From 1ee8623c60213e200093a59d717439567c4ede2e Mon Sep 17 00:00:00 2001 From: jpitalopez Date: Tue, 25 Jun 2024 13:25:38 +0200 Subject: [PATCH] data 'tif' support --- bioMONAI/data.py | 48 +++++---- nbs/03_data.ipynb | 249 +++++++++++++++++++++++++++++++--------------- 2 files changed, 197 insertions(+), 100 deletions(-) diff --git a/bioMONAI/data.py b/bioMONAI/data.py index dc8ab47..938bdce 100644 --- a/bioMONAI/data.py +++ b/bioMONAI/data.py @@ -45,7 +45,7 @@ def tiff_reader(path, # The path to the TIFF file to be read # Return the image data and the affine matrix return data, affine -# %% ../nbs/03_data.ipynb 9 +# %% ../nbs/03_data.ipynb 11 from aicsimageio import AICSImage def lif_reader(path, # The path to the LIF file to be read @@ -83,7 +83,7 @@ def lif_reader(path, # The path to the LIF file to be read # Return the image data and the affine matrix return data, affine -# %% ../nbs/03_data.ipynb 12 +# %% ../nbs/03_data.ipynb 14 from aicsimageio.readers import CziReader def czi_reader(path, # The path to the CZI file to be read @@ -113,7 +113,7 @@ def czi_reader(path, # The path to the CZI file to be read # Return the image data and the affine matrix return data, affine -# %% ../nbs/03_data.ipynb 15 +# %% ../nbs/03_data.ipynb 17 def _image_reader(path, # The file path to the image ): @@ -143,6 +143,16 @@ def _image_reader(path, # The file path to the image return data, affine + if (path[-3:]=="tif"): + + # Reorder for tiff files + data = image_aics.get_image_data("CZYX", T=0) # returns 4D CZYX numpy array + + affine = np.eye(4) #to change + + return data, affine + + # Convert to numpy array data = image_aics.data @@ -156,7 +166,7 @@ def _image_reader(path, # The file path to the image return data, affine -# %% ../nbs/03_data.ipynb 18 +# %% ../nbs/03_data.ipynb 20 import h5py def h5_reader(path, dataset): @@ -170,7 +180,7 @@ def h5_reader(path, dataset): return dataset1 -# %% ../nbs/03_data.ipynb 21 +# %% ../nbs/03_data.ipynb 23 def _preprocess(obj, # The object to preprocess reorder, # Whether to reorder the object resample # Whether to resample the object @@ -204,7 +214,7 @@ def _preprocess(obj, # The object to preprocess return obj, original_size -# %% ../nbs/03_data.ipynb 23 +# %% ../nbs/03_data.ipynb 25 def _load_and_preprocess(file_path, # Image file path reorder=False, # Whether to reorder data for canonical (RAS+) orientation resample=False, # Whether to resample image to different voxel sizes and dimensions @@ -228,7 +238,7 @@ def _load_and_preprocess(file_path, # Image file path return org_img, input_img, org_size -# %% ../nbs/03_data.ipynb 26 +# %% ../nbs/03_data.ipynb 28 def _multi_channel(image_paths: (L, list), # List of image paths (e.g., T1, T2, T1CE, DWI) reorder: bool = False, # Whether to reorder data for canonical (RAS+) orientation resample: list = None, # Whether to resample image to different voxel sizes and dimensions @@ -264,7 +274,7 @@ def _multi_channel(image_paths: (L, list), # List of image paths (e.g., T1, T2, input_img.set_data(tensor) return org_img, input_img, org_size -# %% ../nbs/03_data.ipynb 29 +# %% ../nbs/03_data.ipynb 31 def img_reader(file_path: (str, Path, L, list), # Path to the image dtype=torch.Tensor, # Datatype for the return value. Defaults to torch.Tensor