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81733ef1 · cordina · 787de3c9 · 81733ef1
Commit 81733ef1 authored 3 years ago by cordina
--- a/Notebook_EDA.ipynb
+++ b/Notebook_EDA.ipynb
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install pydicom\nimport matplotlib.pyplot as plt\nimport pydicom\nfrom pydicom.data import get_testdata_file\n\n \nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pathlib import Path\n\nimport os\nimport json\nimport glob\nimport random\nimport collections\nimport cv2\nimport pandas as pd\nimport pydicom\nfrom pydicom.pixel_data_handlers.util import apply_voi_lut\nfrom tqdm.notebook import tqdm\nimport os\nimport glob\n\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score, accuracy_score, log_loss\n\nfrom scipy import ndimage\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras import layers\n\nfrom matplotlib import animation, rc\nrc('animation', html='jshtml')\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-01-20T21:44:06.129718Z","iopub.execute_input":"2022-01-20T21:44:06.130093Z","iopub.status.idle":"2022-01-20T21:44:22.635619Z","shell.execute_reply.started":"2022-01-20T21:44:06.130004Z","shell.execute_reply":"2022-01-20T21:44:22.634807Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"path_data=\"/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/\"\ntrain_df = pd.read_csv(path_data+\"train_labels.csv\")\ndef load_dicom(path):\n    dicom = pydicom.read_file(path,force=True)\n    data = dicom.pixel_array\n    data = data - np.min(data)\n    if np.max(data) != 0:\n        data = data / np.max(data)\n    data = (data * 255).astype(np.uint8)\n    return data\n\n\ndef visualize_sample(\n    brats21id, \n    slice_i,\n    mgmt_value,\n    types=(\"FLAIR\", \"T1w\", \"T1wCE\", \"T2w\")\n):\n    plt.figure(figsize=(10, 3))\n    patient_path = os.path.join(\n        path_data+\"train/\", \n        str(brats21id).zfill(5),\n    )\n    for i, t in enumerate(types, 1):\n        t_paths = sorted(\n            glob.glob(os.path.join(patient_path, t, \"*\")), \n            key=lambda x: int(x[:-4].split(\"-\")[-1]),\n        )\n        data = load_dicom(t_paths[int(len(t_paths) * slice_i)])\n        plt.subplot(1, 4, i)\n        plt.imshow(data, cmap=\"gray\")\n        plt.title(f\"{t}\", fontsize=10)\n        plt.axis(\"off\")\n    plt.show()\n\n\n\n\ndef create_animation(ims):\n    fig = plt.figure(figsize=(5, 5))\n    plt.axis('off')\n    im = plt.imshow(ims[0], cmap=\"gray\")\n\n    def animate_func(i):\n        im.set_array(ims[i])\n        #return [im]\n    return animation.FuncAnimation(fig, animate_func, frames = len(ims), interval = 1000//4)\n\ndef load_dicom_line(path):\n    t_paths = sorted(\n        glob.glob(os.path.join(path, \"*\")), \n        key=lambda x: int(x[:-4].split(\"-\")[-1]),\n    )\n    images = []\n    for filename in t_paths:\n        data = load_dicom(filename)\n        if data.max() == 0:\n            continue\n        images.append(data)\n        \n    return images\n\n\ndef create_animation(ims):\n    fig = plt.figure(figsize=(5, 5))\n    plt.axis('off')\n    im = plt.imshow(ims[0], cmap=\"gray\")\n\n    def animate_func(i):\n        im.set_array(ims[i])\n        #return [im]\n    return animation.FuncAnimation(fig, animate_func, frames = len(ims), interval = 1000//4)\n\ndef load_dicom_line(path):\n    t_paths = sorted(\n        glob.glob(os.path.join(path, \"*\")), \n        key=lambda x: int(x[:-4].split(\"-\")[-1]),\n    )\n    images = []\n    for filename in t_paths:\n        data = load_dicom(filename)\n        if data.max() == 0:\n            continue\n        images.append(data)\n    return images\n\ncreate_animation(load_dicom_line(\"../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00176/T2w\"))\n    ","metadata":{"execution":{"iopub.status.busy":"2022-01-20T21:48:23.386828Z","iopub.execute_input":"2022-01-20T21:48:23.387156Z","iopub.status.idle":"2022-01-20T21:48:25.138332Z","shell.execute_reply.started":"2022-01-20T21:48:23.387123Z","shell.execute_reply":"2022-01-20T21:48:25.137138Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
