def create_features(self):
print('CREATING FEATURES')
for im in self.im_list:
print(os.path.basename(im))
img = cv2.imread(im, 0) #queryimage # left image
img, scale = imsz.imRescaleMaxDim(img,
self.image_max_dim,
boUpscale=False,
interpolation=1)
kp, des = self.detector.detectAndCompute(img, None)
#des=des.astype('float32')
self.feats[im] = (kp, des)
return
jpg_list = imsz.imagelist_in_depth(im_dir, level=1)
i1 = 1
im_file1 = jpg_list[i1]
i2 = 2
im_file2 = jpg_list[i2]
#im_file1=r'd:\\DATA\\MAV1\\Images\\Selection_20190124_120409_2800_3400\\roi_0_2882.jpg'
#im_file2=r'd:\\DATA\\MAV1\\Images\\Selection_20190124_120409_2800_3400\\roi_1_2882.jpg'
img1 = cv2.imread(im_file1, 0) #queryimage # left image
img2 = cv2.imread(im_file2, 0) #trainimage # right image
##
img1, scale = imsz.imRescaleMaxDim(img1,
max_dim,
boUpscale=False,
interpolation=1)
img2, scale = imsz.imRescaleMaxDim(img2,
max_dim,
boUpscale=False,
interpolation=1)
## SIFT
# sift = cv2.xfeatures2d.SIFT_create(800)
detector, matcher = hc.init_feature(det_type, n_feature_point=1000)
kp1, des1 = detector.detectAndCompute(img1, None)
kp2, des2 = detector.detectAndCompute(img2, None)
# draw rich keypoints
#################
base_dir = r'd:\DATA\EON_LOCAL\SESSIONS\20200218_092900'
#run_id='20180907_103336_sel1'
run_id = '20200218_092854964_Agent4_Master'
ext = '.jpg'
image_max_dim = 1014
im_dir = os.path.join(base_dir, run_id)
jpg_list = imsz.imagelist_in_depth(im_dir, level=1)
frame_id = 0
wait_time = 100
while True:
frame = cv2.imread(jpg_list[frame_id], 0) #queryimage # left image
frame, scale = imsz.imRescaleMaxDim(frame,
image_max_dim,
boUpscale=False,
interpolation=1)
(H, W) = frame.shape[:2]
frame_id += 1
if frame_id > len(jpg_list):
break
# check to see if we are currently tracking an object
if initBB is not None:
# grab the new bounding box coordinates of the object
(success, box) = tracker.update(frame)
# check to see if the tracking was a success
if success:
(x, y, w, h) = [int(v) for v in box]
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
refPt_transformed = []
cropping = False
i1=0
i2=3
im1=jpg_list[i1]
im2=jpg_list[i2]
M=hcobj.Ms[im1,im2]
img1 = cv2.imread(im1,0) #queryimage # left image
img2 = cv2.imread(im2,0) #queryimage # left image
image_max_dim=hcobj.image_max_dim
img1, scale = imRescaleMaxDim(img1, image_max_dim, boUpscale = False, interpolation = 1)
img2, scale = imRescaleMaxDim(img2, image_max_dim, boUpscale = False, interpolation = 1)
image_width=img1.shape[1]
#img2 = cv2.warpPerspective(img1, M, (img2.shape[1],img2.shape[0]))
M[0][2]*=scale
M[1][2]*=scale
M[2][0]*=scale
M[2][1]*=scale
def click_and_crop(event, x, y, flags, param):
# grab references to the global variables
global refPt, refPt_transformed, cropping, M, image_width