reorder: bool = False, # Whether to reorder to canonical orientation @@ -303,7 +313,7 @@ def img_reader(file_path: (str, Path, L, list), # Path to the image return org_img, input_img, org_size -# %% ../nbs/03_data.ipynb 33 +# %% ../nbs/03_data.ipynb 35 class MetaResolver(type(torch.Tensor), metaclass=BypassNewMeta): """ A class to bypass metaclass conflict: @@ -312,12 +322,12 @@ class MetaResolver(type(torch.Tensor), metaclass=BypassNewMeta): pass -# %% ../nbs/03_data.ipynb 35 +# %% ../nbs/03_data.ipynb 37 from .core import show_images_grid, mosaic_image_3d from monai.data import MetaTensor -# %% ../nbs/03_data.ipynb 36 +# %% ../nbs/03_data.ipynb 38 class BioImageBase(MetaTensor, metaclass=MetaResolver): """ A class that represents an image object. @@ -389,7 +399,7 @@ def __repr__(self) -> str: """Returns the string representation of the ImageBase instance.""" return f"BioImageBase{self.as_tensor().__repr__()[6:]}" -# %% ../nbs/03_data.ipynb 38 +# %% ../nbs/03_data.ipynb 40 class BioImage(BioImageBase): """Subclass of BioImageBase that represents 2D and 3D image objects.""" _show_args = {'cmap':'gray'} @@ -423,7 +433,7 @@ def __repr__(self) -> str: # return f'{self.__class__.__name__} shape={"x".join([str(d) for d in self.shape])}' return f"BioImage{self.as_tensor().__repr__()[6:]}" -# %% ../nbs/03_data.ipynb 41 +# %% ../nbs/03_data.ipynb 43 class BioImageStack(BioImageBase): """Subclass of BioImageBase that represents a 3D image object.""" @@ -431,7 +441,7 @@ def __repr__(self) -> str: """Returns the string representation of the ImageBase instance.""" return f"BioImageStack{self.as_tensor().__repr__()[6:]}" -# %% ../nbs/03_data.ipynb 44 +# %% ../nbs/03_data.ipynb 46 class BioImageProject(BioImageBase): """Subclass of BioImageBase that represents a 2D image object.""" _show_args = {'cmap':'gray'} @@ -465,7 +475,7 @@ def __repr__(self) -> str: """Returns the string representation of the ImageBase instance.""" return f"BioImage{self.as_tensor().__repr__()[6:]}" -# %% ../nbs/03_data.ipynb 47 +# %% ../nbs/03_data.ipynb 49 class BioImageMulti(BioImageBase): """Subclass of BioImageBase that represents a multi-channel 2D image object.""" @@ -494,7 +504,7 @@ def __repr__(self) -> str: return f"BioImageMulti{self.as_tensor().__repr__()[6:]}" -# %% ../nbs/03_data.ipynb 53 +# %% ../nbs/03_data.ipynb 55 class Tensor2BioImage(DisplayedTransform): def __init__(self, cls:BioImageBase=BioImageStack): self.cls = cls @@ -506,12 +516,12 @@ def encodes(self, o): if isinstance(o, torch.Tensor): return self.cls(o) -# %% ../nbs/03_data.ipynb 56 +# %% ../nbs/03_data.ipynb 58 def BioImageBlock(cls:BioImageBase=BioImageStack): "A `TransformBlock` for images of `cls`" return TransformBlock(type_tfms=cls.create, batch_tfms=[Tensor2BioImage(cls)]) # IntToFloatTensor -# %% ../nbs/03_data.ipynb 59 +# %% ../nbs/03_data.ipynb 61 @typedispatch def show_batch(x:BioImageBase, y:BioImageBase, samples, ctxs=None, max_n=10, nrows=None, ncols=None, figsize=None, **kwargs): if ctxs is None: ctxs = get_grid(min(len(samples), max_n), nrows=nrows, ncols=ncols, figsize=figsize, double=True) @@ -519,7 +529,7 @@ def show_batch(x:BioImageBase, y:BioImageBase, samples, ctxs=None, max_n=10, nro ctxs[i::2] = [b.show(ctx=c, **kwargs) for b,c,_ in