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@ -219,7 +219,7 @@ class sbb_predict:
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added_image = cv2.addWeighted(img,0.5,output,0.1,0)
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return added_image
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return added_image, output
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def predict(self):
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self.start_new_session_and_model()
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@ -444,7 +444,7 @@ class sbb_predict:
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if img.shape[1] < self.img_width:
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img = cv2.resize(img, (self.img_height, img.shape[0]), interpolation=cv2.INTER_NEAREST)
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margin = int(0 * self.img_width)
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margin = int(0.1 * self.img_width)
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width_mid = self.img_width - 2 * margin
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height_mid = self.img_height - 2 * margin
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img = img / float(255.0)
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@ -562,9 +562,10 @@ class sbb_predict:
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print(self.save)
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cv2.imwrite(self.save,res)
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else:
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img_seg_overlayed = self.visualize_model_output(res, self.img_org, self.task)
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img_seg_overlayed, only_prediction = self.visualize_model_output(res, self.img_org, self.task)
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if self.save:
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cv2.imwrite(self.save,img_seg_overlayed)
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cv2.imwrite('./layout.png', only_prediction)
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if self.ground_truth:
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gt_img=cv2.imread(self.ground_truth)
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