opencv capture camera python
import numpy as np
import cv2
cap = cv2.VideoCapture(0)
while(True):
# Capture frame-by-frame
ret, frame = cap.read()
# Our operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Display the resulting frame
cv2.imshow('frame',gray)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()take pictures from webcam python
from cv2 import *
# initialize the camera
cam = VideoCapture(0) # 0 -> index of camera
s, img = cam.read()
if s: # frame captured without any errors
namedWindow("cam-test",CV_WINDOW_AUTOSIZE)
imshow("cam-test",img)
waitKey(0)
destroyWindow("cam-test")
imwrite("filename.jpg",img) #save imageopencv python webcam is blur
import cv2
import time
import numpy as np
# Get the webcam
cap = cv2.VideoCapture(0)
# Time is just used to get the Frames Per Second (FPS)
last_time = time.time()
while True:
# Step 1: Capture the frame
_, frame = cap.read()
# Step 2: Convert to the HSV color space
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# Step 3: Create a mask based on medium to high Saturation and Value
# - These values can be changed (the lower ones) to fit your environment
mask = cv2.inRange(hsv, (0, 75, 40), (180, 255, 255))
# We need a to copy the mask 3 times to fit the frames
mask_3d = np.repeat(mask[:, :, np.newaxis], 3, axis=2)
# Step 4: Create a blurred frame using Gaussian blur
blurred_frame = cv2.GaussianBlur(frame, (25, 25), 0)
# Step 5: Combine the original with the blurred frame based on mask
frame = np.where(mask_3d == (255, 255, 255), frame, blurred_frame)
# Add a FPS label to image
text = f"FPS: {int(1 / (time.time() - last_time))}"
last_time = time.time()
cv2.putText(frame, text, (10, 20), cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0), 2)
# Step 6: Show the frame with blurred background
cv2.imshow("Webcam", frame)
# If q is pressed terminate
if cv2.waitKey(1) == ord('q'):
break
# Release and destroy all windows
cap.release()
cv2.destroyAllWindows()Copyright © 2021 Codeinu
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