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eynollah
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For the CNN-RNN OCR model, long text lines are split into two segments

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pull/140/head
vahidrezanezhad 1 month ago
parent aa72ca3006
commit c8b8529951
1 changed files with 62 additions and 12 deletions
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src/eynollah/eynollah.py
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@ -4961,7 +4961,7 @@ class Eynollah_ocr:
self.model_ocr.to(self.device)
else:
self.model_ocr_dir = dir_models + "/model_1_ocrcnn"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
self.model_ocr_dir = dir_models + "/model_1_new_ocrcnn"#"/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(
@ -5080,6 +5080,18 @@ class Eynollah_ocr:
return [image1, image2]
else:
return None
def preprocess_and_resize_image_for_ocrcnn_model(self, img, image_height, image_width):
ratio = image_height /float(img.shape[0])
w_ratio = int(ratio * img.shape[1])
if w_ratio <= image_width:
width_new = w_ratio
else:
width_new = image_width
img = resize_image(img, image_height, width_new)
img_fin = np.ones((image_height, image_width, 3))*255
img_fin[:,:width_new,:] = img[:,:,:]
img_fin = img_fin / 255.
return img_fin
def run(self):
ls_imgs = os.listdir(self.dir_in)
@ -5214,7 +5226,7 @@ class Eynollah_ocr:
else:
max_len = 512
padding_token = 299
image_width = max_len * 4
image_width = 512#max_len * 4
image_height = 32
b_s = 8
@ -5265,10 +5277,31 @@ class Eynollah_ocr:
mask_poly = mask_poly[y:y+h, x:x+w, :]
img_crop = img_poly_on_img[y:y+h, x:x+w, :]
img_crop[mask_poly==0] = 255
img_crop = tf.reverse(img_crop,axis=[-1])
img_crop = self.distortion_free_resize(img_crop, img_size)
img_crop = tf.cast(img_crop, tf.float32) / 255.0
cropped_lines.append(img_crop)
if h2w_ratio > 0.05:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(0)
else:
splited_images = self.return_textlines_split_if_needed(img_crop)
#print(splited_images)
if splited_images:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[0], image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(1)
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[1], image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(-1)
else:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(0)
#img_crop = tf.reverse(img_crop,axis=[-1])
#img_crop = self.distortion_free_resize(img_crop, img_size)
#img_crop = tf.cast(img_crop, tf.float32) / 255.0
#cropped_lines.append(img_crop)
indexer_text_region = indexer_text_region +1
@ -5282,12 +5315,12 @@ class Eynollah_ocr:
n_start = i*b_s
imgs = cropped_lines[n_start:]
imgs = np.array(imgs)
imgs = imgs.reshape(imgs.shape[0], image_width, image_height, 3)
imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3)
else:
n_start = i*b_s
n_end = (i+1)*b_s
imgs = cropped_lines[n_start:n_end]
imgs = np.array(imgs).reshape(b_s, image_width, image_height, 3)
imgs = np.array(imgs).reshape(b_s, image_height, image_width, 3)
preds = self.prediction_model.predict(imgs, verbose=0)
@ -5296,15 +5329,32 @@ class Eynollah_ocr:
for ib in range(imgs.shape[0]):
pred_texts_ib = pred_texts[ib].strip("[UNK]")
extracted_texts.append(pred_texts_ib)
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_texts_merged = [ind for ind in extracted_texts_merged if ind is not None]
#print(extracted_texts_merged, len(extracted_texts_merged))
unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
#print(len(unique_cropped_lines_region_indexer), 'unique_cropped_lines_region_indexer')
text_by_textregion = []
for ind in unique_cropped_lines_region_indexer:
extracted_texts_merged_un = np.array(extracted_texts)[np.array(cropped_lines_region_indexer)==ind]
extracted_texts_merged_un = np.array(extracted_texts_merged)[np.array(cropped_lines_region_indexer)==ind]
text_by_textregion.append(" ".join(extracted_texts_merged_un))
##unique_cropped_lines_region_indexer = np.unique(cropped_lines_region_indexer)
##text_by_textregion = []
##for ind in unique_cropped_lines_region_indexer:
##extracted_texts_merged_un = np.array(extracted_texts)[np.array(cropped_lines_region_indexer)==ind]
##text_by_textregion.append(" ".join(extracted_texts_merged_un))
indexer = 0
indexer_textregion = 0
for nn in root1.iter(region_tags):
@ -5317,7 +5367,7 @@ class Eynollah_ocr:
if child_textregion.tag.endswith("TextLine"):
text_subelement = ET.SubElement(child_textregion, 'TextEquiv')
unicode_textline = ET.SubElement(text_subelement, 'Unicode')
unicode_textline.text = extracted_texts[indexer]
unicode_textline.text = extracted_texts_merged[indexer]
indexer = indexer + 1
has_textline = True
if has_textline:

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