Red Object Detection

This Python script captures images from a Raspberry Pi camera using PiCamera2 and analyzes them in real-time to detect the presence of red objects. It uses the HSV color space for more robust color detection.

Libaries

from picamera2 import Picamera2
import cv2
import numpy as np
import time

picamera2: Interface for the Raspberry Pi Camera Module.
cv2 (OpenCV): Image processing and computer vision tasks.
numpy: Efficient array operations, used here for masks and color thresholds.
time: Adds delays and manages timing.

Initialization

from picamera2 import Picamera2
import cv2
import numpy as np
import time

Initializes the camera and configures it for still image capture.
Waits for one second to ensure the camera is ready.

Main Loop

while True:
    ...

The loop runs continuously until the program is interrupted by the user (CTRL+C).

Image Capture and Color Conversion

frame = picam2.capture_array()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

Captures a frame from the camera.
Converts the image to HSV (Hue, Saturation, Value) color space, which is better suited for color segmentation than RGB.

Red Color Range Definition

lower_red1 = np.array([0, 100, 100])
upper_red1 = np.array([10, 255, 255])
lower_red2 = np.array([160, 100, 100])
upper_red2 = np.array([179, 255, 255])

Red spans two ranges in HSV: near 0° and near 180° hue values.
Two ranges are defined to cover the full red spectrum.

Mask Creation and Combination

mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
mask = cv2.bitwise_or(mask1, mask2)

Creates binary masks for each red range.
Combines the two masks into a single one that highlights all red areas.

Red Object Detection

contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

Finds contours in the mask to identify red regions.

for cnt in contours:
    contour_area = cv2.contourArea(cnt)
    if contour_area > MIN_CONTOUR_AREA:
        found_red = True
        break

Loops through all contours and calculates their area using cv2.contourArea().
If any contour has an area greater than MIN_CONTOUR_AREA, a red object is considered detected. (MIN_CONTOUR_AREA is a configurable threshold value defined earlier in the code to control the sensitivity of red object detection.)

Output

if found_red:
    print("Red object detected!")
else:
    print("No red object detected.")

time.sleep(1)

Prints the detection result every second.
Adds a delay with time.sleep(1) to reduce processing load and output spam.

Graceful Exit

except KeyboardInterrupt:
    print("\nTerminated by user.")

Handles manual termination (CTRL+C) with a user-friendly message.

finally:
    picam2.stop()

Ensures the camera is properly stopped, whether the loop ends normally or due to an error/interruption.

07 Juli 2025