def Align(image):
# print('[Info] Input image size: {}'.format(image.shape))
landmark = GetRetinaROI(image)
# detected position
facialPoints = [
landmark[5], landmark[7], landmark[9], landmark[11], landmark[13],
landmark[6], landmark[8], landmark[10], landmark[12], landmark[14]
]
facialPoints = np.reshape(facialPoints, (2, 5))
# ideal position
referencePoint = get_reference_facial_points(config.output_size,
config.inner_padding_factor,
config.outer_padding,
config.default_square)
alignedImg = warp_and_crop_face(image,
facialPoints,
reference_pts=referencePoint,
crop_size=config.output_size)
# draw landmark
if config.draw_landmark:
drawImg = draw(image, landmark)
else:
drawImg = None
return alignedImg, drawImg
def image_loader(image_path, out_size, device, transform=None):
global dst_img
raw = cv.imread(image_path)
img = Image.open(image_path).convert('RGB')
_, facial5points = detect_faces(img)
if len(facial5points) != 1:
raise Exception("No face or multi faces...")
facial5points = np.reshape(facial5points[0], (2, 5))
# Default set
crop_size = 224
inner_padding_factor = 0.1
outer_padding = 0
output_size = 224
# get the reference 5 landmarks position in the crop settings
reference_5pts = get_reference_facial_points(
(output_size, output_size), inner_padding_factor,
(outer_padding, outer_padding), True)
dst_img = warp_and_crop_face(raw,
facial5points,
reference_pts=reference_5pts,
crop_size=(crop_size, crop_size))
dst_img = cv.resize(dst_img, (out_size, out_size))[:, :, ::-1]
dst_img_ = Image.fromarray(dst_img)
im = transform(dst_img_).float()
return im.unsqueeze(0).to(device)
def face_align_similarity_transformation(facial_5_points, dir):
##Load as RGB
img = cv2.imread(dir + 'Test/data/test.png')
facial_5_points = np.array([
facial_5_points['left_eye'], facial_5_points['right_eye'],
facial_5_points['nose'], facial_5_points['mouth_left'],
facial_5_points['mouth_right']
],
dtype=np.float32)
default_square = True
inner_padding_factor = 0.001
outer_padding = (0, 0)
output_size = (48, 48)
reference_5pts = get_reference_facial_points(output_size,
inner_padding_factor,
outer_padding, default_square)
dst_img = warp_and_crop_face(img,
facial_5_points,
reference_pts=reference_5pts,
crop_size=(48, 48))
##RGB to gray
dst_img = cv2.cvtColor(dst_img, cv2.COLOR_BGR2GRAY)
cv2.imwrite(dir + 'Test/data/face-align-test.png', dst_img)
def face_align_similarity_transformation(img, facial_5_points, dir_path,
image_path, dataset, count):
count += 1
if facial_5_points != None:
print(facial_5_points)
# #facial_5_points = np.array(facial_5_points, dtype=np.float32)
facial_5_points = np.array([
facial_5_points['left_eye'], facial_5_points['right_eye'],
facial_5_points['nose'], facial_5_points['mouth_left'],
facial_5_points['mouth_right']
],
dtype=np.float32)
default_square = True
inner_padding_factor = 0.001
outer_padding = (0, 0)
output_size = (48, 48)
reference_5pts = get_reference_facial_points(output_size,
inner_padding_factor,
outer_padding,
default_square)
dst_img = warp_and_crop_face(img,
facial_5_points,
reference_pts=reference_5pts,
crop_size=(48, 48))
##RGB to gray
dst_img = cv2.cvtColor(dst_img, cv2.COLOR_BGR2GRAY)
folder = os.path.exists(
"/home/ubuntu/Face-emotion-recognition-master/Preprocessing/data/face-alignment-{}/{}"
