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OCR prediction is now enabled to integrate results from both RGB and binarized images or to be performed on each individually

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vahidrezanezhad 2025-03-31 21:28:05 +02:00
parent b1da0a3327
commit 4de441eaaa
2 changed files with 70 additions and 8 deletions
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16
src/eynollah/cli.py
View file

@ -321,6 +321,12 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
help="directory of images", help="directory of images",
type=click.Path(exists=True, file_okay=False), type=click.Path(exists=True, file_okay=False),
) )
@click.option(
"--dir_in_bin",
"-dib",
help="directory of binarized images. This should be given if you want to do prediction based on both rgb and bin images. And all bin images are png files",
type=click.Path(exists=True, file_okay=False),
)
@click.option( @click.option(
"--out", "--out",
"-o", "-o",
@ -371,6 +377,12 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
is_flag=True, is_flag=True,
help="if this parameter set to true, the predicted texts will be displayed on an image.", help="if this parameter set to true, the predicted texts will be displayed on an image.",
) )
@click.option(
"--prediction_with_both_of_rgb_and_bin",
"-brb/-nbrb",
is_flag=True,
help="If this parameter is set to True, the prediction will be performed using both RGB and binary images. However, this does not necessarily improve results; it may be beneficial for certain document images.",
)
@click.option( @click.option(
"--log_level", "--log_level",
"-l", "-l",
@ -378,7 +390,7 @@ def layout(image, out, overwrite, dir_in, model, save_images, save_layout, save_
help="Override log level globally to this", help="Override log level globally to this",
) )
def ocr(dir_in, out, dir_xmls, dir_out_image_text, model, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, draw_texts_on_image, log_level): def ocr(dir_in, dir_in_bin, out, dir_xmls, dir_out_image_text, model, tr_ocr, export_textline_images_and_text, do_not_mask_with_textline_contour, draw_texts_on_image, prediction_with_both_of_rgb_and_bin, log_level):
if log_level: if log_level:
setOverrideLogLevel(log_level) setOverrideLogLevel(log_level)
initLogging() initLogging()
@ -386,12 +398,14 @@ def ocr(dir_in, out, dir_xmls, dir_out_image_text, model, tr_ocr, export_textlin
dir_xmls=dir_xmls, dir_xmls=dir_xmls,
dir_out_image_text=dir_out_image_text, dir_out_image_text=dir_out_image_text,
dir_in=dir_in, dir_in=dir_in,
dir_in_bin=dir_in_bin,
dir_out=out, dir_out=out,
dir_models=model, dir_models=model,
tr_ocr=tr_ocr, tr_ocr=tr_ocr,
export_textline_images_and_text=export_textline_images_and_text, export_textline_images_and_text=export_textline_images_and_text,
do_not_mask_with_textline_contour=do_not_mask_with_textline_contour, do_not_mask_with_textline_contour=do_not_mask_with_textline_contour,
draw_texts_on_image=draw_texts_on_image, draw_texts_on_image=draw_texts_on_image,
prediction_with_both_of_rgb_and_bin=prediction_with_both_of_rgb_and_bin,
) )
eynollah_ocr.run() eynollah_ocr.run()

62
src/eynollah/eynollah.py
View file

@ -4952,15 +4952,18 @@ class Eynollah_ocr:
dir_models, dir_models,
dir_xmls=None, dir_xmls=None,
dir_in=None, dir_in=None,
dir_in_bin=None,
dir_out=None, dir_out=None,
dir_out_image_text=None, dir_out_image_text=None,
tr_ocr=False, tr_ocr=False,
export_textline_images_and_text=False, export_textline_images_and_text=False,
do_not_mask_with_textline_contour=False, do_not_mask_with_textline_contour=False,
draw_texts_on_image=False, draw_texts_on_image=False,
prediction_with_both_of_rgb_and_bin=False,
logger=None, logger=None,
): ):
self.dir_in = dir_in self.dir_in = dir_in
self.dir_in_bin = dir_in_bin
self.dir_out = dir_out self.dir_out = dir_out
self.dir_xmls = dir_xmls self.dir_xmls = dir_xmls
self.dir_models = dir_models self.dir_models = dir_models
@ -4969,6 +4972,7 @@ class Eynollah_ocr:
self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour self.do_not_mask_with_textline_contour = do_not_mask_with_textline_contour
self.draw_texts_on_image = draw_texts_on_image self.draw_texts_on_image = draw_texts_on_image
