mirror of https://github.com/menyifang/DCT-Net
update
menyifang
3 years ago
parent
c08e84e872
commit
32c9f31e7b
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# Default ignored files
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Before Width: | Height: | Size: 13 MiB |
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from modelscope.hub.snapshot_download import snapshot_download
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model_dir = snapshot_download('damo/cv_unet_person-image-cartoon_compound-models', cache_dir='.')
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import cv2
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from source.cartoonize import Cartoonizer
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import os
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def process():
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algo = Cartoonizer(dataroot='damo/cv_unet_person-image-cartoon_compound-models')
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img = cv2.imread('input.png')[...,::-1]
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result = algo.cartoonize(img)
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cv2.imwrite('res.png', result)
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print('finished!')
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if __name__ == '__main__':
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process()
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import cv2
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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img_cartoon = pipeline(Tasks.image_portrait_stylization, 'damo/cv_unet_person-image-cartoon_compound-models')
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img_cartoon = pipeline('image-portrait-stylization')
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result = img_cartoon('input.png')
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cv2.imwrite('result.png', result['output_img'])
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import os
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import cv2
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import tensorflow as tf
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import numpy as np
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from source.facelib.facer import FaceAna
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import source.utils as utils
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from source.mtcnn_pytorch.src.align_trans import warp_and_crop_face, get_reference_facial_points
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if tf.__version__ >= '2.0':
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tf = tf.compat.v1
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tf.disable_eager_execution()
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class Cartoonizer():
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def __init__(self, dataroot):
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self.facer = FaceAna(dataroot)
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self.sess_head = self.load_sess(
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os.path.join(dataroot, 'cartoon_anime_h.pb'), 'model_head')
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self.sess_bg = self.load_sess(
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os.path.join(dataroot, 'cartoon_anime_bg.pb'), 'model_bg')
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self.box_width = 288
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global_mask = cv2.imread(os.path.join(dataroot, 'alpha.jpg'))
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global_mask = cv2.resize(
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global_mask, (self.box_width, self.box_width),
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interpolation=cv2.INTER_AREA)
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self.global_mask = cv2.cvtColor(
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global_mask, cv2.COLOR_BGR2GRAY).astype(np.float32) / 255.0
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def load_sess(self, model_path, name):
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config = tf.ConfigProto(allow_soft_placement=True)
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config.gpu_options.allow_growth = True
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sess = tf.Session(config=config)
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print(f'loading model from {model_path}')
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with tf.gfile.FastGFile(model_path, 'rb') as f:
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graph_def = tf.GraphDef()
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graph_def.ParseFromString(f.read())
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sess.graph.as_default()
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tf.import_graph_def(graph_def, name=name)
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sess.run(tf.global_variables_initializer())
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print(f'load model {model_path} done.')
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return sess
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def detect_face(self, img):
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src_h, src_w, _ = img.shape
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src_x = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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boxes, landmarks, _ = self.facer.run(src_x)
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if boxes.shape[0] == 0:
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return None
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else:
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return landmarks
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def cartoonize(self, img):
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# img: RGB input