.format(dataset, dir_path))
if not folder:
os.makedirs(
"/home/ubuntu/Face-emotion-recognition-master/Preprocessing/data/face-alignment-{}/{}"
.format(dataset, dir_path))
cv2.imwrite(
"/home/ubuntu/Face-emotion-recognition-master/Preprocessing/data/face-alignment-{}/{}/{}"
.format(dataset, dir_path, image_path), dst_img)
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
folder = os.path.exists(
"/home/ubuntu/Face-emotion-recognition-master/Preprocessing/data/face-alignment-{}/{}"
.format(dataset, dir_path))
if not folder:
os.makedirs(
"/home/ubuntu/Face-emotion-recognition-master/Preprocessing/data/face-alignment-{}/{}"
.format(dataset, dir_path))
cv2.imwrite(
"/home/ubuntu/Face-emotion-recognition-master/Preprocessing/data/face-alignment-{}/{}/{}"
.format(dataset, dir_path, image_path), img)
return count
def align_face(img_fn, facial5points):
raw = cv.imread(img_fn, True)
facial5points = np.reshape(facial5points, (2, 5))
crop_size = (image_h, image_w)
default_square = True
inner_padding_factor = 0.25
outer_padding = (0, 0)
output_size = (image_h, image_w)
# get the reference 5 landmarks position in the crop settings
reference_5pts = get_reference_facial_points(
output_size, inner_padding_factor, outer_padding, default_square)
# dst_img = warp_and_crop_face(raw, facial5points)
dst_img = warp_and_crop_face(raw, facial5points, reference_pts=reference_5pts, crop_size=crop_size)
return dst_img
def crop_test(image,
facial5points,
reference_points=None,
output_size=(96, 112),
align_type='similarity'):
# dst_img = transform_and_crop_face(image, facial5points, coord5points, imgSize)
dst_img = warp_and_crop_face(image, facial5points, reference_points,
output_size, align_type)
print('warped image shape: ', dst_img.shape)
# swap BGR to RGB to show image by pyplot
dst_img_show = dst_img[..., ::-1]
plt.figure()
plt.title(align_type + ' transform ' + str(output_size))
plt.imshow(dst_img_show)
plt.show()
def process(img, output_size):
_, facial5points = detector.detect_faces(img)
facial5points = np.reshape(facial5points[0], (2, 5))
default_square = True
inner_padding_factor = 0.25
outer_padding = (0, 0)
# get the reference 5 landmarks position in the crop settings
reference_5pts = get_reference_facial_points(output_size,
inner_padding_factor,
outer_padding, default_square)
# dst_img = warp_and_crop_face(raw, facial5points, reference_5pts, crop_size)
dst_img = warp_and_crop_face(raw,
facial5points,
reference_pts=reference_5pts,
crop_size=output_size)
cv.imwrite(
'images/{}_mtcnn_aligned_{}x{}.jpg'.format(i, output_size[0],
output_size[1]), dst_img)
def embed_faces(annotations, filename=None, image=None):
global face_model
if len(annotations) < 1:
return True
with torch.no_grad():
if image is None and filename is not None:
image = cv2.imread(filename)
if image is None:
return
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if image is None:
return False
face_model = store_arcface_model.get()
device = store_arcface_model.loaded_device
images = torch.zeros(len(annotations), 3, 112, 112).to(device)
for i, annotation in enumerate(annotations):
face = warp_and_crop_face(image, annotation['landmarks'], reference_pts=align_faces.REFERENCE_FACIAL_POINTS_112, crop_size=(112,112))
