def load_test_img(gray, bbox): # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) face = gray[bbox.top:bbox.bottom, bbox.left:bbox.right] face = cv2.resize(face, (39,39)).reshape(1,1,39,39) face = processImage(np.asarray(face)) return face
def load_test_img(gray, bbox):
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face = gray[bbox.top:bbox.bottom, bbox.left:bbox.right]
face = cv2.resize(face, (40, 40)).reshape(1, 1, 40, 40)
face = processImage(np.asarray(face))
return face
def generate_hdf5(cephaTxt,output,fname,argument=False):
data = getDataFromTxt(cephaTxt)#return [(img_path,landmark,bbox)]
cepha_imgs = []
cepha_landmarks = []
for (imgPath,landmarkGt,bbox) in data:
img = cv2.imread(imgPath,cv2.CV_LOAD_IMAGE_GRAYSCALE)
assert(img is not None)
logger("process %s" % imgPath)
#downsampled by 3: 3x3 patch
height,width = img.shape[:2]
size = (int(width/3),int(height/3))
cephaImg = cv2.resize(img,size,interpolation=cv2.INTER_NEAREST)
cepha_bbox = bbox#.subBBox(-0.05,1.05,-0.05,1.05)
cepha_img = cephaImg[cepha_bbox.top:cepha_bbox.bottom+1,cepha_bbox.left:cepha_bbox.right+1]
if argument and np.random.rand()>-1:
###rotation
if np.random.rand() > 0.5:
cepha_rotated_alpha,landmark_rotated = rotate(cepha_img,cepha_bbox,bbox.reprojectLandmark(landmarkGt),5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)#relative
cepha_rotated_alpha = cv2.resize(cepha_rotated_alpha,(39,39))
cepha_imgs.append(cepha_rotated_alpha.reshape((1,39,39)))
cepha_landmarks.append(landmark_rotated.reshape(38))
if np.random.rand() > 0.5:
cepha_rotated_alpha,landmark_rotated = rotate(cepha_img,cepha_bbox,bbox.reprojectLandmark(landmarkGt),-5)
landmark_rotated = bbox.projectLandmark(landmark_rotated)
cepha_rotated_alpha = cv2.resize(cepha_rotated_alpha,(39,39))
cepha_imgs.append(cepha_rotated_alpha.reshape((1,39,39)))
cepha_landmarks.append(landmark_rotated.reshape(38))
cepha_img = cv2.resize(cepha_img,(39,39))
cepha_img = cepha_img.reshape((1,39,39))
cepha_landmark = landmarkGt.reshape((38))
cepha_imgs.append(cepha_img)
cepha_landmarks.append(cepha_landmark)
cepha_imgs,cepha_landmarks = np.asarray(cepha_imgs),np.asarray(cepha_landmarks)
cepha_imgs = processImage(cepha_imgs)
shuffle_in_unison_scary(cepha_imgs,cepha_landmarks)
#save file
base = join(OUTPUT,'1_cepha')#train/1_cepha (or test)
createDir(base)
output = join(base,fname)#train/1_cepha/train.h5 (or test)
output = output.replace('\\','/')
logger("generate %s" % output)
with h5py.File(output,'w') as h5:
h5['data'] = cepha_imgs.astype(np.float32)
h5['landmark'] = cepha_landmarks.astype(np.float32)
def generate_h5py(data, h5_path, txt_path, augment=False):
'''
Get images and turn them into h5py files
Input:
- data: a tuple of [imgpath, bbox, landmark]
- h5_path: h5py file name
- txt_path: h5 txt name
'''
F_imgs = []
F_landmarks = []
for (imgpath, bbox, landmark) in data:
img = cv2.imread(imgpath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
print("processing %s") % imgpath
if not check_bbox(img, bbox):
print 'BBox out of range.'
continue
face = img[bbox.top:bbox.bottom, bbox.left:bbox.right]
if augment:
face_flip, landmark_flip = flip(face, landmark)
face_flip = cv2.resize(face_flip, (39, 39)).reshape(1, 39, 39)
landmark_flip = landmark_flip.reshape(10)
F_imgs.append(face_flip)
F_landmarks.append(landmark_flip)
face = cv2.resize(face, (39, 39)).reshape(1, 39, 39)
landmark = landmark.reshape(10)
F_imgs.append(face)
F_landmarks.append(landmark)
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
F_imgs = processImage(F_imgs)
F_imgs, F_landmarks = shuffle(F_imgs, F_landmarks, random_state=42)
with h5py.File(h5_path, 'w') as f:
f['data'] = F_imgs.astype(np.float32)
f['landmarks'] = F_landmarks.astype(np.float32)
with open(txt_path, 'w') as f:
f.write(h5_path)
def generate_h5py(data, h5_path, txt_path, augment=False):
'''
Get images and turn them into h5py files
Input:
- data: a tuple of [imgpath, bbox, landmark]
- h5_path: h5py file name
- txt_path: h5 txt name
'''
F_imgs = []
F_landmarks = []
img_size = 40
num_landmark = 68 * 2
num_sample = 0
for (imgpath, bbox, landmarkGt) in data:
img = cv2.imread(imgpath, cv2.CV_LOAD_IMAGE_GRAYSCALE)
print("processing %s") % imgpath
if not check_bbox(img, bbox):
print 'BBox out of range.'
