opencv
arrow keys
key = cv2.waitKeyEx(0)
if key == 27:
break
elif key == 2555904: #right
print("right")
elif key == 2424832: #left
print("left")
filter pixels based on number of neighbours
_, labeled_image = cv2.connectedComponents(fg)
unique_labels, label_counts = np.unique(labeled_image, return_counts=True)
fg[label_counts[labeled_image] < 4] = 0
sharpen
kernel = np.array([[-1,-1,-1], [-1,9,-1], [-1,-1,-1]])
gray = cv2.filter2D(gray, -1, kernel)
remove lonely pixels
kernel = np.array([[1, 1, 1],[1, 0, 1],[1, 1, 1]])
convolved_image = convolve2d(gray, kernel, mode='same', boundary='wrap')
no_neighbor_mask = convolved_image == 0
gray[no_neighbor_mask] = 0
replace pixels with value 0 by value 255 using numpy
img[img == 0] = 255
convert gray to rgb
rgb = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
homography
import cv2
import numpy as np
# Lees de puntparen in (LET OP OMGEWISSELT)
dst_points = np.loadtxt("data/src_points.txt")
src_points = np.loadtxt("data/dst_points.txt")
# Bereken de projectieve transformatiematrix (homografie)
homography_matrix, _ = cv2.findHomography(src_points, dst_points)
print(homography_matrix)
# Pas de homografie toe op een afbeelding
src = cv2.imread("data/vis_200.png")
dst = cv2.warpPerspective(src, homography_matrix, (src.shape[1], src.shape[0]))
cv2.imshow("src", src)
cv2.imshow("dst", dst)
cv2.waitKey(0)
cv2.destroyAllWindows()
mouse
def mouse_callback(event, x, y, flags, param):
if event == cv2.EVENT_LBUTTONDOWN:
print(f"LEFT ({x}, {y})")
elif event == cv2.EVENT_RBUTTONDOWN:
print(f"RIGHT ({x}, {y})")
elif event == cv2.EVENT_MOUSEMOVE:
print(f"MOVE ({x}, {y})")
## ...
cv2.setMouseCallback("img", mouse_callback)
new rgb image
img = np.zeros((480, 640, 3), dtype=np.uint8)
set focus to image window opencv
cv2.namedWindow('img',cv2.WINDOW_NORMAL)
cv2.setWindowProperty('img',cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
cv2.setWindowProperty('img',cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_NORMAL)
cv2.imshow('img', img)
cv2.waitKey()
cv2.destroyWindow('img')
sample images in opencv github repo
https://github.com/opencv/opencv/tree/master/samples/data
see also
[[openFrameworks]], [[Processing]]
Notes on findContours
Source image is modified by this function.- Also, the function does not take into account 1-pixel border of the image (it's filled with 0's and used for neighbor analysis in the algorithm), therefore the contours touching the image border will be clipped.
Subtract vs absdiff
Subtract can be useful instead of absdiff if you want only difference when something 'appears or moves' and not when it 'disappears'
Optical Flow
- use Farneback for a dense flow field, (voor een dense flow field met een vector per row/col)
- use PyrLK for specific features (voor feature herkenning. kan objecten volgen)
Thresholding
Various
- http://indranilsinharoy.com/2012/11/01/installing-opencv-on-linux/
- see also: [[cv]]
- matrix set all values to 0:
mat.setTo(0);
- background subtraction: http://www-staff.it.uts.edu.au/~massimo/BackgroundSubtractionReview-Piccardi.pdf
- [[openFrameworks]]
- http://code.google.com/p/cvblob/
- http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html