58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
from picamera2 import Picamera2
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import cv2
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import numpy as np
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import time
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# Initialize the camera
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picam2 = Picamera2()
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picam2.configure(picam2.create_preview_configuration(main={"format": "XRGB8888", "size": (640, 480)}))
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picam2.start()
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time.sleep(2) # Give the camera some time to initialize
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# Read the initial frames
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frame1 = picam2.capture_array()
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frame2 = picam2.capture_array()
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while True:
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# Calculate the absolute difference between the two frames
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diff = cv2.absdiff(frame1, frame2)
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# Convert the difference to grayscale
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gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
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# Apply a Gaussian blur to the grayscale image
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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# Threshold the blurred image to highlight the motion
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_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
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# Dilate the thresholded image to fill in holes
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dilated = cv2.dilate(thresh, None, iterations=3)
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# Find contours (motion areas) in the dilated image
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contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# Draw bounding boxes around the contours
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for contour in contours:
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if cv2.contourArea(contour) < 500:
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continue
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x, y, w, h = cv2.boundingRect(contour)
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cv2.rectangle(frame1, (x, y), (x + w, y + h), (0, 255, 0), 2)
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# Display the result
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cv2.imshow("Motion Detection", frame1)
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# Update frames: frame1 becomes frame2, and a new frame is read as frame2
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frame1 = frame2
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frame2 = picam2.capture_array()
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# Press 'q' to exit the loop
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if cv2.waitKey(10) & 0xFF == ord('q'):
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break
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# Release resources and close all OpenCV windows
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picam2.stop()
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cv2.destroyAllWindows()
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