I want to pipe images to a virtual video device (e.g. /dev/video0), the images are created inside a loop with the desired frame rate.
In this minimal example i only two arrays which alternate in the cv2 window. Now i look for a good solution to pipe the arrays to the virtual device.
I saw that ffmpeg-python can run asynchronous with ffmpeg.run_async(), but so far i could not make anything work with this package.
example code without the ffmpeg stuff:
#!/usr/bin/env python3
import cv2
import numpy as np
import time
window_name = 'virtual-camera'
cv2.namedWindow(window_name, cv2.WINDOW_GUI_EXPANDED)
img1 = np.random.uniform(0, 255, (1080, 1440, 3)).astype('uint8')
img2 = np.random.uniform(0, 255, (1080, 1440, 3)).astype('uint8')
for i in range(125):
time.sleep(0.04)
if i % 2:
img = img1
else:
img = img2
cv2.imshow(window_name, img)
cv2.waitKey(1)
cv2.destroyAllWindows()
First of all, you would have to setup a virtual camera, with for example v4l2loopback. See here for how to install it (ignore the usage examples).
Then, you can just write to the virtual camera like to a normal file (that is, let openCV write the images to say /dev/video0; how to do that you have to find out yourself because im not an expert with openCV).
In the end, you can use ffmpeg-python with /dev/video0 as input file, do something with the video, and that's it !
As Programmer wrote in his answer, it is possible to create a dummy device with the package v4l2loopback. To publish images, videos or the desktop to the dummy device was already easy with ffmpeg, but i want to pipe it directly from the python script - where i capture the images - to the dummy device. I still think it's possible with ffmpeg-python, but i found this great answer from Alp which sheds light on the darkness. The package pyfakewebcam is a perfect solution for the problem.
For the sake of completeness, here is my extended minimal working example:
#!/usr/bin/env python3
import time
import cv2
import numpy as np
import pyfakewebcam
WIDTH = 1440
HEIGHT = 1080
DEVICE = '/dev/video0'
fake_cam = pyfakewebcam.FakeWebcam(DEVICE, WIDTH, HEIGHT)
window_name = 'virtual-camera'
cv2.namedWindow(window_name, cv2.WINDOW_GUI_EXPANDED)
img1 = np.random.uniform(0, 255, (HEIGHT, WIDTH, 3)).astype('uint8')
img2 = np.random.uniform(0, 255, (HEIGHT, WIDTH, 3)).astype('uint8')
for i in range(125):
time.sleep(0.04)
if i % 2:
img = img1
else:
img = img2
fake_cam.schedule_frame(img)
cv2.imshow(window_name, img)
cv2.waitKey(1)
cv2.destroyAllWindows()
Related
I have a sequence of grayscale images. using python code of opencv, I made a video out of these grayscale images.
import cv2
import numpy as np
A = readimages(img_path) # A is a list of uint8 images of size similar to framesize
framesize = (480,640)
out = cv2.VideoWriter('output_video.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 30.0, framesize, isColor = False)
for img in A:
out.write(cv2.flip(img,0))
out.release()
the resultant video as shown in the image below (left part) just shows a fraction of the input images along with multiple lines. any help please.
In this case, the images dtype must uint8, before writing into video file. The new code works fine.
import cv2
import numpy as np
A = readimages(img_path) # A is a list of uint8 images of size similar to framesize
framesize = (480,640)
out = cv2.VideoWriter('output_video.avi',cv2.VideoWriter_fourcc('M','J','P','G'), 30.0, framesize, isColor = False)
for img in A:
out.write(img.astype('uint8'))
out.release()
I am trying to count seeds in an image using cv2 thresholding. The test image is below:
When I run the below code to create a mask:
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('S__14278933.jpg')
#img = cv2.fastNlMeansDenoisingColored(img,None,10,10,7,21)
mask = cv2.threshold(img[:, :, 0], 255, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
plt.imshow(mask)
I get the following mask:
But ideally it should give a small yellow dot at the centre. I have tried this with other images and it works just fine.
Can someone help?
The lighting in your image seems not uniform. Try using Adaptive Thresholding:
import cv2
import numpy as np
# image path
path = "D://opencvImages//"
fileName = "c6pBO.jpg"
# Reading an image in default mode:
inputImage = cv2.imread(path + fileName)
# Convert the image to grayscale:
grayImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)
# Get binary image via Adaptive Thresholding :
windowSize = 31
windowConstant = 40
binaryImage = cv2.adaptiveThreshold( grayImage, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY_INV, windowSize, windowConstant )
cv2.imshow("binaryImage", binaryImage)
cv2.waitKey(0)
You might want to apply an Area Filter after this, though, as dark portions on the image will yield noise.
Try it
img=cv2.imread('foto.jpg',0)
mask = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV )[1]
I'm creating a script that will read the state of a supermarket and tell me if there is products missing.
for example in the image below there is some places where there is products missing. I'm using FAST method to find all the corners in the frame. but sometimes the scripts detects the floor corners. What I want to do is remove the floor from the frame before I find the corners.
import cv2
import numpy as np
image = cv2.imread('gondola_imagem.jpeg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
fast = cv2.FastFeatureDetector_create()
# Obtain Key points, by default non max suppression is On
# to turn off set fast.setBool('nonmaxSuppression', False)
keypoints = fast.detect(gray, None)
print ("Number of keypoints Detected: ", len(keypoints))
image = cv2.drawKeypoints(image, keypoints, None,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imshow('Feature Method - FAST', image)
cv2.waitKey()
cv2.destroyAllWindows()
You can use a mask to remove the areas you are not interested. For example with the following image as a mask you can get the bellow results.
