Count Objects Crossing the Line¶

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Click the Open in Colab button to run the cookbook on Google Colab.

Before you start¶

Let's make sure that we have access to GPU. We can use nvidia-smi command to do that. In case of any problems navigate to Edit -> Notebook settings -> Hardware accelerator, set it to GPU, and then click Save.

In [1]:

!nvidia-smi

!nvidia-smi

Mon Feb 12 13:03:38 2024       
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |
|-----------------------------------------+----------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
|                                         |                      |               MIG M. |
|=========================================+======================+======================|
|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |
| N/A   48C    P8              10W /  70W |      0MiB / 15360MiB |      0%      Default |
|                                         |                      |                  N/A |
+-----------------------------------------+----------------------+----------------------+
                                                                                         
+---------------------------------------------------------------------------------------+
| Processes:                                                                            |
|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
|        ID   ID                                                             Usage      |
|=======================================================================================|
|  No running processes found                                                           |
+---------------------------------------------------------------------------------------+

NOTE: To make it easier for us to manage datasets, images and models we create a HOME constant.

In [2]:

import os
HOME = os.getcwd()
print(HOME)

import os HOME = os.getcwd() print(HOME)

/content

Install required packages¶

In this cookbook, we'll leverage two Python packages - ultralytics for running object detection, and supervision for tracking, visualizing detections, and crucially, counting objects that cross a line.

In [ ]:

!pip install -q ultralytics supervision==0.18.0

!pip install -q ultralytics supervision==0.18.0

Imports¶

In [4]:

import numpy as np
import supervision as sv

from ultralytics import YOLO
from supervision.assets import download_assets, VideoAssets

import numpy as np import supervision as sv from ultralytics import YOLO from supervision.assets import download_assets, VideoAssets

Download video¶

As an example input video, we will use one of the videos available in supervision.assets. Supervision offers an assets download utility that allows you to download video files that you can use in your demos.

In [ ]:

download_assets(VideoAssets.VEHICLES)

download_assets(VideoAssets.VEHICLES)

NOTE: If you want to run the cookbook using your own file as input, simply upload video to Google Colab and replace SOURCE_VIDEO_PATH with the path to your file.

In [6]:

SOURCE_VIDEO_PATH = f"{HOME}/vehicles.mp4"

SOURCE_VIDEO_PATH = f"{HOME}/vehicles.mp4"

As a result of executing the above commands, you will download a video file and save it at the SOURCE_VIDEO_PATH. Keep in mind that the video preview below works only in the web version of the cookbooks and not in Google Colab.

Read single frame from video¶

The get_video_frames_generator enables us to easily iterate over video frames. Let's create a video generator for our sample input file and display its first frame on the screen.

In [7]:

generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH)
frame = next(generator)

sv.plot_image(frame, (12, 12))

generator = sv.get_video_frames_generator(SOURCE_VIDEO_PATH) frame = next(generator) sv.plot_image(frame, (12, 12))