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log num_cols-dependent resizing
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1 changed files with 4 additions and 0 deletions
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@ -1861,6 +1861,8 @@ class Eynollah:
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if num_col_classifier == 1 or num_col_classifier == 2:
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if num_col_classifier == 1 or num_col_classifier == 2:
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if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
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if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
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self.logger.debug("resized to %dx%d for %d cols",
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img_resized.shape[1], img_resized.shape[0], num_col_classifier)
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prediction_regions_org = self.do_prediction_new_concept(
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prediction_regions_org = self.do_prediction_new_concept(
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True, img_resized, self.model_region_1_2, n_batch_inference=1,
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True, img_resized, self.model_region_1_2, n_batch_inference=1,
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thresholding_for_some_classes_in_light_version=True)
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thresholding_for_some_classes_in_light_version=True)
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@ -1873,6 +1875,8 @@ class Eynollah:
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else:
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else:
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new_h = (900+ (num_col_classifier-3)*100)
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new_h = (900+ (num_col_classifier-3)*100)
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img_resized = resize_image(img_bin, int(new_h * img_bin.shape[0] /img_bin.shape[1]), new_h)
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img_resized = resize_image(img_bin, int(new_h * img_bin.shape[0] /img_bin.shape[1]), new_h)
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self.logger.debug("resized to %dx%d (new_h=%d) for %d cols",
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img_resized.shape[1], img_resized.shape[0], new_h, num_col_classifier)
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prediction_regions_org = self.do_prediction_new_concept(
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prediction_regions_org = self.do_prediction_new_concept(
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True, img_resized, self.model_region_1_2, n_batch_inference=2,
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True, img_resized, self.model_region_1_2, n_batch_inference=2,
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thresholding_for_some_classes_in_light_version=True)
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thresholding_for_some_classes_in_light_version=True)
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