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correct qc pix2pix and datatype bmz export
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@esgomezm
esgomezm authored Jan 11, 2024
1 parent 728412e commit 7720488
Showing 1 changed file with 33 additions and 17 deletions.
50 changes: 33 additions & 17 deletions Colab_notebooks/pix2pix_ZeroCostDL4Mic.ipynb
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Expand Up @@ -229,11 +229,12 @@
"colab": {
"background_save": true
},
"id": "fq21zJVFNASx"
"id": "fq21zJVFNASx",
"tags": []
},
"outputs": [],
"source": [
"Notebook_version = '1.16.3'\n",
"Notebook_version = '1.16.4'\n",
"Network = 'pix2pix'\n",
"\n",
"\n",
Expand Down Expand Up @@ -361,8 +362,11 @@
"# average function\n",
"def Average(lst):\n",
" return sum(lst) / len(lst)\n",
"def ssim(img1, img2):\n",
" return structural_similarity(img1,img2,data_range=1.,full=True, gaussian_weights=True, use_sample_covariance=False, sigma=1.5)\n",
"def ssim(img1, img2, multichannel=False):\n",
" if multichannel:\n",
" return structural_similarity(img1,img2,data_range=1.,full=True, multichannel=True)\n",
" else:\n",
" return structural_similarity(img1,img2,data_range=1.,full=True, gaussian_weights=True, use_sample_covariance=False, sigma=1.5)\n",
"\n",
"\n",
"def normalize(x, pmin=3, pmax=99.8, axis=None, clip=False, eps=1e-20, dtype=np.float32):\n",
Expand Down Expand Up @@ -522,8 +526,8 @@
" test_source_matched = match_histograms(test_source, test_GT, multichannel=True)\n",
" # -------------------------------- Calculate the metric maps and save them --------------------------------\n",
" # Calculate the SSIM maps\n",
" index_SSIM_GTvsPrediction, img_SSIM_GTvsPrediction = ssim(test_GT, test_prediction_matched)\n",
" index_SSIM_GTvsSource, img_SSIM_GTvsSource = ssim(test_GT, test_source_matched)\n",
" index_SSIM_GTvsPrediction, img_SSIM_GTvsPrediction = ssim(test_GT, test_prediction_matched, multichannel=True)\n",
" index_SSIM_GTvsSource, img_SSIM_GTvsSource = ssim(test_GT, test_source_matched, multichannel=True)\n",
" ssim_score_list.append(index_SSIM_GTvsPrediction)\n",
"\n",
" #Save ssim_maps\n",
Expand Down Expand Up @@ -607,8 +611,8 @@
"\n",
" # -------------------------------- Calculate the metric maps and save them --------------------------------\n",
" # Calculate the SSIM maps\n",
" index_SSIM_GTvsPrediction, img_SSIM_GTvsPrediction = ssim(test_GT_norm, test_prediction_norm)\n",
" index_SSIM_GTvsSource, img_SSIM_GTvsSource = ssim(test_GT_norm, test_source_norm)\n",
" index_SSIM_GTvsPrediction, img_SSIM_GTvsPrediction = ssim(test_GT_norm, test_prediction_norm, multichannel=False)\n",
" index_SSIM_GTvsSource, img_SSIM_GTvsSource = ssim(test_GT_norm, test_source_norm, multichannel=False)\n",
" ssim_score_list.append(index_SSIM_GTvsPrediction)\n",
"\n",
" #Save ssim_maps\n",
Expand Down Expand Up @@ -1895,7 +1899,8 @@
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "i6LXhHf61w3K"
"id": "i6LXhHf61w3K",
"tags": []
},
"outputs": [],
"source": [
Expand Down Expand Up @@ -1973,7 +1978,8 @@
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "eHUa1opwWi9G"
"id": "eHUa1opwWi9G",
"tags": []
},
"outputs": [],
"source": [
Expand Down Expand Up @@ -2002,7 +2008,7 @@
"Normalisation_QC_target = \"Contrast stretching\" #@param [\"None\", \"Contrast stretching\", \"Adaptive Equalization\"]\n",
"\n",
"#@markdown ###Did you evaluate all the checkpoints and only want to visualise the results? (It reduces significantly the processing time if you did it already)\n",
"avoid_evaluating_again = False #@param {type:\"boolean\"}\n",
