I was trying to figure out a way to read the veins in an video capture (i am using special camera) using OpenCV in Python, but there are too many noise from the results i got. Can someone help?
here is the result: https://ibb.co/cbdxY5F
i want all in the red circle to be clear without nosie: https://ibb.co/C9SPjyX
import cv2
import numpy as np
import time
def multi_clahe(img, num):
for i in range(num):
img = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4+i*2,4+i*2)).apply(img)
return img
img = cv2.VideoCapture(1)
while(True):
ret, frame = img.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
cl3 = multi_clahe(cl1, 5)
cv2.imshow('image', cl3)
k = cv2.waitKey(1) & 0xFF
if k == ord("a")
cv2.imwrite(time.strftime("Screenshot%Y%m%d%H%M%S.jpg"),final)
cv2.imwrite(time.strftime("1.jpg"),cl3)
cv2.imwrite("temp.jpg",cl3)
break
if cv2.waitKey(1) & 0xFF == ord('q'):
break
img.release()
cv2.destroyAllWindows()
I need to remove noises from CLAHE in Python.
Related
I tried this code to draw an animated dot on a video
from collections import deque
from imutils.video import VideoStream
import numpy as np
import cv2
import imutils
import time
vs = cv2.VideoCapture('/media/intercept.mp4')
pts = deque(maxlen=64) #buffer size
# keep looping
while True:
ret,frame = vs.read()
if frame is None:
break
# resize the frame, blur it, and convert it to the HSV
# color space
frame = imutils.resize(frame, width=600)
blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
for i in range(10,260,20):
time.sleep(0.5) #To visualise dots one by one
cv2.circle(frame,(i, i),10, (0,0,255), -1) #draw circle
cv2.imshow('frame',frame) #show output image
if cv2.waitKey(1) == ord('q'):
break
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
cv2.destroyAllWindows()
vs.release()
But the entire animation takes place over a single frame rather than being continous over consecutive frames. Also I want to add a certain element of jitter/randomness to the red ball/circle.
How can I achieve both ?
Ahh solved it by tweaking the sleep timer, and skipping the frames
from collections import deque
from imutils.video import VideoStream
import numpy as np
import cv2
import imutils
import time
vs = cv2.VideoCapture('/media/intercept.mp4')
pts = deque(maxlen=64) #buffer size
i=0
ct=0
# keep looping
while True:
ret,frame = vs.read()
# resize the frame, blur it, and convert it to the HSV
# color space
frame = imutils.resize(frame, width=600)
blurred = cv2.GaussianBlur(frame, (11, 11), 0)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
i+=2
ct+=10
#for i in range(10,260,20):
#time.sleep(0.5) #To visualise dots one by one
if ct%10==0:
cv2.circle(frame,(i, i),10, (0,0,255), -1) #draw circle
#cv2.imshow('frame',frame) #show output image
if cv2.waitKey(1) == ord('q'):
break
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the 'q' key is pressed, stop the loop
if key == ord("q"):
break
cv2.destroyAllWindows()
vs.release()
I’m using OpenCV3 and Python 3.7 to capture a live video stream from my webcam and I want to control the brightness and contrast. I cannot control the camera settings using OpenCV's cap.set(cv2.CAP_PROP_BRIGHTNESS, float) and cap.set(cv2.CAP_PROP_BRIGHTNESS, int) commands so I want to apply the contrast and brightness after each frame is read. The Numpy array of each captured image is (480, 640, 3). The following code properly displays the video stream without any attempt to change the brightness or contrast.
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
I get a washed-out video stream when I use Numpy’s clip() method to control the contrast and brightness, even when I set contrast = 1.0 (no change to contrast) and brightness = 0 (no change to brightness). Here is my attempt to control contrast and brightness.
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
contrast = 1.0
brightness = 0
frame = np.clip(contrast * frame + brightness, 0, 255)
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
How can I control the contrast and brightness of a video stream using OpenCV?
I found the solution using the numpy.clip() method and #fmw42 provided a solution using the cv2.normalize() method. I like the cv2.normalize() solution slightly better because it normalizes the pixel values to 0-255 rather than clip them at 0 or 255. Both solutions are provided here.
The cv2.normalize() solution:
Brightness - shift the alpha and beta values the same amount. Alpha
can be negative and beta can be higher than 255. (If alpha >= 255,
then the picture is white and if beta <= 0, then the picure is black.
Contrast - Widen or shorten the gap between alpha and beta.