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ori_h, ori_w, _ = img.shape
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img = utils.resize_size(img, size=720)
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img_brg = img[:, :, ::-1]
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# background process
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pad_bg, pad_h, pad_w = utils.padTo16x(img_brg)
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bg_res = self.sess_bg.run(
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self.sess_bg.graph.get_tensor_by_name(
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'model_bg/output_image:0'),
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feed_dict={'model_bg/input_image:0': pad_bg})
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res = bg_res[:pad_h, :pad_w, :]
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landmarks = self.detect_face(img_brg)
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if landmarks is None:
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print('No face detected!')
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return res
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|
||||||
|
print('%d faces detected!'%len(landmarks))
|
||||||
|
for landmark in landmarks:
|
||||||
|
# get facial 5 points
|
||||||
|
f5p = utils.get_f5p(landmark, img_brg)
|
||||||
|
|
||||||
|
# face alignment
|
||||||
|
head_img, trans_inv = warp_and_crop_face(
|
||||||
|
img,
|
||||||
|
f5p,
|
||||||
|
ratio=0.75,
|
||||||
|
reference_pts=get_reference_facial_points(default_square=True),
|
||||||
|
crop_size=(self.box_width, self.box_width),
|
||||||
|
return_trans_inv=True)
|
||||||
|
|
||||||
|
# head process
|
||||||
|
head_res = self.sess_head.run(
|
||||||
|
self.sess_head.graph.get_tensor_by_name(
|
||||||
|
'model_head/output_image:0'),
|
||||||
|
feed_dict={
|
||||||
|
'model_head/input_image:0': head_img[:, :, ::-1]
|
||||||
|
})
|
||||||
|
|
||||||
|
# merge head and background
|
||||||
|
head_trans_inv = cv2.warpAffine(
|
||||||
|
head_res,
|
||||||
|
trans_inv, (np.size(img, 1), np.size(img, 0)),
|
||||||
|
borderValue=(0, 0, 0))
|
||||||
|
|
||||||
|
mask = self.global_mask
|
||||||
|
mask_trans_inv = cv2.warpAffine(
|
||||||
|
mask,
|
||||||
|
trans_inv, (np.size(img, 1), np.size(img, 0)),
|
||||||
|
borderValue=(0, 0, 0))
|
||||||
|
mask_trans_inv = np.expand_dims(mask_trans_inv, 2)
|
||||||
|
|
||||||
|
res = mask_trans_inv * head_trans_inv + (1 - mask_trans_inv) * res
|
||||||
|
|
||||||
|
res = cv2.resize(res, (ori_w, ori_h), interpolation=cv2.INTER_AREA)
|
||||||
|
|
||||||
|
return res
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@ -0,0 +1,4 @@
|
|||||||
|
|
||||||
|
Copyright (c) Peppa_Pig_Face_Engine
|
||||||
|
|
||||||
|
https://github.com/610265158/Peppa_Pig_Face_Engine
|
||||||
@ -0,0 +1,97 @@
|
|||||||
|
import numpy as np
|
||||||
|
|
||||||
|
from modelscope.models.cv.cartoon.facelib.config import config as cfg
|
||||||
|
|
||||||
|
|
||||||
|
class GroupTrack():
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
self.old_frame = None
|
||||||
|
self.previous_landmarks_set = None
|
||||||
|
self.with_landmark = True
|
||||||
|
self.thres = cfg.TRACE.pixel_thres
|
||||||
|
self.alpha = cfg.TRACE.smooth_landmark
|
||||||
|
self.iou_thres = cfg.TRACE.iou_thres
|
||||||
|
|
||||||
|
def calculate(self, img, current_landmarks_set):
|
||||||
|
if self.previous_landmarks_set is None:
|
||||||
|
self.previous_landmarks_set = current_landmarks_set
|
||||||
|
result = current_landmarks_set
|
||||||
|
else:
|
||||||
|
previous_lm_num = self.previous_landmarks_set.shape[0]
|
||||||
|
if previous_lm_num == 0:
|
||||||
|
self.previous_landmarks_set = current_landmarks_set
|
||||||
|
result = current_landmarks_set
|
||||||
|
return result
|
||||||
|
else:
|
||||||
|
result = []
|
||||||
|
for i in range(current_landmarks_set.shape[0]):
|
||||||
|
not_in_flag = True
|
||||||
|
for j in range(previous_lm_num):
|
||||||
|
if self.iou(current_landmarks_set[i],
|
||||||
|
self.previous_landmarks_set[j]
|
||||||
|
) > self.iou_thres:
|
||||||
|
result.append(
|
||||||
|
self.smooth(current_landmarks_set[i],
|
||||||
|
self.previous_landmarks_set[j]))
|
||||||
|
not_in_flag = False
|
||||||
|
break
|
||||||
|
if not_in_flag:
|
||||||
|
result.append(current_landmarks_set[i])
|
||||||
|
|
||||||
|
result = np.array(result)
|
||||||
|
self.previous_landmarks_set = result
|
||||||
|
|
||||||
|
return result
|
||||||
|
|
||||||
|
def iou(self, p_set0, p_set1):
|
||||||
|
rec1 = [
|
||||||
|
np.min(p_set0[:, 0]),
|
||||||
|
np.min(p_set0[:, 1]),
|
||||||
|
np.max(p_set0[:, 0]),
|
||||||
|
np.max(p_set0[:, 1])
|
||||||
|
]
|
||||||
|
rec2 = [
|
||||||
|
np.min(p_set1[:, 0]),
|
||||||
|
np.min(p_set1[:, 1]),
|
||||||
|
np.max(p_set1[:, 0]),
|
||||||
|
np.max(p_set1[:, 1])
|
||||||
|
]
|
||||||
|
|
||||||
|
# computing area of each rectangles
|
||||||
|
S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
|
||||||
|
S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
|
||||||
|
|
||||||
|
# computing the sum_area
|
||||||
|
sum_area = S_rec1 + S_rec2
|
||||||
|
|
||||||
|
# find the each edge of intersect rectangle
|
||||||
|
x1 = max(rec1[0], rec2[0])
|
||||||
|
y1 = max(rec1[1], rec2[1])
|
||||||
|
x2 = min(rec1[2], rec2[2])
|
||||||
|
y2 = min(rec1[3], rec2[3])
|
||||||
|
|
||||||
|
# judge if there is an intersect
|
||||||
|
intersect = max(0, x2 - x1) * max(0, y2 - y1)
|
||||||
|
|
||||||
|
iou = intersect / (sum_area - intersect)
|
||||||
|
return iou
|
||||||
|
|
||||||
|
def smooth(self, now_landmarks, previous_landmarks):
|
||||||
|
result = []
|
||||||
|
for i in range(now_landmarks.shape[0]):
|
||||||
|
x = now_landmarks[i][0] - previous_landmarks[i][0]
|
||||||
|
y = now_landmarks[i][1] - previous_landmarks[i][1]
|
||||||
|
dis = np.sqrt(np.square(x) + np.square(y))
|
||||||
|
if dis < self.thres:
|
||||||
|
result.append(previous_landmarks[i])
|
||||||
|
else:
|
||||||
|
result.append(
|
||||||
|
self.do_moving_average(now_landmarks[i],
|
||||||
|
previous_landmarks[i]))
|
||||||
