def query_image(image, refName, show=False):
if True:
IMG = cv2.imread(image, 0)
exif_focal = getFocal(image)
img_heigh, img_width = IMG.shape[:2]
query_kps_cv2, query_features = detector.detectAndCompute(
IMG, None)
ref = KnnIndex()
ref.load(refName)
dstPoints = []
srcPoints = []
obj_points = ref.query(query_features, K=8, mapped=True)
for it, kp in enumerate(query_kps_cv2):
L = obj_points[it]
for o_pt in L:
dstPoints.append(o_pt[0:3])
srcPoints.append(kp.pt)
dstPoints2d = np.array(dstPoints, np.float32)[:, 0:2].copy()
srcPoints = np.array(srcPoints, np.float32)
H, mask = cv2.findHomography(srcPoints, dstPoints2d, cv2.RANSAC,
5.0)
obj_pt = []
img_pt = []
for it, m in enumerate(mask):
if m:
obj_pt.append(dstPoints[it])
img_pt.append(srcPoints[it])
if True:
obj_pt = np.array(obj_pt)
img_pt = np.array(img_pt)
return obj_pt, img_pt
def query_image(image,refName,show=False):
if True:
IMG=cv2.imread(image,0)
exif_focal=getFocal(image)
img_heigh,img_width=IMG.shape[:2]
query_kps_cv2, query_features=detector.detectAndCompute(IMG,None )
ref=KnnIndex()
ref.load(refName)
dstPoints=[]
srcPoints=[]
obj_points=ref.query(query_features,K=8,mapped=True)
for it,kp in enumerate(query_kps_cv2):
L=obj_points[it]
for o_pt in L:
dstPoints.append(o_pt[0:3])
srcPoints.append(kp.pt)
dstPoints2d=np.array(dstPoints,np.float32)[:,0:2].copy()
srcPoints=np.array(srcPoints,np.float32)
H,mask = cv2.findHomography(srcPoints, dstPoints2d,cv2.RANSAC,5.0)
obj_pt=[]
img_pt=[]
for it,m in enumerate(mask):
if m:
obj_pt.append(dstPoints[it])
img_pt.append(srcPoints[it])
if True:
obj_pt=np.array(obj_pt)
img_pt=np.array(img_pt)
return obj_pt,img_pt
def build(image, width=None, height=None):
if not width and not height:
name, x, y, _discard = re.split('x|y|\.', image)
width = int(x)
height = int(y)
print name
print 'width ', width, ' mm'
print 'height ', height, ' mm'
if not name:
name = image.split('.')[0] + '.reference'
IMG = cv2.imread(image, 0)
img_heigh, img_width = IMG.shape[:2]
x_scale = img_width / width
y_scale = img_heigh / height
kps_cv2, features = detector.detectAndCompute(IMG, None)
obj_points = []
for kp in kps_cv2:
_x, _y = kp.pt
_x = _x / x_scale
_y = _y / y_scale
_z = 0.
_o = kp.angle
_s = kp.octave
_v = [_x, _y, _z, _s, _o]
obj_points.append(_v)
obj_points = np.array(obj_points, np.float32)
Index = KnnIndex()
Index.train(features)
Index.set_dataset_key_map(obj_points)
Index.save(name)
return name
def build(image,width=None,height=None):
if not width and not height:
name,x,y,_discard=re.split('x|y|\.',image)
width=int(x)
height=int(y)
print name
print 'width ',width,' mm'
print 'height ',height,' mm'
if not name:
name=image.split('.')[0]+'.reference'
IMG=cv2.imread(image,0)
img_heigh,img_width=IMG.shape[:2]
x_scale=img_width/width
y_scale=img_heigh/height
kps_cv2, features=detector.detectAndCompute(IMG ,None)
obj_points=[]
for kp in kps_cv2:
_x,_y=kp.pt
_x=_x/x_scale
_y=_y/y_scale
_z=0.
_o=kp.angle
_s=kp.octave
_v=[_x,_y,_z,_s,_o]
obj_points.append(_v)
obj_points=np.array(obj_points,np.float32)
Index=KnnIndex()
Index.train(features)
Index.set_dataset_key_map(obj_points)
Index.save(name)
return name