+%% Cell type:code id: tags:
+``` python
+!pip install pydicom
+import matplotlib.pyplot as plt
+import pydicom
+from pydicom.data import get_testdata_file
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pathlib import Path
+import os
+import json
+import glob
+import random
+import collections
+import cv2
+import pandas as pd
+import pydicom
+from pydicom.pixel_data_handlers.util import apply_voi_lut
+from tqdm.notebook import tqdm
+import os
+import glob
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import roc_auc_score, accuracy_score, log_loss
+from scipy import ndimage
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras.utils import to_categorical
+from tensorflow.keras import layers
+from matplotlib import animation, rc
+rc('animation', html='jshtml')
+```
+%% Cell type:code id: tags:
+``` python
+path_data="/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification/"
+train_df = pd.read_csv(path_data+"train_labels.csv")
+def load_dicom(path):
+    dicom = pydicom.read_file(path,force=True)
+    data = dicom.pixel_array
+    data = data - np.min(data)
+    if np.max(data) != 0:
+        data = data / np.max(data)
+    data = (data * 255).astype(np.uint8)
+    return data
+def visualize_sample(
+    brats21id, 
+    slice_i,
+    mgmt_value,
+    types=("FLAIR", "T1w", "T1wCE", "T2w")
+):
+    plt.figure(figsize=(10, 3))
+    patient_path = os.path.join(
+        path_data+"train/", 
+        str(brats21id).zfill(5),
+    )
+    for i, t in enumerate(types, 1):
+        t_paths = sorted(
+            glob.glob(os.path.join(patient_path, t, "*")), 
+            key=lambda x: int(x[:-4].split("-")[-1]),
+        )
+        data = load_dicom(t_paths[int(len(t_paths) * slice_i)])
+        plt.subplot(1, 4, i)
+        plt.imshow(data, cmap="gray")
+        plt.title(f"{t}", fontsize=10)
+        plt.axis("off")
+    plt.show()
+def create_animation(ims):
+    fig = plt.figure(figsize=(5, 5))
+    plt.axis('off')
+    im = plt.imshow(ims[0], cmap="gray")
+    def animate_func(i):
+        im.set_array(ims[i])
+        #return [im]
+    return animation.FuncAnimation(fig, animate_func, frames = len(ims), interval = 1000//4)
+def load_dicom_line(path):
+    t_paths = sorted(
+        glob.glob(os.path.join(path, "*")), 
+        key=lambda x: int(x[:-4].split("-")[-1]),
+    )
+    images = []
+    for filename in t_paths:
+        data = load_dicom(filename)
+        if data.max() == 0:
+            continue
+        images.append(data)
+    return images
+def create_animation(ims):
+    fig = plt.figure(figsize=(5, 5))
+    plt.axis('off')
+    im = plt.imshow(ims[0], cmap="gray")
+    def animate_func(i):
+        im.set_array(ims[i])
+        #return [im]
+    return animation.FuncAnimation(fig, animate_func, frames = len(ims), interval = 1000//4)
+def load_dicom_line(path):
+    t_paths = sorted(
+        glob.glob(os.path.join(path, "*")), 
+        key=lambda x: int(x[:-4].split("-")[-1]),
+    )
+    images = []
+    for filename in t_paths:
+        data = load_dicom(filename)
+        if data.max() == 0:
+            continue
+        images.append(data)
+    return images
+create_animation(load_dicom_line("../input/rsna-miccai-brain-tumor-radiogenomic-classification/train/00176/T2w"))
+```
+%% Cell type:code id: tags:
+``` python
+```