|
|
||||||
|
return np.array(result)
|
||||||
|
|
||||||
|
def do_moving_average(self, p_now, p_previous):
|
||||||
|
p = self.alpha * p_now + (1 - self.alpha) * p_previous
|
||||||
|
return p
|
||||||
@ -0,0 +1,23 @@
|
|||||||
|
import os
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from easydict import EasyDict as edict
|
||||||
|
|
||||||
|
config = edict()
|
||||||
|
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
|
||||||
|
|
||||||
|
config.DETECT = edict()
|
||||||
|
config.DETECT.topk = 10
|
||||||
|
config.DETECT.thres = 0.8
|
||||||
|
config.DETECT.input_shape = (512, 512, 3)
|
||||||
|
config.KEYPOINTS = edict()
|
||||||
|
config.KEYPOINTS.p_num = 68
|
||||||
|
config.KEYPOINTS.base_extend_range = [0.2, 0.3]
|
||||||
|
config.KEYPOINTS.input_shape = (160, 160, 3)
|
||||||
|
config.TRACE = edict()
|
||||||
|
config.TRACE.pixel_thres = 1
|
||||||
|
config.TRACE.smooth_box = 0.3
|
||||||
|
config.TRACE.smooth_landmark = 0.95
|
||||||
|
config.TRACE.iou_thres = 0.5
|
||||||
|
config.DATA = edict()
|
||||||
|
config.DATA.pixel_means = np.array([123., 116., 103.]) # RGB
|
||||||
@ -0,0 +1,116 @@
|
|||||||
|
import time
|
||||||
|
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
import tensorflow as tf
|
||||||
|
|
||||||
|
from .config import config as cfg
|
||||||
|
|
||||||
|
if tf.__version__ >= '2.0':
|
||||||
|
tf = tf.compat.v1
|
||||||
|
|
||||||
|
|
||||||
|
class FaceDetector:
|
||||||
|
|
||||||
|
def __init__(self, dir):
|
||||||
|
|
||||||
|
self.model_path = dir + '/detector.pb'
|
||||||
|
self.thres = cfg.DETECT.thres
|
||||||
|
self.input_shape = cfg.DETECT.input_shape
|
||||||
|
|
||||||
|
self._graph = tf.Graph()
|
||||||
|
|
||||||
|
with self._graph.as_default():
|
||||||
|
self._graph, self._sess = self.init_model(self.model_path)
|
||||||
|
|
||||||
|
self.input_image = tf.get_default_graph().get_tensor_by_name(
|
||||||
|
'tower_0/images:0')
|
||||||
|
self.training = tf.get_default_graph().get_tensor_by_name(
|
||||||
|
'training_flag:0')
|
||||||
|
self.output_ops = [
|
||||||
|
tf.get_default_graph().get_tensor_by_name('tower_0/boxes:0'),
|
||||||
|
tf.get_default_graph().get_tensor_by_name('tower_0/scores:0'),
|
||||||
|
tf.get_default_graph().get_tensor_by_name(
|
||||||
|
'tower_0/num_detections:0'),
|
||||||
|
]
|
||||||
|
|
||||||
|
def __call__(self, image):
|
||||||
|
|
||||||
|
image, scale_x, scale_y = self.preprocess(
|
||||||
|
image,
|
||||||
|
target_width=self.input_shape[1],
|
||||||
|
target_height=self.input_shape[0])
|
||||||
|
|
||||||
|
image = np.expand_dims(image, 0)
|
||||||
|
|
||||||
|
boxes, scores, num_boxes = self._sess.run(
|
||||||
|
self.output_ops,
|
||||||
|
feed_dict={
|
||||||
|
self.input_image: image,
|
||||||
|
self.training: False
|
||||||
|
})
|
||||||
|
|
||||||
|
num_boxes = num_boxes[0]
|
||||||
|
boxes = boxes[0][:num_boxes]
|
||||||
|
|
||||||
|
scores = scores[0][:num_boxes]
|
||||||
|
|
||||||
|
to_keep = scores > self.thres
|
||||||
|
boxes = boxes[to_keep]
|
||||||
|
scores = scores[to_keep]
|
||||||
|
|
||||||
|
y1 = self.input_shape[0] / scale_y
|
||||||
|
x1 = self.input_shape[1] / scale_x
|
||||||
|
y2 = self.input_shape[0] / scale_y
|
||||||
|
x2 = self.input_shape[1] / scale_x
|
||||||
|
scaler = np.array([y1, x1, y2, x2], dtype='float32')
|
||||||
|
boxes = boxes * scaler
|
||||||
|
|
||||||
|
scores = np.expand_dims(scores, 0).reshape([-1, 1])
|
||||||
|
|
||||||
|
for i in range(boxes.shape[0]):
|
||||||
|
boxes[i] = np.array(
|
||||||
|
[boxes[i][1], boxes[i][0], boxes[i][3], boxes[i][2]])
|
||||||
|
return np.concatenate([boxes, scores], axis=1)
|
||||||
|
|
||||||
|
def preprocess(self, image, target_height, target_width, label=None):
|
||||||
|
|
||||||
|
h, w, c = image.shape
|
||||||
|
|
||||||
|
bimage = np.zeros(
|
||||||
|
shape=[target_height, target_width, c],
|
||||||
|
dtype=image.dtype) + np.array(
|
||||||
|
cfg.DATA.pixel_means, dtype=image.dtype)
|
||||||
|
long_side = max(h, w)
|
||||||
|
|
||||||
|
scale_x = scale_y = target_height / long_side
|
||||||
|
|
||||||
|
image = cv2.resize(image, None, fx=scale_x, fy=scale_y)
|
||||||
|
|
||||||
|
h_, w_, _ = image.shape
|
||||||
|
bimage[:h_, :w_, :] = image
|
||||||
|
|
||||||
|
return bimage, scale_x, scale_y
|
||||||
|
|
||||||
|
def init_model(self, *args):
|
||||||
|
pb_path = args[0]
|
||||||
|
|
||||||
|
def init_pb(model_path):
|
||||||
|
config = tf.ConfigProto()
|
||||||
|
config.gpu_options.per_process_gpu_memory_fraction = 0.2
|
||||||
|
compute_graph = tf.Graph()
|
||||||
|
compute_graph.as_default()
|
||||||
|
sess = tf.Session(config=config)
|
||||||
|
with tf.gfile.GFile(model_path, 'rb') as fid:
|
||||||
|
graph_def = tf.GraphDef()
|
||||||
|
graph_def.ParseFromString(fid.read())
|
||||||
|
tf.import_graph_def(graph_def, name='')
|
||||||
|
|
||||||
|
return (compute_graph, sess)
|
||||||
|
|
||||||
|
model = init_pb(pb_path)
|
||||||
|
|
||||||
|
graph = model[0]
|
||||||
|
sess = model[1]
|
||||||
|
|
||||||
|
return graph, sess
|
||||||
@ -0,0 +1,154 @@
|
|||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
import tensorflow as tf
|
||||||
|
|
||||||
|
from .config import config as cfg
|
||||||
|
|
||||||
|
if tf.__version__ >= '2.0':
|
||||||
|
tf = tf.compat.v1
|
||||||
|
|
||||||
|
|
||||||
|
class FaceLandmark:
|
||||||
|
|
||||||
|
def __init__(self, dir):
|
||||||
|
self.model_path = dir + '/keypoints.pb'
|
||||||
|
self.min_face = 60
|
||||||
|
self.keypoint_num = cfg.KEYPOINTS.p_num * 2
|
||||||
|
|
||||||
|
self._graph = tf.Graph()
|
||||||
|
|
||||||
|
with self._graph.as_default():
|
||||||
|
|
||||||
|
self._graph, self._sess = self.init_model(self.model_path)
|
||||||
|
self.img_input = tf.get_default_graph().get_tensor_by_name(
|
||||||
|
'tower_0/images:0')
|
||||||
|
self.embeddings = tf.get_default_graph().get_tensor_by_name(
|
||||||
|
'tower_0/prediction:0')
|
||||||
|
self.training = tf.get_default_graph().get_tensor_by_name(
|
||||||
|
'training_flag:0')
|
||||||
|
|
||||||
|
self.landmark = self.embeddings[:, :self.keypoint_num]
|
||||||
|
self.headpose = self.embeddings[:, -7:-4] * 90.
