def normalize_shape():
delta_cy,delta_cx,delta_sx,delta_sy=0,0,0,0
face_points=np.load("ferns/face_point.npy")
shape=np.zeros((total_img,p*2),dtype=np.float32)
bounding_box=np.zeros((total_img,8),dtype=np.int16)
for i in range(0,total_img):
for j in range(0,p):
shape[i,j*2+1],shape[i,j*2]=face_points[i,j,0],face_points[i,j,1]
np.save("ferns/shape(1520,40).npy",shape)
for i in range(0,total_img):
detect_ok=0
filename="/home/wcs/opencv_assignments/lfw/images_reindex/"+str(i)+".jpg"
img=cv2.imread(filename)
if (img!=None):
img0=img.copy()
bounding_box0=detect_face(img,1)
if (bounding_box0[0,0]!=0):
for j in range(0,bounding_box0.shape[0]):
mean_x0,mean_y0,sx0,sy0=bounding_box0[j,0],bounding_box0[j,1],bounding_box0[j,2]/2,bounding_box0[j,3]/2
print (shape[i,40]-mean_y0-sy0*0.2)/sy0,(shape[i,41]-mean_x0)/sx0
if (abs(shape[i,40]-mean_y0-sy0*0.2)<sy0*0.2 and abs(shape[i,41]-mean_x0)<sx0*0.2):
mean_x,mean_y,sx,sy=bounding_box0[j,0],bounding_box0[j,1],bounding_box0[j,2]/2,bounding_box0[j,3]/2
detect_ok=1
if (detect_ok==0):
bounding_box[i,1]=min(shape[i,::2])
bounding_box[i,0]=max(shape[i,::2])
bounding_box[i,2]=(bounding_box[i,0]+bounding_box[i,1])/2*(1+(np.random.random_sample((1,5))[0,0]/10)-0.05)+delta_cy/i
bounding_box[i,3]=(bounding_box[i,0]-bounding_box[i,1])/2*(1+(np.random.random_sample((1,5))[0,0]/10)-0.05)+delta_sy/i
bounding_box[i,4]=max(shape[i,1::2])
bounding_box[i,5]=min(shape[i,1::2])
bounding_box[i,6]=(bounding_box[i,4]+bounding_box[i,5])/2*(1+(np.random.random_sample((1,5))[0,0]/10)-0.05)+delta_cx/i
bounding_box[i,7]=(bounding_box[i,4]-bounding_box[i,5])/2*(1+(np.random.random_sample((1,5))[0,0]/10)-0.05)+delta_sx/i
if (detect_ok==1):
bounding_box[i,0]=mean_y+sy
bounding_box[i,1]=mean_y-sy
bounding_box[i,2]=mean_y
bounding_box[i,3]=sy
bounding_box[i,4]=mean_x+sx
bounding_box[i,5]=mean_x-sx
bounding_box[i,6]=mean_x
bounding_box[i,7]=sx
print bounding_box[i,3],bounding_box[i,7],i
#if ( bounding_box[i,3]==np.NaN or bounding_box[i,7] ==np.NaN):
#print i
print bounding_box[i,2]
delta_cy=bounding_box[i,2]-(max(shape[i,::2])+min(shape[i,::2]))/2+delta_cy
delta_cx=bounding_box[i,6]-(max(shape[i,1::2])+min(shape[i,1::2]))/2+delta_cx
delta_sy=bounding_box[i,3]-(max(shape[i,::2])-min(shape[i,::2]))/2 +delta_sy
delta_sx=bounding_box[i,7]-(max(shape[i,1::2])-min(shape[i,1::2]))/2+delta_sx
shape[i,::2]=(shape[i,::2]-bounding_box[i,2])/bounding_box[i,3]
shape[i,1::2]=(shape[i,1::2]-bounding_box[i,6])/bounding_box[i,7]
"""
if (detect_ok==0):
cv2.rectangle(img, (bounding_box[i,6]- bounding_box[i,7],bounding_box[i,2]- bounding_box[i,3]), (bounding_box[i,6]+bounding_box[i,7],bounding_box[i,2]+bounding_box[i,3]), (0, 255, 0), 2)
cv2.imshow("fews",img)
cv2.waitKey(0)
"""
mean_shape=np.mean(shape,axis=0)
print mean_shape
np.save("ferns/bound_box(1520,8).npy",bounding_box)
return shape
#for lfpw
delta_x=shape0[0,40]-mean_x
delta_y=shape0[0,41]-mean_y
#for bioid
#delta_x=shape0[0,28]-mean_x-0.18*sizex
#delta_y=shape0[0,29]-mean_y
#print delta_x,delta_y,shape0[0,28],shape0[0,29],mean_x,mean_y
new_shape[0,::2]=shape0[0,::2]-delta_x+0.2*sizex
new_shape[0,1::2]=shape0[0,1::2]-delta_y
#new_shape[0,28*2]=new_shape[0,28*2]+4
#for i in range(18,22):
#new_shape[0,i*2]=new_shape[0,i*2]-2
return new_shape
if __name__ == '__main__':
#shape=vectorize_shape()
shape=normalize_shape()
print shape
np.save("ferns/align_shape(1520,40).npy",shape)
#print shape[5,:]
img=cv2.imread("face_detect/build/BioID-FaceDatabase-V1.2/BioID_0001.pgm")
mean_y,mean_x,sx,sy=detect_face(img)
shape=get_shape(np.mean(shape,axis=0),sx/2,sy/2,mean_y,mean_x)
for i in range(0,p):
y,x=shape[0,i*2+1],shape[0,i*2]
img[x:x+5,y:y+5]=[255,0,0]
cv2.namedWindow("imshow")
cv2.imshow("imshow",img)
cv2.waitKey(0)
