qurator-spk/sbb_pixelwise_segmentation
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save only layout output. different from overlayed layout on image

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vahidrezanezhad 2024-08-09 12:46:18 +02:00
parent b6bdf942fd
commit f4bad09083
1 changed files with 19 additions and 13 deletions
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32
inference.py
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@ -32,6 +32,7 @@ class sbb_predict:
self.image=image self.image=image
self.patches=patches self.patches=patches
self.save=save self.save=save
self.save_layout=save_layout
self.model_dir=model self.model_dir=model
self.ground_truth=ground_truth self.ground_truth=ground_truth
self.task=task self.task=task
@ -181,6 +182,7 @@ class sbb_predict:
prediction = prediction * -1 prediction = prediction * -1
prediction = prediction + 1 prediction = prediction + 1
added_image = prediction * 255 added_image = prediction * 255
layout_only = None
else: else:
unique_classes = np.unique(prediction[:,:,0]) unique_classes = np.unique(prediction[:,:,0])
rgb_colors = {'0' : [255, 255, 255], rgb_colors = {'0' : [255, 255, 255],
@ -200,26 +202,26 @@ class sbb_predict:
'14' : [255, 125, 125], '14' : [255, 125, 125],
'15' : [255, 0, 255]} '15' : [255, 0, 255]}
output = np.zeros(prediction.shape) layout_only = np.zeros(prediction.shape)
for unq_class in unique_classes: for unq_class in unique_classes:
rgb_class_unique = rgb_colors[str(int(unq_class))] rgb_class_unique = rgb_colors[str(int(unq_class))]
output[:,:,0][prediction[:,:,0]==unq_class] = rgb_class_unique[0] layout_only[:,:,0][prediction[:,:,0]==unq_class] = rgb_class_unique[0]
output[:,:,1][prediction[:,:,0]==unq_class] = rgb_class_unique[1] layout_only[:,:,1][prediction[:,:,0]==unq_class] = rgb_class_unique[1]
output[:,:,2][prediction[:,:,0]==unq_class] = rgb_class_unique[2] layout_only[:,:,2][prediction[:,:,0]==unq_class] = rgb_class_unique[2]
img = self.resize_image(img, output.shape[0], output.shape[1]) img = self.resize_image(img, layout_only.shape[0], layout_only.shape[1])
output = output.astype(np.int32) layout_only = layout_only.astype(np.int32)
img = img.astype(np.int32) img = img.astype(np.int32)
added_image = cv2.addWeighted(img,0.5,output,0.1,0) added_image = cv2.addWeighted(img,0.5,layout_only,0.1,0)
return added_image, output return added_image, layout_only
def predict(self): def predict(self):
self.start_new_session_and_model() self.start_new_session_and_model()
@ -559,13 +561,12 @@ class sbb_predict:
pass pass
elif self.task == 'enhancement': elif self.task == 'enhancement':
if self.save: if self.save:
print(self.save)
cv2.imwrite(self.save,res) cv2.imwrite(self.save,res)
else: else:
img_seg_overlayed, only_prediction = self.visualize_model_output(res, self.img_org, self.task) img_seg_overlayed, only_layout = self.visualize_model_output(res, self.img_org, self.task)
if self.save: if self.save:
cv2.imwrite(self.save,img_seg_overlayed) cv2.imwrite(self.save,img_seg_overlayed)
cv2.imwrite('./layout.png', only_prediction) cv2.imwrite(self.save_layout, only_layout)
if self.ground_truth: if self.ground_truth:
gt_img=cv2.imread(self.ground_truth) gt_img=cv2.imread(self.ground_truth)
@ -595,6 +596,11 @@ class sbb_predict:
"-s", "-s",
help="save prediction as a png file in current folder.", help="save prediction as a png file in current folder.",
) )
@click.option(
"--save_layout",
"-sl",
help="save layout prediction only as a png file in current folder.",
)
@click.option( @click.option(
"--model", "--model",
"-m", "-m",
@ -618,7 +624,7 @@ class sbb_predict:
"-min", "-min",
help="min area size of regions considered for reading order detection. The default value is zero and means that all text regions are considered for reading order.", help="min area size of regions considered for reading order detection. The default value is zero and means that all text regions are considered for reading order.",
) )
def main(image, model, patches, save, ground_truth, xml_file, out, min_area): def main(image, model, patches, save, save_layout, ground_truth, xml_file, out, min_area):
with open(os.path.join(model,'config.json')) as f: with open(os.path.join(model,'config.json')) as f:
config_params_model = json.load(f) config_params_model = json.load(f)
task = config_params_model['task'] task = config_params_model['task']
@ -626,7 +632,7 @@ def main(image, model, patches, save, ground_truth, xml_file, out, min_area):
if not save: if not save:
print("Error: You used one of segmentation or binarization task but not set -s, you need a filename to save visualized output with -s") print("Error: You used one of segmentation or binarization task but not set -s, you need a filename to save visualized output with -s")
sys.exit(1) sys.exit(1)
x=sbb_predict(image, model, task, config_params_model, patches, save, ground_truth, xml_file, out, min_area) x=sbb_predict(image, model, task, config_params_model, patches, save, save_layout, ground_truth, xml_file, out, min_area)
x.run() x.run()
if __name__=="__main__": if __name__=="__main__":