zip(samples.itemgot(i),ctxs[i::2],range(max_n))] return ctxs -# %% ../nbs/03_data.ipynb 61 +# %% ../nbs/03_data.ipynb 63 @typedispatch def show_results(x:BioImageBase, y:BioImageBase, samples, outs, ctxs=None, max_n=10, figsize=None, **kwargs): if ctxs is None: ctxs = get_grid(3*min(len(samples), max_n), ncols=3, figsize=figsize, title='Input/Target/Prediction') diff --git a/nbs/03_data.ipynb b/nbs/03_data.ipynb index 36e9dab..1edafcb 100644 --- a/nbs/03_data.ipynb +++ b/nbs/03_data.ipynb @@ -99,13 +99,64 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ - "# file_path = 'data_examples/example_tiff.tiff'\n", + "# file_path = 'data_examples/example_tiff.tiff's\n", + "\n", + "# d, _ = tiff_reader(file_path)\n", + "\n", + "\n", + "file_path = '../../bioMONAI_0/_data/Thunder_20230308/nuevos_datos/dataset_2/targets/1.tif'\n", + "\n", + "d, _ = tiff_reader(file_path)\n", + "\n", + "file_path = '../../bioMONAI_0/_data/Babesia/TRITC/O11_TRITC_frame01.tiff'\n", "\n", - "# d, _ = tiff_reader(file_path)" + "e, _ = tiff_reader(file_path)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 1, 2048, 2048)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 1, 512, 512)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "e.shape" ] }, { @@ -235,7 +286,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -270,6 +321,16 @@ " \n", " return data, affine\n", "\n", + " if (path[-3:]==\"tif\"):\n", + "\n", + " # Reorder for tif files\n", + " data = image_aics.get_image_data(\"CZYX\", T=0) # returns 4D CZYX numpy array\n", + " \n", + " affine = np.eye(4) #to change\n", + " \n", + " return data, affine\n", + " \n", + "\n", " # Convert to numpy array \n", " data = image_aics.data\n", "\n", @@ -285,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -303,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -323,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -339,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -387,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -418,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -435,7 +496,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -479,7 +540,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -497,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -543,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -566,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -590,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -691,7 +752,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -733,14 +794,35 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 96, 512, 512])\n", + "torch.Size([1, 512, 512])\n", + "torch.Size([1, 56, 2048, 2048])\n", + "torch.Size([1, 2048, 2048])\n" + ] + } + ], "source": [ - "# a = BioImage.create('../../bioMONAI_0/_data/Babesia/RI/O11_RI_frame01.tiff')\n", - "# print(a.shape)\n", - "# a = BioImage.create('../../bioMONAI_0/_data/Babesia/TRITC/O11_TRITC_frame01.tiff')\n", - "# a.shape" + "a = BioImage.create('../../bioMONAI_0/_data/Babesia/RI/O11_RI_frame01.tiff')\n", + "print(a.shape)\n", + "a = BioImage.create('../../bioMONAI_0/_data/Babesia/TRITC/O11_TRITC_frame01.tiff')\n", + "print(a.shape)\n", + "\n", + "\n", + "\n", + "a = BioImage.create('../../bioMONAI_0/_data/Thunder_20230308/nuevos_datos/dataset_2/inputs/1.tif')\n", + "print(a.shape)\n", + "a = BioImage.create('../../bioMONAI_0/_data/Thunder_20230308/nuevos_datos/dataset_2/targets/1.tif')\n", + "print(a.shape)\n", + "\n", + "\n", + "\n" ] }, { @@ -754,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -770,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -791,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -833,12 +915,24 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 76, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 512, 512])" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# a = BioImageProject.create('../../bioMONAI_0/_data/Babesia/RI/O11_RI_frame01.tiff')\n", - "# a.shape" + "a = BioImageProject.create('../../bioMONAI_0/_data/Babesia/RI/O11_RI_frame01.tiff')\n", + "#a = BioImageProject.create('../../bioMONAI_0/_data/Thunder_20230308/nuevos_datos/dataset_2/inputs/1.tif')\n", + "a.shape" ] }, { @@ -852,7 +946,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -889,7 +983,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -908,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -944,7 +1038,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -974,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -1008,7 +1102,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -1035,7 +1129,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -1058,7 +1152,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -1082,7 +1176,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -1092,7 +1186,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -1103,7 +1197,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -1112,7 +1206,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -1125,7 +1219,7 @@ }, { "data": { - "image/png": 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2dXzcT56+te6naH/9yM9lX//lgyfc9tZdPxmzt0+nax+ZWnHbVSL35rZOWVQp2nudSgZemNfpv80z/nOj0o+jlN+p835y3qpIuY1SdlWarUpxzkk4rpLalyitec7SU4bVvpx98zm3fUrsl5neM5VL+60X/ORlM52+LNconrscUfJx9LzuVbW79z73vqpMS47mmzpOlLCcmpjcu3T7gfYVpRXnpC8/SDlpzdBy0myjlGFFpfmqJN+c8XXKrj6G6LWkJ+aqNeak71aZCp0jdS1RWnPq+hd/R69j+jv+PW4s+Gn8zWcnZV/RnD8L39gCAAAAAIrGwRYAAAAAUDQOtgAAAACAonGwBQAAAAAUjYMtAAAAAKBoO47D+saXf+y2/9H84/Kavjt+e+uWnw641KvTbH994rLb/sOl9JTTK1dEMnFbJLB1dMpc896I237txpjbfvignxZsZnbho0fddpWW/N69w7KvpZafZKzazcz2D6657V8/9IHb/uPLx2VfjX4/HW35lj/+f1x8Svb1q08suO2tLT/ldKhPJ5x+dGfcbVcp2mvbOhUwSoXey1TKcJSMuy4Si1X6sHp/RPU1Nq/vgUpSVinOUTJu6rzM0lN2VSL0wqz+OZaWy1q91MRglXwc9XX26e+67eeuPSn7Wn71pNvePJOecBylUu9Vnbsttz36a7W6xmYf8vtq+mmWZjrJOCetucpU5igxuaq+diNF2kynH6v05dQUZ7M4lVlR88rpK3WMKHm5PZSecLsXqATYq8/o330qZVe156Ql5/SlrlFridKCVZKySuY98JbeL5X+q9aSk2Kck7Csxp95rSmvWXhhym3XydPp1Fqmv5OeVrw2o6vBpI6/E3xjCwAAAAAoGgdbAAAAAEDRONgCAAAAAIrGwRYAAAAAUDQOtgAAAACAonGwBQAAAAAUbcd5yksbflmb3qBET+t4Wlz2vetD8rWfTfilbZZujrrt7XW9tN4lPxK8fcCPse4d1RH2K62Gf81FP3b/2nVdIqT3qF9C4MO1/W773HU/8tvMrLfHL1GiyvCYmS3e8CO5NztfdNvHRnVZB1U+56GRFbd9YWVC9qVK9KjyRF89MC/7mpryyy39y8tfdduXW7p8w8hgelmRvUCVnImoMjnjcytu++rsuOxLlWPJKcXz0D/6odv+4T/4y8l96bI26fNSVBmiiCpFo8rdmJmtzqb9zXJ8zi8f8gn/Z6waIyoPpO7xy/O6RJCyLp4jVTro7JvPyb4WLohyS7+RPK1d0zPs/76UJX2Ca7oZJXJyyvpUJWfsKssTVVnWR5W1MdNlfZSNST2vwYt+6abdKhG0G2Psxrw+D6q0SU7JGSUqBZNaWiWalypd1DqW/rlDzWvgY79ETuuY7iu1PFKOKksqNZ+dTL5GicpGqedCX6OfldTSSTn7tRN8YwsAAAAAKBoHWwAAAABA0TjYAgAAAACKxsEWAAAAAFA0DrYAAAAAgKLtOArtrZ9Pu+19l3ViaHvMT+btXam77V2/2czMfvruQ/4L/f