mirror of
https://github.com/qurator-spk/eynollah.git
synced 2025-06-14 22:59:53 +02:00
do_prediction: avoid code duplication
This commit is contained in:
Robert Sachunsky
parent
c3163caefd
commit
ad748d0039
1 changed files with 9 additions and 160 deletions
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@ -912,7 +912,10 @@ class Eynollah:
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batch_indexer = batch_indexer + 1
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if batch_indexer == n_batch_inference:
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if (batch_indexer == n_batch_inference or
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# last batch
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i == nxf - 1 and j == nyf - 1):
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self.logger.debug("predicting patches on %s", str(img_patch.shape))
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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@ -994,88 +997,6 @@ class Eynollah:
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img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
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elif i==(nxf-1) and j==(nyf-1):
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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if thresholding_for_some_classes_in_light_version:
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seg_not_base = label_p_pred[:,:,:,4]
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seg_not_base[seg_not_base>0.03] =1
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seg_not_base[seg_not_base<1] =0
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seg_line = label_p_pred[:,:,:,3]
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seg_line[seg_line>0.1] =1
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seg_line[seg_line<1] =0
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seg_background = label_p_pred[:,:,:,0]
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seg_background[seg_background>0.25] =1
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seg_background[seg_background<1] =0
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seg[seg_not_base==1]=4
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seg[seg_background==1]=0
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seg[(seg_line==1) & (seg==0)]=3
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if thresholding_for_artificial_class_in_light_version:
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seg_art = label_p_pred[:,:,:,2]
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seg_art[seg_art<0.2] = 0
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seg_art[seg_art>0] =1
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seg[seg_art==1]=2
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch,:,:]
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seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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if i_batch == 0 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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else:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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indexer_inside_batch = indexer_inside_batch +1
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
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prediction_true = prediction_true.astype(np.uint8)
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#del model
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#gc.collect()
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@ -1111,7 +1032,7 @@ class Eynollah:
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return img_scaled_padded#, label_scaled_padded
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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):
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self.logger.debug("enter do_prediction")
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self.logger.debug("enter do_prediction_new_concept")
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img_height_model = model.layers[len(model.layers) - 1].output_shape[1]
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img_width_model = model.layers[len(model.layers) - 1].output_shape[2]
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@ -1207,7 +1128,10 @@ class Eynollah:
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batch_indexer = batch_indexer + 1
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if batch_indexer == n_batch_inference:
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if (batch_indexer == n_batch_inference or
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# last batch
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i == nxf - 1 and j == nyf - 1):
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self.logger.debug("predicting patches on %s", str(img_patch.shape))
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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@ -1284,81 +1208,6 @@ class Eynollah:
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img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
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elif i==(nxf-1) and j==(nyf-1):
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label_p_pred = model.predict(img_patch,verbose=0)
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seg = np.argmax(label_p_pred, axis=3)
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if thresholding_for_some_classes_in_light_version:
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seg_art = label_p_pred[:,:,:,4]
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seg_art[seg_art<0.2] =0
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seg_art[seg_art>0] =1
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seg_line = label_p_pred[:,:,:,3]
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seg_line[seg_line>0.1] =1
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seg_line[seg_line<1] =0
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seg[seg_art==1]=4
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seg[(seg_line==1) & (seg==0)]=3
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if thresholding_for_artificial_class_in_light_version:
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seg_art = label_p_pred[:,:,:,2]
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seg_art[seg_art<0.2] = 0
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seg_art[seg_art>0] =1
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seg[seg_art==1]=2
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indexer_inside_batch = 0
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for i_batch, j_batch in zip(list_i_s, list_j_s):
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seg_in = seg[indexer_inside_batch,:,:]
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seg_color = np.repeat(seg_in[:, :, np.newaxis], 3, axis=2)
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index_y_u_in = list_y_u[indexer_inside_batch]
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index_y_d_in = list_y_d[indexer_inside_batch]
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index_x_u_in = list_x_u[indexer_inside_batch]
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index_x_d_in = list_x_d[indexer_inside_batch]
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if i_batch == 0 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch == 0 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, 0 : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + 0 : index_x_u_in - margin, :] = seg_color
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elif i_batch == nxf - 1 and j_batch != 0 and j_batch != nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - 0, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - 0, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == 0:
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seg_color = seg_color[0 : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + 0 : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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elif i_batch != 0 and i_batch != nxf - 1 and j_batch == nyf - 1:
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seg_color = seg_color[margin : seg_color.shape[0] - 0, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - 0, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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else:
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seg_color = seg_color[margin : seg_color.shape[0] - margin, margin : seg_color.shape[1] - margin, :]
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prediction_true[index_y_d_in + margin : index_y_u_in - margin, index_x_d_in + margin : index_x_u_in - margin, :] = seg_color
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indexer_inside_batch = indexer_inside_batch +1
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list_i_s = []
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list_j_s = []
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list_x_u = []
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list_x_d = []
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list_y_u = []
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list_y_d = []
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batch_indexer = 0
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img_patch = np.zeros((n_batch_inference, img_height_model, img_width_model, 3))
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prediction_true = prediction_true.astype(np.uint8)
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return prediction_true
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