{
"cells": [
{
"cell_type": "markdown",
"id": "2a66f904",
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"status": "completed"
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"tags": []
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"source": [
"# Advanced Object Tracking & Counting with DETR and SORT\n",
"\n",
"## Project Overview\n",
"\n",
"This project demonstrates an advanced computer vision pipeline that goes beyond simple detection to perform robust **object tracking and cumulative counting**. The solution leverages the official **Facebook (Meta AI) DETR model** for high-accuracy object detection and the **SORT (Simple Online and Realtime Tracking) algorithm** to assign unique IDs to objects and track them across video frames.\n",
"\n",
"The primary application shown here is traffic analysis, where a virtual \"counting line\" is established. The system provides a cumulative total for each object category (`car`, `person`, etc.) only when an object crosses this line, providing accurate metrics instead of a simple per-frame count. This is a portfolio-ready project showcasing skills in modern AI models and practical, real-world application logic."
]
},
{
"cell_type": "markdown",
"id": "91555f1e",
"metadata": {
"papermill": {
"duration": 0.00192,
"end_time": "2025-09-13T14:52:34.763826",
"exception": false,
"start_time": "2025-09-13T14:52:34.761906",
"status": "completed"
},
"tags": []
},
"source": [
"## 1. Setup and Library Installation\n",
"\n",
"We will install all necessary libraries. `sort-tracker` is a lightweight and efficient library for implementing the SORT algorithm."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "395aef60",
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"exception": false,
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"status": "completed"
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"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.0/178.0 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.4/363.4 MB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.8/13.8 MB\u001b[0m \u001b[31m83.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.6/24.6 MB\u001b[0m \u001b[31m71.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m883.7/883.7 kB\u001b[0m \u001b[31m36.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.8/664.8 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.5/211.5 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.3/56.3 MB\u001b[0m \u001b[31m28.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m13.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m75.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
"\u001b[?25h Building wheel for filterpy (setup.py) ... \u001b[?25l\u001b[?25hdone\r\n"
]
}
],
"source": [
"# Install Libraries\n",
"!pip install transformers timm opencv-python filterpy scikit-image -q"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b99bb35b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T14:54:07.975873Z",
"iopub.status.busy": "2025-09-13T14:54:07.975305Z",
"iopub.status.idle": "2025-09-13T14:54:08.405070Z",
"shell.execute_reply": "2025-09-13T14:54:08.404103Z"
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"papermill": {
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"end_time": "2025-09-13T14:54:08.406224",
"exception": false,
"start_time": "2025-09-13T14:54:07.955673",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2025-09-13 14:54:08-- https://raw.githubusercontent.com/abewley/sort/master/sort.py\r\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.111.133, ...\r\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\r\n",
"HTTP request sent, awaiting response... 200 OK\r\n",
"Length: 11739 (11K) [text/plain]\r\n",
"Saving to: ‘sort.py’\r\n",
"\r\n",
"sort.py 100%[===================>] 11.46K --.-KB/s in 0.001s \r\n",
"\r\n",
"2025-09-13 14:54:08 (15.0 MB/s) - ‘sort.py’ saved [11739/11739]\r\n",
"\r\n",
"File 'sort.py' downloaded and patched successfully.\n"
]
}
],
"source": [
"# Download AND Patch the SORT code\n",
"\n",
"# Step 1: Download the original sort.py file, forcing the output name to be 'sort.py'\n",
"!wget https://raw.githubusercontent.com/abewley/sort/master/sort.py -O sort.py\n",