continue
face = img[bbox.top:bbox.bottom, bbox.left:bbox.right]
if augment:
# flip the face
#face_flip, landmark_flip = flip(face, landmarkGt)
#face_flip = cv2.resize(face_flip, (img_size,img_size)).reshape(1,img_size,img_size)
#fit=0
#for i in range(0,num_landmark/2):
# if landmark_flip[i,0]<0 or landmark_flip[i,0]>1 or landmark_flip[i,1]<0 or landmark_flip[i,1]>1:
# fit=1
# break
#if fit==0:
#print landmark_flipped_alpha
# F_imgs.append(face_flip)
# F_landmarks.append(landmark_flip.reshape(num_landmark))
#print landmark_flip
#angles=[5,10,15,20,25,30,-5,-10,-15,-20,-25,-30]
#for alpha in angles:
# rotate the face
#face_rotated_by_alpha, landmark_rotated_alpha = rotate(img, bbox,bbox.reprojectLandmark(landmarkGt), alpha)
#landmark_rotated_alpha = bbox.projectLandmark(landmark_rotated_alpha)
#face_rotated_by_alpha = cv2.resize(face_rotated_by_alpha, (img_size,img_size))
#fit=0
#for i in range(0,num_landmark/2):
# if landmark_rotated_alpha[i,0]<0 or landmark_rotated_alpha[i,0]>1 or landmark_rotated_alpha[i,1]<0 or landmark_rotated_alpha[i,1]>1:
# fit=1
# break
#if fit==0:
#print landmark_rotated_alpha
# F_imgs.append(face_rotated_by_alpha.reshape((1, img_size,img_size)))
# F_landmarks.append(landmark_rotated_alpha.reshape(num_landmark))
#print landmark_rotated_5
# flip with the rotation
#face_flipped_alpha, landmark_flipped_alpha = flip(face_rotated_by_alpha, landmark_rotated_alpha)
#face_flipped_alpha = cv2.resize(face_flipped_alpha, (img_size,img_size))
#fit=0
#for i in range(0,num_landmark/2):
# if landmark_flipped_alpha[i,0]<0 or landmark_flipped_alpha[i,0]>1 or landmark_flipped_alpha[i,1]<0 or landmark_flipped_alpha[i,1]>1:
# fit=1
# break
#if fit==0:
#print landmark_flipped_alpha
# F_imgs.append(face_flipped_alpha.reshape((1, img_size,img_size)))
# F_landmarks.append(landmark_flipped_alpha.reshape(num_landmark))
# debug
#center = ((bbox.left+bbox.right)/2, (bbox.top+bbox.bottom)/2)
#rot_mat = cv2.getRotationMatrix2D(center, alpha, 1)
#img_rotated_by_alpha = cv2.warpAffine(img, rot_mat, img.shape)
#landmark_rotated_alpha = bbox.reprojectLandmark(landmark_rotated_alpha) # will affect the flip "landmark_rotated_alpha"
#img_rotated_by_alpha = drawLandmark(img_rotated_by_alpha, bbox, landmark_rotated_alpha)
#fp = 'debug_results/'+ str(num_sample)+'.jpg'
#cv2.imwrite(fp, img_rotated_by_alpha)
#num_sample=num_sample+1
# debug
#use bounding box perturbation
y = bbox.top
x = bbox.left
h = bbox.bottom - bbox.top
w = bbox.right - bbox.left
# original landmark position
landmarkGT_scale = bbox.reprojectLandmark(landmarkGt)
for cur_scale in [0.83, 0.91, 1.0, 1.10, 1.21]:
for cur_x in [-0.17, 0, 0.17]:
for cur_y in [-0.17, 0, 0.17]:
s_n = 1 / cur_scale
x_n = -cur_x / cur_scale
y_n = -cur_y / cur_scale
x_temp = int(x - (x_n * w / s_n))
y_temp = int(y - (y_n * h / s_n))
w_temp = int(w / s_n)
h_temp = int(h / s_n)
# generate new bounding box
bbox_left = x_temp
bbox_right = x_temp + w_temp
bbox_top = y_temp
bbox_bottom = y_temp + h_temp
new_bbox = map(
int,
[bbox_left, bbox_right, bbox_top, bbox_bottom])
new_bbox = BBox(new_bbox)
if not check_bbox(img, new_bbox):
print 'BBox out of range.'
