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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Start to finish - DINOv2 feature extraction"
]
},
{
"cell_type": "markdown",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3AdjGBwjnr-5"
},
"outputs": [],
"source": [
"from transformers import AutoImageProcessor, AutoModel\n",
"from PIL import Image\n",
"\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import requests\n",
"import torch\n",
"import cv2\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qvTYvSVOkLLL"
},
"source": [
"## Initialize pre-trained image processor and model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aRlCk-Tlj8Iv",
"outputId": "fb51843c-598f-48ad-a1c0-cf8d9bab53f4",
"scrolled": true
},
"outputs": [],
"source": [
"# Adjust for cuda - takes up 2193 MiB on device\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"processor = AutoImageProcessor.from_pretrained('facebook/dinov2-large')\n",
"model = AutoModel.from_pretrained('facebook/dinov2-large').to(device)"
]
},
{
"cell_type": "markdown",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## DINOv2 Feature Extraction"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tqdm import tqdm\n",
"import gc\n",
"\n",
"torch.cuda.empty_cache() \n",
"gc.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Crq7KD84qz5d"
},
"outputs": [],
"source": [
"# Path to your videos\n",
"path_to_videos = './dataset-tacdec/videos'\n",
"\n",
"# Directory paths\n",
"processed_features_dir = './processed_features'\n",
"last_hidden_states_dir = os.path.join(processed_features_dir, 'last_hidden_states/')\n",
"pooler_outputs_dir = os.path.join(processed_features_dir, 'pooler_outputs/')\n",
"\n",
"# Create directories if they don't exist\n",
"os.makedirs(last_hidden_states_dir, exist_ok=True)\n",
"os.makedirs(pooler_outputs_dir, exist_ok=True)\n",
"\n",
"# Dictonary with filename as key, all feature extracted frames as values\n",
"feature_extracted_videos = {}\n",
"\n",
"# Define batch size\n",
"batch_size = 32\n",
"\n",
"# Process each video\n",
"for video_file in tqdm(os.listdir(path_to_videos)):\n",
" full_path = os.path.join(path_to_videos, video_file)\n",
"\n",
" if not os.path.isfile(full_path):\n",
" continue\n",
"\n",
" cap = cv2.VideoCapture(full_path)\n",
"\n",
" # List to hold all batch outputs, clear for each video\n",
" batch_last_hidden_states = []\n",
" batch_pooler_outputs = []\n",
" \n",
" batch_frames = []\n",
"\n",
" while True:\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" \n",
" # Process the last batch\n",
" if len(batch_frames) > 0:\n",
" inputs = processor(images=batch_frames, return_tensors=\"pt\").to(device)\n",
" \n",
" with torch.no_grad():\n",
" outputs = model(**inputs)\n",
" \n",
" for key, value in outputs.items():\n",
" if key == 'last_hidden_state':\n",
" # batch_last_hidden_states.append(value.cpu().numpy())\n",
" batch_last_hidden_states.append(value)\n",
" elif key == 'pooler_output':\n",
" # batch_pooler_outputs.append(value.cpu().numpy())\n",
" batch_pooler_outputs.append(value)\n",
" else:\n",
" print('Error in key, expected last_hidden_state or pooler_output, got: ', key)\n",
" break\n",
"\n",
" # cv2 comes in BGR, but transformer takes RGB\n",
" frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" batch_frames.append(frame_rgb)\n",
"\n",
" # Check if batch is full\n",
" if len(batch_frames) == batch_size:\n",
" inputs = processor(images=batch_frames, return_tensors=\"pt\").to(device)\n",
" # outputs = model(**inputs)\n",
" with torch.no_grad():\n",
" outputs = model(**inputs)\n",
" for key, value in outputs.items():\n",
" if key == 'last_hidden_state':\n",
" batch_last_hidden_states.append(value)\n",
" elif key == 'pooler_output':\n",
" batch_pooler_outputs.append(value)\n",
" else:\n",
" print('Error in key, expected last_hidden_state or pooler_output, got: ', key)\n",
"\n",
" # Clear batch\n",
" batch_frames = []\n",
"\n",
" \n",
" all_last_hidden_states = torch.cat(batch_last_hidden_states, dim=0)\n",
" all_pooler_outputs = torch.cat(batch_pooler_outputs, dim=0)\n",
"\n",
" # Save the tensors with the video name as filename\n",
" pt_filename = video_file.replace('.mp4', '.pt')\n",
" torch.save(all_last_hidden_states, os.path.join(last_hidden_states_dir, f'{pt_filename}'))\n",
" torch.save(all_pooler_outputs, os.path.join(pooler_outputs_dir, f'{pt_filename}'))\n",
" \n",
"print('Features extracted')"
]
},
{
"cell_type": "markdown",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Reload features to verify "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lhs_torch = torch.load('./processed_features/last_hidden_states/1738_avxeiaxxw6ocr.pt')\n",
"po_torch = torch.load('./processed_features/pooler_outputs/1738_avxeiaxxw6ocr.pt')\n",
"\n",
"print('LHS Torch size: ', lhs_torch.size())\n",
"print('PO Torch size: ', po_torch.size())\n",