"\n",
"# Step 2: Automatically edit the file to comment out the problematic line\n",
"# This 'sed' command finds the line \"matplotlib.use('TkAgg')\" and adds a '#' at the beginning, disabling it.\n",
"!sed -i \"s/matplotlib.use('TkAgg')/# matplotlib.use('TkAgg')/\" sort.py\n",
"\n",
"print(\"File 'sort.py' downloaded and patched successfully.\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "17c56c08",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T14:54:08.445410Z",
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"shell.execute_reply": "2025-09-13T14:54:47.452310Z"
},
"papermill": {
"duration": 39.047475,
"end_time": "2025-09-13T14:54:47.472848",
"exception": false,
"start_time": "2025-09-13T14:54:08.425373",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-09-13 14:54:26.595957: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"E0000 00:00:1757775266.958994 19 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"E0000 00:00:1757775267.062505 19 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"All libraries imported successfully.\n"
]
}
],
"source": [
"# Imports\n",
"\n",
"import matplotlib\n",
"matplotlib.use('Agg') # Best practice to set the backend early\n",
"\n",
"import torch\n",
"from transformers import AutoImageProcessor, AutoModelForObjectDetection\n",
"import cv2\n",
"from PIL import Image\n",
"import numpy as np\n",
"import os\n",
"import base64\n",
"from IPython.display import HTML, display\n",
"\n",
"# Import the Sort class from our now-patched 'sort.py' file\n",
"from sort import Sort\n",
"\n",
"print(\"All libraries imported successfully.\")"
]
},
{
"cell_type": "markdown",
"id": "acddf947",
"metadata": {
"papermill": {
"duration": 0.019119,
"end_time": "2025-09-13T14:54:47.510996",
"exception": false,
"start_time": "2025-09-13T14:54:47.491877",
"status": "completed"
},
"tags": []
},
"source": [
"## 2. Initialize Model and Tracker\n",
"\n",
"Here, we load the official `facebook/detr-resnet-50` model from the Hugging Face Hub. We then initialize the SORT tracker and create data structures to hold our cumulative counts (`total_counts`) and the IDs of objects that have already been counted (`counted_ids`)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ddda0f60",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T14:54:47.549718Z",
"iopub.status.busy": "2025-09-13T14:54:47.549203Z",
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},
"papermill": {
"duration": 9.12414,
"end_time": "2025-09-13T14:54:56.653897",
"exception": false,
"start_time": "2025-09-13T14:54:47.529757",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e978fd9222c54d91ab0ccde0a3fba65d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"preprocessor_config.json: 0%| | 0.00/290 [00:00 0:\n",
" tracked_objects_raw = tracker.update(np.array(detections_for_sort))\n",
"\n",
" for obj in tracked_objects_raw:\n",
" x1, y1, x2, y2, obj_id = [int(val) for val in obj]\n",
" center_x = int((x1 + x2) / 2)\n",
"\n",
" # Re-associate label using IoU\n",
" best_iou = 0\n",
" best_label = None\n",
" for det in original_detections:\n",
" iou_score = iou([x1, y1, x2, y2], det['box'])\n",
" if iou_score > best_iou:\n",
" best_iou = iou_score\n",
" best_label = det['label']\n",
"\n",
" # Count the object if its center enters the zone and it hasn't been counted before\n",
" if best_label and obj_id not in counted_ids:\n",
" if center_x > ZONE_START_X and center_x < ZONE_END_X:\n",
" total_counts[best_label] += 1\n",
" counted_ids.add(obj_id)\n",
" \n",
" # Draw bounding box and ID (no changes here)\n",
" if best_label:\n",
" cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)\n",
" cv2.putText(frame, f'{best_label} ID: {obj_id}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)\n",
"\n",
" overlay = frame.copy()\n",
" cv2.rectangle(overlay, (ZONE_START_X, 0), (ZONE_END_X, frame_height), (255, 0, 0, 0.2), -1)\n",
" # Gabungkan overlay dengan frame asli\n",
" alpha = 0.2 # Tingkat transparansi\n",
" frame = cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0)\n",
" \n",
" # Display the cumulative total counts (no changes here)\n",
" y_offset = 30\n",