images[i] = face_transforms(face).to(device)
embedding = face_model(images)
embedding = embedding / embedding.norm(dim=-1, keepdim=True)
embedding = embedding.cpu().numpy()
for i, annotation in enumerate(annotations):
annotation['embedding'] = embedding[i]
return True
def process(filename, type, savafilename, output_size):
img = cv2.imread(filename)
bbox, facial5points = detector.detect_faces(img)
if bbox.size:
# # visualization
# draw_bbox(filename, bbox)
# draw_lms(filename, facial5points)
# draw_all(filename, bbox, facial5points)
# # show ref_5pts
if output_size[0] == 112 and output_size[1] == 112:
tmp_pts = np.array([[
38.29459953, 73.53179932, 56.02519989, 41.54930115,
70.72990036, 51.69630051, 51.50139999, 71.73660278, 92.3655014,
92.20410156
]])
empty_face = np.zeros(
(output_size[1], output_size[0], 3)) # output_size[0] == 列
cv2.imwrite('empty_face.jpg', empty_face)
draw_lms('empty_face.jpg', tmp_pts)
elif output_size[0] == 96 and output_size[1] == 112:
tmp_pts = np.array([[
30.29459953, 65.53179932, 48.02519989, 33.54930115,
62.72990036, 51.69630051, 51.50139999, 71.73660278, 92.3655014,
92.20410156
]])
empty_face = np.zeros((output_size[1], output_size[0], 3))
cv2.imwrite(
'empty_face_' + str(output_size[0]) + '_' +
str(output_size[1]) + '.jpg', empty_face)
draw_lms(
'empty_face_' + str(output_size[0]) + '_' +
str(output_size[1]) + '.jpg', tmp_pts)
elif output_size[0] == 120 and output_size[1] == 120:
tmp_pts = np.array([[
42.29459953, 77.53179932, 60.02519989, 45.54930115,
74.72990036, 63.69630051, 63.50139999, 83.73660278,
104.3655014, 104.20410156
]])
empty_face = np.zeros((output_size[1], output_size[0], 3))
cv2.imwrite(
'empty_face_' + str(output_size[0]) + '_' +
str(output_size[1]) + '.jpg', empty_face)
draw_lms(
'empty_face_' + str(output_size[0]) + '_' +
str(output_size[1]) + '.jpg', tmp_pts)
default_square = True
inner_padding_factor = 0.25
outer_padding = (0, 0)
# get the reference 5 landmarks position in the crop settings
reference_5pts = get_reference_facial_points(output_size,
inner_padding_factor,
outer_padding,
default_square)
if (len(bbox) > 0):
if type == 'labeled':
# find the max bbox
max_bb = []
for box in bbox:
x, y, r, b, _ = list(map(int, box))
w = r - x + 1
h = b - y + 1
max_bb.append(w * h)
# 得到最大值的序号
index = max_bb.index(max(max_bb))
facial5points = facial5points[[index]]
facial5point = np.reshape(facial5points, (2, 5))
dst_img = warp_and_crop_face(img,
facial5point,
reference_pts=reference_5pts,
crop_size=output_size)
# dst_name = filename[: filename.rfind('.')]
dst = filename.split("\\")
dst_dir = savafilename + '/train/' + dst[1] + '/' + dst[2] + '/'
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
dst_dir = dst_dir + dst[3][:dst[3].rfind('.')]
cv2.imwrite(
'{}_{}_mtcnn_aligned_{}x{}.jpg'.format(
dst_dir, type, output_size[0], output_size[1]),
dst_img)
elif type == 'unlabelled':
for i in range(bbox.shape[0]):
facial5point = np.reshape(facial5points[i], (2, 5))
dst_img = warp_and_crop_face(img,
facial5point,
reference_pts=reference_5pts,
crop_size=output_size)
dst = filename.split("\\")
dst_dir = savafilename + '/train/' + dst[1] + '/' + dst[
2] + '/'
if not os.path.exists(dst_dir):
os.makedirs(dst_dir)
dst_dir = dst_dir + dst[3][:dst[3].rfind('.')]