Mask
Result
Code is as follow:
import numpy as np
import cv2
image = cv2.imread('test.jpg')
mask = cv2.imread('mask.jpg', 0)
cv2.imshow('Original', image)
cv2.imshow('Mask', mask)
res = cv2.bitwise_and(image,image,mask = mask)
gray = cv2.cvtColor(res, cv2.COLOR_BGR2GRAY)
fast = cv2.FastFeatureDetector_create()
# Obtain Key points, by default non max suppression is On
# to turn off set fast.setBool('nonmaxSuppression', False)
keypoints = fast.detect(gray, None)
print ("Number of keypoints Detected: ", len(keypoints))
image = cv2.drawKeypoints(image, keypoints, None,
flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)
cv2.imwrite('result.jpg', image)
cv2.imshow('Feature Method - FAST', image)
cv2.waitKey()
cv2.destroyAllWindows()
Edit:
If you want to do this in realtime (video from webcam) you just need to do it for every frame you get from the video camera. As long as the camera is not moving you should be able to use the same mask for all the frames. You could make the code above a function and then call it with an image as a parameter, as per the following code:
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
# Following function will have to be created with the previews code
CallFunctionToPreviewsCode(frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
The code above was taken from OpenCV Python-Tutorials It is a good place for learning OpenCV for Python programming language.
I am developing an application to read the numbers from an image using opencv in Python 3. I first converted the image to gray scale,then Apply dilation and erosion to remove some noise, then Apply threshold to get image with only black and white, then Write the image to local disk to do some ..., then apply tesseract to recognise the number for python.
I need to extract the numbers from the image. I am new to openCV. Does anybody know any other method to get the result??
I have share the image link bellow, i was trying to extract from that image. Thanks in advance
https://drive.google.com/file/d/141y-3okLPGP_STje14ukSqSHcgtwMdRO/view?usp=sharing
import cv2
import numpy as np
import pytesseract
from PIL import Image
from pytesseract import image_to_string
# Path of working folder on Disk
src_path = "/Users/sougata.a.roy/Desktop/Images/"
def get_string(img_path):
# Read image with opencv
img = cv2.imread(img_path)
# Convert to gray
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply dilation and erosion to remove some noise
kernel = np.ones((1, 1), np.uint8)
img = cv2.dilate(img, kernel, iterations=1)
img = cv2.erode(img, kernel, iterations=1)
# Write image after removed noise
cv2.imwrite(src_path + "removed_noise.jpg", img)
# Apply threshold to get image with only black and white
img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 31, 2)
# Write the image after apply opencv to do some ...
cv2.imwrite(src_path + 'thres.jpg', img)
# Recognize text with tesseract for python
result = pytesseract.image_to_string(Image.open(src_path + "thres.jpg"), lang='eng')
return result
print('--- Start recognize text from image ---')
print(get_string(src_path + 'abcdefg195.jpg'))
print("------ Done -------")
365
I am unable to print the text in Orange colored.I identified the edges of the image and then printed a text on it.
%matplotlib inline
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('ind_maharashtra.png',0)
edges = cv2.Canny(img,100,20)
cv2.imwrite('Edged_img.jpg',edges)
#plt.subplot(121)
img1 = cv2.imread('Edged_img.jpg',0)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img1,'JAI MAHARASHTRA !!',(70,150), font, 0.7,(255,69,0),2,cv2.LINE_8)
cv2.imshow('Maharashtra Map',img1)
#cv2.imshow('Maharashtra Map',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
The problem is that the image on which you are trying to draw ( the image named img1) is a gray-scale image since the 2nd argument of cv2.imread is 0 in the following line:
img1 = cv2.imread('Edged_img.jpg',0)
You have 2 options to fix this issue. First one is to load the image as a color image as follows:
img1 = cv2.imread('Edged_img.jpg')
Alternatively, if you want your canvas to have a gray-ish look, you can just replicate the single channel to form a 3 channel image as follows:
img1 = cv2.imread('Edged_img.jpg', 0)
img1 = cv2.cvtColor(img1, cv2.COLOR_GRAY2BGR)
You are loading your jpg in grayscale, so you will only be able to write grayscale to img1
OpenCV Imread docs
change this line
img1 = cv2.imread('Edged_img.jpg',0)
to
img1 = cv2.imread('Edged_img.jpg',1)
As you can see from the above linked docs, using these numbers is OK but you are actually setting a flag, so you could use the flag definition to make your code clearer. Coincidentally, if you had used the flags you would likely not have had this issue.
You can change your line to
img1 = cv2.imread('Edged_img', cv2.IMREAD_COLOR)
Look how much clearer, and understandable that is. Especially when you come back to this code/hand it over to another developer in a few months time.