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annotate main.py @ 5:37b9ead209da draft default tip

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/bioimaging commit 399a62ba78a6a3d7885c7df03cc7de37e1a7ec6f
author bgruening
date Tue, 15 Apr 2025 15:39:20 +0000
parents 2b61d8fcfa52
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9f9ae2ac7820 planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/bioimaging commit 57f46739f4365f59cd52c515bdd3fae2e01b734e
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1 """
9f9ae2ac7820 planemo upload for repository https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/bioimaging commit 57f46739f4365f59cd52c515bdd3fae2e01b734e
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2 Predict images using AI models from BioImage.IO
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3 """
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5 import argparse
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7 import imageio
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8 import numpy as np
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9 import torch
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10 import torch.nn.functional as F
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13 def dynamic_resize(image: torch.Tensor, target_shape: tuple):
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14 """
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15 Resize an input tensor dynamically to the target shape.
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17 Parameters:
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18 - image: Input tensor with shape (C, D1, D2, ..., DN) (any number of spatial dims)
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19 - target_shape: Tuple specifying the target shape (C', D1', D2', ..., DN')
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21 Returns:
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22 - Resized tensor with target shape target_shape.
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23 """
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24 # Extract input shape
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25 input_shape = image.shape
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26 num_dims = len(input_shape) # Includes channels and spatial dimensions
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28 # Ensure target shape matches the number of dimensions
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29 if len(target_shape) != num_dims:
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30 raise ValueError(
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31 f"Target shape {target_shape} must match input dimensions {num_dims}"
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32 )
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34 # Extract target channels and spatial sizes
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35 target_channels = target_shape[0] # First element is the target channel count
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36 target_spatial_size = target_shape[1:] # Remaining elements are spatial dimensions
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38 # Add batch dim (N=1) for resizing
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39 image = image.unsqueeze(0)
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41 # Choose the best interpolation mode based on dimensionality
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42 if num_dims == 4:
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43 interp_mode = "trilinear"
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44 elif num_dims == 3:
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45 interp_mode = "bilinear"
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46 elif num_dims == 2:
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47 interp_mode = "bicubic"
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48 else:
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49 interp_mode = "nearest"
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51 # Resize spatial dimensions dynamically
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52 image = F.interpolate(
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53 image, size=target_spatial_size, mode=interp_mode, align_corners=False
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54 )
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56 # Adjust channels if necessary
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57 current_channels = image.shape[1]
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59 if target_channels > current_channels:
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60 # Expand channels by repeating existing ones
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61 expand_factor = target_channels // current_channels
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62 remainder = target_channels % current_channels
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63 image = image.repeat(1, expand_factor, *[1] * (num_dims - 1))
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65 if remainder > 0:
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66 extra_channels = image[
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67 :, :remainder, ...
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68 ] # Take the first few channels to match target
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69 image = torch.cat([image, extra_channels], dim=1)
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71 elif target_channels < current_channels:
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72 # Reduce channels by averaging adjacent ones
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73 image = image[:, :target_channels, ...] # Simply slice to reduce channels
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74 return image.squeeze(0) # Remove batch dimension before returning
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77 if __name__ == "__main__":
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78 arg_parser = argparse.ArgumentParser()
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79 arg_parser.add_argument(
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80 "-im", "--imaging_model", required=True, help="Input BioImage model"
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81 )
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82 arg_parser.add_argument(
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83 "-ii", "--image_file", required=True, help="Input image file"
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84 )
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85 arg_parser.add_argument(
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86 "-is", "--image_size", required=True, help="Input image file's size"
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87 )
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88 arg_parser.add_argument(
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89 "-ia", "--image_axes", required=True, help="Input image file's axes"
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90 )
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92 # get argument values
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93 args = vars(arg_parser.parse_args())
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94 model_path = args["imaging_model"]
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95 input_image_path = args["image_file"]
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96 input_size = args["image_size"]
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98 # load all embedded images in TIF file
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99 test_data = imageio.v3.imread(input_image_path, index="...")
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100 test_data = test_data.astype(np.float32)
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101 test_data = np.squeeze(test_data)
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102
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103 target_image_dim = input_size.split(",")[::-1]
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104 target_image_dim = [int(i) for i in target_image_dim if i != "1"]
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105 target_image_dim = tuple(target_image_dim)
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106
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107 exp_test_data = torch.tensor(test_data)
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108 # check if image dimensions are reversed
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109 reversed_order = list(reversed(range(exp_test_data.dim())))
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110 exp_test_data_T = exp_test_data.permute(*reversed_order)
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111 if exp_test_data_T.shape == target_image_dim and exp_test_data.shape != target_image_dim:
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112 exp_test_data = exp_test_data_T
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113 # check if image dimensions are not equal to target image dimensions
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114 if exp_test_data.shape != target_image_dim:
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115 for i in range(len(target_image_dim) - exp_test_data.dim()):
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116 exp_test_data = exp_test_data.unsqueeze(i)
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117 try:
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118 exp_test_data = dynamic_resize(exp_test_data, target_image_dim)
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119 except Exception as e:
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120 raise RuntimeError(f"Error during resizing: {e}") from e
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121
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122 current_dimension = len(exp_test_data.shape)
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123 input_axes = args["image_axes"]
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124 target_dimension = len(input_axes)
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125 # expand input image based on the number of target dimensions
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126 for i in range(target_dimension - current_dimension):
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127 exp_test_data = torch.unsqueeze(exp_test_data, i)
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128
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129 # load model
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130 model = torch.load(model_path)
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131 model.eval()
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132
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133 # make prediction
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134 pred_data = model(exp_test_data)
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135 pred_data_output = pred_data.detach().numpy()
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136
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137 # save original image matrix
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138 np.save("output_predicted_image_matrix.npy", pred_data_output)
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139
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140 # post process predicted file to correctly save as TIF file
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141 pred_data = torch.squeeze(pred_data)
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142 pred_numpy = pred_data.detach().numpy()
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143
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144 # write predicted TIF image to file
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145 imageio.v3.imwrite("output_predicted_image.tiff", pred_numpy, extension=".tiff")