While extracting frames from a video using OpenCV, how can I set a particular value at which the frame extraction will occur?
I have seen many available sample codes for image extraction, but they are not shown any option for the frame rate.
There are many ways of frame extraction, one is to use ffmg for frame extraction.
other is, You can try this code, but we can't use any random value that you will understand while trying at different values.
change directories ap per your system.
import math
count = 0
videoFile = "train/train.mp4"
cap = cv2.VideoCapture(videoFile)
frameRate = cap.get(5) #frame rate
x=1
while(cap.isOpened()):
frameId = cap.get(1)
ret, frame = cap.read()
if (ret != True):
break
else (frameId % math.floor(frameRate) == 0):
filename ="train/frame2/frame%d.jpg" % count;count+=1
cv2.imwrite(filename, frame)
cap.release()
print ("Done!")
Related
I'm trying to make a python program with OpenCV, which opens the webcam and takes several images with different exposures in real time (40ms,95ms,150ms) and averages them in the end.
I tried to create a loop in which I change the exposure time, update the rendering (frame) and save it in a list, but the problem is that the display remains static and the rendering hardly changes (which gives after merging the images an image whose exposure time is almost 40)
I supposed that after setting exposure time, the frame update needs some time so I added the method time.sleep to suspend the execution for 3 seconds, but it was in vain.
Here is my code
import numpy as np
import cv2
import os
import time
capture = cv2.VideoCapture(0, cv2.CAP_V4L2)
while True:
(grabbed, frame) = capture.read()
if not grabbed:
break
# Resize frame
width = 1500
height = 1000
dim = (width, height)
frame = cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)
cv2.imshow('RGB', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if cv2.waitKey(1) == ord('h') or cv2.waitKey(1) == ord('H'):
#repertory = input("Enter the name of the directory: ")
if not os.path.exists(repertory):
os.mkdir(repertory)
exposure = [40,95,150]
ims = []
for i in exposure:
capture.set(cv2.CAP_PROP_EXPOSURE, i) # Setting Exposure
(grabbed, frame) = capture.read() # Updating frame
if grabbed:
cv2.imshow('RGB', frame) #Display
ims.append(frame)
# Convert to numpy
ims = np.array([np.array(im) for im in ims])
# average et conversion en uint8
imave = np.average(ims, axis=0)
imave = imave.astype(np.uint8)
# image HDR
cv2.imwrite(repertory + '/' + repertory + '_HDR8.jpg', imave)
capture.release()
cv2.destroyAllWindows()
Is there an optimal solution that allows to take pictures with differents exposure time in real time and in an automatic way?
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 want to create a csv file which should have the coordinates of each object as i am getting in my python shell window.
import cv2
import pandas as pd
# capture frames from a video
cap = cv2.VideoCapture('video.avi')
# Trained XML classifiers describes some features of some object we want
to detect
car_cascade = cv2.CascadeClassifier('cars.xml')
no_obj_det=0
frames_got_processed = 0
frame_processed = []
number_of_object_detected= []
# loop runs if capturing has been initialized.
while True:
# reads frames from a video
try:
ret, frames = cap.read()
frames_got_processed += 1
# convert to gray scale of each frames
gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)
# Detects cars of different sizes in the input image
cars = car_cascade.detectMultiScale(gray, 1.1, 1)
# To draw a rectangle in each cars
for (x,y,w,h) in cars:
cv2.rectangle(frames,(x,y),(x+w,y+h),(0,0,255),2)
cv2.imshow('video2', frames)
if cv2.waitKey(33) == 27:
break
# loop to count the number of objects detected at every 5th frame
if frames_got_processed % 5 == 0:
print "appended in frame
number",frames_got_processed,len(cars),cars
frame_processed.append(frames_got_processed)
number_of_object_detected.append(len(cars))
df.to_csv('example.csv')
# De-allocate any associated memory usage
cv2.destroyAllWindows()
Output on python shell same output i want in my csv file
[1]: https://i.stack.imgur.com/vfPEP.png
As you probably need to write data to the CSV continuously, it is probably better to do this as you go rather than trying to append all of the data and then write it at the end. Doing it this way would avoid you eventually running out of memory.