self.dir_out_image_text = dir_out_image_text self.dir_out_image_text = dir_out_image_text
self.prediction_with_both_of_rgb_and_bin = prediction_with_both_of_rgb_and_bin
if tr_ocr: if tr_ocr:
self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed") self.processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-printed")
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@ -4977,7 +4981,7 @@ class Eynollah_ocr:
self.model_ocr.to(self.device) self.model_ocr.to(self.device)
else: else:
self.model_ocr_dir = dir_models + "/model_step_150000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn" self.model_ocr_dir = dir_models + "/model_step_50000_ocr"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
model_ocr = load_model(self.model_ocr_dir , compile=False) model_ocr = load_model(self.model_ocr_dir , compile=False)
self.prediction_model = tf.keras.models.Model( self.prediction_model = tf.keras.models.Model(
@ -5104,15 +5108,20 @@ class Eynollah_ocr:
return ImageFont.truetype(font_path, 10) # Smallest font fallback return ImageFont.truetype(font_path, 10) # Smallest font fallback
def return_textlines_split_if_needed(self, textline_image): def return_textlines_split_if_needed(self, textline_image, textline_image_bin):
split_point = self.return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image) split_point = self.return_start_and_end_of_common_text_of_textline_ocr_without_common_section(textline_image)
if split_point: if split_point:
image1 = textline_image[:, :split_point,:]# image.crop((0, 0, width2, height)) image1 = textline_image[:, :split_point,:]# image.crop((0, 0, width2, height))
image2 = textline_image[:, split_point:,:]#image.crop((width1, 0, width, height)) image2 = textline_image[:, split_point:,:]#image.crop((width1, 0, width, height))
return [image1, image2] if self.prediction_with_both_of_rgb_and_bin:
image1_bin = textline_image_bin[:, :split_point,:]# image.crop((0, 0, width2, height))
image2_bin = textline_image_bin[:, split_point:,:]#image.crop((width1, 0, width, height))
return [image1, image2], [image1_bin, image2_bin]
else:
return [image1, image2], None
else: else:
return None return None, None
def preprocess_and_resize_image_for_ocrcnn_model(self, img, image_height, image_width): def preprocess_and_resize_image_for_ocrcnn_model(self, img, image_height, image_width):
ratio = image_height /float(img.shape[0]) ratio = image_height /float(img.shape[0])
w_ratio = int(ratio * img.shape[1]) w_ratio = int(ratio * img.shape[1])
@ -5123,7 +5132,7 @@ class Eynollah_ocr:
img = resize_image(img, image_height, width_new) img = resize_image(img, image_height, width_new)
img_fin = np.ones((image_height, image_width, 3))*255 img_fin = np.ones((image_height, image_width, 3))*255
img_fin[:,:width_new,:] = img[:,:,:] img_fin[:,:+width_new,:] = img[:,:,:]
img_fin = img_fin / 255. img_fin = img_fin / 255.
return img_fin return img_fin
@ -5183,7 +5192,7 @@ class Eynollah_ocr:
cropped_lines.append(img_crop) cropped_lines.append(img_crop)
cropped_lines_meging_indexing.append(0) cropped_lines_meging_indexing.append(0)
else: else:
splited_images = self.return_textlines_split_if_needed(img_crop) splited_images, _ = self.return_textlines_split_if_needed(img_crop, None)
#print(splited_images) #print(splited_images)
if splited_images: if splited_images:
cropped_lines.append(splited_images[0]) cropped_lines.append(splited_images[0])
@ -5274,6 +5283,10 @@ class Eynollah_ocr:
dir_xml = os.path.join(self.dir_xmls, file_name+'.xml') dir_xml = os.path.join(self.dir_xmls, file_name+'.xml')
out_file_ocr = os.path.join(self.dir_out, file_name+'.xml') out_file_ocr = os.path.join(self.dir_out, file_name+'.xml')
img = cv2.imread(dir_img) img = cv2.imread(dir_img)
if self.prediction_with_both_of_rgb_and_bin:
cropped_lines_bin = []
dir_img_bin = os.path.join(self.dir_in_bin, file_name+'.png')
img_bin = cv2.imread(dir_img_bin)
if self.draw_texts_on_image: if self.draw_texts_on_image:
out_image_with_text = os.path.join(self.dir_out_image_text, file_name+'.png') out_image_with_text = os.path.join(self.dir_out_image_text, file_name+'.png')
@ -5315,6 +5328,10 @@ class Eynollah_ocr:
h2w_ratio = h/float(w) h2w_ratio = h/float(w)
img_poly_on_img = np.copy(img) img_poly_on_img = np.copy(img)