|
||||||
|
self.state = tf.nn.sigmoid(self.embeddings[:, -4:])
|
||||||
|
|
||||||
|
def __call__(self, img, bboxes):
|
||||||
|
landmark_result = []
|
||||||
|
state_result = []
|
||||||
|
for i, bbox in enumerate(bboxes):
|
||||||
|
landmark, state = self._one_shot_run(img, bbox, i)
|
||||||
|
if landmark is not None:
|
||||||
|
landmark_result.append(landmark)
|
||||||
|
state_result.append(state)
|
||||||
|
return np.array(landmark_result), np.array(state_result)
|
||||||
|
|
||||||
|
def simple_run(self, cropped_img):
|
||||||
|
with self._graph.as_default():
|
||||||
|
|
||||||
|
cropped_img = np.expand_dims(cropped_img, axis=0)
|
||||||
|
landmark, p, states = self._sess.run(
|
||||||
|
[self.landmark, self.headpose, self.state],
|
||||||
|
feed_dict={
|
||||||
|
self.img_input: cropped_img,
|
||||||
|
self.training: False
|
||||||
|
})
|
||||||
|
|
||||||
|
return landmark, states
|
||||||
|
|
||||||
|
def _one_shot_run(self, image, bbox, i):
|
||||||
|
|
||||||
|
bbox_width = bbox[2] - bbox[0]
|
||||||
|
bbox_height = bbox[3] - bbox[1]
|
||||||
|
if (bbox_width <= self.min_face and bbox_height <= self.min_face):
|
||||||
|
return None, None
|
||||||
|
add = int(max(bbox_width, bbox_height))
|
||||||
|
bimg = cv2.copyMakeBorder(
|
||||||
|
image,
|
||||||
|
add,
|
||||||
|
add,
|
||||||
|
add,
|
||||||
|
add,
|
||||||
|
borderType=cv2.BORDER_CONSTANT,
|
||||||
|
value=cfg.DATA.pixel_means)
|
||||||
|
bbox += add
|
||||||
|
|
||||||
|
one_edge = (1 + 2 * cfg.KEYPOINTS.base_extend_range[0]) * bbox_width
|
||||||
|
center = [(bbox[0] + bbox[2]) // 2, (bbox[1] + bbox[3]) // 2]
|
||||||
|
|
||||||
|
bbox[0] = center[0] - one_edge // 2
|
||||||
|
bbox[1] = center[1] - one_edge // 2
|
||||||
|
bbox[2] = center[0] + one_edge // 2
|
||||||
|
bbox[3] = center[1] + one_edge // 2
|
||||||
|
|
||||||
|
bbox = bbox.astype(np.int)
|
||||||
|
crop_image = bimg[bbox[1]:bbox[3], bbox[0]:bbox[2], :]
|
||||||
|
h, w, _ = crop_image.shape
|
||||||
|
crop_image = cv2.resize(
|
||||||
|
crop_image,
|
||||||
|
(cfg.KEYPOINTS.input_shape[1], cfg.KEYPOINTS.input_shape[0]))
|
||||||
|
crop_image = crop_image.astype(np.float32)
|
||||||
|
|
||||||
|
keypoints, state = self.simple_run(crop_image)
|
||||||
|
|
||||||
|
res = keypoints[0][:self.keypoint_num].reshape((-1, 2))
|
||||||
|
res[:, 0] = res[:, 0] * w / cfg.KEYPOINTS.input_shape[1]
|
||||||
|
res[:, 1] = res[:, 1] * h / cfg.KEYPOINTS.input_shape[0]
|
||||||
|
|
||||||
|
landmark = []
|
||||||
|
for _index in range(res.shape[0]):
|
||||||
|
x_y = res[_index]
|
||||||
|
landmark.append([
|
||||||
|
int(x_y[0] * cfg.KEYPOINTS.input_shape[0] + bbox[0] - add),
|
||||||
|
int(x_y[1] * cfg.KEYPOINTS.input_shape[1] + bbox[1] - add)
|
||||||
|
])
|
||||||
|
|
||||||
|
landmark = np.array(landmark, np.float32)
|
||||||
|
|
||||||
|
return landmark, state
|
||||||
|
|
||||||
|
def init_model(self, *args):
|
||||||
|
|
||||||
|
if len(args) == 1:
|
||||||
|
use_pb = True
|
||||||
|
pb_path = args[0]
|
||||||
|
else:
|
||||||
|
use_pb = False
|
||||||
|
meta_path = args[0]
|
||||||
|
restore_model_path = args[1]
|
||||||
|
|
||||||
|
def ini_ckpt():
|
||||||
|
graph = tf.Graph()
|
||||||
|
graph.as_default()
|
||||||
|
configProto = tf.ConfigProto()
|
||||||
|
configProto.gpu_options.allow_growth = True
|
||||||
|
sess = tf.Session(config=configProto)
|
||||||
|
# load_model(model_path, sess)
|
||||||
|
saver = tf.train.import_meta_graph(meta_path)
|
||||||
|
saver.restore(sess, restore_model_path)
|
||||||
|
|
||||||
|
print('Model restred!')
