I am taking input from a video and I want to take the median value of the first 5 frames so that I can use it as background image for motion detection using deferential.
Also, I want to use a time condition that, say if motion is not detected then calculate the background again, else wait t seconds. I am new to opencv and I don't know how to do it.. Please help
Also, I want to take my video in 1 fps but this does not work. Here is the code I have:
import cv2
BLUR_SIZE = 3
NOISE_CUTOFF = 12
cam = cv2.VideoCapture('gh10fps.mp4')
cam.set(3, 640)
cam.set(4, 480)
cam.set(cv2.cv.CV_CAP_PROP_FPS, 1)
fps=cam.get(cv2.cv.CV_CAP_PROP_FPS)
print "Current FPS: ",fps
If you really want the median of the first 5 frames, then following should do what you are looking for:
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
frames = []
for _ in range(5):
ret, frame = cap.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
frames.append(gray)
median = np.median(frames, axis=0).astype(dtype=np.uint8)
cv2.imshow('frame', median)
cv2.waitKey(0)
cap.release()
cv2.destroyAllWindows()
Note, this is just taking the source from a webcam as an example.
Related
I am trying to write a script to manipulate video from a webcam. I am trying to do this through OpenCV with Python, but I am running into some issues.
If I run the video capture stream with no pixel manipulation applied, the stream works fine and has a smooth frame rate. However, I applied a threshold loop as a test, and my stream undergoes major lag and updates once every few seconds. Any ideas if it is possible to optimise this? Ideally, I am looking to get a 30 fps stream with the video manipulation applied. Here is the code:
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
T = 100
while True:
ret, frame = cap.read()
height, width, channels = frame.shape
for x in range(width):
for y in range(height):
if frame[y,x,0] < T:
frame[y,x]=0
else:
frame[y,x]=255
cv2.imshow('frame', frame)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
accessing Pixel by pixel in image processing in general is very bad practice as it slow the performance very much, packages like opencv and numpy has optimized this by doing matrix operations allowing your program to be much more faster, here is a sample code that will perform your task but much more faster.
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
T = 100
while True:
ret, frame = cap.read()
height, width, channels = frame.shape
B,G,R = cv2.split(frame)
# for x in range(width):
# for y in range(height):
# if frame[y,x,0] < T:
# frame[y,x]=0
# else:
# frame[y,x]=255
_,B = cv2.threshold(B,T,255,cv2.THRESH_BINARY)
frame = cv2.merge((B,G,R))
cv2.imshow('frame', frame)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
I have conferance call video with different people's tiles arranged on a grid.
Example:
gallery view zoom
Can I crop every video tile to a separate file using python or nodejs?
Yes, you can achieve that using OpenCV library
Read the video in OpenCV using VideoCapture API. Note down framerate while reading.
Parse through each frame and crop the frame:
Write the frame in a video using OpenCV VideoWriter
Here is the example code using (640,480) to be the new dimensions:
cap = cv2.VideoCapture(<video_file_name>)
fps = cap.get(cv2.CAP_PROP_FPS)
out = cv2.VideoWriter('<output video file name>, -1, fps, (640,480))
while(cap.isOpened()):
ret, frame = cap.read()
crop_frame = frame[y:y+h, x:x+w]
# write the crooped frame
out.write(crop_frame)
# Release reader wand writer after parsing all frames
cap.release()
out.release()
Here's the code (tested). It works by initialising a number of video outputs, then for each frame of the input video: cropping the region of interest (roi) and assigning each to the relevent output video. You might need to make tweaks depending on input video dimensions, number of times, offsets etc.
import numpy as np
import cv2
import time
cap = cv2.VideoCapture('in.mp4')
ret, frame = cap.read()
(h, w, d) = np.shape(frame)
horiz_divisions = 5 # Number of tiles stacked horizontally
vert_divisions = 5 # Number of tiles stacked vertically
divisions = horiz_divisions*vert_divisions # Total number of tiles
seg_h = int(h/vert_divisions) # Tile height
seg_w = int(w/horiz_divisions) # Tile width
# Initialise the output videos
outvideos = [0] * divisions
for i in range(divisions):
outvideos[i] = cv2.VideoWriter('out{}.avi'.format(str(i)),cv2.VideoWriter_fourcc('M','J','P','G'), 10, (seg_w,seg_h))
# main code
while(cap.isOpened()):
ret, frame = cap.read()
if ret == True:
vid = 0 # video counter
for i in range(vert_divisions):
for j in range(horiz_divisions):
# Get the coordinates (top left corner) of the current tile
row = i * seg_h
col = j * seg_w
roi = frame[row:row+seg_h,col:col+seg_w,0:3] # Copy the region of interest
outvideos[vid].write(roi)
vid += 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
# Release all the objects
cap.release()
for i in range(divisions):
outvideos[i].release()
# Release everything if job is finished
cv2.destroyAllWindows()
Hope this helps!