4Y9WWdAjZ82U/iuiu2o/eK7ssf3azb76eAqXYzs82Wn46oEoPvfTAu+9r3yIrb/pWDH8prvn/zS257f49Ovk711Nii2/6hSD42M7uyeNBtP35qxW1fXNcJyz+4Oeu2qzVubOh7PzH04FJB74dKsx2bV0+zfu3SN8bd9pxkXOXk91bka5dE+rGab5QWrNKiVfJz5L2X/LT01LHNzJbNTz+OEpZVf+v+4/8Z801Lco7ub+p9qTRhOegrGmevUunH218WN9nMTKTG9jT9xNzOlJ+Ub2ZWS0xSjtKHq0wszhlfUXtcy0hFVunHqcnHZmbtST+pvLflJ9Ka6fTj1PtYtZxU5l82Kkk4J802NeE4Gj+nryoTnm8+5a/xwFv+vqj3fzJ+eiqzkpr+a5Z+j3NSrHP6UnKSjFOvydmvneAbWwAAAABA0TjYAgAAAACKxsEWAAAAAFA0DrYAAAAAgKJxsAUAAAAAFG3HkWejf+on123oAFrb97Efc1wTIbvbQThee8vva99VfwmNZZ3wOnDLT9vqXfP7ageBoeuTfnv/qp8C1u3Rf0vYFvty3cb8F8Z0athfO/Gu2/7OrWPymr45P+nx4qKfSP383/iB7Guj4+/lzfaQ275/cE32tXZ01W2/teHPt7+uU5z/10V/LV1x73uGdZrcpfcP+y+clpfsCTmJweoa1X7ye/p+mvmvrZzykz6jxN6p8/rfuSdKflbUvKJUYpXYm7P3SpSkPPHiJf+FV9MTlh97xb9f6r7MnL4o+1qwh+VrnmiNqyL8V42/cCFt7FJFKbsqGXhrxv9ZVn9nXval8ix7hv2f8Sph2Cz9L+xRWrNKeM5JXlZrUWuPEn7rIpF6/WG9lsaffeS294k5V5liHa1F9aX2Uu191NdeTXH+PKiU25xkXJ1aqz/upybQTr2+JF9beGHKbc9JcVbpx8rj374lX2s+Kz6sZ8hZi0osVsnAjQX/59gn41S3FkU9E1Ov63mpPVb7FaV+R+N8Fr6xBQAAAAAUjYMtAAAAAKBoHGwBAAAAAEXjYAsAAAAAKBoHWwAAAABA0TjYAgAAAACKtuNyPwMrfiR1bVtFi5vt/7kfb7/4W37pnsf+lS5TcHfGL7mx3e+PP3JRlxZoD/e77UPX/Hjrnm0V7m/WueiP3x7y/2awNqH/ltDeJ0oaNf0xVmd0X/95+C+47du3/DIkZmZDIkV//YhfomTutih3Y2Z/8IU/dttXO37pkN++PiP7unXNv/crW+Nu+2Ldj5s3M7N+Ue5FtDf+TJea2Q7KQO1lqrTM+uxGcFVa+ZpL3xiXPaWW3IlLvlT3t7nUeamSPjl9Re9XpXiikkqytI0okROpv+KXUBi8MO62z9lR3Zl4xtQ9jsoQnZq+4rYvi5JGY6b3uMrnaC9TJXdUiaD2l/UDE5UCSqXKwbQn/Z/90djqLteCkjOyL1WiSJTVUaWGor4GzC9BZ2a29sRxt33wYnopDFU+J5pzVaKSSrIM0y9hWZ9UUTkUVSYmKhFUlah0jpqXKoUTlRpKvWZtRpe72g261JLeF1U6KdrjqNxSVX2pa6osNaT2JBp/J/58/DYHAAAAAPzS4mALAAAAACgaB1sAAAAAQNE42AIAAAAAisbBFgAAAABQtB2nIvff9bMG++/qa5a/5KcPP/Lv7rjtG1NDsq99H/uper03/L5sw09kNjOrHd7vtqv8uVpbp2n23Fj1Xxjw1z4yoqN02+N+amGn7v/9YfhjPa+rW37SYJTkq5Kvf/0rc277cxM/kn2p9OPVzrbb/oenviP7+trm33TbO//poNu+NaKT6WoigG9t0l97R4cSWl2H0u5pKoF35rSfMmumk27H5oMNShQl4CoqTXd8zk95jdKaU5NxT35vRb62cspPc1XzUu83i1Kh9QOo5qYSjnOoFO0oLXrm9EW3fe6M/3xFfalnclDcx+h+Nc+UF3HeM+z/vuxcuy6vqR0+5LarxFyVlmxm1p3103w78x/Ka+S8RGqu+oCiszTNTMyrPeT/Tu4LEnvVfqm+VFpy9FqUStwQr+1GknCUZCzTohOfLzO9frXGSDTnvSxK062KShiOxleptVGScZTk7NPzUuPkJCynrjFH1Jea28ILfhWPqK/Ua6J9UUnS0TWp1DPxeSV4840tAAAAAKBoHGwBAAAAAEXjYAsAAAAAKBoHWwAAAABA0TjYAgAAAACKtuNU5KHLfvxxlBjcv+qn2q1P+gmUjasiac/M6rf0a57OwTH5Ws+Cn/66/chxf+xrOkm02/aTw7q3/bTmHpuQffWv+HvcOnXYbW/X67Kv0UX/vmwGicH9d/xEtfdX/KTDf7xyRvb13yf99NONjv/ILdz1E47NzPrqfpLypt9sB3/sp7WamXV7/fWvLfvzqm/q53vgZnWpcbtJpQ+vXnhYXqMzLf39UcnLEZWAG/WlkoxXZ8fddpXka2b22Ct+yrBKLI6SjJX3XlLpuzodUO2LWmPoQvo1av/9XEaz1Vnd1/KrJ912lWQc3S+VFt08418TJWKfmvZ/Xu1lMhk3SJNNTY3NSbM1kdacQ42vEqHNdCpzr0jsjaSmNefISf9V+7Kt0prNrG/hWtIYnSjhWaUiC1Hys3yOxd5H84qe171MpdlGackqtVYl0EYpu1Wm01aZMpyafhwlP6euMepLOfjqG/K1zb/+pNuu5pUzvhIlVaferygtubHg/zufWUgawsx0WvNO8I0tAAAAAKBoHGwBAAAAAEXjYAsAAAAAKBoHWwAAAABA0TjYAgAAAACKxsEWAAAAAFC0HedJb074ZSq2hnTJGVUiaOCjdbe9M6Kj2ru9/jjdq0tue+1WelR2/VLTf2HQL7dhpsv62JYf4d25oiP3aw1/jweX/Nj7bl3/XaJ32I/9Hz+fnrs9/ytf9Me4p6Pobzx33W1//R2/rwP/Q9+vnk2/fXDFL0OycUD31an7c95/QezL2Ijsq/WoLt20l6nyMRFV8mV87rbbnlMKR5d20fNV81JlgKK1v/eS3z44n16KRo2Ts/cnv7fitkd7rO6LUn9FlzRbML8MlNr7qDyTKjWlSi1FJXpUqSlVBigqQ7T90n7/hf8ZDP+ApZbuMdOlVXLKpKiyK6q0SycoEdO5I8rjiTHCEjliHLXGnH1Uor7UftVa/uehqL/2pP9vv94SvywzRCV61L/wKvfyz5OorE+qKsvHVFlypso1jl5OvyZ1LVFZG9nXi88kjfFZ4yip5aGqLOcU2X5/3m1XpY6iMkT3g29sAQAAAABF42ALAAAAACgaB1sAAAAAQNE42AIAAAAAisbBFgAAAABQtB3HhPXeTU/Vqt/wkzm7636a6PYRnfJZe1/EoIn0YevVS+uu+Qmcpto3/bTiiEo47rbTE9DU2nuOTMpren626I+v9svMamP+/s/+Uz/pzA6KJFEz++CDU277Yxf9pGyVum1m1vjpFbe9e8+/X7X9Y7KvtS8c9F8Qa6mt6eTbobcX5Wt7WZTmq4zN++l1cWptNeNHKbsq/ViNEaUSq9dy9ktJTXE203sc7UtqKnVzUf/7m8qYs5KXfuxTyc85feVcs1dF6cPqjuWkIkepuZ7a4UPytd7hIf8FMcbWzGHZV12sRc03SlhOTfmN+soZXyUp52hf9Ssy1B+Z8d8vkpfNzHqXxGc78f7uNb9KgplZTaw/NXW7ZCrlNlJlYrHqKyexV/Wl1piTPrwb840SezeO+enjB97S16i5NRb85/n2Cf35XlF7rMYwM1ubSfsZEz53GanQnwe+sQUAAAAAFI2DLQAAAACgaBxsAQAAAABF42ALAAAAACgaB1sAAAAAQNE42AIAAAAAirbzcj8/80vO9AWlVTo3V9z22omjbnv/4g3ZV1eU75Eh6Ud1VHbtxi35WjJVIkjMt5ZThki5HsTeB2V95PiifI6aV23Vj/w3MxuZv+S290z40eIDqpyTmamiJrV9oqTSrVXZ1+Db/mudGf+ZvP5VHYV+4N11+dpepsraROVjmmf85ykqn6NMnfcj8VX5mG/+/jnZ19k3n6tsXqlOTftlqMzM5sx/ntZn/ferPTHT+xKV21ElitQ4qgyPmdnKKf+a6HlJdfJ7K257VIbnl6lET47U8i1mZp3Esj61lv4ZF5UVcvsKytqokjeq3E7fgr92M10mJnUMM73GaC2KGj8qtSRL22SUAeo94pdIUp+hoj1WotJFSmpJpbAE0vyHyeOXqu+3/RJKzSvjbntUiqZ1rCZe8T+v6vebzbzWdNt/9nt+ScXRy7ovRZXOiUr0KKpETlSCaejj9HGU5rPpZX3U+lUpnqikj9oztf7o3is5e3w/+MYWAAAAAFA0DrYAAAAAgKJxsAUAAAAAFI2DLQAAAACgaBxsAQAAAABF23Eqcrftp3BZKy3RzszMRCpxmI/V7yd39YwO+31dWZJdqTRdRa7dzKzPn1fnzh23vWdkJLmvnITjWiNtjWZ6nTl9qfRnlbwcpUWre69E6dJyjSKVeWziMdlXbau6VNjdpNJsVfKxmU4ZVum7USqxSvOdOX3RbX/5tef1vER7zrySr5mWXUmqr+YZf+xw/IxxdMZ4OnUfo7TklVOjSX2pe5Ij597vZakpt2Y65Vgl00Yptz3DQ0nXpKbfmgUJuEH6rfprvZpXlLKbmn6cs8YoFXlrxr/HvUt+RYJo/JxU5qqoZyWi5hUlddcyxtnLVPqtmdnVt/00Xf1TLkr59V/T4+vPayrld+BjP0336jOyq4A/fpSyq9J8VXtOYm90v1KvUQnHn/WaJ0qLTl3nkTf050Q1r5x9SV3jL+IbWwAAAABA0TjYAgAAAACKxsEWAAAAAFA0DrYAAAAAgKJxsAUAAAAAFC0/dupTmzohSybdqmuC9NvakEj0u7XqX3DUT2YzM+tcvuKPIeYbpSjXRv2UY5ULGiX2ylRkJdgvmT4crKVHvLbd9BOmo7Rktc7amJ+K2l31Ux7NzEwlGYv5hinOav9F8vTAG+/JrsIk5z1MpR9HqbEqsXhu8ajbHqXMTp33n9u5Wb+vsy+ck32du/ZkZfM6Ne3/XFiYfzhpjBw5yccRvU5/nPG59DFU+rFKOI6uUe3rs3r8x17x/y1f+sa4vuiXSE76sEwGvtvy+wpSgdU1u/HX8pyUXbUv0X7JfTl8KHl81ZepdjOzL/v/ANqT/u/RvmAtOeuvSpSu3b123W1Xz1GUYr0bCc+fh5tP+b+Tp173K4iYmQ2d8D/j5qTspqbWhmnNIoE3J2W4Sqnj56wxOlKpezz9HT+VORr/v/3rf+G2f+XsN932aO1qnMbCTbf9Z7+3X/Z14C2//X4SjnPwjS0AAAAAoGgcbAEAAAAAReNgCwAAAAAoGgdbAAAAAEDRONgCAAAAAIrGwRYAAAAAULT7zmCOysd0bt/xr0ktA2Rm28f9eP+6eH97qF/21XtgXL7mjv3xVflaWFrGe78od2MWlLwRZYBUSR8zk+Vr7KAuUdK94pf16ZkQ8frB/TLxXMg5B6WOksvqBGWQVOkgtV+llvSJ5JSWWX71pN+XKO0SldWZePGS2756wS+rc9aek32ptUxllKKxaf2SR5UHMksvBZRTHilci6DK6rz3kv45dmrav1/qmVBjmOk5R+tXVk75P0vV+Oq5MzPbfkmUMPjd5GkVSZWviUrB1DJK7iSb/9Bt1k9YXimg1L6qLFwSzre1WeFIvpyyUbJ0kRCVjZKlk1R5otZ60tglOPCW/zN+4YUpec2DLJ+jy92kz6t1zC93k9NXzjg5Yxx5I/jsK6jSTc1ndWlSRZX1mXrd/wy/NqNLZMnyTKKc1PR3dBmixkLa+FU+R7+Ib2wBAAAAAEXjYAsAAAAAKBoHWwAAAABA0TjYAgAAAACKxsEWAAAAAFC0HUe+qnRYlXxsZjJptqsSewP1G35icOfmitveu6HTBLcP+wmYaoysZFy1xs2gryAZ2BMlUqtxoqTD1AyyrkoYNjOZc5dx703sv0pY7hkdSe9LvL27ppOnUxOx94qcBFqz9CTlVGpeYfqw+enD67P++6NEaJlknLFfas5qvpHmGf/fTE66tUoGngiukfuSMS91j9U10bM6Puf/vK6/4qdPLojUbTOzk7YiXyuNSrk1i3/+e6L02yrTh9U4MpU4WKNK01VjdO7ozzC9Rw7741+77rZHSdE5+6USgLtDg8l9yTESn4mIWmNH7JdZkIosqL3P6Wuvy0mGvX0i/fOqSqfNSf9NlbPGm0/581Lp0rnjKGqPRy/rz8Qq/VilNUd7r8ZXY0Tz2jjmn5WOvOHPK36+0tOPlWjOn4VvbAEAAAAAReNgCwAAAAAoGgdbAAAAAEDRONgCAAAAAIrGwRYAAAAAULQdx6epBNwwNValCYv03yjlV6UfqwTczS9M6b7q/nlepSJHacVq/VlJyrug09SJgopMON4l8hkT9yVKa1bU/YoSvKM0zb0sJ01XyUkyVtQ1zx9+W17zz1/9Hbddpf8unz8ZzMDfl5nTF932KGU3dV9k8rDp+6XmFc1NpTJXer+e1vfr7JvPJY+jrJwaddubi