def get_hw(pt, return_rot=False):
pt = pt.copy()
R = su.unrotate2d(pt)
mu = np.median(pt, axis=0)
pt = (pt - mu[None, :]).dot(R.T) + mu[None, :]
h, w = np.max(pt, axis=0) - np.min(pt, axis=0)
if return_rot:
return h, w, R
return h, w
def get_hw(pt,return_rot=False):
pt = pt.copy()
R = su.unrotate2d(pt)
mu = np.median(pt,axis=0)
pt = (pt-mu[None,:]).dot(R.T) + mu[None,:]
h,w = np.max(pt,axis=0) - np.min(pt,axis=0)
if return_rot:
return h,w,R
return h,w
def get_text_placement_mask(xyz, mask, plane, pad=2, viz=False):
"""
Returns a binary mask in which text can be placed.
Also returns a homography from original image
to this rectified mask.
XYZ : (HxWx3) image xyz coordinates
MASK : (HxW) : non-zero pixels mark the object mask
REGION : DICT output of TextRegions.get_regions
PAD : number of pixels to pad the placement-mask by
"""
contour, hier = cv2.findContours(mask.copy().astype('uint8'),
mode=cv2.RETR_CCOMP,
method=cv2.CHAIN_APPROX_SIMPLE)
contour = [np.squeeze(c).astype('float') for c in contour]
# plane = np.array([plane[1],plane[0],plane[2],plane[3]])
H, W = mask.shape[:2]
# bring the contour 3d points to fronto-parallel config:
pts, pts_fp = [], []
center = np.array([W, H]) / 2
n_front = np.array([0.0, 0.0, -1.0])
for i in range(len(contour)):
cnt_ij = contour[i]
xyz = su.DepthCamera.plane2xyz(center, cnt_ij, plane)
R = su.rot3d(plane[:3], n_front)
xyz = xyz.dot(R.T)
pts_fp.append(xyz[:, :2])
pts.append(cnt_ij)
# unrotate in 2D plane:
rect = cv2.minAreaRect(pts_fp[0].copy().astype('float32'))
box = np.array(cv2.boxPoints(rect))
R2d = su.unrotate2d(box.copy())
box = np.vstack([box, box[0, :]]) # close the box for visualization
mu = np.median(pts_fp[0], axis=0)
pts_tmp = (pts_fp[0] - mu[None, :]).dot(R2d.T) + mu[None, :]
boxR = (box - mu[None, :]).dot(R2d.T) + mu[None, :]
# rescale the unrotated 2d points to approximately
# the same scale as the target region:
s = rescale_frontoparallel(pts_tmp, boxR, pts[0])
boxR *= s
for i in range(len(pts_fp)):
pts_fp[i] = s * ((pts_fp[i] - mu[None, :]).dot(R2d.T) + mu[None, :])
# paint the unrotated contour points:
minxy = -np.min(boxR, axis=0) + pad // 2
ROW = np.max(ssd.pdist(np.atleast_2d(boxR[:, 0]).T))
COL = np.max(ssd.pdist(np.atleast_2d(boxR[:, 1]).T))
place_mask = 255 * np.ones(
(int(np.ceil(COL)) + pad, int(np.ceil(ROW)) + pad), 'uint8')
pts_fp_i32 = [(pts_fp[i] + minxy[None, :]).astype('int32')
for i in range(len(pts_fp))]
cv2.drawContours(place_mask,
pts_fp_i32,
-1,
0,
thickness=cv2.FILLED,
lineType=8,
hierarchy=hier)
if not TextRegions.filter_rectified((~place_mask).astype('float') / 255):
return
# calculate the homography
H, _ = cv2.findHomography(pts[0].astype('float32').copy(),
pts_fp_i32[0].astype('float32').copy(),
method=0)
Hinv, _ = cv2.findHomography(pts_fp_i32[0].astype('float32').copy(),
pts[0].astype('float32').copy(),
method=0)
if viz:
plt.subplot(1, 2, 1)
plt.imshow(mask)
plt.subplot(1, 2, 2)
plt.imshow(~place_mask)
for i in range(len(pts_fp_i32)):
plt.scatter(pts_fp_i32[i][:, 0],
pts_fp_i32[i][:, 1],
edgecolors='none',
facecolor='g',
alpha=0.5)
plt.show()
return place_mask, H, Hinv
def get_text_placement_mask(xyz,mask,plane,pad=2,viz=False):
"""
Returns a binary mask in which text can be placed.