|
||||||
|
return (graph, sess)
|
||||||
|
|
||||||
|
def init_pb(model_path):
|
||||||
|
config = tf.ConfigProto()
|
||||||
|
config.gpu_options.per_process_gpu_memory_fraction = 0.2
|
||||||
|
compute_graph = tf.Graph()
|
||||||
|
compute_graph.as_default()
|
||||||
|
sess = tf.Session(config=config)
|
||||||
|
with tf.gfile.GFile(model_path, 'rb') as fid:
|
||||||
|
graph_def = tf.GraphDef()
|
||||||
|
graph_def.ParseFromString(fid.read())
|
||||||
|
tf.import_graph_def(graph_def, name='')
|
||||||
|
|
||||||
|
# saver = tf.train.Saver(tf.global_variables())
|
||||||
|
# saver.save(sess, save_path='./tmp.ckpt')
|
||||||
|
return (compute_graph, sess)
|
||||||
|
|
||||||
|
if use_pb:
|
||||||
|
model = init_pb(pb_path)
|
||||||
|
else:
|
||||||
|
model = ini_ckpt()
|
||||||
|
|
||||||
|
graph = model[0]
|
||||||
|
sess = model[1]
|
||||||
|
|
||||||
|
return graph, sess
|
||||||
@ -0,0 +1,150 @@
|
|||||||
|
import time
|
||||||
|
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
from .config import config as cfg
|
||||||
|
from .face_detector import FaceDetector
|
||||||
|
from .face_landmark import FaceLandmark
|
||||||
|
from .LK.lk import GroupTrack
|
||||||
|
|
||||||
|
|
||||||
|
class FaceAna():
|
||||||
|
'''
|
||||||
|
by default the top3 facea sorted by area will be calculated for time reason
|
||||||
|
'''
|
||||||
|
|
||||||
|
def __init__(self, model_dir):
|
||||||
|
self.face_detector = FaceDetector(model_dir)
|
||||||
|
self.face_landmark = FaceLandmark(model_dir)
|
||||||
|
self.trace = GroupTrack()
|
||||||
|
|
||||||
|
self.track_box = None
|
||||||
|
self.previous_image = None
|
||||||
|
self.previous_box = None
|
||||||
|
|
||||||
|
self.diff_thres = 5
|
||||||
|
self.top_k = cfg.DETECT.topk
|
||||||
|
self.iou_thres = cfg.TRACE.iou_thres
|
||||||
|
self.alpha = cfg.TRACE.smooth_box
|
||||||
|
|
||||||
|
def run(self, image):
|
||||||
|
|
||||||
|
boxes = self.face_detector(image)
|
||||||
|
|
||||||
|
if boxes.shape[0] > self.top_k:
|
||||||
|
boxes = self.sort(boxes)
|
||||||
|
|
||||||
|
boxes_return = np.array(boxes)
|
||||||
|
landmarks, states = self.face_landmark(image, boxes)
|
||||||
|
|
||||||
|
if 1:
|
||||||
|
track = []
|
||||||
|
for i in range(landmarks.shape[0]):
|
||||||
|
track.append([
|
||||||
|
np.min(landmarks[i][:, 0]),
|
||||||
|
np.min(landmarks[i][:, 1]),
|
||||||
|
np.max(landmarks[i][:, 0]),
|
||||||
|
np.max(landmarks[i][:, 1])
|
||||||
|
])
|
||||||
|
tmp_box = np.array(track)
|
||||||
|
|
||||||
|
self.track_box = self.judge_boxs(boxes_return, tmp_box)
|
||||||
|
|
||||||
|
self.track_box, landmarks = self.sort_res(self.track_box, landmarks)
|
||||||
|
return self.track_box, landmarks, states
|
||||||
|
|
||||||
|
def sort_res(self, bboxes, points):
|
||||||
|
area = []
|
||||||
|
for bbox in bboxes:
|
||||||
|
bbox_width = bbox[2] - bbox[0]
|
||||||
|
bbox_height = bbox[3] - bbox[1]
|
||||||
|
area.append(bbox_height * bbox_width)
|
||||||
|
|
||||||
|
area = np.array(area)
|
||||||
|
picked = area.argsort()[::-1]
|
||||||
|
sorted_bboxes = [bboxes[x] for x in picked]
|
||||||
|
sorted_points = [points[x] for x in picked]
|
||||||
|
return np.array(sorted_bboxes), np.array(sorted_points)
|
||||||
|
|
||||||
|
def diff_frames(self, previous_frame, image):
|
||||||
|
if previous_frame is None:
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
_diff = cv2.absdiff(previous_frame, image)
|
||||||
|
diff = np.sum(
|
||||||
|
_diff) / previous_frame.shape[0] / previous_frame.shape[1] / 3.
|
||||||
|
return diff > self.diff_thres
|
||||||
|
|
||||||
|
def sort(self, bboxes):
|
||||||
|
if self.top_k > 100:
|
||||||
|
return bboxes
|
||||||
|
area = []
|
||||||
|
for bbox in bboxes:
|
||||||
|
|
||||||
|
bbox_width = bbox[2] - bbox[0]
|
||||||
|
bbox_height = bbox[3] - bbox[1]
|
||||||
|
area.append(bbox_height * bbox_width)
|
||||||
|
|
||||||
|
area = np.array(area)
|
||||||
|
|
||||||
|
picked = area.argsort()[-self.top_k:][::-1]
|
||||||
|
sorted_bboxes = [bboxes[x] for x in picked]
|
||||||
|
return np.array(sorted_bboxes)
|
||||||
|
|
||||||
|
def judge_boxs(self, previuous_bboxs, now_bboxs):
|
||||||
|
|
||||||
|
def iou(rec1, rec2):
|
||||||
|
|
||||||
|
# computing area of each rectangles
|
||||||
|
S_rec1 = (rec1[2] - rec1[0]) * (rec1[3] - rec1[1])
|
||||||
|
S_rec2 = (rec2[2] - rec2[0]) * (rec2[3] - rec2[1])
|
||||||
|
|
||||||
|
# computing the sum_area
|
||||||
|
sum_area = S_rec1 + S_rec2
|
||||||
|
|
||||||
|
# find the each edge of intersect rectangle
|
||||||
|
x1 = max(rec1[0], rec2[0])
|
||||||
|
y1 = max(rec1[1], rec2[1])
|
||||||
|
x2 = min(rec1[2], rec2[2])
|
||||||
|
y2 = min(rec1[3], rec2[3])
|
||||||
|
|
||||||
|
# judge if there is an intersect
|
||||||
|
intersect = max(0, x2 - x1) * max(0, y2 - y1)
|
||||||
|
|
||||||
|
return intersect / (sum_area - intersect)
|
||||||
|
|
||||||
|
if previuous_bboxs is None:
|
||||||
|
return now_bboxs
|
||||||
|
|
||||||
|
result = []
|
||||||
|
|
||||||
|
for i in range(now_bboxs.shape[0]):
|
||||||
|
contain = False
|
||||||
|
for j in range(previuous_bboxs.shape[0]):
|
||||||
|
if iou(now_bboxs[i], previuous_bboxs[j]) > self.iou_thres:
|
||||||
|
result.append(
|
||||||
|
self.smooth(now_bboxs[i], previuous_bboxs[j]))
|
||||||
|
contain = True
|
||||||
|
break
|
||||||
|
if not contain:
|
||||||
|
result.append(now_bboxs[i])
|
||||||
|
|
||||||
|
return np.array(result)
|
||||||
|
|
||||||
|
def smooth(self, now_box, previous_box):
|
||||||
|
|
||||||
|
return self.do_moving_average(now_box[:4], previous_box[:4])
|
||||||
|
|
||||||
|
def do_moving_average(self, p_now, p_previous):
|
||||||
|
p = self.alpha * p_now + (1 - self.alpha) * p_previous
|
||||||
|
return p
|
||||||
|
|
||||||
|
def reset(self):
|
||||||
|
'''
|
||||||
|
reset the previous info used foe tracking,
|
||||||
|
:return:
|
||||||
|
'''
|
||||||
|
self.track_box = None
|
||||||
|
self.previous_image = None
|
||||||
|
self.previous_box = None
|
||||||
@ -0,0 +1,21 @@
|
|||||||
|
MIT License
|
||||||
|
|
||||||
|
Copyright (c) 2017 Dan Antoshchenko
|
||||||
|
|
||||||
|
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||||
|
of this software and associated documentation files (the "Software"), to deal
|
||||||
|
in the Software without restriction, including without limitation the rights
|
||||||
|
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||||
|
copies of the Software, and to permit persons to whom the Software is
|
||||||
|
furnished to do so, subject to the following conditions:
|
||||||
|
|
||||||
|
The above copyright notice and this permission notice shall be included in all
|
||||||
|
copies or substantial portions of the Software.
