Realtime Distance Estimation Using Python OpenCV


Python and OpenCV have revolutionized the field of computer vision, enabling developers and researchers to explore a wide range of applications. With its simplicity, versatility, and extensive library support, Python has become a popular programming language for various domains, including computer vision. Complementing Python's capabilities, OpenCV (Open Source Computer Vision Library) provides a powerful toolkit for image and video processing, making it a go−to choice for implementing computer vision algorithms.

In this article, we will guide you through the process of setting up your development environment, capturing real−time video feeds, calibrating the camera, and implementing object detection and tracking techniques using OpenCV. We will then explore various distance calculation methods, including triangulation, and showcase how these techniques can be seamlessly integrated to estimate distances between objects in real time. In the article's next section, we will cover the basics of camera calibration and its significance in distance estimation.

Realtime Distance Estimation Using Python OpenCV

To get started, we need to install OpenCV and its dependencies.

Install Python: If you don't have Python installed, head over to the official Python website (python.org) and download the latest version compatible with your operating system.

Install OpenCV: OpenCV can be installed using a package manager like pip. Open your terminal or command prompt and run the following command:

pip install opencv-python

This command will download and install the OpenCV package for Python.

Install Additional Libraries: In addition to OpenCV, we will be using other Python libraries for this project. Install them by running the following pip commands:

pip install numpy
pip install matplotlib

NumPy is a powerful library for numerical computations, while Matplotlib is useful for visualizing data and displaying images.

Capturing Realtime Video by Accessing the camera feed using OpenCV

To perform real−time distance estimation, we first need to access the camera feed using OpenCV. Luckily, OpenCV provides straightforward functions to accomplish this task. In this tutorial, we will be using the `VideoCapture` class, which allows us to capture video frames from a camera.

Here's an example code snippet that demonstrates how to access the camera feed:

import cv2

# Create a VideoCapture object
cap = cv2.VideoCapture(0)

# Check if the camera is opened successfully
if not cap.isOpened():
    print("Failed to open the camera")
    exit()

# Read and display frames from the camera
while True:
    ret, frame = cap.read()
    if not ret:
        print("Failed to read frame from the camera")
        break

    # Display the frame
    cv2.imshow("Camera Feed", frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the VideoCapture object and close the windows
cap.release()
cv2.destroyAllWindows()

In the above code, we have accessed the video camera feed. Now let’s understand how can we track the objects.

Object Detection and Tracking in OpenCV

Object detection is a critical task in computer vision that involves locating and classifying objects within an image or video stream. OpenCV provides several algorithms for object detection, including Haar cascades and Histogram of Oriented Gradients (HOG).

Haar cascades: Haar cascades are classifiers trained to detect objects based on specific visual features, such as edges, corners, and texture patterns. These classifiers use a cascade of weak classifiers trained with positive and negative samples to progressively narrow down the search space for the object of interest.

HOG (Histogram of Oriented Gradients): HOG is a feature descriptor that captures local object appearance and shape information. It works by calculating and analyzing the distribution of gradient orientations in an image.

In this tutorial, we will focus on the Haar cascade−based object detection approach using OpenCV. Here's an example code snippet that demonstrates object detection and tracking in a video stream:

import cv2

# Load the pre-trained Haar cascade classifier
cascade_path = "haarcascade_frontalface_default.xml"
face_cascade = cv2.CascadeClassifier(cascade_path)

# Access the camera feed
cap = cv2.VideoCapture(0)

while True:
    # Read a frame from the camera
    ret, frame = cap.read()
    if not ret:
        print("Failed to read frame from the camera")
        break

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the grayscale frame
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Draw rectangles around the detected faces
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow("Object Detection", frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the VideoCapture object and close the windows
cap.release()
cv2.destroyAllWindows()

In the above code, we first load the Haar cascade classifier for face detection using the `CascadeClassifier` class provided by OpenCV. You can experiment with different pre−trained Haar cascades and parameters to detect other objects or improve the detection accuracy.

Distance Calculation using Triangulation

Triangulation is a widely used method for estimating distances based on the principles of geometry and perspective. Triangulation involves using the concept of parallax and known geometric relationships to calculate distances.

