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OpenCV Edge Detector
Loads and displays a video.
# Importing OpenCV
import cv2
import numpy as np
# Create a VideoCapture object and read from input file
# If the input is the camera, pass 0 instead of the video file name
cap = cv2.VideoCapture('videos/cafe.mp4')
# Check if camera opened successfully
if (cap.isOpened()== False):
print("Error opening video stream or file")
fgbg = cv2.createBackgroundSubtractorMOG2(
# Read the video
# Capture frame-by-frame
ret, frame =
if ret == True:
# Converting the image to grayscale.
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Extract the foreground
edges_foreground = cv2.bilateralFilter(gray, 9, 75, 75)
foreground = fgbg.apply(edges_foreground)
# Smooth out to get the moving area
kernel = np.ones((50,50),np.uint8)
foreground = cv2.morphologyEx(foreground, cv2.MORPH_CLOSE, kernel)
# Applying static edge extraction
edges_foreground = cv2.bilateralFilter(gray, 9, 75, 75)
edges_filtered = cv2.Canny(edges_foreground, 60, 120)
# Crop off the edges out of the moving area
cropped = (foreground // 255) * edges_filtered
# Stacking the images to print them together for comparison
images = np.hstack((gray, edges_filtered, cropped))
# Display the resulting frame
cv2.imshow('Frame', canny_images)
# Press Q on keyboard to exit
if cv2.waitKey(25) & 0xFF == ord('q'):
# Break the loop
# When everything done, release the video capture object
# Closes all the frames
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