Table of Contents
Introduction
In this tutorial, we will learn how to create a real-time object detection system using Python. Object detection is a computer vision task that involves identifying and locating objects in images or videos. We will leverage the power of Python libraries and modules to build a system that can detect objects in real-time using a webcam feed.
By the end of this tutorial, you will have a working object detection system that can identify objects in real-time video streams. We will go through each step of the process, from setting up the required software to building the object detection model and integrating it with the webcam feed. No prior experience in object detection or computer vision is required, but basic knowledge of Python programming is recommended.
Prerequisites
To follow along with this tutorial, you should have:
- Basic knowledge of Python programming
- Python installed on your machine
- Webcam connected to your computer
Setup
Before we can start building our object detection system, we need to install some Python libraries and modules. Open your terminal and run the following commands to install the necessary dependencies:
pip install opencv-python
pip install numpy
pip install tensorflow
pip install pillow
Once the installation is complete, we can proceed to the next step.
Building the Object Detection System
Step 1: Importing the Required Libraries
First, let’s import the required libraries and modules to begin building our object detection system. Open a new Python script and add the following lines of code:
python
import cv2
import numpy as np
import tensorflow as tf
from PIL import Image
Here, we imported the cv2 library for video input/output, numpy for array manipulation, tensorflow for applying a pre-trained object detection model, and PIL for image processing.
Step 2: Loading the Pre-trained Model
To detect objects in real-time, we will use a pre-trained object detection model. TensorFlow provides a selection of pre-trained models through the TensorFlow Model Zoo. In this tutorial, we will use the MobileNetV2 model.
Download the MobileNetV2 model from the TensorFlow Model Zoo by navigating to the following link: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
Once downloaded, extract the contents of the ZIP file. Inside the extracted folder, you will find a file with a .pb extension. Copy this file to your project directory.
Step 3: Loading the Model into Memory
Now, let’s load the pre-trained model into memory using TensorFlow. Add the following code to your script:
python
model_path = 'path/to/mobilenet_v2_frozen_inference_graph.pb'
model = tf.saved_model.load(model_path)
Replace 'path/to/mobilenet_v2_frozen_inference_graph.pb' with the actual path to the downloaded .pb file.
Step 4: Defining the Object Detection Function
Next, we will define a function that performs object detection on each frame of the video feed. Add the following code to your script:
python
def detect_objects(frame):
image = Image.fromarray(frame)
input_tensor = np.expand_dims(image, 0)
input_tensor = tf.convert_to_tensor(input_tensor)
detections = model(input_tensor)
num_detections = int(detections.pop('num_detections'))
detections = {key: value[0, :num_detections].numpy() for key, value in detections.items()}
detections['num_detections'] = num_detections
detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
return detections
This function takes a frame from the webcam feed as input and returns the detected objects along with their classifications and bounding box coordinates.
Step 5: Capturing and Processing the Webcam Feed
Now, let’s capture the webcam feed and process each frame using the object detection function. Add the following code to your script: ```python cap = cv2.VideoCapture(0) # 0 represents the default webcam while True: ret, frame = cap.read() if not ret: break
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
detections = detect_objects(frame_rgb)
# Draw bounding boxes on the frame
for i in range(detections['num_detections']):
class_id = detections['detection_classes'][i]
score = detections['detection_scores'][i]
bbox = detections['detection_boxes'][i]
if score > 0.5: # Set a threshold for classification score
height, width, _ = frame.shape
ymin, xmin, ymax, xmax = bbox
xmin = int(xmin * width)
xmax = int(xmax * width)
ymin = int(ymin * height)
ymax = int(ymax * height)
class_name = class_names[class_id]
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 0), 2)
cv2.putText(frame, class_name, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
cv2.imshow('Object Detection', frame)
if cv2.waitKey(1) & 0xFF == ord('q'): # Press 'q' to exit
break
cap.release()
cv2.destroyAllWindows()
``` Here, we used OpenCV to capture the video feed from the webcam and processed each frame using the `detect_objects` function. We draw bounding boxes around the detected objects and display the resulting frame in a window. Press 'q' to exit the program.
Step 6: Running the Object Detection System
Save your script and run it. You should see a window displaying the webcam feed with bounding boxes around the detected objects. Move different objects in front of the webcam and observe how the system detects and classifies them in real-time.
Congratulations! You have successfully created a real-time object detection system using Python.
Conclusion
In this tutorial, we have learned how to build a real-time object detection system using Python. We started by setting up the required software and dependencies, loaded a pre-trained object detection model, and integrated it with the webcam feed. We then captured and processed each frame of the video feed, drawing bounding boxes around the detected objects. Finally, we ran the system and tested its performance in real-time. Object detection is a powerful technique with numerous applications in various domains, including autonomous vehicles, security systems, and robotics. By mastering this skill, you can unlock a range of exciting possibilities in computer vision and beyond.