|
||||||
|
|
||||||
|
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||||
|
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||||
|
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||||
|
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||||
|
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||||
|
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||||
|
SOFTWARE.
|
||||||
@ -0,0 +1,26 @@
|
|||||||
|
# MTCNN
|
||||||
|
|
||||||
|
`pytorch` implementation of **inference stage** of face detection algorithm described in
|
||||||
|
[Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://arxiv.org/abs/1604.02878).
|
||||||
|
|
||||||
|
## Example
|
||||||
|
|
||||||
|
|
||||||
|
## How to use it
|
||||||
|
Just download the repository and then do this
|
||||||
|
```python
|
||||||
|
from src import detect_faces
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
image = Image.open('image.jpg')
|
||||||
|
bounding_boxes, landmarks = detect_faces(image)
|
||||||
|
```
|
||||||
|
For examples see `test_on_images.ipynb`.
|
||||||
|
|
||||||
|
## Requirements
|
||||||
|
* pytorch 0.2
|
||||||
|
* Pillow, numpy
|
||||||
|
|
||||||
|
## Credit
|
||||||
|
This implementation is heavily inspired by:
|
||||||
|
* [pangyupo/mxnet_mtcnn_face_detection](https://github.com/pangyupo/mxnet_mtcnn_face_detection)
|
||||||
@ -0,0 +1,187 @@
|
|||||||
|
"""
|
||||||
|
Created on Mon Apr 24 15:43:29 2017
|
||||||
|
@author: zhaoy
|
||||||
|
"""
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
from .matlab_cp2tform import get_similarity_transform_for_cv2
|
||||||
|
|
||||||
|
# reference facial points, a list of coordinates (x,y)
|
||||||
|
dx = 1
|
||||||
|
dy = 1
|
||||||
|
REFERENCE_FACIAL_POINTS = [
|
||||||
|
[30.29459953 + dx, 51.69630051 + dy], # left eye
|
||||||
|
[65.53179932 + dx, 51.50139999 + dy], # right eye
|
||||||
|
[48.02519989 + dx, 71.73660278 + dy], # nose
|
||||||
|
[33.54930115 + dx, 92.3655014 + dy], # left mouth
|
||||||
|
[62.72990036 + dx, 92.20410156 + dy] # right mouth
|
||||||
|
]
|
||||||
|
|
||||||
|
DEFAULT_CROP_SIZE = (96, 112)
|
||||||
|
|
||||||
|
global FACIAL_POINTS
|
||||||
|
|
||||||
|
|
||||||
|
class FaceWarpException(Exception):
|
||||||
|
|
||||||
|
def __str__(self):
|
||||||
|
return 'In File {}:{}'.format(__file__, super.__str__(self))
|
||||||
|
|
||||||
|
|
||||||
|
def get_reference_facial_points(output_size=None,
|
||||||
|
inner_padding_factor=0.0,
|
||||||
|
outer_padding=(0, 0),
|
||||||
|
default_square=False):
|
||||||
|
|
||||||
|
tmp_5pts = np.array(REFERENCE_FACIAL_POINTS)
|
||||||
|
tmp_crop_size = np.array(DEFAULT_CROP_SIZE)
|
||||||
|
|
||||||
|
# 0) make the inner region a square
|
||||||
|
if default_square:
|
||||||
|
size_diff = max(tmp_crop_size) - tmp_crop_size
|
||||||
|
tmp_5pts += size_diff / 2
|
||||||
|
tmp_crop_size += size_diff
|
||||||
|
|
||||||
|
h_crop = tmp_crop_size[0]
|
||||||
|
w_crop = tmp_crop_size[1]
|
||||||
|
if (output_size):
|
||||||
|
if (output_size[0] == h_crop and output_size[1] == w_crop):
|
||||||
|
return tmp_5pts
|
||||||
|
|
||||||
|
if (inner_padding_factor == 0 and outer_padding == (0, 0)):
|
||||||
|
if output_size is None:
|
||||||
|
return tmp_5pts
|
||||||
|
else:
|
||||||
|
raise FaceWarpException(
|
||||||
|
'No paddings to do, output_size must be None or {}'.format(
|
||||||
|
tmp_crop_size))
|
||||||
|
|
||||||
|
# check output size
|
||||||
|
if not (0 <= inner_padding_factor <= 1.0):
|
||||||
|
raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)')
|
||||||
|
|
||||||
|
factor = inner_padding_factor > 0 or outer_padding[0] > 0
|
||||||
|
factor = factor or outer_padding[1] > 0
|
||||||
|
if (factor and output_size is None):
|
||||||
|
output_size = tmp_crop_size * \
|
||||||
|
(1 + inner_padding_factor * 2).astype(np.int32)
|
||||||
|
output_size += np.array(outer_padding)
|
||||||
|
|
||||||
|
cond1 = outer_padding[0] < output_size[0]
|
||||||
|
cond2 = outer_padding[1] < output_size[1]
|
||||||
|
if not (cond1 and cond2):
|
||||||
|
raise FaceWarpException('Not (outer_padding[0] < output_size[0]'
|
||||||
|
'and outer_padding[1] < output_size[1])')
|
||||||
|
|
||||||
|
# 1) pad the inner region according inner_padding_factor
|
||||||
|
if inner_padding_factor > 0:
|
||||||
|
size_diff = tmp_crop_size * inner_padding_factor * 2
|
||||||
|
tmp_5pts += size_diff / 2
|
||||||
|
tmp_crop_size += np.round(size_diff).astype(np.int32)
|
||||||
|
|
||||||
|
# 2) resize the padded inner region
|
||||||
|
size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2
|
||||||
|
|
||||||
|
if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[
|
||||||
|
