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Project Details #4

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Project Details
aayushrai1288 Jul 5, 2024
7532106
Update README.md
aayushrai1288 Jul 5, 2024
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107 changes: 107 additions & 0 deletions Data/Dataset Download.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-21T04:28:58.977554Z",
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"shell.execute_reply": "2024-06-21T04:29:13.185664Z",
"shell.execute_reply.started": "2024-06-21T04:28:58.977523Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-06-21 04:29:01.304109: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-06-21 04:29:01.304202: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-06-21 04:29:01.478767: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
}
],
"source": [
"import os\n",
"import glob\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from matplotlib import pyplot as plt\n",
"tf.random.set_seed(1234)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-21T04:29:16.466913Z",
"iopub.status.busy": "2024-06-21T04:29:16.465874Z",
"iopub.status.idle": "2024-06-21T04:34:43.035673Z",
"shell.execute_reply": "2024-06-21T04:34:43.034856Z",
"shell.execute_reply.started": "2024-06-21T04:29:16.466878Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from http://download.cs.stanford.edu/downloads/completion3d/dataset2019.zip\n",
"\u001b[1m1585860897/1585860897\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m300s\u001b[0m 0us/step\n"
]
}
],
"source": [
"DATA_DIR = tf.keras.utils.get_file(\n",
" \"dataset2019.zip\",\n",
" \"http://download.cs.stanford.edu/downloads/completion3d/dataset2019.zip\",\n",
" extract=True,\n",
")\n",
"DATA_DIR = os.path.join (os.path.dirname(DATA_DIR),\"shapenet\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kaggle": {
"accelerator": "nvidiaTeslaT4",
"dataSources": [],
"dockerImageVersionId": 30733,
"isGpuEnabled": true,
"isInternetEnabled": true,
"language": "python",
"sourceType": "notebook"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
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"nbformat": 4,
"nbformat_minor": 4
}
218 changes: 218 additions & 0 deletions Notebooks used/Data preprocessing .ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-21T04:28:58.977554Z",
"iopub.status.busy": "2024-06-21T04:28:58.977192Z",
"iopub.status.idle": "2024-06-21T04:29:13.186468Z",
"shell.execute_reply": "2024-06-21T04:29:13.185664Z",
"shell.execute_reply.started": "2024-06-21T04:28:58.977523Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-06-21 04:29:01.304109: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-06-21 04:29:01.304202: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-06-21 04:29:01.478767: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
}
],
"source": [
"import os\n",
"import glob\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from matplotlib import pyplot as plt\n",
"tf.random.set_seed(1234)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-21T04:29:16.466913Z",
"iopub.status.busy": "2024-06-21T04:29:16.465874Z",
"iopub.status.idle": "2024-06-21T04:34:43.035673Z",
"shell.execute_reply": "2024-06-21T04:34:43.034856Z",
"shell.execute_reply.started": "2024-06-21T04:29:16.466878Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from http://download.cs.stanford.edu/downloads/completion3d/dataset2019.zip\n",
"\u001b[1m1585860897/1585860897\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m300s\u001b[0m 0us/step\n"
]
}
],
"source": [
"DATA_DIR = tf.keras.utils.get_file(\n",
" \"dataset2019.zip\",\n",
" \"http://download.cs.stanford.edu/downloads/completion3d/dataset2019.zip\",\n",
" extract=True,\n",
")\n",
"DATA_DIR = os.path.join (os.path.dirname(DATA_DIR),\"shapenet\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-21T04:34:51.538206Z",
