import cv2
import numpy as np
# Define the color ranges for the objects to count
color_ranges = {
"golden": [(10, 100, 100), (25, 255, 255)], # HSV range for golden
"white": [(0, 0, 200), (180, 25, 255)], # HSV range for white
}
# Start video capture from the webcam
cap = cv2.VideoCapture(0)
while True:
# Capture frame-by-frame
ret, frame = cap.read()
if not ret:
break
# Convert the frame to HSV color space
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
object_count = {color: 0 for color in color_ranges}
# Process each color range
for color, (lower, upper) in color_ranges.items():
# Create a mask for the specified color
mask = cv2.inRange(hsv, np.array(lower), np.array(upper))
# Find contours in the mask
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Count the number of objects (contours) for the specified color
for contour in contours:
if cv2.contourArea(contour) > 500: # Filter small contours
object_count[color] += 1
# Draw the contour and a label
cv2.drawContours(frame, [contour], -1, (0, 255, 0), 2)
# Get the centroid of the contour for labeling
M = cv2.moments(contour)
if M["m00"] != 0:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
cv2.putText(frame, color, (cX - 20, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)
# Display the object counts on the frame
for idx, (color, count) in enumerate(object_count.items()):
cv2.putText(frame, f'Count of {color}: {count}', (10, 30 + idx * 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)
# Display the resulting frame
cv2.imshow('Object Counting', frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the capture and close any open windows
cap.release()
cv2.destroyAllWindows()