1] * tmp_crop_size[0]:
|
||||||
|
raise FaceWarpException(
|
||||||
|
'Must have (output_size - outer_padding)'
|
||||||
|
'= some_scale * (crop_size * (1.0 + inner_padding_factor)')
|
||||||
|
|
||||||
|
scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0]
|
||||||
|
tmp_5pts = tmp_5pts * scale_factor
|
||||||
|
|
||||||
|
# 3) add outer_padding to make output_size
|
||||||
|
reference_5point = tmp_5pts + np.array(outer_padding)
|
||||||
|
|
||||||
|
return reference_5point
|
||||||
|
|
||||||
|
|
||||||
|
def get_affine_transform_matrix(src_pts, dst_pts):
|
||||||
|
|
||||||
|
tfm = np.float32([[1, 0, 0], [0, 1, 0]])
|
||||||
|
n_pts = src_pts.shape[0]
|
||||||
|
ones = np.ones((n_pts, 1), src_pts.dtype)
|
||||||
|
src_pts_ = np.hstack([src_pts, ones])
|
||||||
|
dst_pts_ = np.hstack([dst_pts, ones])
|
||||||
|
|
||||||
|
A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_)
|
||||||
|
|
||||||
|
if rank == 3:
|
||||||
|
tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]],
|
||||||
|
[A[0, 1], A[1, 1], A[2, 1]]])
|
||||||
|
elif rank == 2:
|
||||||
|
tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]])
|
||||||
|
|
||||||
|
return tfm
|
||||||
|
|
||||||
|
|
||||||
|
def warp_and_crop_face(src_img,
|
||||||
|
facial_pts,
|
||||||
|
ratio=0.84,
|
||||||
|
reference_pts=None,
|
||||||
|
crop_size=(96, 112),
|
||||||
|
align_type='similarity'
|
||||||
|
'',
|
||||||
|
return_trans_inv=False):
|
||||||
|
|
||||||
|
if reference_pts is None:
|
||||||
|
if crop_size[0] == 96 and crop_size[1] == 112:
|
||||||
|
reference_pts = REFERENCE_FACIAL_POINTS
|
||||||
|
else:
|
||||||
|
default_square = False
|
||||||
|
inner_padding_factor = 0
|
||||||
|
outer_padding = (0, 0)
|
||||||
|
output_size = crop_size
|
||||||
|
|
||||||
|
reference_pts = get_reference_facial_points(
|
||||||
|
output_size, inner_padding_factor, outer_padding,
|
||||||
|
default_square)
|
||||||
|
|
||||||
|
ref_pts = np.float32(reference_pts)
|
||||||
|
|
||||||
|
factor = ratio
|
||||||
|
ref_pts = (ref_pts - 112 / 2) * factor + 112 / 2
|
||||||
|
ref_pts *= crop_size[0] / 112.
|
||||||
|
|
||||||
|
ref_pts_shp = ref_pts.shape
|
||||||
|
if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2:
|
||||||
|
raise FaceWarpException(
|
||||||
|
'reference_pts.shape must be (K,2) or (2,K) and K>2')
|
||||||
|
|
||||||
|
if ref_pts_shp[0] == 2:
|
||||||
|
ref_pts = ref_pts.T
|
||||||
|
|
||||||
|
src_pts = np.float32(facial_pts)
|
||||||
|
src_pts_shp = src_pts.shape
|
||||||
|
if max(src_pts_shp) < 3 or min(src_pts_shp) != 2:
|
||||||
|
raise FaceWarpException(
|
||||||
|
'facial_pts.shape must be (K,2) or (2,K) and K>2')
|
||||||
|
|
||||||
|
if src_pts_shp[0] == 2:
|
||||||
|
src_pts = src_pts.T
|
||||||
|
|
||||||
|
if src_pts.shape != ref_pts.shape:
|
||||||
|
raise FaceWarpException(
|
||||||
|
'facial_pts and reference_pts must have the same shape')
|
||||||
|
|
||||||
|
if align_type == 'cv2_affine':
|
||||||
|
tfm = cv2.getAffineTransform(src_pts, ref_pts)
|
||||||
|
tfm_inv = cv2.getAffineTransform(ref_pts, src_pts)
|
||||||
|
|
||||||
|
elif align_type == 'affine':
|
||||||
|
tfm = get_affine_transform_matrix(src_pts, ref_pts)
|
||||||
|
tfm_inv = get_affine_transform_matrix(ref_pts, src_pts)
|
||||||
|
else:
|
||||||
|
tfm, tfm_inv = get_similarity_transform_for_cv2(src_pts, ref_pts)
|
||||||
|
|
||||||
|
face_img = cv2.warpAffine(
|
||||||
|
src_img,
|
||||||
|
tfm, (crop_size[0], crop_size[1]),
|
||||||
|
borderValue=(255, 255, 255))
|
||||||
|
|
||||||
|
if return_trans_inv:
|
||||||
|
return face_img, tfm_inv
|
||||||
|
else:
|
||||||
|
return face_img
|
||||||
@ -0,0 +1,339 @@
|
|||||||
|
"""
|
||||||
|
Created on Tue Jul 11 06:54:28 2017
|
||||||
|
|
||||||
|
@author: zhaoyafei
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from numpy.linalg import inv, lstsq
|
||||||
|
from numpy.linalg import matrix_rank as rank
|
||||||
|
from numpy.linalg import norm
|
||||||
|
|
||||||
|
|
||||||
|
class MatlabCp2tormException(Exception):
|
||||||
|
|
||||||
|
def __str__(self):
|
||||||
|
return 'In File {}:{}'.format(__file__, super.__str__(self))
|
||||||
|
|
||||||
|
|
||||||
|
def tformfwd(trans, uv):
|
||||||
|
"""
|
||||||
|
Function:
|
||||||
|
----------
|
||||||
|
apply affine transform 'trans' to uv
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
----------
|
||||||
|
@trans: 3x3 np.array
|
||||||
|
transform matrix
|
||||||
|
@uv: Kx2 np.array
|
||||||
|
each row is a pair of coordinates (x, y)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
----------
|
||||||
|
@xy: Kx2 np.array
|
||||||
|
each row is a pair of transformed coordinates (x, y)
|
||||||
|
