red_color,py

import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Convert to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # Define red color range in HSV
    lower_red1 = np.array([0, 120, 70])
    upper_red1 = np.array([10, 255, 255])
    lower_red2 = np.array([170, 120, 70])
    upper_red2 = np.array([180, 255, 255])

    # Create masks
    mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
    mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
    red_mask = cv2.bitwise_or(mask1, mask2)

    # Clean the mask a bit
    red_mask = cv2.medianBlur(red_mask, 5)

    # Find contours in the mask
    contours, _ = cv2.findContours(red_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    detected = False
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area > 1000:  # Ignore small areas (noise)
            x, y, w, h = cv2.boundingRect(cnt)
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
            cv2.putText(frame, "Red Object Detected", (x, y - 10),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2)
            detected = True

    if not detected:
        cv2.putText(frame, "No Red Object", (30, 50),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (100, 100, 100), 2)

    # Show result
    cv2.imshow("Red Object Detection", frame)
    cv2.imshow("Red Mask", red_mask)  # Optional: show binary red mask

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()