Motion detection on the webcam

It is surprisingly easy to make a small Python script that takes a webcam or any other video and detects when something is moving there. It uses the OpenCV library.

1. Difference between frames

Compares two frames and displays only what are change. The rest is black.

import cv2

# Select camera. Usualy 0, or 1 and so on
cam = cv2.VideoCapture(0)

try:
	while cam.isOpened():
		ret, frame1 = cam.read()
		ret, frame2 = cam.read()
		diff = cv2.absdiff(frame1, frame2)
    
		# To exit press 'q'    
		if cv2.waitKey(10) == ord('q'):
			break
    	
		# Display
		cv2.imshow('Erinevus', diff)
except:
	print("Error.")

2. Binary image

Turn it into binary: only black and white. To make it easy to find contours.

import cv2

# Select camera. Usualy 0, or 1 and so on
cam = cv2.VideoCapture(0)

try:
	while cam.isOpened():
		ret, frame1 = cam.read()
		ret, frame2 = cam.read()
		# Compare frames
		diff = cv2.absdiff(frame1, frame2)
		# Convert diff to grayscale image
		gray = cv2.cvtColor(diff, cv2.COLOR_RGB2GRAY)
		# Blur gray image
		blur = cv2.GaussianBlur(gray, (5, 5), 0)
		# Converts to Binary images. Only black and white colour.
		_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
		# Expand moving image part
		dilated = cv2.dilate(thresh, None, iterations=3)
    
		# To exit press 'q'    
		if cv2.waitKey(10) == ord('q'):
			break
    	
		# Display
		cv2.imshow('Erinevus', dilated)
except:
	print("Error.")

3. Contours

Now displays founded contours over the original image.

import cv2

# Select camera. Usualy 0, or 1 and so on
cam = cv2.VideoCapture(0)

try:
	while cam.isOpened():
		ret, frame1 = cam.read()
		ret, frame2 = cam.read()
		# Compare frames
		diff = cv2.absdiff(frame1, frame2)
		# Convert diff to grayscale image
		gray = cv2.cvtColor(diff, cv2.COLOR_RGB2GRAY)
		# Blur gray image
		blur = cv2.GaussianBlur(gray, (5, 5), 0)
		# Converts to Binary images. Only black and white colour.
		_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
		# Expand moving image part
		dilated = cv2.dilate(thresh, None, iterations=3)
		# Find moving part contures
		contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
		# Draw contours
		cv2.drawContours(frame1, contours, -1, (0, 255, 0), 2) #
    
		# To exit press 'q'    
		if cv2.waitKey(10) == ord('q'):
			break
    	
		# Display
		cv2.imshow('Erinevus', frame1)
except:
	print("Error.")

Rectangle

When we know where the contours are. Where are the coordinates of the beginning on the x and y axes. We can draw rectangles around these regions.

import cv2

# Select camera. Usualy 0, or 1 and so on
cam = cv2.VideoCapture(0)

try:
	while cam.isOpened():
		ret, frame1 = cam.read()
		ret, frame2 = cam.read()
		# Compare frames
		diff = cv2.absdiff(frame1, frame2)
		# Convert diff to grayscale image
		gray = cv2.cvtColor(diff, cv2.COLOR_RGB2GRAY)
		# Blur gray image
		blur = cv2.GaussianBlur(gray, (5, 5), 0)
		# Converts to Binary images. Only black and white colour.
		_, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
		# Expand moving image part
		dilated = cv2.dilate(thresh, None, iterations=3)
		# Find moving part contures
		contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

		for c in contours:
			# Select movement size area.
			# If contour is smaller it will be ignored.
			if cv2.contourArea(c) < 2000:
				continue
			# Contour position and size
			x, y, w, h = cv2.boundingRect(c)
			# Draw rectangle
			cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
			# To something
    
		# To exit press 'q'    
		if cv2.waitKey(10) == ord('q'):
			break
    	
		# Display
		cv2.imshow('Liikumine', frame1)
except:
	print("Error.")

Demo video

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