From 57f46739f4365f59cd52c515bdd3fae2e01b734e Mon Sep 17 00:00:00 2001 From: Anup Kumar Date: Fri, 2 Aug 2024 17:26:57 +0200 Subject: [PATCH] Add tool for analysing images using Bioimage AI models (#1391) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add model inference tool * add tool tests and test data * fix linting issues * Update tools/bioimaging/bioimage_inference.xml Fix suggestion in name Co-authored-by: Leonid Kostrykin * Update tools/bioimaging/bioimage_inference.xml Fix suggestion to include edam annotation Co-authored-by: Leonid Kostrykin * update * add model size * fix dymanic input shapes * remove comments * upate * fix linting error * replace test files * update test files * add image size to test * fix remove comments * Apply suggestions from code review Fix review comments Co-authored-by: Beatriz Serrano-Solano Co-authored-by: Leonid Kostrykin * use correct model name * use original value of predicted matrix * add support for png * add creator * update bioimage name * fix review comments * Apply suggestions from code review Fix review comments Co-authored-by: Leonid Kostrykin * fix review comments * Apply suggestions from code review Fix review comments Co-authored-by: Leonid Kostrykin * Apply suggestions from code review Add missing articles and replace shape by size Co-authored-by: Leonid Kostrykin * Apply suggestions from code review Fix review Co-authored-by: Björn Grüning * fix review * restore tool version prefix * Apply suggestions from code review Co-authored-by: Leonid Kostrykin --------- Co-authored-by: Leonid Kostrykin Co-authored-by: Beatriz Serrano-Solano Co-authored-by: Björn Grüning --- tools/bioimaging/.shed.yml | 11 +++ tools/bioimaging/bioimage_inference.xml | 80 ++++++++++++++++ tools/bioimaging/main.py | 86 ++++++++++++++++++ .../input_nucleisegboundarymodel.png | Bin 0 -> 26189 bytes .../output_nucleisegboundarymodel.tif | Bin 0 -> 524846 bytes .../output_nucleisegboundarymodel_matrix.npy | Bin 0 -> 524416 bytes 6 files changed, 177 insertions(+) create mode 100644 tools/bioimaging/.shed.yml create mode 100644 tools/bioimaging/bioimage_inference.xml create mode 100644 tools/bioimaging/main.py create mode 100644 tools/bioimaging/test-data/input_nucleisegboundarymodel.png create mode 100644 tools/bioimaging/test-data/output_nucleisegboundarymodel.tif create mode 100644 tools/bioimaging/test-data/output_nucleisegboundarymodel_matrix.npy diff --git a/tools/bioimaging/.shed.yml b/tools/bioimaging/.shed.yml new file mode 100644 index 0000000000..fdbebf7d3d --- /dev/null +++ b/tools/bioimaging/.shed.yml @@ -0,0 +1,11 @@ +name: bioimage_inference +owner: bgruening +description: "Load model from BioImage.IO and make inferences" +homepage_url: https://github.com/bgruening/galaxytools +long_description: + Load model from BioImage.IO and make inferences +remote_repository_url: https://github.com/bgruening/galaxytools/tree/recommendation_training/tools/bioimaging +type: unrestricted +categories: +- Imaging + diff --git a/tools/bioimaging/bioimage_inference.xml b/tools/bioimaging/bioimage_inference.xml new file mode 100644 index 0000000000..f6e182d868 --- /dev/null +++ b/tools/bioimaging/bioimage_inference.xml @@ -0,0 +1,80 @@ + + with PyTorch + + 2.3.1 + 0 + + + + + + + + + operation_3443 + + + pytorch + pytorch + + + python + pytorch + torchvision + imageio + + echo "@VERSION@" + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + config > test_information > inputs > size) + + **Output files** + - Predicted image: Predicted image using the BioImage.IO model + - Predicted image matrix: Predicted image matrix in original dimensions + ]]> + + + 10.1145/3620665.3640366 + 10.1101/2022.06.07.495102 + + diff --git a/tools/bioimaging/main.py b/tools/bioimaging/main.py new file mode 100644 index 0000000000..87a9c6ddee --- /dev/null +++ b/tools/bioimaging/main.py @@ -0,0 +1,86 @@ +""" +Predict images using AI models from BioImage.IO +""" + +import argparse + +import imageio +import numpy as np +import torch + + +def find_dim_order(user_in_shape, input_image): + """ + Find the correct order of input image's + shape. For a few models, the order of input size + mentioned in the RDF.yaml file is reversed compared + to the input image's original size. If it is reversed, + transpose the image to find correct order of image's + dimensions. + """ + image_shape = list(input_image.shape) + # reverse the input shape provided from RDF.yaml file + correct_order = user_in_shape.split(",")[::-1] + # remove 1s from the original dimensions + correct_order = [int(i) for i in correct_order if i != "1"] + if (correct_order[0] == image_shape[-1]) and (correct_order != image_shape): + input_image = torch.tensor(input_image.transpose()) + return input_image, correct_order + + +if __name__ == "__main__": + arg_parser = argparse.ArgumentParser() + arg_parser.add_argument("-im", "--imaging_model", required=True, help="Input BioImage model") + arg_parser.add_argument("-ii", "--image_file", required=True, help="Input image file") + arg_parser.add_argument("-is", "--image_size", required=True, help="Input image file's size") + + # get argument values + args = vars(arg_parser.parse_args()) + model_path = args["imaging_model"] + input_image_path = args["image_file"] + + # load all embedded images in TIF file + test_data = imageio.v3.imread(input_image_path, index="...") + test_data = np.squeeze(test_data) + test_data = test_data.astype(np.float32) + + # assess the correct dimensions of TIF input image + input_image_shape = args["image_size"] + im_test_data, shape_vals = find_dim_order(input_image_shape, test_data) + + # load model + model = torch.load(model_path) + model.eval() + + # find the number of dimensions required by the model + target_dimension = 0 + for param in model.named_parameters(): + target_dimension = len(param[1].shape) + break + current_dimension = len(list(im_test_data.shape)) + + # update the dimensions of input image if the required image by + # the model is smaller + slices = tuple(slice(0, s_val) for s_val in shape_vals) + + # apply the slices to the reshaped_input + im_test_data = im_test_data[slices] + exp_test_data = torch.tensor(im_test_data) + + # expand input image's dimensions + for i in range(target_dimension - current_dimension): + exp_test_data = torch.unsqueeze(exp_test_data, i) + + # make prediction + pred_data = model(exp_test_data) + pred_data_output = pred_data.detach().numpy() + + # save original image matrix + np.save("output_predicted_image_matrix.npy", pred_data_output) + + # post process predicted file to correctly save as TIF file + pred_data = torch.squeeze(pred_data) + pred_numpy = pred_data.detach().numpy() + + # write predicted TIF image to file + imageio.v3.imwrite("output_predicted_image.tif", pred_numpy, extension=".tif") diff --git a/tools/bioimaging/test-data/input_nucleisegboundarymodel.png b/tools/bioimaging/test-data/input_nucleisegboundarymodel.png new file mode 100644 index 0000000000000000000000000000000000000000..d041852239e3855ba692bf8488cd025c4fa97d0f GIT binary patch literal 26189 zcmV(>K-j;DP)J{N zW#+vnT#JY}`2Z4z?)tuZFZ14WB0StZBKW%>W=PfvIsusgfKI^#rwNcrFo}Y}0fKh* z^Go{E!!PfFX@-k%ktV|Fy$K@nf9QnNNNcwJ{qxP{!}q^>>iN?sP$q!_X#l4$K0n)B 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