import glob
import cv2
import numpy as np
from PIL import Image
import init_auv as auv
imdir = '/Users/Mackeprang/Dropbox (Personlig)/Master Thesis/Pictures/20181005_084733.9640_Mission_1'
#imdir = auv.datasetPath_Mads()["Images"]
filenames = auv.imagesFilePath(imdir, '*.png')
prev_frame = None
orb = cv2.ORB_create()
bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
for i, frame in enumerate(filenames):
if auv.image_broken(frame):
continue
img = cv2.imread(frame)
gray = auv.preprocess_image(img, unsharp=False)
if prev_frame is None:
prev_frame = gray
kp1, des1 = orb.detectAndCompute(prev_frame, None)
print "Numbers of features: " + str(len(kp1))
kp2, des2 = orb.detectAndCompute(gray, None)
matches = bf.match(des1, des2)
matches = sorted(matches, key=lambda x: x.distance)
img_match = cv2.drawMatches(prev_frame,
kp1,
gray,
kp2,
matches[:10],
None,
import glob
import cv2
import numpy as np
from PIL import Image
import init_auv as auv
im_filename = '*.png'
imdir = '/Users/Mackeprang/Dropbox (Personlig)/Master Thesis/Pictures/20181005_084733.9640_Mission_1'
filenames = auv.imagesFilePath(imdir)
images = []
prev_frame = None
hsv = np.zeros_like(cv2.imread(filenames[0]))
hsv[...,1] = 255
for i,frame in enumerate(filenames):
if auv.image_broken(frame):
continue
img = cv2.imread(frame)
gray = auv.preprocess_image(img,size=None)
if prev_frame is None:
prev_frame = gray
continue
flow = cv2.calcOpticalFlowFarneback(prev_frame, gray, None, 0.5, 3, 30, 3, 5, 1.2, 0)
mag, ang = cv2.cartToPolar(flow[..., 0], flow[..., 1])
hsv[..., 0] = ang * 180 / np.pi / 2
hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)
bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
cv2.imshow("Match", bgr)
key = cv2.waitKey()
if key & 0xFF == ord('q'):
break