{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# [Angle closure Glaucoma Evaluation Challenge](https://age.grand-challenge.org/Details/)\n",
"## Scleral spur localization Baseline （ResNet50+UNet)\n",
"\n",
"- To keep model training stable, images with coordinate == -1, were removed.\n",
"\n",
"- For real inference, you MIGHT keep all images in val_file_path file."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## requirement install"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install xlrd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Zip File Extract\n",
"\n",
"Assume `Training100.zip` and `Validation_ASOCT_Image.zip` are stored @ `./AGE_challenge Baseline/datasets/`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!unzip -q ../datasets/Training100.zip -d ../datasets/\n",
"!unzip -q ../datasets/Validation_ASOCT_Image.zip -d ../datasets/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore Data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import csv\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"import os, shutil\n",
"import pprint\n",
"import pandas as pd\n",
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_root_path = \"../datasets/Training100/\"\n",
"xlsx_file_path = os.path.join(data_root_path, \"Training100_Location.xlsx\")\n",
"\n",
"image_path = os.path.join(data_root_path, \"ASOCT_Image\")\n",
"label_file_path = os.path.join(data_root_path, \"train_loc.csv\")\n",
"\n",
"train_file_path = os.path.join(data_root_path, \"loc_train_split.csv\")\n",
"val_file_path = os.path.join(data_root_path, \"loc_val_split.csv\")\n",
"\n",
"img_save_path = os.path.join(data_root_path, \"ASOCT_Image_loc\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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