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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "37048f21",
"metadata": {},
"outputs": [],
"source": [
"# Copyright (c) Meta Platforms, Inc. and affiliates."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "154d8663",
"metadata": {},
"outputs": [],
"source": [
"using_colab = False"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b85d99d9",
"metadata": {},
"outputs": [],
"source": [
"if using_colab:\n",
" import torch\n",
" import torchvision\n",
" print(\"PyTorch version:\", torch.__version__)\n",
" print(\"Torchvision version:\", torchvision.__version__)\n",
" print(\"CUDA is available:\", torch.cuda.is_available())\n",
" import sys\n",
" !{sys.executable} -m pip install opencv-python matplotlib scikit-learn\n",
" !{sys.executable} -m pip install 'git+https://github.com/facebookresearch/sam3.git'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da21a3bc",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from glob import glob\n",
"\n",
"import numpy as np\n",
"import utils\n",
"\n",
"from matplotlib import pyplot as plt\n",
"\n",
"COLORS = utils.pascal_color_map()[1:]"
]
},
{
"cell_type": "markdown",
"id": "57e85e7e",
"metadata": {},
"source": [
"1. Load the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a796734e",
"metadata": {},
"outputs": [],
"source": [
"# Preapre the data path\n",
"DATA_DIR = \"./sam3_saco_veval_data\" # PUT YOUR DATA PATH HERE\n",
"ANNOT_DIR = os.path.join(DATA_DIR, \"annotation\")\n",
"\n",
"# Load the SACO/Veval annotation files\n",
"annot_file_list = glob(os.path.join(ANNOT_DIR, \"*veval*.json\"))\n",
"annot_dfs = utils.get_annot_dfs(file_list=annot_file_list)"
]
},
{
"cell_type": "markdown",
"id": "74bf92b1",
"metadata": {},
"source": [
"Show the annotation files being loaded"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a95620ec",
"metadata": {},
"outputs": [],
"source": [
"annot_dfs.keys()"
]
},
{
"cell_type": "markdown",
"id": "5ce211d3",
"metadata": {},
"source": [
"2. Examples of the data format"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6ba749db",
"metadata": {},
"outputs": [],
"source": [
"annot_dfs[\"saco_veval_yt1b_val\"].keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b6dc186",
"metadata": {},
"outputs": [],
"source": [
"annot_dfs[\"saco_veval_yt1b_val\"][\"info\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c41091b3",
"metadata": {},
"outputs": [],
"source": [
"annot_dfs[\"saco_veval_yt1b_val\"][\"videos\"].head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7df5771",
"metadata": {},
"outputs": [],
"source": [
"annot_dfs[\"saco_veval_yt1b_val\"][\"annotations\"].head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24d2861c",
"metadata": {},
"outputs": [],
"source": [
"annot_dfs[\"saco_veval_yt1b_val\"][\"categories\"].head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9f98f27",
"metadata": {},
"outputs": [],
"source": [
"annot_dfs[\"saco_veval_yt1b_val\"][\"video_np_pairs\"].head(3)"
]
},
{
"cell_type": "markdown",
"id": "5673a63f",
"metadata": {},
"source": [
"3. Visualize the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "da827d09",
"metadata": {},
"outputs": [],
"source": [
"# Select a target dataset\n",
"target_dataset_name = \"saco_veval_yt1b_val\"\n",
"\n",
"# visualize a random positive video-np pair\n",
"df_pairs = annot_dfs[target_dataset_name][\"video_np_pairs\"]\n",
"df_positive_pairs = df_pairs[df_pairs.num_masklets > 0]\n",
"rand_idx = np.random.randint(len(df_positive_pairs))\n",
"pair_row = df_positive_pairs.iloc[rand_idx]\n",
"video_id = pair_row.video_id\n",
"noun_phrase = pair_row.noun_phrase\n",
"print(f\"Randomly selected video-np pair: video_id={video_id}, noun_phrase={noun_phrase}\")\n",
"\n",
"def display_image_in_subplot(img, axes, row, col, title=\"\"):\n",
" axes[row, col].imshow(img)\n",
" axes[row, col].set_title(title)\n",
" axes[row, col].axis('off')\n",
"\n",
"num_frames_to_show = 5 # Number of frames to show per dataset\n",
"every_n_frames = 4 # Interval between frames to show\n",
"\n",
"fig, axes = plt.subplots(num_frames_to_show, 3, figsize=(15, 5 * num_frames_to_show))\n",
"\n",
"for idx in range(0, num_frames_to_show):\n",
" sampled_frame_idx = idx * every_n_frames\n",
" print(f\"Reading annotations for frame {sampled_frame_idx}\")\n",
" # Get the frame and the corresponding masks and noun phrases\n",
" frame, annot_masks, annot_noun_phrases = utils.get_all_annotations_for_frame(\n",
" annot_dfs[target_dataset_name], video_id=video_id, frame_idx=sampled_frame_idx, data_dir=DATA_DIR, dataset=target_dataset_name\n",
" )\n",
" # Filter masks and noun phrases by the selected noun phrase\n",
" annot_masks = [m for m, np in zip(annot_masks, annot_noun_phrases) if np == noun_phrase]\n",
"\n",
" # Show the frame\n",
" display_image_in_subplot(frame, axes, idx, 0, f\"{target_dataset_name} - {noun_phrase} - Frame {sampled_frame_idx}\")\n",
"\n",
" # Show the annotated masks\n",
" if annot_masks is None:\n",
" print(f\"No masks found for video_id {video_id} at frame {sampled_frame_idx}\")\n",
" else:\n",
" # Show all masks over a white background\n",
" all_masks = utils.draw_masks_to_frame(\n",
" frame=np.ones_like(frame)*255, masks=annot_masks, colors=COLORS[: len(annot_masks)]\n",
" )\n",
" display_image_in_subplot(all_masks, axes, idx, 1, f\"{target_dataset_name} - {noun_phrase} - Frame {sampled_frame_idx} - Masks\")\n",
" \n",
" # Show masks overlaid on the frame\n",
" masked_frame = utils.draw_masks_to_frame(\n",
" frame=frame, masks=annot_masks, colors=COLORS[: len(annot_masks)]\n",
" )\n",
" display_image_in_subplot(masked_frame, axes, idx, 2, f\"Dataset: {target_dataset_name} - {noun_phrase} - Frame {sampled_frame_idx} - Masks overlaid\")\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2a23152",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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",
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