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"iopub.status.idle": "2024-06-21T04:34:51.543451Z",
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"shell.execute_reply.started": "2024-06-21T04:34:51.538175Z"
}
},
"outputs": [],
"source": [
"import re\n",
"import h5py\n",
"import numpy as np\n",
"\n",
"def read_point_cloud_from_h5_file(file_path):\n",
" with h5py.File(file_path, 'r') as file:\n",
" \n",
" point_cloud_data = file['data'][:]\n",
" return point_cloud_data"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"execution": {
"iopub.execute_input": "2024-06-21T05:20:26.419374Z",
"iopub.status.busy": "2024-06-21T05:20:26.418692Z",
"iopub.status.idle": "2024-06-21T05:20:54.621883Z",
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"shell.execute_reply.started": "2024-06-21T05:20:26.419344Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Loading 02691156: 100%|██████████| 3795/3795 [00:03<00:00, 1088.31it/s]\n",
"Loading 03636649: 100%|██████████| 2068/2068 [00:01<00:00, 1095.93it/s]\n",
"Loading 02933112: 100%|██████████| 1322/1322 [00:01<00:00, 1084.20it/s]\n",
"Loading 02958343: 100%|██████████| 5677/5677 [00:05<00:00, 1079.16it/s]\n",
"Loading 03001627: 100%|██████████| 5750/5750 [00:05<00:00, 1068.83it/s]\n",
"Loading 04256520: 100%|██████████| 2923/2923 [00:02<00:00, 1080.93it/s]\n",
"Loading 04379243: 100%|██████████| 5750/5750 [00:05<00:00, 1037.04it/s]\n",
"Loading 04530566: 100%|██████████| 1689/1689 [00:01<00:00, 983.45it/s] \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded 28974 partial point clouds and 28974 ground truth point clouds.\n"
]
}
],
"source": [
"import os\n",
"import h5py\n",
"import numpy as np\n",
"from tqdm import tqdm\n",
"\n",
"def load_h5_file(file_path, dataset_name='data'):\n",
" \"\"\"Load point cloud data from an .h5 file.\"\"\"\n",
" with h5py.File(file_path, 'r') as file:\n",
" data = file[dataset_name][:]\n",
" return data\n",
"\n",
"def load_dataset(base_dir, categories, dataset_name='data'):\n",
" \"\"\"Load partial and gt datasets from the given base directory.\"\"\"\n",
" partials = []\n",
" gts = []\n",
"\n",
" for category in categories:\n",
" partial_dir = os.path.join(base_dir, 'partial', category)\n",
" gt_dir = os.path.join(base_dir, 'gt', category)\n",
"\n",
" # Check if the directories exist\n",
" if not os.path.exists(partial_dir):\n",
" print(f\"Partial directory does not exist: {partial_dir}\")\n",
" continue\n",
" if not os.path.exists(gt_dir):\n",
" print(f\"GT directory does not exist: {gt_dir}\")\n",
" continue\n",
"\n",
" partial_files = sorted([f for f in os.listdir(partial_dir) if f.endswith('.h5')])\n",
" gt_files = sorted([f for f in os.listdir(gt_dir) if f.endswith('.h5')])\n",
"\n",
" for p_file, gt_file in tqdm(zip(partial_files, gt_files), total=len(partial_files), desc=f\"Loading {category}\"):\n",
" partial_path = os.path.join(partial_dir, p_file)\n",
" gt_path = os.path.join(gt_dir, gt_file)\n",
"\n",
" partial_data = load_h5_file(partial_path, dataset_name)\n",
" gt_data = load_h5_file(gt_path, dataset_name)\n",
"\n",
" partials.append(partial_data)\n",
" gts.append(gt_data)\n",
"\n",
" return np.array(partials), np.array(gts)\n",
"\n",
"# Example usage\n",
"base_dir = os.path.join(DATA_DIR , \"train\")\n",
"categories = ['02691156', '03636649', '02933112', '02958343', '03001627' , '04256520' , '04379243' , '04530566'] # Replace with your actual category names\n",
"partial_dataset, gt_dataset = load_dataset(base_dir, categories)\n",
"print(f'Loaded {len(partial_dataset)} partial point clouds and {len(gt_dataset)} ground truth point clouds.')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kaggle": {
"accelerator": "nvidiaTeslaT4",
"dataSources": [],
"dockerImageVersionId": 30733,
"isGpuEnabled": true,
"isInternetEnabled": true,
"language": "python",
"sourceType": "notebook"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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