def get_refimage(self,R,THRESH,MIN,MAX,NPTS,save=0,out_dir='',TIME=0,loop=''):
''' Finds reference position in image using perspective.imagepoints()
_Parameters_:
R,NPTS,THRESH,MIN,MAX: parameters used by perspective.imagepoints()
save: set to 1 if you wish to save results (images)
out_dir: directory where to save images.
TIME: start time of program (for file naming)
_Returns_:
Nothing
'''
img_org,circ_org,pts_img,th_org = persp.imagepoints(self.img,R,NPTS,THRESH,
MIN,MAX)
self.ref_img=pts_img
if save:
h,s,__ = cv2.split(cv2.cvtColor(self.img,cv2.COLOR_BGR2HSV))
imgs={'img_h_'+str(self.n)+'_'+TIME:h,'img_s_'+str(self.n)+'_'+TIME:s}
persp.visualization(imgs,self.n,1)
imgs={'img_org_'+str(self.n)+'_'+TIME:img_org,
'circ_org_'+str(self.n)+'_'+TIME:circ_org,
'img_'+str(self.n)+'_'+TIME:self.img}
persp.visualization(imgs,self.n)
persp.save_open_images(out_dir,loop)
plt.close('all')
def get_robotimage(self,R,THRESH,MIN,MAX,save=0,out_dir='',TIME=0,loop=''):
''' Finds robot position in image using perspective.imagepoints().
_Parameters_:
R,THRESH,MIN,MAX: parameters used by perspective.imagepoints().
save: set to 1 if you wish to save results (images)
out_dir: directory where to save images.
TIME: start time of program (for file naming)
_Returns_:
Nothing
'''
img_red,circ_red,p,__ = persp.imagepoints(self.img,R,1,THRESH,MIN,MAX)
self.r_img=p
if save:
imgs={'img_red_'+str(self.n)+'_'+TIME:img_red,
'circ_red_'+str(self.n)+'_'+TIME:circ_red,
'img_'+str(self.n)+'_'+TIME:self.img}
persp.visualization(imgs,self.n)
persp.save_open_images(out_dir,loop)
plt.close('all')
def get_refimage(self,R,THRESH,MIN,MAX,NPTS,save=0,out_dir='',TIME=0,loop=''):
''' Finds reference position in image using perspective.imagepoints()
_Parameters_:
R,NPTS,THRESH,MIN,MAX: parameters used by perspective.imagepoints()
save: set to 1 if you wish to save results (images)
out_dir: directory where to save images.
TIME: start time of program (for file naming)
_Returns_:
Nothing
'''
img_org,circ_org,pts_img,th_org = persp.imagepoints(self.img,R,NPTS,THRESH,
MIN,MAX)
self.ref_img=pts_img
if save:
h,s,__ = cv2.split(cv2.cvtColor(self.img,cv2.COLOR_BGR2HSV))
imgs={'img_h_'+str(self.n)+'_'+TIME:h,'img_s_'+str(self.n)+'_'+TIME:s}
persp.visualization(imgs,self.n,1)
imgs={'img_org_'+str(self.n)+'_'+TIME:img_org,
'circ_org_'+str(self.n)+'_'+TIME:circ_org,
'img_'+str(self.n)+'_'+TIME:self.img}
persp.visualization(imgs,self.n)
persp.save_open_images(out_dir,loop)
plt.close('all')
def get_robotimage(self,R,THRESH,MIN,MAX,save=0,out_dir='',TIME=0,loop=''):
''' Finds robot position in image using perspective.imagepoints().
_Parameters_:
R,THRESH,MIN,MAX: parameters used by perspective.imagepoints().
save: set to 1 if you wish to save results (images)
out_dir: directory where to save images.
TIME: start time of program (for file naming)
_Returns_:
Nothing
'''
img_red,circ_red,p,__ = persp.imagepoints(self.img,R,1,THRESH,MIN,MAX)
self.r_img=p
if save:
imgs={'img_red_'+str(self.n)+'_'+TIME:img_red,
'circ_red_'+str(self.n)+'_'+TIME:circ_red,
'img_'+str(self.n)+'_'+TIME:self.img}
persp.visualization(imgs,self.n)
persp.save_open_images(out_dir,loop)
plt.close('all')
def get_checkerboard(self,in_dir,fisheye,w,h,MARGIN,
R,THRESH,MIN,MAX,save,out_dir='',TIME=0,loop=''):
cam = Camera(self.n)
cam.read(in_dir,fisheye)
ipts,opts,img_out=cam.get_checkpoints_file(out_dir,w,h,fisheye,self.img)
plt.close('all')
if len(ipts) == 0:
return 0
img_org,circ_org,pts_img,__ = persp.imagepoints(self.img,R,3,THRESH,MIN,MAX)
A = pts_img[0]
B = pts_img[1]
C = pts_img[2]
a = np.array([10000,10000])
b = np.array([10000,10000])
c = np.array([10000,10000])
min_a=10000
min_b=10000