continue
# project landmark onto the new bounding box
new_landmarkGT = new_bbox.projectLandmark(
landmarkGT_scale)
new_landmarkGT_org = new_landmarkGT.copy()
angles = [
5, 10, 15, 20, 25, 30, -5, -10, -15, -20, -25, -30
]
for alpha in angles:
# rotate the face
face_rotated_by_alpha, landmark_rotated_alpha = rotate(
img, new_bbox,
new_bbox.reprojectLandmark(new_landmarkGT),
alpha)
landmark_rotated_alpha = new_bbox.projectLandmark(
landmark_rotated_alpha)
face_rotated_by_alpha = cv2.resize(
face_rotated_by_alpha, (img_size, img_size))
fit = 0
for i in range(0, num_landmark / 2):
if landmark_rotated_alpha[
i, 0] < 0 or landmark_rotated_alpha[
i,
0] > 1 or landmark_rotated_alpha[
i,
1] < 0 or landmark_rotated_alpha[
i, 1] > 1:
fit = 1
break
if fit == 0:
#print landmark_rotated_alpha
F_imgs.append(
face_rotated_by_alpha.reshape(
(1, img_size, img_size)))
F_landmarks.append(
landmark_rotated_alpha.reshape(
num_landmark))
# debug
#center = ((new_bbox.left+new_bbox.right)/2, (new_bbox.top+new_bbox.bottom)/2)
#rot_mat = cv2.getRotationMatrix2D(center, alpha, 1)
#img_rotated_by_alpha = cv2.warpAffine(img, rot_mat, img.shape)
#landmark_rotated_alpha = new_bbox.reprojectLandmark(landmark_rotated_alpha) # will affect the flip "landmark_rotated_alpha"
#img_rotated_by_alpha = drawLandmark(img_rotated_by_alpha, new_bbox, landmark_rotated_alpha)
#fp = 'debug_results/'+ str(num_sample)+'.jpg'
#cv2.imwrite(fp, img_rotated_by_alpha)
#num_sample=num_sample+1
# debug
# project landmark onto the new bounding box
landmarkGT_project = new_landmarkGT_org.copy(
) # error is fixed here
#print landmarkGT_project
fit = 0
for i in range(0, num_landmark / 2):
if landmarkGT_project[
i, 0] < 0 or landmarkGT_project[
i, 0] > 1 or landmarkGT_project[
i, 1] < 0 or landmarkGT_project[
i, 1] > 1:
fit = 1
break
if fit == 0:
#print landmarkGT_project
#if landmarkGT_project[i,0]<0 or landmarkGT_project[i,0]>1 or landmarkGT_project[i,1]<0 or landmarkGT_project[i,1]>1:
# pdb.set_trace()
cropped_face = img[new_bbox.top:new_bbox.bottom,
new_bbox.left:new_bbox.right]
cropped_face = cv2.resize(
cropped_face, (img_size, img_size)).reshape(
1, img_size, img_size)
F_imgs.append(cropped_face)
F_landmarks.append(
landmarkGT_project.reshape(num_landmark))
# debug on the bounding box perturbation
#landmark_org = new_bbox.reprojectLandmark(landmarkGT_project) # will affect the flip "landmark_rotated_alpha"
#img_debug = drawLandmark(img, new_bbox, landmark_org)
#fp = 'debug_results/'+ str(num_sample)+'_bbox.jpg'
#cv2.imwrite(fp, img_debug)
#num_sample=num_sample+1
face = cv2.resize(face,
(img_size, img_size)).reshape(1, img_size, img_size)
fit = 0
for i in range(0, num_landmark / 2):
if landmarkGt[i, 0] < 0 or landmarkGt[i, 0] > 1 or landmarkGt[
i, 1] < 0 or landmarkGt[i, 1] > 1:
fit = 1
break
if fit == 0:
#print landmark_flipped_alpha
F_imgs.append(face)
F_landmarks.append(landmarkGt.reshape(num_landmark))
F_imgs, F_landmarks = np.asarray(F_imgs), np.asarray(F_landmarks)
F_imgs = processImage(F_imgs)
F_imgs, F_landmarks = shuffle(F_imgs, F_landmarks, random_state=42)
with h5py.File(h5_path, 'w') as f:
f['data'] = F_imgs.astype(np.float32)
f['landmarks'] = F_landmarks.astype(np.float32)
with open(txt_path, 'w') as f:
f.write(h5_path)
f.write(str(len(F_landmarks)))