"avoid_evaluating_again = True #@param {type:\"boolean\"}\n",
"#@markdown ####Choose the frequency of checkpoints to evaluate. If 1, it will evaluate all the model checkpoints available.\n",
"QC_evaluation_checkpoint_freq = 1 #@param {type:\"number\"}\n",
"QC_freq = np.int(QC_evaluation_checkpoint_freq)\n",
Expand Down Expand Up @@ -2042,7 +2048,10 @@
"\n",
" #Here we copy and normalise the data\n",
" if prepare_testdata:\n",
" normalise_data(Source_QC_folder, Target_QC_folder, Normalisation_QC_source, Normalisation_QC_target, path2im)\n"
" normalise_data(Source_QC_folder, Target_QC_folder, Normalisation_QC_source, Normalisation_QC_target, path2im)\n",
"else:\n",
" # Here we count how many images are in our folder to be predicted and we had a few\n",
" Nb_files_Data_folder = len(os.listdir(Source_QC_folder))"
]
},
{
Expand Down Expand Up @@ -2499,7 +2508,8 @@
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "9WvxYBkzEa1d"
"id": "9WvxYBkzEa1d",
"tags": []
},
"outputs": [],
"source": [
Expand All @@ -2511,8 +2521,8 @@
"#@markdown ##Introduce the information to document your model:\n",
"Trained_model_name = \"\" #@param {type:\"string\"}\n",
"Trained_model_authors = \"\\\"[Author 1, Author 2]\\\"\" #@param {type:\"string\"}\n",
"Trained_model_authors_affiliation = \"\\\"[Affiliation of author 1, Affiliation of author 2]\\\"\" #@param {type:\"string\"}\n",
"Trained_model_description = \"A conditional cycleGAN trained to infer XXXX from XXX\" #@param {type:\"string\"}\n",
"Trained_model_authors_affiliation = \"\\\"[Author 1 Affiliation, Author 2 Affiliation]\\\"\" #@param {type:\"string\"}\n",
"Trained_model_description = \"A conditional cycleGAN trained to infer XXX from XXX \" #@param {type:\"string\"}\n",
"Trained_model_license = \"CC-BY-NC-1.0\" #@param [\"BSD-Protection\", \"CC-BY-SA-2.0\", \"Unicode-DFS-2016\", \"CC-BY-ND-2.5\", \"CC-BY-NC-ND-3.0\", \"BSD-3-Clause-LBNL\", \"NCGL-UK-2.0\", \"GPL-1.0+\", \"OSL-1.0\", \"MIT\", \"MPL-2.0\", \"CC-BY-NC-1.0\", \"CC-BY-NC-ND-2.5\", \"CC-BY-SA-2.0-UK\", \"BSD-4-Clause-Shortened\"]\n",
"\n",
"\n",
Expand Down Expand Up @@ -2610,12 +2620,14 @@
"\n",
"kwargs = dict(\n",
" input_names=[\"input\"],\n",
" input_data_range=[[0., 255.]],\n",
" input_axes=[\"bcyx\"],\n",
" pixel_sizes=[pixel_size],\n",
" preprocessing = bmz_preprpocess)\n",
"\n",
"output_spec = dict(\n",
" output_names=[\"output\"],\n",
" output_data_range=[[-np.inf, np.inf]],\n",
" output_axes=[\"bcyx\"],\n",
" postprocessing=bmz_postprocess,\n",
" output_reference=[\"input\"],\n",
Expand Down Expand Up @@ -2697,7 +2709,7 @@
"#else:\n",
"# y = np.expand_dims(y, axis=[0, 1]) # add batch dimension\n",
"#np.save(os.path.join(output_root, \"test-output.npy\"), y)\n",
"np.save(os.path.join(output_root, \"test-output.npy\"), x)\n",
"np.save(os.path.join(output_root, \"test-output.npy\"), x.astype(np.float32))\n",
"# Build the bioimage model zoo model\n",
"# ---------------------------------------\n",
"for i in range(2):\n",
Expand Down Expand Up @@ -3103,6 +3115,10 @@
"# **7. Version log**\n",
"---\n",
"\n",
"<font size = 4>**v1.16.4**:\n",
"* Corrects data type and range in the bioimageio specs.\n",
"* Corrects for RGB data quality control SSIM.\n",
"\n",
"<font size = 4>**v1.16.3**:\n",
"* Import the `bioimageio.core` library and export bioimage model zoo compatible models.\n",
"\n",
Expand Down Expand Up @@ -3181,7 +3197,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
"version": "3.8.18"
}
},
"nbformat": 4,
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