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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple Object Tracking\n",
"- Andre Hu\n",
"- Professor Pantelis\n",
"- Introduction to Artificial Intelligence\n",
"- 4/16/2023"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mkdir: files: File exists\n",
"--2023-04-17 14:41:33-- https://raw.githubusercontent.com/sseshadr/auvsi-cv-all/master/objectTracking/examples/ball.mp4\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1368142 (1.3M) [application/octet-stream]\n",
"Saving to: ‘files/videos/ball.mp4’\n",
"\n",
"ball.mp4 100%[===================>] 1.30M --.-KB/s in 0.09s \n",
"\n",
"2023-04-17 14:41:34 (14.3 MB/s) - ‘files/videos/ball.mp4’ saved [1368142/1368142]\n",
"\n",
"--2023-04-17 14:41:34-- https://github.com/sseshadr/auvsi-cv-all/raw/master/objectTracking/examples/multiObject.avi\n",
"Resolving github.com (github.com)... 140.82.112.4\n",
"Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
"HTTP request sent, awaiting response... 302 Found\n",
"Location: https://raw.githubusercontent.com/sseshadr/auvsi-cv-all/master/objectTracking/examples/multiObject.avi [following]\n",
"--2023-04-17 14:41:34-- https://raw.githubusercontent.com/sseshadr/auvsi-cv-all/master/objectTracking/examples/multiObject.avi\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1769608 (1.7M) [video/msvideo]\n",
"Saving to: ‘files/videos/multiObject.avi’\n",
"\n",
"multiObject.avi 100%[===================>] 1.69M 5.22MB/s in 0.3s \n",
"\n",
"2023-04-17 14:41:35 (5.22 MB/s) - ‘files/videos/multiObject.avi’ saved [1769608/1769608]\n",
"\n",
"Requirement already satisfied: numpy in /Users/bhu/.virtualenvs/mot/lib/python3.9/site-packages (1.24.2)\n",
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]
}
],
"source": [
"!mkdir files\n",
"!rm -rf files/*\n",
"\n",
"# Downloading assignment videos\n",
"!wget -P files/videos https://raw.githubusercontent.com/sseshadr/auvsi-cv-all/master/objectTracking/examples/ball.mp4\n",
"!wget -P files/videos https://github.com/sseshadr/auvsi-cv-all/raw/master/objectTracking/examples/multiObject.avi\n",
"\n",
"!pip install numpy\n",
"!pip install filterpy\n",
"!pip install opencv-python\n",
"!pip install -r https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import torch\n",
"import numpy as np\n",
"from scipy.linalg import block_diag\n",
"from filterpy.kalman import KalmanFilter\n",
"from filterpy.common import Q_discrete_white_noise"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 2: Object Detector"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using cache found in /Users/bhu/.cache/torch/hub/ultralytics_yolov5_master\n",
"YOLOv5 🚀 2023-4-15 Python-3.9.6 torch-2.0.0 CPU\n",
"\n",
"Fusing layers... \n",
"YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
"Adding AutoShape... \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31m\u001b[1mrequirements:\u001b[0m /Users/bhu/.cache/torch/hub/requirements.txt not found, check failed.\n"
]
}
],
"source": [
"# Loading YOLOv5 model\n",
"model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)\n",
"\n",
"# Filter out classes, only want to detect sports ball\n",
"model.classes = [32]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"class Ball:\n",
" def __init__(self, x, y):\n",
" self.actual_points = []\n",
" self.predicted_points = []\n",
" self.filter = self.createFilter(x, y)\n",
"\n",
" def createFilter(self, x, y):\n",
" \"\"\"\n",
" Based off of https://share.cocalc.com/share/7557a5ac1c870f1ec8f01271959b16b49df9d087/Kalman-and-Bayesian-Filters-in-Python/08-Designing-Kalman-Filters.ipynb?viewer=share\n",
" \"\"\"\n",
" kf = KalmanFilter(dim_x=4, dim_z=2)\n",
" dt = 1.0\n",
"\n",
" kf.F = np.array([[1, dt, 0, 0],\n",
" [0, 1, 0, 0],\n",
" [0, 0, 1, dt],\n",
" [0, 0, 0, 1]])\n",
"\n",
" q = Q_discrete_white_noise(dim=2, dt=dt, var=0.05)\n",
" kf.Q = block_diag(q, q)\n",
"\n",
" kf.H = np.array([[1, 0, 0, 0],\n",
" [0, 0, 1, 0]])\n",
"\n",