if self.prediction_with_both_of_rgb_and_bin:
img_poly_on_img_bin = np.copy(img_bin)
img_crop_bin = img_poly_on_img_bin[y:y+h, x:x+w, :]
mask_poly = np.zeros(img.shape) mask_poly = np.zeros(img.shape)
mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1)) mask_poly = cv2.fillPoly(mask_poly, pts=[textline_coords], color=(1, 1, 1))
@ -5322,14 +5339,22 @@ class Eynollah_ocr:
img_crop = img_poly_on_img[y:y+h, x:x+w, :] img_crop = img_poly_on_img[y:y+h, x:x+w, :]
if not self.do_not_mask_with_textline_contour: if not self.do_not_mask_with_textline_contour:
img_crop[mask_poly==0] = 255 img_crop[mask_poly==0] = 255
if self.prediction_with_both_of_rgb_and_bin:
img_crop_bin[mask_poly==0] = 255
if not self.export_textline_images_and_text: if not self.export_textline_images_and_text:
if h2w_ratio > 0.1: if h2w_ratio > 0.1:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width) img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin) cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(0) cropped_lines_meging_indexing.append(0)
if self.prediction_with_both_of_rgb_and_bin:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop_bin, image_height, image_width)
cropped_lines_bin.append(img_fin)
else: else:
splited_images = self.return_textlines_split_if_needed(img_crop) if self.prediction_with_both_of_rgb_and_bin:
splited_images, splited_images_bin = self.return_textlines_split_if_needed(img_crop, img_crop_bin)
else:
splited_images, splited_images_bin = self.return_textlines_split_if_needed(img_crop, None)
if splited_images: if splited_images:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[0], image_height, image_width) img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images[0], image_height, image_width)
cropped_lines.append(img_fin) cropped_lines.append(img_fin)
@ -5338,10 +5363,21 @@ class Eynollah_ocr:
cropped_lines.append(img_fin) cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(-1) cropped_lines_meging_indexing.append(-1)
if self.prediction_with_both_of_rgb_and_bin:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images_bin[0], image_height, image_width)
cropped_lines_bin.append(img_fin)
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(splited_images_bin[1], image_height, image_width)
cropped_lines_bin.append(img_fin)
else: else:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width) img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop, image_height, image_width)
cropped_lines.append(img_fin) cropped_lines.append(img_fin)
cropped_lines_meging_indexing.append(0) cropped_lines_meging_indexing.append(0)
if self.prediction_with_both_of_rgb_and_bin:
img_fin = self.preprocess_and_resize_image_for_ocrcnn_model(img_crop_bin, image_height, image_width)
cropped_lines_bin.append(img_fin)
if self.export_textline_images_and_text: if self.export_textline_images_and_text:
if child_textlines.tag.endswith("TextEquiv"): if child_textlines.tag.endswith("TextEquiv"):
@ -5370,14 +5406,26 @@ class Eynollah_ocr:
imgs = cropped_lines[n_start:] imgs = cropped_lines[n_start:]
imgs = np.array(imgs) imgs = np.array(imgs)
imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3) imgs = imgs.reshape(imgs.shape[0], image_height, image_width, 3)
if self.prediction_with_both_of_rgb_and_bin:
imgs_bin = cropped_lines_bin[n_start:]
imgs_bin = np.array(imgs_bin)
imgs_bin = imgs_bin.reshape(imgs_bin.shape[0], image_height, image_width, 3)
else: else:
n_start = i*b_s n_start = i*b_s
n_end = (i+1)*b_s n_end = (i+1)*b_s
imgs = cropped_lines[n_start:n_end] imgs = cropped_lines[n_start:n_end]
imgs = np.array(imgs).reshape(b_s, image_height, image_width, 3) imgs = np.array(imgs).reshape(b_s, image_height, image_width, 3)
if self.prediction_with_both_of_rgb_and_bin:
imgs_bin = cropped_lines_bin[n_start:n_end]
imgs_bin = np.array(imgs_bin).reshape(b_s, image_height, image_width, 3)
preds = self.prediction_model.predict(imgs, verbose=0) preds = self.prediction_model.predict(imgs, verbose=0)
if self.prediction_with_both_of_rgb_and_bin:
preds_bin = self.prediction_model.predict(imgs_bin, verbose=0)
preds = (preds + preds_bin) / 2.
pred_texts = self.decode_batch_predictions(preds) pred_texts = self.decode_batch_predictions(preds)
for ib in range(imgs.shape[0]): for ib in range(imgs.shape[0]):