/7vkan5juzrvZfKSzhvT/rr710Sv8dMpwlXmYybQ/0sVSm3uzXf9tVrbnv9kRm3PUr/zUpFTlxnmBadOEaVidjh+8X4ai3htzEP+DnOpRJgowRalaarRCm7rWNpqbVRwnDqWqL0W3WNSj+O9kTNWV+Tvvejl+Ul8pqctObUPY6eo8e/veS2L7zgn6Gi+ar0Y3VNzr3fCb6xBQAAAAAUjYMtAAAAAKBoHGwBAAAAAEXjYAsAAAAAKBoHWwAAAABA0TjYAgAAAACKtuM8ZVm+Jihr03P0sNu+ffkj//0HxvX4g37JC1kGaG1C9tUZ7vdfqNf9sYMyRIq6pnsvvTxSTlmZzuPTbnv9UlNfs3zTfyGjfE5UBsoVlFSSRCmebk7ivyrrE8zrQZdByqVK0VRZBigqX6Oo8jFhiZgz/n1bFeVu1s/4a48sv+qXCIr6UmtRfQ3O6r8xqvsVUddMnfef57kz6eWGVOmkcy9+xuQcY6IUz/qsvqYp7r2abzO4X1U++7tFlfXpDg3Ka3qa4me8en9GiZocPSN+2b6uKJ9TC+YVlalJ1XvE/wwTlfXZDapET1TuJ7V0UHTv1R5X+bzk3Pucckd7WVQORX18V9eoUixm6SVncsq0xGtJo/vS55HUa6L5qrI+UYkaVW4pp0RPldZmDrjtqfPNvUa5n+eFb2wBAAAAAEXjYAsAAAAAKBoHWwAAAABA0TjYAgAAAACKxsEWAAAAAFC0+47dqjV0YvD2fj+9rjbyiNveWfDTks3MbOa43970k/PaKvk40G34yZi17W190aZI5l31EytDIklZ7rHaEzO7+aVht32yuSKv2fyrv+q2DzT9BMTa5Wuyrygt29MNkpdVWnRXJBlH6drd23fd9m2RPN0rkr3NzDrXl+Vre1lOAmxqkvLM6YuyL5WY/Pzht932s4tBKrKQmgpsZjbx4iW3fWHWT1hWycdmZgsVpjKnjmFmZmL9qUnCZnovVWJxM0jEVnumUpmjPVbPkXr2VCK1WZyYXJoo+bgz5Sdg1lrrfnuQpJuaPpyTmKuuicZOHSen6kDOvHYjSTg1+dis2hRppcq05ir72itUYrFKmY2oBNqor9Rk3tYxXRNCjaPWGCUyq2TcKpOXp15fcttVWnA0fqSxoH4u++NEY6TuSyT1muje5yRMfx74xhYAAAAAUDQOtgAAAACAonGwBQAAAAAUjYMtAAAAAKBoHGwBAAAAAEW771Tk7tqafK3203m/fWzUbd9+fFr21SvSfLuir26vTu5q/PyG/4JIP1ZJuqE+PwFOJfya6SRllYq8PqXTFEc+Fgl4bZ3wXGv76XRrR/2E5caCTjqLngt37CBdW/Yl9ji6X+tP+lGujZ/66atLXz8i+xpf2C9f28tyEoPN0tKPVWJt5OXXnvdfEPPNsTqr/5an0nxP5awxcc45SdU5ydM51HORk7Bs036zumbO0teh1j6V3BNy7Eb6cZVJwpH2VT/5v2dkJLmvnDmrBOAq0393Yy9z5ivnVWjycWTmtabb3nx2Ul6j0mlzkpSrTNlNTT+++VQ03+gzyf8vZ74qlTgSJwP7VMpylQnPVd5HPf59Hxs/d3xjCwAAAAAoGgdbAAAAAEDRONgCAAAAAIrGwRYAAAAAUDQOtgAAAACAonGwBQAAAAAUbee5zYdEJPbVpYqmYla/5Eeem5ltfvGY29737sd+Xxu6rE331mrSvFJL14R9belo8/ZfetxtV2sc+JOf6HHaIqr7xHF5Tf/b77vt20/MuO21fbpET+fOHf8aVe4o2JfO+rrb3iPK/Vi/jogfXLzltq99yS8RMvGOX4LJzGxzQq+/RKp8S0SVVskpX6PKEEXUOKoUzsL8w7KvU9N+yacqS+fs1r6otSjRvqjn4uzT33Xbz00/KfuSe1nhvVeaZ/QYqftVqlrL/1mqyrGocjtmZrXDh9x2v6hHXPJlN8r6RGtJlTOvnPFTv3lQ5YHMqi0RtFdVeY9308ILfjEyVSInek2V24movnJKCqnSMqqvA2+lzzen3E7qfuXsfTSv1HGqLdGTfk1e6SRflX3tBN/YAgAAAACKxsEWAAAAAFA0DrYAAAAAgKJxsAUAAAAAFI2DLQAAAACgaDuOquqK9OPaCZ0Y2p2/5LercL4gGXftkJ/a2feOf03Pmk4Hq40Ou+2dmyv+BSp9N6DSf2VasZn1vbPo96XSh2dP6gmIve9cX9bXiP3vvXHXbb/9NZ2kOvrOiNvebfS77bV2R/bVc9lPLJX7MuaPbaaf4wExr80JnTLZeE+neO9lKk02St9NTQz+1gvnZF9n33zObZ867/87m3jRf5bNzP7rme+77b/1/m+67Sot2cxs+VXx70mkAkepvGqcOfP3K0rlVXscpTWn3q+zwf06d02nHHsWLuifCyr9WN371Vn9t1f1vOakW8vXfkNe8sB1hwaT2s3Mepo33fasVGKRshsl81YlSr9Vc85KMhbp/g+a2uMo+Tj1vuSkWKuk7Jy+clSZor2bVMpwlHCsrhm97L8/6kul+VaZ2BulDFfVV5RKnJPwnKp1LP2soPtKT35OKXTzqcaC/zvBzK+EEz1H6r7kpDXfD76xBQAAAAAUjYMtAAAAAKBoHGwBAAAAAEXjYAsAAAAAKBoHWwAAAABA0XYeoTVz3G2urfiJuWZmXZUmLPpqj+s0x07dTwhr/ZVH3PbhH32k+1oWKWBVph+vrSWPIROT74m+VLuZ1Rp+YnD3xGF9zTU/MbnW8sfZaui/i7QP+snTfddW3factGYzkYrcl54MZ+/5Kbb9wf3qiOd4r4vSjxWZdCv6ipJ0ZWrtGT+ZdjVI2T114e/I1zxRKnIzMf046kul7Kq+VFpydE3OfTz79Hf9dpFUHTln/j3OmZfa+xzPH37bbT+7mL7GvazWWnfbo1RkRaXJRkm6KulWteckGWelNQs5faXOa7eoPc5JpI4SixX5vIj3R/Pi25VqqcTiKJVYJfDmJAnnJCYrqWnNG8c2ZV8H3vI/y6n5Rkm+OXuspK4xR7SW5rOTSX1VOa/PCz9TAAAAAABF42ALAAAAACgaB1sAAAAAQNE42AIAAAAAisbBFgAAAABQNA62AAAAAICi7TiX+84jo2770GXdRV1FvK/5kdybD43Ivrp1v3170I/KvvXVE7Kv8fN++ZrOjCi58eP39LxEuyoDpNrNdLkfWQYoKjcjytesHfXL8JiZ7RPlftT4B/9El1RqT437fbX80gLbT8zIvuotEeEunqOtg0HJg4NfcJv73ll