OpenCV provides functions and tools to implement triangulation algorithms easily. Here's an example code snippet that demonstrates stereo triangulation using OpenCV:

import cv2
import numpy as np

# Load the camera calibration parameters
camera_matrix_1 = np.load("camera_matrix_1.npy")
dist_coeff_1 = np.load("dist_coeff_1.npy")
camera_matrix_2 = np.load("camera_matrix_2.npy")
dist_coeff_2 = np.load("dist_coeff_2.npy")
R = np.load("rotation_matrix.npy")
T = np.load("translation_vector.npy")

# Load the corresponding image points in each view
image_points_1 = np.load("image_points_1.npy")
image_points_2 = np.load("image_points_2.npy")

# Perform stereo triangulation
points_4d = cv2.triangulatePoints(camera_matrix_1, camera_matrix_2, image_points_1, image_points_2)

# Convert 4D homogeneous coordinates to 3D Cartesian coordinates
points_3d = cv2.convertPointsFromHomogeneous(points_4d.T)

# Extract the X, Y, and Z coordinates
x = points_3d[:, 0, 0]
y = points_3d[:, 0, 1]
z = points_3d[:, 0, 2]

# Calculate the distances
distances = np.sqrt(x**2 + y**2 + z**2)

# Print the calculated distances
for i, distance in enumerate(distances):
    print(f"Point {i+1} distance: {distance}")

In the above code, we have implemented the triangulation algorithms for distance calculation. By implementing these triangulation algorithms using OpenCV, we can accurately estimate distances in real−time applications.

Realtime Distance Estimation

Now that we have covered the individual components of object detection, camera calibration, and distance calculation, it's time to bring everything together and create a complete system for real−time distance estimation.

The code implementation of our real−time distance estimation application with look something like this:

import cv2
import numpy as np

# Load the camera calibration parameters
camera_matrix_1 = np.load("camera_matrix_1.npy")
dist_coeff_1 = np.load("dist_coeff_1.npy")
camera_matrix_2 = np.load("camera_matrix_2.npy")
dist_coeff_2 = np.load("dist_coeff_2.npy")
R = np.load("rotation_matrix.npy")
T = np.load("translation_vector.npy")

# Load the pre-trained Haar cascade classifier
cascade_path = "haarcascade_frontalface_default.xml"
face_cascade = cv2.CascadeClassifier(cascade_path)

# Access the camera feed
cap = cv2.VideoCapture(0)

while True:
    # Read a frame from the camera
    ret, frame = cap.read()
    if not ret:
        print("Failed to read frame from the camera")
        break

    # Convert the frame to grayscale
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the grayscale frame
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    for (x, y, w, h) in faces:
        # Calculate the 2D image points for triangulation
        image_points_1 = np.array([[x, y+h/2]], dtype=np.float32)
        image_points_2 = np.array([[x+w, y+h/2]], dtype=np.float32)

        # Perform stereo triangulation
        points_4d = cv2.triangulatePoints(camera_matrix_1, camera_matrix_2, image_points_1, image_points_2)

        # Convert 4D homogeneous coordinates to 3D Cartesian coordinates
        points_3d = cv2.convertPointsFromHomogeneous(points_4d.T)

        # Extract the X, Y, and Z coordinates
        x = points_3d[0, 0, 0]
        y = points_3d[0, 0, 1]
        z = points_3d[0, 0, 2]

        # Calculate the distance
        distance = np.sqrt(x**2 + y**2 + z**2)

        # Draw a bounding box and display the distance on the frame
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
        cv2.putText(frame, f"Distance: {distance:.2f} units", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow("Real-time Distance Estimation", frame)

    # Break the loop if 'q' is pressed
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release the capture and close the windows
cap.release()
cv2.destroyAllWindows()

In the above code, we have successfully implemented a real−time distance estimation application using Python and OpenCV.

Conclusion

In this tutorial, we learned how to perform real−time distance estimation using Python and OpenCV. By combining object detection, tracking, and triangulation techniques, we created a complete system for estimating distances between objects in real−time. With the provided code examples, you can now explore applications in various domains, such as robotics, augmented reality, and security systems. Enjoy delving into the exciting possibilities of computer vision!

Updated on: 25-Jul-2023

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