" for obj_name, count in total_counts.items():\n",
" text = f'Total {obj_name.capitalize()}: {count}'\n",
" cv2.putText(frame, text, (15, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 5)\n",
" cv2.putText(frame, text, (15, y_offset), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)\n",
" y_offset += 30\n",
"\n",
" out.write(frame)\n",
"\n",
" cap.release()\n",
" out.release()\n",
" print(f\"\\nVideo processing complete! Output saved to: {output_video_path}\")"
]
},
{
"cell_type": "markdown",
"id": "4a6caa77",
"metadata": {
"papermill": {
"duration": 0.019812,
"end_time": "2025-09-13T14:56:37.128777",
"exception": false,
"start_time": "2025-09-13T14:56:37.108965",
"status": "completed"
},
"tags": []
},
"source": [
"## 5. Final Visualization\n",
"\n",
"This final cell will display the processed video directly in the notebook output. This allows for immediate review without needing to download the file first."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9f762f1c",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T14:56:37.169919Z",
"iopub.status.busy": "2025-09-13T14:56:37.169687Z",
"iopub.status.idle": "2025-09-13T14:56:37.176019Z",
"shell.execute_reply": "2025-09-13T14:56:37.175482Z"
},
"papermill": {
"duration": 0.028108,
"end_time": "2025-09-13T14:56:37.176931",
"exception": false,
"start_time": "2025-09-13T14:56:37.148823",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Video file found! Size: 57.01 MB\n",
"Click the link below to download your processed video:\n"
]
},
{
"data": {
"text/html": [
"/kaggle/working/output_video_final_zone.mp4
"
],
"text/plain": [
"/kaggle/working/output_video_final_zone.mp4"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Kode Visualisasi Baru yang Lebih Andal (Menampilkan Link Unduhan)\n",
"\n",
"import os\n",
"from IPython.display import FileLink, display\n",
"\n",
"# Path ke video output Anda\n",
"output_video_path = '/kaggle/working/output_video_final_zone.mp4'\n",
"\n",
"# Periksa apakah file ada\n",
"if os.path.exists(output_video_path):\n",
" # Dapatkan ukuran file dalam Megabyte\n",
" file_size_mb = os.path.getsize(output_video_path) / (1024 * 1024)\n",
" \n",
" print(f\"Video file found! Size: {file_size_mb:.2f} MB\")\n",
" print(\"Click the link below to download your processed video:\")\n",
" \n",
" # Tampilkan link unduhan yang bisa diklik\n",
" display(FileLink(output_video_path))\n",
"else:\n",
" print(f\"File video output tidak ditemukan di path: {output_video_path}\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "10d3d22b",
"metadata": {
"execution": {
"iopub.execute_input": "2025-09-13T14:56:37.218640Z",
"iopub.status.busy": "2025-09-13T14:56:37.217997Z",
"iopub.status.idle": "2025-09-13T14:56:37.221352Z",
"shell.execute_reply": "2025-09-13T14:56:37.220579Z"
},
"papermill": {
"duration": 0.025305,
"end_time": "2025-09-13T14:56:37.222531",
"exception": false,
"start_time": "2025-09-13T14:56:37.197226",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# !rm -f /kaggle/working/output_video_final.mp4"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0da184ad",
"metadata": {
"papermill": {
"duration": 0.021484,
"end_time": "2025-09-13T14:56:37.264219",
"exception": false,
"start_time": "2025-09-13T14:56:37.242735",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kaggle": {
"accelerator": "nvidiaTeslaT4",
"dataSources": [
{
"datasetId": 8261882,
"sourceId": 13047166,
"sourceType": "datasetVersion"
}
],
"dockerImageVersionId": 31090,
"isGpuEnabled": true,
"isInternetEnabled": true,
"language": "python",
"sourceType": "notebook"
},
"kernelspec": {
"display_name": "Python 3",
"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": "3.11.13"
},
"papermill": {
"default_parameters": {},
"duration": 252.23122,
"end_time": "2025-09-13T14:56:40.923845",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2025-09-13T14:52:28.692625",
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"model_name": "HTMLStyleModel",
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"_model_name": "HTMLStyleModel",
"_view_count": null,
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"_view_module_version": "2.0.0",
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