Here is the code:
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
cv2.normalize(frame, frame, 0, 255, cv2.NORM_MINMAX)
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
The numpy.clip() solution:
This helped me solve the problem: How to fast change image brightness with python + OpenCV?. I need to:
Convert Red-Green Blue (RGB) to Hue-Saturation-Value (HSV) first
(“Value” is the same as “Brightness”)
“Slice” the Numpy array to the Value portion of the Numpy array and adjust brightness and contrast on that slice
Convert back from HSV to RGB.
Here is the working solution. Vary the contrast and brightness values. numpy.clip() ensures that all the pixel values remain between 0 and 255 in each on the channels (R, G, and B).
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
contrast = 1.25
brightness = 50
frame[:,:,2] = np.clip(contrast * frame[:,:,2] + brightness, 0, 255)
frame = cv2.cvtColor(frame, cv2.COLOR_HSV2BGR)
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()
import cv2 as cv
cap = cv.VideoCapture(0)
while True:
# Capture frame-by-frame
ret, frame = cap.read()
# normalize the frame
frame = cv.normalize(
frame, None, alpha=0, beta=255, norm_type=cv.NORM_MINMAX, dtype=cv.CV_8UC1
)
# Display the resulting frame
cv.imshow("frame", frame)
# press q to quit
if cv.waitKey(1) & 0xFF == ord("q"):
break
I am writing code to extract text from live video. I write the code and it,s executing well. But the problem is that it is taking all the frames and running slowly. Means giving delay of 10 seconds. Is there any command to kill past frames and take present frame for processing.
import cv2
import numpy as np
import time
import math
from PIL import Image
from pytesseract import image_to_string
cap = cv2.VideoCapture(0)
while(True):
cap.open
ret, img = cap.read()
img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
kernel = np.ones((1, 1), np.uint8)
img = cv2.dilate(img, kernel, iterations=1)
img = cv2.erode(img, kernel, iterations=1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(img,(5,5),0)
ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
cv2.imshow('frame', th3)
text = image_to_string(th3)
print text
if cv2.waitKey(1) & 0xFF == ord('q'):
out = cv2.imwrite('capture.jpg', frame)
break
cap.release()
cv2.destroyAllWindows()
Not exactly.
VideoCapture works by giving you the next frame to process in order. If it is slow, it is up to you to process it or not.
One common solution is just process every i frames.
But you can also change the Frame Per Seconds rate with:
cap.set(cv2.CAP_PROP_FPS, 10)
import cv2
import numpy as np
cap = cv2.VideoCapture()
while True:
_, frame = cap.read()
laplacia = cv2.Laplacian(frame, cv2.CV_64F)
cv2.imshow('original', frame)
cv2.imshow('laplacian', laplacia)
k = cv2.waitKey(5) & 0xFF
if k==27:
break
cv2.destroyAllWindows()
cap.release()
I am getting this error
#laplacia = cv2.Laplacian(frame, cv2.CV_64F)
cv2.error: C:\build\master_winpack-bindings-win64-vc14-static\opencv\modules\core\src\matrix.cpp:981: error: (-215) dims <= 2 && step[0] > 0 in function cv::Mat::locateROI
cv2.Laplacian() won't work with Color images.
You can go through OpenCV Documentation for knowing more..Image Gradients
You must convert the frame you have captured to gray scale and then apply Laplacian
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
You can convert to gray scale as shown above..
I am working on a project that requires that I display 3 (and possibly more) webcam feeds side by side. To tackle this project, I am using OpenCV Beta 3.0.0 and Python 2.7.5 because I am slightly familiar with the language. Also, how do I display the video in color?
Here is my current code:
import cv2
import numpy as np
capture = cv2.VideoCapture(0)
capture1 = cv2.VideoCapture(1)
while True:
ret, frame = capture.read()
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
cv2.imshow("frame",gray)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
capture.release()
cv2.destroyAllWindows()
import cv2
import numpy as np
capture = cv2.VideoCapture(0)
capture1 = cv2.VideoCapture(1)
while True:
_, frame1 = capture.read()
_, frame2 = capture1.read()
frame1 = cv2.cvtColor(frame1,cv2.COLOR_BGR2RGB)
frame2 = cv2.cvtColor(frame2,cv2.COLOR_BGR2RGB)
cv2.imshow("frame1",frame1)
cv2.imshow("frame2",frame2)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
capture1.release()
capture2.release()
cv2.destroyAllWindows()
To display color you simply don't convert to grayscale. To show two frames simultaneously just call imshow() twice. As for side by side, you can play with the frame positions if you really want. Also notice that the I converted the frames from BGR to RGB.