I'm attempting to take multiple snapshots squentially with different exposure times from an attached USB camera. However, upon changing the exposure time before each aquisition, the exposure time remains the same as that for the first snapshot as can been seen by looking at the image (even though cap.get() still returns the new exposure time). Furthermore, the camera doesn't turn off when using .release(). Perhaps a driver issue? The result is the same with the attached webcam on my Dell.
openCV v3.4.2 on Python v3.7.0
import numpy as np
import cv2
from matplotlib import pyplot as plt
cap = cv2.VideoCapture(1)
exp = cap.get(15)
width = cap.get(3)
height = cap.get(4)
print(f'original exporsure {exp:1}')
cap.set(15,-5) #set first exposure
ret, frame = cap.read() #get a single frame
print(f'exposure 1 {cap.get(15):1}')
cv2.imshow('1',frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
cap.set(15,-10) #set second exposure
ret2, frame2 = cap.read()
print(f'exposure 2 {cap.get(15):1}')
cv2.imshow('2',frame2)
cv2.waitKey(0)
cap.release()
cv2.destroyAllWindows()
I'm trying to use MOG background subtraction but the "history" function doesn't seem to work.
OpenCV 2.4.13
Python (2.7.6)
Observation: The program appears to use the very first frame it captures for all future background subtractions.
Expectation: The background should slowly evolve based on the "history" parameter, so that if the camera angle changes, or if a person/object leaves the field-of-view, the "background" image will change accordingly.
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
mog = cv2.BackgroundSubtractorMOG(history=10, nmixtures=5, backgroundRatio=0.25)
while True:
ret, img1 = cap.read()
if ret is True:
cv2.imshow("original", img1)
img_mog = mog.apply(img1)
cv2.imshow("mog", img_mog)
if cv2.waitKey(10) & 0xFF == ord(q):
break
video_capture.release()
cv2.destroyAllWindows()
Thank you in advance for your help!
What I need to do is fairly simple:
load a 5 frames video file
detect background
On every frames, one by one :
subtract background (create foreground mask)
do some calculations on foreground mask
save both original frame and foreground mask
Just to see the 5 frames and the 5 corresponding fgmasks :
import numpy as np
import cv2
cap = cv2.VideoCapture('test.avi')
fgbg = cv2.BackgroundSubtractorMOG()
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
fgmask = fgbg.apply(frame)
# Display the fgmask frame
cv2.imshow('fgmask',fgmask)
# Display original frame
cv2.imshow('img', frame)
k = cv2.waitKey(0) & 0xff
if k == 5:
break
cap.release()
cv2.destroyAllWindows()
Every frame gets opened and displayed correctly but the showed fgmask do not correspond to the showed original frame. Somewhere in the process, the order of the fgmasks gets mixed up.
The background does get subtracted correctly but I don't get the 5 expected fgmasks.
What am I missing ? I feel like this should be straightforward : the while loop runs over the 5 frames of the video and fgbg.apply apply the background subtraction function to each frame.
OpenCV version that I use is opencv-2.4.9-3
As bikz05 suggested, running average method worked pretty good on my 5 images sets. Thanks for the tip !
import cv2
import numpy as np
c = cv2.VideoCapture('test.avi')
_,f = c.read()
avg1 = np.float32(f)
avg2 = np.float32(f)
# loop over images and estimate background
for x in range(0,4):
_,f = c.read()
cv2.accumulateWeighted(f,avg1,1)
cv2.accumulateWeighted(f,avg2,0.01)
res1 = cv2.convertScaleAbs(avg1)
res2 = cv2.convertScaleAbs(avg2)
cv2.imshow('img',f)
cv2.imshow('avg1',res1)
cv2.imshow('avg2',res2)
k = cv2.waitKey(0) & 0xff
if k == 5:
break