w_scaled < 750:#1.5*image_width:
img_fin = preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin)
if abs(better_des_slope) > 45:
@ -5580,6 +5580,7 @@ class Eynollah_ocr:
if not self.export_textline_images_and_text:
extracted_texts = []
extracted_conf_value = []
n_iterations = math.ceil(len(cropped_lines) / self.b_s)
@ -5700,12 +5701,19 @@ class Eynollah_ocr:
preds_bin[indices_to_be_replaced,:,:] = preds_flipped[indices_where_flipped_conf_value_is_higher, :, :]
preds = (preds + preds_bin) / 2.
pred_texts = decode_batch_predictions(preds, self.num_to_char)
preds_max = np.max(preds, axis=2 )
preds_max_args = np.argmax(preds, axis=2 )
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=256
masked_means = np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / np.sum(pred_max_not_unk_mask_bool, axis=1)
for ib in range(imgs.shape[0]):
pred_texts_ib = pred_texts[ib].replace("[UNK]", "")
extracted_texts.append(pred_texts_ib)
extracted_conf_value.append(masked_means[ib])
del cropped_lines
if self.prediction_with_both_of_rgb_and_bin:
@ -5713,7 +5721,10 @@ class Eynollah_ocr:
gc.collect()
extracted_texts_merged = [extracted_texts[ind] if cropped_lines_meging_indexing[ind]==0 else extracted_texts[ind]+" "+extracted_texts[ind+1] if cropped_lines_meging_indexing[ind]==1 else None for ind in range(len(cropped_lines_meging_indexing))]
extracted_conf_value_merged = [extracted_conf_value[ind] if cropped_lines_meging_indexing[ind]==0 else (extracted_conf_value[ind]+extracted_conf_value[ind+1])/2. if cropped_lines_meging_indexing[ind]==1 else None for ind in range(len(cropped_lines_meging_indexing))]
extracted_conf_value_merged = [extracted_conf_value_merged[ind_cfm] for ind_cfm in range(len(extracted_texts_merged)) if extracted_texts_merged[ind_cfm] is not None]
extracted_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
@ -5791,6 +5802,7 @@ class Eynollah_ocr:
if not is_textline_text:
text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
text_subelement.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
unicode_textline = ET.SubElement(text_subelement, 'Unicode')
unicode_textline.text = extracted_texts_merged[indexer]
else:
@ -5798,6 +5810,7 @@ class Eynollah_ocr:
if childtest3.tag.endswith("TextEquiv"):
for child_uc in childtest3:
if child_uc.tag.endswith("Unicode"):
childtest3.set('conf', f"{extracted_conf_value_merged[indexer]:.2f}")
child_uc.text = extracted_texts_merged[indexer]
indexer = indexer + 1