From ad748d003978b643dab6ec482542b03fdb3dc1e4 Mon Sep 17 00:00:00 2001 From: Robert Sachunsky Date: Mon, 9 Dec 2024 10:55:41 +0000 Subject: [PATCH] do_prediction: avoid code duplication --- src/eynollah/eynollah.py | 169 +++------------------------------------ 1 file changed, 9 insertions(+), 160 deletions(-) diff --git a/src/eynollah/eynollah.py b/src/eynollah/eynollah.py index d483cac..50f0f34 100644 --- a/src/eynollah/eynollah.py +++ b/src/eynollah/eynollah.py @@ -912,7 +912,10 @@ class Eynollah: batch_indexer = batch_indexer + 1 - if batch_indexer == n_batch_inference: + if (batch_indexer == n_batch_inference or + # last batch + i == nxf - 1 and j == nyf - 1): + self.logger.debug("predicting patches on %s", str(img_patch.shape)) label_p_pred = model.predict(img_patch,verbose=0) seg = np.argmax(label_p_pred, axis=3) @@ -994,88 +997,6 @@ class Eynollah: img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) - elif i==(nxf-1) and j==(nyf-1): - label_p_pred = model.predict(img_patch,verbose=0) - - seg = np.argmax(label_p_pred, axis=3) - if thresholding_for_some_classes_in_light_version: - seg_not_base = label_p_pred[:,:,:,4] - seg_not_base[seg_not_base>0.03] =1 - seg_not_base[seg_not_base<1] =0 - - seg_line = label_p_pred[:,:,:,3] - seg_line[seg_line>0.1] =1 - seg_line[seg_line<1] =0 - - seg_background = label_p_pred[:,:,:,0] - seg_background[seg_background>0.25] =1 - seg_background[seg_background<1] =0 - - seg[seg_not_base==1]=4 - seg[seg_background==1]=0 - seg[(seg_line==1) & (seg==0)]=3 - - if thresholding_for_artificial_class_in_light_version: - seg_art = label_p_pred[:,:,:,2] - - seg_art[seg_art<0.2] = 0 - seg_art[seg_art>0] =1 - - seg[seg_art==1]=2 - - indexer_inside_batch = 0 - for i_batch, j_batch in zip(list_i_s, list_j_s): - seg_in = seg[indexer_inside_batch,:,:] - seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2) - - index_y_u_in = list_y_u[indexer_inside_batch] - index_y_d_in = list_y_d[indexer_inside_batch] - - index_x_u_in = list_x_u[indexer_inside_batch] - index_x_d_in = list_x_d[indexer_inside_batch] - - if i_batch == 0 and j_batch == 0: - seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color - elif i_batch == nxf - 1 and j_batch == nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :] - prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color - elif i_batch == 0 and j_batch == nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color - elif i_batch == nxf - 1 and j_batch == 0: - seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] - prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color - elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color - elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] - prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color - elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: - seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color - elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color - else: - seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color - - indexer_inside_batch = indexer_inside_batch +1 - - - list_i_s = [] - list_j_s = [] - list_x_u = [] - list_x_d = [] - list_y_u = [] - list_y_d = [] - - batch_indexer = 0 - - img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) - prediction_true = prediction_true.astype(np.uint8) #del model #gc.collect() @@ -1111,7 +1032,7 @@ class Eynollah: return img_scaled_padded#, label_scaled_padded def do_prediction_new_concept(self, patches, img, model, n_batch_inference=1, marginal_of_patch_percent=0.1, thresholding_for_some_classes_in_light_version=False, thresholding_for_artificial_class_in_light_version=False): - self.logger.debug("enter do_prediction") + self.logger.debug("enter do_prediction_new_concept") img_height_model = model.layers[len(model.layers) - 1].output_shape[1] img_width_model = model.layers[len(model.layers) - 1].output_shape[2] @@ -1207,7 +1128,10 @@ class Eynollah: batch_indexer = batch_indexer + 1 - if batch_indexer == n_batch_inference: + if (batch_indexer == n_batch_inference or + # last batch + i == nxf - 1 and j == nyf - 1): + self.logger.debug("predicting patches on %s", str(img_patch.shape)) label_p_pred = model.predict(img_patch,verbose=0) seg = np.argmax(label_p_pred, axis=3) @@ -1284,81 +1208,6 @@ class Eynollah: img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) - elif i==(nxf-1) and j==(nyf-1): - label_p_pred = model.predict(img_patch,verbose=0) - - seg = np.argmax(label_p_pred, axis=3) - if thresholding_for_some_classes_in_light_version: - seg_art = label_p_pred[:,:,:,4] - seg_art[seg_art<0.2] =0 - seg_art[seg_art>0] =1 - - seg_line = label_p_pred[:,:,:,3] - seg_line[seg_line>0.1] =1 - seg_line[seg_line<1] =0 - - seg[seg_art==1]=4 - seg[(seg_line==1) & (seg==0)]=3 - - if thresholding_for_artificial_class_in_light_version: - seg_art = label_p_pred[:,:,:,2] - - seg_art[seg_art<0.2] = 0 - seg_art[seg_art>0] =1 - - seg[seg_art==1]=2 - - indexer_inside_batch = 0 - for i_batch, j_batch in zip(list_i_s, list_j_s): - seg_in = seg[indexer_inside_batch,:,:] - seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2) - - index_y_u_in = list_y_u[indexer_inside_batch] - index_y_d_in = list_y_d[indexer_inside_batch] - - index_x_u_in = list_x_u[indexer_inside_batch] - index_x_d_in = list_x_d[indexer_inside_batch] - - if i_batch == 0 and j_batch == 0: - seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color - elif i_batch == nxf - 1 and j_batch == nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :] - prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color - elif i_batch == 0 and j_batch == nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color - elif i_batch == nxf - 1 and j_batch == 0: - seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] - prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color - elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color - elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :] - prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color - elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0: - seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color - elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1: - seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color - else: - seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :] - prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color - - indexer_inside_batch = indexer_inside_batch +1 - - list_i_s = [] - list_j_s = [] - list_x_u = [] - list_x_d = [] - list_y_u = [] - list_y_d = [] - - batch_indexer = 0 - img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3)) - prediction_true = prediction_true.astype(np.uint8) return prediction_true