min_c=10000
a_ind = b_ind = c_ind = 0
for i,pt in enumerate(ipts[0]):
if np.linalg.norm(pt-A)<min_a:
min_a = np.linalg.norm(pt-A)
a=pt
a_ind = i
if np.linalg.norm(pt-B)<min_b:
min_b = np.linalg.norm(pt-B)
b=pt
b_ind = i
if np.linalg.norm(pt-C)<min_c:
min_c = np.linalg.norm(pt-C)
c=pt
c_ind = i
''' reorder points from a to b and a to c '''
ipts_new=np.zeros(ipts[0].shape,dtype=np.float32)
j = np.zeros((ipts[0].shape[0],1))
index = np.zeros((ipts[0].shape[0],1))
for i in range(ipts[0].shape[0]):
j[i] = i + 1 - np.mod(i,w)
index[i]=a_ind+(c_ind-a_ind)/(w*(h-1))*(j[i]-1)+(b_ind-a_ind)/(w-1)*(i+1-j[i])
ipts_new[i,:] = ipts[0][int(index[i]),:]
''' choose 4 corner points for homography'''
ipts_selec = np.array([ipts_new[0],ipts_new[w-1],
ipts_new[w*(h-1)],ipts_new[w*h-1]])
opts_selec = np.array([opts[0][0],opts[0][w-1],
opts[0][w*(h-1)],opts[0][w*h-1]])
pts_obj = opts_selec.copy()
pts_obj[:,0]+=MARGIN[0]
pts_obj[:,1]+=MARGIN[1]
size = np.amax(pts_obj[:,:2],axis=0)+MARGIN
size = (int(size[0]),int(size[1]))
img_flat,M = persp.geometric_transformationN(self.img,pts_obj[:,:2],
ipts_selec,size)
ref_obj=opts[0].copy()
ref_obj[:,0]+=MARGIN[0]
ref_obj[:,1]+=MARGIN[1]
self.ref_img = ipts_new
self.ref_obj = ref_obj
self.M = M
if save:
img_summary = persp.create_summary(img_flat,pts_obj)
imgs={'summary_'+str(self.n)+'_'+TIME:img_summary}
persp.visualization(imgs,self.n,0,1)
imgs={'img_org_'+str(self.n)+'_'+TIME:img_org,
'circ_org_'+str(self.n)+'_'+TIME:circ_org,
'img_out_'+str(self.n)+'_'+TIME:img_out}
persp.visualization(imgs,self.n)
persp.save_open_images(out_dir)
return 1
def get_checkerboard(self,in_dir,fisheye,w,h,MARGIN,
R,THRESH,MIN,MAX,save,out_dir='',TIME=0,loop=''):
cam = Camera(self.n)
cam.read(in_dir,fisheye)
ipts,opts,img_out=cam.get_checkpoints_file(out_dir,w,h,fisheye,self.img)
plt.close('all')
if len(ipts) == 0:
return 0
img_org,circ_org,pts_img,__ = persp.imagepoints(self.img,R,3,THRESH,MIN,MAX)
A = pts_img[0]
B = pts_img[1]
C = pts_img[2]
a = np.array([10000,10000])
b = np.array([10000,10000])
c = np.array([10000,10000])
min_a=10000
min_b=10000
min_c=10000
a_ind = b_ind = c_ind = 0
for i,pt in enumerate(ipts[0]):
if np.linalg.norm(pt-A)<min_a:
min_a = np.linalg.norm(pt-A)
a=pt
a_ind = i
if np.linalg.norm(pt-B)<min_b:
min_b = np.linalg.norm(pt-B)
b=pt
b_ind = i
if np.linalg.norm(pt-C)<min_c:
min_c = np.linalg.norm(pt-C)
c=pt
c_ind = i
''' reorder points from a to b and a to c '''
ipts_new=np.zeros(ipts[0].shape,dtype=np.float32)
j = np.zeros((ipts[0].shape[0],1))
index = np.zeros((ipts[0].shape[0],1))
for i in range(ipts[0].shape[0]):
j[i] = i + 1 - np.mod(i,w)
index[i]=a_ind+(c_ind-a_ind)/(w*(h-1))*(j[i]-1)+(b_ind-a_ind)/(w-1)*(i+1-j[i])
ipts_new[i,:] = ipts[0][int(index[i]),:]
''' choose 4 corner points for homography'''
ipts_selec = np.array([ipts_new[0],ipts_new[w-1],
ipts_new[w*(h-1)],ipts_new[w*h-1]])
opts_selec = np.array([opts[0][0],opts[0][w-1],
opts[0][w*(h-1)],opts[0][w*h-1]])
pts_obj = opts_selec.copy()
pts_obj[:,0]+=MARGIN[0]
pts_obj[:,1]+=MARGIN[1]
size = np.amax(pts_obj[:,:2],axis=0)+MARGIN
size = (int(size[0]),int(size[1]))
img_flat,M = persp.geometric_transformationN(self.img,pts_obj[:,:2],
ipts_selec,size)
ref_obj=opts[0].copy()
ref_obj[:,0]+=MARGIN[0]
ref_obj[:,1]+=MARGIN[1]
self.ref_img = ipts_new
self.ref_obj = ref_obj
self.M = M
if save:
img_summary = persp.create_summary(img_flat,pts_obj)
imgs={'summary_'+str(self.n)+'_'+TIME:img_summary}
persp.visualization(imgs,self.n,0,1)
imgs={'img_org_'+str(self.n)+'_'+TIME:img_org,
'circ_org_'+str(self.n)+'_'+TIME:circ_org,
'img_out_'+str(self.n)+'_'+TIME:img_out}
persp.visualization(imgs,self.n)
persp.save_open_images(out_dir)
return 1
