32 lines
1.0 KiB
Python
32 lines
1.0 KiB
Python
import cv2
|
|
import numpy as np
|
|
import operator
|
|
|
|
methode=cv2.ADAPTIVE_THRESH_GAUSSIAN_C
|
|
v1=9
|
|
|
|
cap=cv2.VideoCapture(0)
|
|
while True:
|
|
ret, frame=cap.read()
|
|
gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
|
|
gray=cv2.GaussianBlur(gray, (5, 5), 0)
|
|
thresh=cv2.adaptiveThreshold(gray, 255, methode, cv2.THRESH_BINARY_INV, v1, 2)
|
|
cv2.imshow("thresh", thresh)
|
|
txt="ADAPTIVE_THRESH_MEAN_C" if methode==cv2.ADAPTIVE_THRESH_MEAN_C else "ADAPTIVE_THRESH_GAUSSIAN_C"
|
|
cv2.putText(frame, "[p|m]v1: {:2d} [o]methode: {}".format(v1, txt), (10, 20), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.9, (0, 0, 255), 1)
|
|
cv2.imshow("frame", frame)
|
|
key=cv2.waitKey(1)&0xFF
|
|
if key==ord('q'):
|
|
break
|
|
if key==ord('p'):
|
|
v1=min(21, v1+2)
|
|
if key==ord('m'):
|
|
v1=max(3, v1-2)
|
|
if key==ord('o'):
|
|
if methode==cv2.ADAPTIVE_THRESH_GAUSSIAN_C:
|
|
methode=cv2.ADAPTIVE_THRESH_MEAN_C
|
|
else:
|
|
methode=cv2.ADAPTIVE_THRESH_GAUSSIAN_C
|
|
cap.release()
|
|
cv2.destroyAllWindows()
|