From ab4bb7cd7b76d4d04d3dd2273b398153058e7cc4 Mon Sep 17 00:00:00 2001 From: Robert Sachunsky Date: Sat, 11 Feb 2023 11:58:40 +0000 Subject: [PATCH] silentium! --- qurator/eynollah/eynollah.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/qurator/eynollah/eynollah.py b/qurator/eynollah/eynollah.py index d6f70c3..22c45ad 100644 --- a/qurator/eynollah/eynollah.py +++ b/qurator/eynollah/eynollah.py @@ -220,7 +220,8 @@ class Eynollah: index_y_d = img_h - img_height_model img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) + label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]), + verbose=0) seg = label_p_pred[0, :, :, :] seg = seg * 255 @@ -355,7 +356,7 @@ class Eynollah: img_in[0, :, :, 1] = img_1ch[:, :] img_in[0, :, :, 2] = img_1ch[:, :] - label_p_pred = model_num_classifier.predict(img_in) + label_p_pred = model_num_classifier.predict(img_in, verbose=0) num_col = np.argmax(label_p_pred[0]) + 1 self.logger.info("Found %s columns (%s)", num_col, label_p_pred) @@ -428,7 +429,7 @@ class Eynollah: - label_p_pred = model_num_classifier.predict(img_in) + label_p_pred = model_num_classifier.predict(img_in, verbose=0) num_col = np.argmax(label_p_pred[0]) + 1 self.logger.info("Found %s columns (%s)", num_col, label_p_pred) session_col_classifier.close() @@ -534,7 +535,8 @@ class Eynollah: img = img / float(255.0) img = resize_image(img, img_height_model, img_width_model) - label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2])) + label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), + verbose=0) seg = np.argmax(label_p_pred, axis=3)[0] seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) @@ -586,7 +588,8 @@ class Eynollah: index_y_d = img_h - img_height_model img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] - label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2])) + label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]), + verbose=0) seg = np.argmax(label_p_pred, axis=3)[0] seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)