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@chiarasch
chiarasch committed Jul 29, 2024
1 parent 9997992 commit 910b76a
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203 changes: 187 additions & 16 deletions notebooks/SignalStar_concatenate_tiles.ipynb
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"cells": [
{
"cell_type": "code",
"execution_count": 49,
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# load libraries\n",
"\n",
"import os\n",
"import os\n",
"import numpy as np\n",
"import tifffile as tiff"
"import tifffile as tiff\n",
"from skimage import util"
]
},
{
"cell_type": "code",
"execution_count": 62,
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# Paths to input and output directories\n",
"input_dir = './../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/test9/'\n",
"output_file = './../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/numpy_stacked_output9.tif'"
"input_dir = './../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/test/'\n",
"output_file = './../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/stacked_image_4tiles_float32.tif'"
]
},
{
"cell_type": "code",
"execution_count": 63,
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Stacked image saved to ./../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/numpy_stacked_output9.tif\n"
"Stacked image saved to ./../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/stacked_image_4tiles_float32.tif\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/79/cjwcycvd2_9dyfffbym60x200000gn/T/ipykernel_44301/1808586505.py:27: DeprecationWarning: <tifffile.imsave> is deprecated. Use tifffile.imwrite\n",
"/var/folders/79/cjwcycvd2_9dyfffbym60x200000gn/T/ipykernel_9976/1808586505.py:27: DeprecationWarning: <tifffile.imsave> is deprecated. Use tifffile.imwrite\n",
" tiff.imsave(output_file, stacked_image)\n"
]
}
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},
{
"cell_type": "code",
"execution_count": 64,
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"float32\n",
"0.0 100.97945\n"
"0.0 145.08916\n"
]
}
],
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},
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"#rescale float32 to uint16 with scikit image:\n",
"numpy_stacked_tif = './../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/numpy_stacked_output9.tif'\n",
"#rescale float32 to uint16 with scikit image with max value per image\n",
"numpy_stacked_tif = output_file\n",
"from skimage import util\n",
"\n",
"# Read the float TIFF\n",
"img = tiff.imread(numpy_stacked_tif)\n",
"# Assuming img is your image data\n",
"\n",
"# Normalize to [0, 1] !!!per channel!!! TODO\n",
"# Normalize to [0, 1] per channel\n",
"\n",
"img_normalized = img / img.max()\n",
"\n",
"# Convert to uint16\n",
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"# Save the converted image\n",
"#io.imsave('./output_uint16.tif', img_uint16)\n",
"# Save the converted image\n",
"tiff.imwrite('./../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/numpy_stacked_output9_uint16.tif', img_uint16)"
"tiff.imwrite('./../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/numpy_stacked_output_day2_uint16.tif', img_uint16)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20.023974266052278\n",
"3.3319761753082275\n",
"3.9181082248687744\n",
"1.088370442390442\n",
"66.18979965209974\n",
"11.126164436340332\n"
]
}
],
"source": [
"# CORRECT normalize with channel 99th percentile\n",
"import numpy as np\n",
"\n",
"numpy_stacked_tif = output_file\n",
"\n",
"# Read the float TIFF\n",
"img = tiff.imread(numpy_stacked_tif)\n",
"\n",
"# Initialize an empty array for the normalized image\n",
"img_normalized = np.empty_like(img, dtype=np.float32)\n",
"\n",
"# Normalize each channel separately\n",
"for ch in range(img.shape[1]): # Now channels are the second dimension\n",
" percentile_99 = np.percentile(img[:, ch, :], 99)\n",
" img_normalized[:, ch, :] = img[:, ch, :] / percentile_99\n",
" img_normalized[:, ch, :][img_normalized[:, ch, :] > 1] = 1 # Cap values at 1\n",
" print(percentile_99)\n",
"\n",
"# Save the converted image\n",
"tiff.imwrite('./../../../../data/Human_squamous_cell_carcinoma_stained_with_SignalStar_mIHC_technology/numpy_stacked_output_day2_min99th_uint16.tif', img_normalized)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from skimage import util\n",
"\n",
"numpy_stacked_tif = output_file\n",
"# Read the float TIFF\n",
"img = tiff.imread(numpy_stacked_tif)\n",
"\n",
"# Initialize an empty array for the normalized image\n",
"img_normalized = np.empty_like(img, dtype=np.float32)\n",
"img.shape[1]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 54,
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