" kf.R = np.array([[0.5, 0],\n",
" [0, 0.5]])\n",
"\n",
" kf.P = np.eye(4)\n",
" kf.x = np.array([[x, 0, y, 0]]).T\n",
"\n",
" return kf\n",
"\n",
" def calculateDistance(self, x, y):\n",
" \"\"\"\n",
" Calculates the distance between the current position and the predicted position\n",
" \"\"\"\n",
" return np.sqrt((x - self.filter.x[0]) ** 2 + (y - self.filter.x[2]) ** 2)\n",
"\n",
" def predict(self):\n",
" \"\"\"\n",
" Have the filter predict the next point\n",
" \"\"\"\n",
" self.filter.predict()\n",
" predicted_x = int(self.filter.x[0])\n",
" predicted_y = int(self.filter.x[2])\n",
"\n",
" self.predicted_points.append((predicted_x, predicted_y))\n",
"\n",
" def update(self, x, y):\n",
" \"\"\"\n",
" Update the filter with a new point\n",
" \"\"\"\n",
" self.actual_points.append((x, y))\n",
" self.filter.update(np.array([[x, y]]))\n",
"\n",
" def addPointsToFrame(self, frame):\n",
" \"\"\"\n",
" Draw the actual and predicted points on the frame\n",
" \"\"\"\n",
" for i in self.actual_points:\n",
" cv2.circle(frame, i, 1, (0, 255, 0), 2) # green\n",
" for i in self.predicted_points:\n",
" cv2.circle(frame, i, 1, (204, 51, 255), 2) # purple\n",
"\n",
"\n",
"def getNumberOfBalls(filename):\n",
" \"\"\"\n",
" Retrieves the maximum number of balls in a video\n",
" \"\"\"\n",
" cap = cv2.VideoCapture(filename)\n",
" t = []\n",
" while (cap.isOpened()):\n",
" ret, frame = cap.read()\n",
" if ret == True:\n",
" results = model(frame)\n",
" t.append(len(results.xyxy[0]))\n",
" else:\n",
" break\n",
" return max(t)\n",
"\n",
"\n",
"def calculateCenter(box):\n",
" \"\"\"\n",
" Calculates the center of a bounding box\n",
" \"\"\"\n",
" return int((box[0] + box[2]) / 2), int((box[1] + box[3]) / 2)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def ballDetector(filename, outfile):\n",
" # Keep track of all the balls in the video\n",
" balls = [] \n",
"\n",
" # Get the number of balls in the video\n",
" numberOfBalls = getNumberOfBalls(filename)\n",
"\n",
" cap = cv2.VideoCapture(filename)\n",
" \n",
" # Defining the codec to export the frames\n",
" fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n",
" fps = int(cap.get(cv2.CAP_PROP_FPS))\n",
" frame_size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))\n",
" out = cv2.VideoWriter(outfile, fourcc, fps, frame_size)\n",
"\n",
" # The code below renders the video and applies the YOLOv5 model\n",
" # loaded previously to detect objects in the video\n",
" while(cap.isOpened()):\n",
" # Read a frame from the video\n",
" ret, frame = cap.read()\n",
"\n",
" if ret == True:\n",
" # Apply the YOLOv5 model to the frame\n",
" results = model(frame)\n",
" frame = results.render()[0]\n",
"\n",
" # Find first frame where all the balls are detected\n",
" if len(balls) == 0 and len(results.xyxy[0]) == numberOfBalls:\n",
" for detection in results.xyxy[0]:\n",
" cx, cy = calculateCenter(detection)\n",
" balls.append(Ball(cx, cy))\n",
"\n",
" if len(balls) == numberOfBalls:\n",
"\n",
" # Update the filters for the balls\n",
" for detection in results.xyxy[0]:\n",
" cx, cy = calculateCenter(detection)\n",
" \n",
" distances = [b.calculateDistance(cx, cy) for b in balls]\n",
" indexOfMin = distances.index(min(distances))\n",
"\n",
" ball = balls[indexOfMin]\n",
" ball.update(cx, cy)\n",
"\n",
" for b in balls:\n",
" b.predict()\n",
" b.addPointsToFrame(frame)\n",
" \n",
" # Render the results\n",
" cv2.imshow('Object Detection', frame)\n",
" out.write(frame)\n",
" \n",
" # Used for exiting the video\n",
" if cv2.waitKey(1) & 0xFF == ord('q'):\n",
" break\n",
" else:\n",
" break\n",
"\n",
" # Release the video and close all windows\n",
" cap.release()\n",
" out.release()\n",
" cv2.destroyAllWindows()\n",
" cv2.waitKey(1)\n",
" \n",
"\n",
"# Open the videos\n",
"ballDetector(\"files/videos/ball.mp4\", \"ball_detection.mp4\")\n",
"ballDetector(\"files/videos/multiObject.avi\", \"multiObject_detection.mp4\")"
]
}
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|