"\n",
"for i in range(all_last_hidden_states.size(0)):\n",
" print(f\"Frame {i}:\")\n",
" print(all_last_hidden_states[i])\n",
" print() \n",
" break\n",
"\n",
"for i in range(lhs_torch.size(0)):\n",
" print(f\"Frame {i}:\")\n",
" print(all_last_hidden_states[i])\n",
" print() \n",
" break\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Different sorts of plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Histogram of video length in seconds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import cv2\n",
"import numpy as np\n",
"\n",
"path_to_videos = './dataset-tacdec/videos'\n",
"video_lengths = []\n",
"frame_counts = []\n",
"\n",
"# Iterate through each file in the directory\n",
"for video_file in os.listdir(path_to_videos):\n",
" full_path = os.path.join(path_to_videos, video_file)\n",
"\n",
" if not os.path.isfile(full_path):\n",
" continue\n",
"\n",
" cap = cv2.VideoCapture(full_path)\n",
"\n",
" # Calculate the length of the video\n",
" # Note: Assuming the frame rate information is accurate\n",
" if cap.isOpened():\n",
" fps = cap.get(cv2.CAP_PROP_FPS) # Frame rate\n",
" frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
" duration = frame_count / fps if fps > 0 else 0\n",
" video_lengths.append(duration)\n",
" frame_counts.append(frame_count)\n",
"\n",
" cap.release()\n",
"\n",
"np.save('./video_durations', video_lengths)\n",
"np.save('./frame_counts', frame_counts)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"\n",
"# Set the aesthetic style of the plots\n",
"sns.set(style=\"darkgrid\")\n",
"\n",
"# Plotting the histogram for video lengths\n",
"plt.figure(figsize=(12, 6))\n",
"sns.histplot(video_lengths, kde=True, color=\"blue\")\n",
"plt.title('Histogram - Video Lengths')\n",
"plt.xlabel('Length of Videos (seconds)')\n",
"plt.ylabel('Number of Videos')\n",
"\n",
"# Plotting the histogram for frame counts\n",
"plt.figure(figsize=(12, 6))\n",
"sns.histplot(frame_counts, kde=True, color=\"green\")\n",
"plt.title('Histogram - Number of Frames')\n",
"plt.xlabel('Frame Count')\n",
"plt.ylabel('Number of Videos')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Frame count and vid lengths"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.boxplot(x=video_lengths)\n",
"plt.title('Box Plot of Video Lengths')\n",
"plt.xlabel('Video Length (seconds)')\n",
"plt.show()\n",
"\n",
"sns.boxplot(x=frame_counts, color=\"r\")\n",
"plt.title('Box Plot of Frame Counts')\n",
"plt.xlabel('Frame Count')\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Class distributions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import os\n",
"import json\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"path_to_labels = './dataset-tacdec/full_labels'\n",
"class_counts = {'background': 0, 'tackle-live': 0, 'tackle-replay': 0, 'tackle-live-incomplete': 0, 'tackle-replay-incomplete': 0, 'dummy_class': 0}\n",
"\n",
"# Iterate through each JSON file in the labels directory\n",
"for label_file in os.listdir(path_to_labels):\n",
" full_path = os.path.join(path_to_labels, label_file)\n",
"\n",
" if not os.path.isfile(full_path):\n",
" continue\n",
"\n",
" with open(full_path, 'r') as file:\n",
" data = json.load(file)\n",
" frame_sections = data['frames_sections']\n",
"\n",
" # Extract annotations\n",
" for section in frame_sections:\n",
" for frame_number, frame_data in section.items():\n",
" class_label = frame_data['radio_answer']\n",
" if class_label in class_counts:\n",
" class_counts[class_label] += 1\n",
"\n",
"# Convert the dictionary to a DataFrame for Seaborn\n",
"df_class_counts = pd.DataFrame(list(class_counts.items()), columns=['Class', 'Occurrences'])\n",
"\n",
"# Save the DataFrame to a CSV file\n",
"df_class_counts.to_csv('class_distribution.csv', sep=',', index=False, encoding='utf-8')\n",
"\n",
"# Plotting the distribution using Seaborn\n",
"plt.figure(figsize=(10, 6))\n",
"sns.barplot(x='Class', y='Occurrences', data=df_class_counts, palette='viridis', alpha=0.75)\n",
"plt.title('Distribution of Frame Classes')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Number of Occurrences')\n",
"plt.xticks(rotation=45) # Rotate class names for better readability\n",
"plt.tight_layout() # Adjust layout to make room for the rotated x-axis labels\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Ensure df_class_counts is already created as in the previous script\n",
"\n",
"# Create a pie chart\n",
"plt.figure(figsize=(8, 8))\n",
"plt.pie(df_class_counts['Occurrences'], labels=df_class_counts['Class'], \n",
" autopct=lambda p: '{:.1f}%'.format(p), startangle=140, \n",
" colors=sns.color_palette('bright', len(df_class_counts)))\n",
"plt.title('Distribution of Frame Classes', fontweight='bold')\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [
"uzdIsbuEpF2w"
],
"provenance": []
},
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