022ujer+6l6/pcQqkyvCYmS3M65I7HlXuJqJK0Zyb1qWDVBmisfmO/34L1iHK1KjyNTlrVCWCcvrKocowRfdeUXOO+lp+9aTbrsr9hM+kuPcvzz/vX5BRhqhEqgyQmVln6kDSNTmlYKos0VM7fMh/IWNeSlSKpnvtutveuXMneZyeEf35JtVulfVJpfZL3sdAlc/RXpdTPubqM375GlWKJyrDk1rCRY0dUWNEpWhGL/vtai2qpE/VVImeSOoe33xKl1Sqcp1Try+57aoMUHS/FPW8RHuSM86n+MYWAAAAAFA0DrYAAAAAgKJxsAUAAAAAFI2DLQAAAACgaBxsAQAAAABFq3W73bSoLgAAAAAA9hC+sQUAAAAAFI2DLQAAAACgaBxsAQAAAABF42ALAAAAACgaB1sAAAAAQNE42AIAAAAAisbBFgAAAABQNA62AAAAAICicbAFAAAAABTtfwMZWVf8UY8jTAAAAABJRU5ErkJggg==", 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"text/plain": [ "
" ] @@ -1135,11 +1229,11 @@ } ], "source": [ - "file_folder = '../_data/Babesia/RI/'\n", + "file_folder = '../../bioMONAI_0/_data/Thunder_20230308/nuevos_datos/dataset_2'\n", "\n", "dblock = DataBlock(blocks=(BioImageBlock(cls=BioImageProject), BioImageBlock(cls=BioImageProject)),\n", " get_items=get_image_files,\n", - " get_y=get_target('../_data/Babesia/TRITC', same_filename=False, signal_file_prefix='RI', target_file_prefix='TRITC'),\n", + " get_y=get_target('../../bioMONAI_0/_data/Thunder_20230308/nuevos_datos/dataset_2/targets', same_filename=True),\n", " splitter=RandomSplitter(valid_pct=0.2),\n", " item_tfms=item_tfms,\n", " )\n", @@ -1151,7 +1245,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -1161,7 +1255,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -1170,7 +1264,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -1179,7 +1273,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -1188,7 +1282,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -1235,13 +1329,6 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Applied workaround for CuDNN issue, install nvrtc.so (Triggered internally at /opt/conda/conda-bld/pytorch_1682343995622/work/aten/src/ATen/native/cudnn/Conv_v8.cpp:80.)\n" - ] - }, { "data": { "text/plain": [ @@ -1343,7 +1430,7 @@ "Total trainable params: 954,401\n", "Total non-trainable params: 0\n", "\n", - "Optimizer used: \n", + "Optimizer used: \n", "Loss function: SSIMLoss()\n", "\n", "Callbacks:\n", @@ -1353,7 +1440,7 @@ " - ProgressCallback" ] }, - "execution_count": 46, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -1364,7 +1451,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -1409,33 +1496,33 @@ " \n", " \n", " 0\n", - " 0.424637\n", - " 0.204382\n", - " 00:08\n", + " 0.999406\n", + " 0.999052\n", + " 00:10\n", " \n", " \n", " 1\n", - " 0.151923\n", - " 0.096799\n", - " 00:07\n", + " 0.995351\n", + " 0.998416\n", + " 00:10\n", " \n", " \n", " 2\n", - " 0.088924\n", - " 0.079339\n", - " 00:07\n", + " 0.971927\n", + " 0.943298\n", + " 00:09\n", " \n", " \n", " 3\n", - " 0.078792\n", - " 0.053730\n", - " 00:07\n", + " 0.860400\n", + " 0.580867\n", + " 00:10\n", " \n", " \n", " 4\n", - " 0.075960\n", - " 0.081879\n", - " 00:07\n", + " 0.698286\n", + " 0.279059\n", + " 00:09\n", " \n", " \n", "" @@ -1454,12 +1541,12 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 108, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1474,7 +1561,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -1523,7 +1610,7 @@ }, { "data": { - "image/png": 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", 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", 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