cv2.imwrite(
'{}_{}_mtcnn_aligned_{}x{}_{}.jpg'.format(
dst_dir, type, output_size[0], output_size[1], i),
dst_img)
else:
file_write_obj = open("bbox-none-name.txt", 'w')
file_write_obj.writelines(filename) # write 写入
file_write_obj.write('\n')
file_write_obj.close()
import numpy as np
from PIL import Image
from align_faces import warp_and_crop_face, get_reference_facial_points
from mtcnn.detector import detect_faces
if __name__ == "__main__":
for i in range(10):
img_fn = 'images/{}_raw.jpg'.format(i)
print('Loading image {}'.format(img_fn))
raw = cv.imread(img_fn, True)
img = Image.open(img_fn).convert('RGB')
_, facial5points = detect_faces(img)
facial5points = np.reshape(facial5points[0], (2, 5))
crop_size = (224, 224)
default_square = True
inner_padding_factor = 0.25
outer_padding = (0, 0)
output_size = (224, 224)
# get the reference 5 landmarks position in the crop settings
reference_5pts = get_reference_facial_points(
output_size, inner_padding_factor, outer_padding, default_square)
# dst_img = warp_and_crop_face(raw, facial5points, reference_5pts, crop_size)
dst_img = warp_and_crop_face(raw, facial5points, reference_pts=reference_5pts, crop_size=crop_size)
cv.imwrite('images/{}_warped.jpg'.format(i), dst_img)
img = cv.resize(raw, (224, 224))
cv.imwrite('images/{}_img.jpg'.format(i), img)
def face_align_similarity_transformation(img, facial_5_points, dir_path,
image_path, dataset):
if facial_5_points != None:
print(facial_5_points)
# #facial_5_points = np.array(facial_5_points, dtype=np.float32)
facial_5_points = np.array([
facial_5_points['left_eye'], facial_5_points['right_eye'],
facial_5_points['nose'], facial_5_points['mouth_left'],
facial_5_points['mouth_right']
],
dtype=np.float32)
#
# coord_5_points = np.array([
# [30.2946, 51.6963],
# [65.5318, 51.5014],
# [48.0252, 71.7366],
# [33.5493, 92.3655],
# [62.7299, 92.2041]], dtype=np.float32)
#
# transform = trans.SimilarityTransform()
# transform.estimate(facial_5_points, coord_5_points)
# M = transform.params[0:2,:]
# width = img.shape[1]
# height = img.shape[0]
# warped = cv2.warpAffine(img, M, (48,48), borderValue=(255,255,255))
default_square = True
inner_padding_factor = 0.001
outer_padding = (0, 0)
output_size = (48, 48)
reference_5pts = get_reference_facial_points(output_size,
inner_padding_factor,
outer_padding,
default_square)
dst_img = warp_and_crop_face(img,
facial_5_points,
reference_pts=reference_5pts,
crop_size=(48, 48))
# transform = trans.SimilarityTransform()
# transform.estimate(facial_5_points, reference_5pts)
# M = transform.params[0:2,:]
#
# warped = cv2.warpAffine(img, M, output_size, borderValue=0)
##RGB to gray
dst_img = cv2.cvtColor(dst_img, cv2.COLOR_BGR2GRAY)
folder = os.path.exists(
"/home/ubuntu/face-emotion-detection/data/face-alignment-{}/{}".
format(dataset, dir_path))
if not folder:
os.makedirs(
"/home/ubuntu/face-emotion-detection/data/face-alignment-{}/{}"
.format(dataset, dir_path))
cv2.imwrite(
"/home/ubuntu/face-emotion-detection/data/face-alignment-{}/{}/{}".
format(dataset, dir_path, image_path), dst_img)
else:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
folder = os.path.exists(
"/home/ubuntu/face-emotion-detection/data/face-alignment-{}/{}".
format(dataset, dir_path))
if not folder:
os.makedirs(
"/home/ubuntu/face-emotion-detection/data/face-alignment-{}/{}"
.format(dataset, dir_path))
cv2.imwrite(
"/home/ubuntu/face-emotion-detection/data/face-alignment-{}/{}/{}".
format(dataset, dir_path, image_path), img)