The Python csv library could be used to do this as follows:
import cv2
import csv
# capture frames from a video
cap = cv2.VideoCapture('video.avi')
# Trained XML classifiers describes some features of some object we want to detect
car_cascade = cv2.CascadeClassifier('cars.xml')
frames_got_processed = 0
with open('example.csv', 'w', newline='') as f_output:
csv_output = csv.writer(f_output)
# loop runs if capturing has been initialized.
while True:
# reads frames from a video
try:
ret, frames = cap.read()
frames_got_processed += 1
# convert to gray scale of each frames
gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)
# Detects cars of different sizes in the input image
cars = car_cascade.detectMultiScale(gray, 1.1, 1)
# To draw a rectangle in each cars
for (x,y,w,h) in cars:
cv2.rectangle(frames,(x,y),(x+w,y+h),(0,0,255),2)
cv2.imshow('video2', frames)
if cv2.waitKey(33) == 27:
break
# loop to count the number of objects detected at every 5th frame
if frames_got_processed % 5 == 0:
print "appended in frame number", frames_got_processed, len(cars), cars
csv_output.writerow([frames_got_processed, len(cars)] + list(cars))
except:
pass
# De-allocate any associated memory usage
cv2.destroyAllWindows()
This should give you a row containing the frame number, the number of cars, followed by the co-ordinates for each car.
I am trying to write a openCV program where i am breaking down the video into frames and comparing two frames one after the other if both are the same i reject the frame else append the frame to a output file.
How can i achieve it?
OpenCV 2.4.13 Python 2.7
The following example captures frames from the first camera connected to your system, compares each frame to the previous frame, and when different, the frame is added to a file. If you sit still in front of the camera, you might see the diagnostic 'no change' message printed if you run the program from a console terminal window.
There are a number of ways to measure how different one frame is from another. For simplicity we have used the average difference, pixel by pixel, between the new frame and the previous frame, compared to a threshold.
Note that frames are returned as numpy arrays by the openCV read function.
import numpy as np
import cv2
interval = 100
fps = 1000./interval
camnum = 0
outfilename = 'temp.avi'
threshold=100.
cap = cv2.VideoCapture(camnum)
ret, frame = cap.read()
height, width, nchannels = frame.shape
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter( outfilename,fourcc, fps, (width,height))
while(True):
# previous frame
frame0 = frame
# new frame
ret, frame = cap.read()
if not ret:
break
# how different is it?
if np.sum( np.absolute(frame-frame0) )/np.size(frame) > threshold:
out.write( frame )
else:
print( 'no change' )
# show it
cv2.imshow('Type "q" to close',frame)
# check for keystroke
key = cv2.waitKey(interval) & 0xFF
# exit if so-commanded
if key == ord('q'):
print('received key q' )
break
# When everything done, release the capture
cap.release()
out.release()
print('VideoDemo - exit' )
We are doing a project on license plate recognition using python(2.7.12). We have divided the video into frames using the following code:
import cv2 #importing opencv library
import numpy #importing numpy
cap = cv2.VideoCapture('C:/Python27/project/license.avi') #read the video
success,image = cap.read() #divide into frames
count = 0
success = True
while (cap.isOpened()):
success,image = cap.read()
print 'Read a new frame: ', success #when frame has been read successfully
if (type(image) == type(None)): #check for invalid frame
break
else:
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #convert frame into grey
cv2.imwrite("frame%d.jpg" % count, gray_image) # save frame as JPEG file
count += 1 #repeat for all the frames
cap.release()
We are trying to get the best frame(with high quality).Is it possible to automatically select a frame that has the complete license plate?
Any suggestions would be helpful.