Also returns a homography from original image
to this rectified mask.
XYZ : (HxWx3) image xyz coordinates
MASK : (HxW) : non-zero pixels mark the object mask
REGION : DICT output of TextRegions.get_regions
PAD : number of pixels to pad the placement-mask by
"""
img, contour, hier = cv2.findContours(mask.copy().astype('uint8'),
mode=cv2.RETR_CCOMP,
method=cv2.CHAIN_APPROX_SIMPLE)
contour = [np.squeeze(c).astype('float') for c in contour]
#plane = np.array([plane[1],plane[0],plane[2],plane[3]])
H,W = mask.shape[:2]
# bring the contour 3d points to fronto-parallel config:
pts,pts_fp = [],[]
center = np.array([W,H])/2
n_front = np.array([0.0,0.0,-1.0])
for i in range(len(contour)):
cnt_ij = contour[i]
xyz = su.DepthCamera.plane2xyz(center, cnt_ij, plane)
R = su.rot3d(plane[:3],n_front)
xyz = xyz.dot(R.T)
pts_fp.append(xyz[:,:2])
pts.append(cnt_ij)
# unrotate in 2D plane:
rect = cv2.minAreaRect(pts_fp[0].copy().astype('float32'))
box = np.array(cv2.boxPoints(rect))
R2d = su.unrotate2d(box.copy())
box = np.vstack([box,box[0,:]]) #close the box for visualization
mu = np.median(pts_fp[0],axis=0)
pts_tmp = (pts_fp[0]-mu[None,:]).dot(R2d.T) + mu[None,:]
boxR = (box-mu[None,:]).dot(R2d.T) + mu[None,:]
# rescale the unrotated 2d points to approximately
# the same scale as the target region:
s = rescale_frontoparallel(pts_tmp,boxR,pts[0])
boxR *= s
for i in range(len(pts_fp)):
pts_fp[i] = s*((pts_fp[i]-mu[None,:]).dot(R2d.T) + mu[None,:])
# paint the unrotated contour points:
minxy = -np.min(boxR,axis=0) + pad//2
ROW = np.max(ssd.pdist(np.atleast_2d(boxR[:,0]).T))
COL = np.max(ssd.pdist(np.atleast_2d(boxR[:,1]).T))
place_mask = 255*np.ones((int(np.ceil(COL)+pad),int(np.ceil(ROW)+pad)),'uint8')
pts_fp_i32 = [(pts_fp[i]+minxy[None,:]).astype('int32') for i in range(len(pts_fp))]
cv2.drawContours(place_mask,pts_fp_i32,-1,0,
thickness=cv2.FILLED,
lineType=8,hierarchy=hier)
if not TextRegions.filter_rectified((~place_mask).astype('float')/255):
return
# calculate the homography
H,_ = cv2.findHomography(pts[0].astype('float32').copy(),
pts_fp_i32[0].astype('float32').copy(),
method=0)
Hinv,_ = cv2.findHomography(pts_fp_i32[0].astype('float32').copy(),
pts[0].astype('float32').copy(),
method=0)
if viz:
plt.subplot(1,2,1)
plt.imshow(mask)
plt.subplot(1,2,2)
plt.imshow(~place_mask)
plt.hold(True)
for i in range(len(pts_fp_i32)):
plt.scatter(pts_fp_i32[i][:,0],pts_fp_i32[i][:,1],
edgecolors='none',facecolor='g',alpha=0.5)
plt.show()
return place_mask,H,Hinv
