distance_to_person.py

# Let's consolidate the explanation into a single Python script that demonstrates how to detect a person
# and estimate the distance from the camera using OpenCV. This script will rely on a simplistic approach
# for distance estimation based on the size of the detected person in the frame. This method is highly
# dependent on the camera's field of view and requires prior calibration to achieve reasonable accuracy.
# Note: This script does not incorporate depth sensors or stereo vision for actual distance measurement.

# Import necessary libraries
import cv2
import numpy as np

# Load a pre-trained model for person detection. For simplicity, we use a Haar Cascade for face detection,
# although for full-body detection in a more complex scenario, you would use models like YOLO or SSD with OpenCV's DNN module.
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# Function to estimate distance from the camera to the person based on the size of the detected face
# This function needs calibration with known distances and object sizes to be accurate
def estimate_distance(frame_width, detected_width):
    # Placeholder function for distance estimation based on the width of the detected face
    # This requires calibration and conversion based on the camera's field of view and the actual size of a human face
    # Here, we simply use a placeholder conversion factor (which you need to calibrate)
    conversion_factor = 0.05  # This is a made-up value for demonstration; you must calibrate this value
    distance = (frame_width / detected_width) * conversion_factor
    return distance

# Initialize video capture
cap = cv2.VideoCapture(0)

while True:
    # Capture frame-by-frame
    ret, frame = cap.read()
    if not ret:
        break

    # Convert frame to grayscale for face detection
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Detect faces in the frame
    faces = face_cascade.detectMultiScale(gray, 1.1, 4)

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

        # Estimate distance and display it
        distance = estimate_distance(frame.shape[1], w)
        cv2.putText(frame, f"Distance: {distance:.2f} units", (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)

    # Display the resulting frame
    cv2.imshow('Frame', frame)

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

# When everything is done, release the capture
cap.release()
cv2.destroyAllWindows()

# Remember, this code needs actual calibration to provide meaningful distance measurements.
# The distance estimation function provided is highly simplified and serves as a placeholder for a more complex calibration process.
# For more accurate distance measurements, consider using depth sensors or stereo vision techniques, which require different hardware and software setups.