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do_prediction_new_concept: aggregate confidence for all classes…

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(not just text; will still have to pass that on to the writer...)
This commit is contained in:
Robert Sachunsky 2026-04-16 05:02:20 +02:00
parent 5a27e46b22
commit 0f82b568ba
1 changed files with 4 additions and 3 deletions
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7
src/eynollah/eynollah.py
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@ -657,8 +657,9 @@ class Eynollah:
dilate=3, dilate=3,
keep=separator_class) keep=separator_class)
conf_text = resize_image(label_p_pred[:, :, 1], img_h_page, img_w_page) conf = label_p_pred[tuple(np.indices(seg.shape)) + (seg,)]
return prediction, conf_text conf = resize_image(conf, img_h_page, img_w_page)
return prediction, conf
if img.shape[0] < img_height_model: if img.shape[0] < img_height_model:
img = resize_image(img, img_height_model, img.shape[1]) img = resize_image(img, img_height_model, img.shape[1])
@ -717,7 +718,7 @@ class Eynollah:
self.logger.debug("predicting patches on %s", str(img_patch.shape)) self.logger.debug("predicting patches on %s", str(img_patch.shape))
label_p_pred = model.predict(img_patch,verbose=0) label_p_pred = model.predict(img_patch,verbose=0)
seg = np.argmax(label_p_pred, axis=3) seg = np.argmax(label_p_pred, axis=3)
conf = label_p_pred[:, :, :, 1] conf = label_p_pred[tuple(np.indices(seg.shape)) + (seg,)]
if thresholding_for_artificial_class: if thresholding_for_artificial_class:
seg_art = label_p_pred[:, :, :, artificial_class] >= threshold_art_class seg_art = label_p_pred[:, :, :, artificial_class] >= threshold_art_class