"""
|
||||||
|
uv = np.hstack((uv, np.ones((uv.shape[0], 1))))
|
||||||
|
xy = np.dot(uv, trans)
|
||||||
|
xy = xy[:, 0:-1]
|
||||||
|
return xy
|
||||||
|
|
||||||
|
|
||||||
|
def tforminv(trans, uv):
|
||||||
|
"""
|
||||||
|
Function:
|
||||||
|
----------
|
||||||
|
apply the inverse of affine transform 'trans' to uv
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
----------
|
||||||
|
@trans: 3x3 np.array
|
||||||
|
transform matrix
|
||||||
|
@uv: Kx2 np.array
|
||||||
|
each row is a pair of coordinates (x, y)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
----------
|
||||||
|
@xy: Kx2 np.array
|
||||||
|
each row is a pair of inverse-transformed coordinates (x, y)
|
||||||
|
"""
|
||||||
|
Tinv = inv(trans)
|
||||||
|
xy = tformfwd(Tinv, uv)
|
||||||
|
return xy
|
||||||
|
|
||||||
|
|
||||||
|
def findNonreflectiveSimilarity(uv, xy, options=None):
|
||||||
|
|
||||||
|
options = {'K': 2}
|
||||||
|
|
||||||
|
K = options['K']
|
||||||
|
M = xy.shape[0]
|
||||||
|
x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector
|
||||||
|
y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector
|
||||||
|
# print('--->x, y:\n', x, y
|
||||||
|
|
||||||
|
tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1))))
|
||||||
|
tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1))))
|
||||||
|
X = np.vstack((tmp1, tmp2))
|
||||||
|
# print('--->X.shape: ', X.shape
|
||||||
|
# print('X:\n', X
|
||||||
|
|
||||||
|
u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector
|
||||||
|
v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector
|
||||||
|
U = np.vstack((u, v))
|
||||||
|
# print('--->U.shape: ', U.shape
|
||||||
|
# print('U:\n', U
|
||||||
|
|
||||||
|
# We know that X * r = U
|
||||||
|
if rank(X) >= 2 * K:
|
||||||
|
r, _, _, _ = lstsq(X, U)
|
||||||
|
r = np.squeeze(r)
|
||||||
|
else:
|
||||||
|
raise Exception('cp2tform:twoUniquePointsReq')
|
||||||
|
|
||||||
|
# print('--->r:\n', r
|
||||||
|
|
||||||
|
sc = r[0]
|
||||||
|
ss = r[1]
|
||||||
|
tx = r[2]
|
||||||
|
ty = r[3]
|
||||||
|
|
||||||
|
Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]])
|
||||||
|
|
||||||
|
# print('--->Tinv:\n', Tinv
|
||||||
|
|
||||||
|
T = inv(Tinv)
|
||||||
|
# print('--->T:\n', T
|
||||||
|
|
||||||
|
T[:, 2] = np.array([0, 0, 1])
|
||||||
|
|
||||||
|
return T, Tinv
|
||||||
|
|
||||||
|
|
||||||
|
def findSimilarity(uv, xy, options=None):
|
||||||
|
|
||||||
|
options = {'K': 2}
|
||||||
|
|
||||||
|
# uv = np.array(uv)
|
||||||
|
# xy = np.array(xy)
|
||||||
|
|
||||||
|
# Solve for trans1
|
||||||
|
trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options)
|
||||||
|
|
||||||
|
# Solve for trans2
|
||||||
|
|
||||||
|
# manually reflect the xy data across the Y-axis
|
||||||
|
xyR = xy
|
||||||
|
xyR[:, 0] = -1 * xyR[:, 0]
|
||||||
|
|
||||||
|
trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options)
|
||||||
|
|
||||||
|
# manually reflect the tform to undo the reflection done on xyR
|
||||||
|
TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]])
|
||||||
|
|
||||||
|
trans2 = np.dot(trans2r, TreflectY)
|
||||||
|
|
||||||
|
# Figure out if trans1 or trans2 is better
|
||||||
|
xy1 = tformfwd(trans1, uv)
|
||||||
|
norm1 = norm(xy1 - xy)
|
||||||
|
|
||||||
|
xy2 = tformfwd(trans2, uv)
|
||||||
|
norm2 = norm(xy2 - xy)
|
||||||
|
|
||||||
|
if norm1 <= norm2:
|
||||||
|
return trans1, trans1_inv
|
||||||
|
else:
|
||||||
|
trans2_inv = inv(trans2)
|
||||||
|
return trans2, trans2_inv
|
||||||
|
|
||||||
|
|
||||||
|
def get_similarity_transform(src_pts, dst_pts, reflective=True):
|
||||||
|
"""
|
||||||
|
Function:
|
||||||
|
----------
|
||||||
|
Find Similarity Transform Matrix 'trans':
|
||||||
|
u = src_pts[:, 0]
|
||||||
|
v = src_pts[:, 1]
|
||||||
|
x = dst_pts[:, 0]
|
||||||
|
y = dst_pts[:, 1]
|
||||||
|
[x, y, 1] = [u, v, 1] * trans
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
----------
|
||||||
|
@src_pts: Kx2 np.array
|
||||||
|
source points, each row is a pair of coordinates (x, y)
|
||||||
|
@dst_pts: Kx2 np.array
|
||||||
|
destination points, each row is a pair of transformed
|
||||||
|
coordinates (x, y)
|
||||||
|
@reflective: True or False
|
||||||
|
if True:
|
||||||
|
use reflective similarity transform
|
||||||
|
else:
|
||||||
|
use non-reflective similarity transform
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
----------
|
||||||
|
@trans: 3x3 np.array
|
||||||
|
transform matrix from uv to xy
|
||||||
|
trans_inv: 3x3 np.array
|
||||||
|
inverse of trans, transform matrix from xy to uv
|
||||||
|
"""
|
||||||
|
|
||||||
|
if reflective:
|
||||||
|
trans, trans_inv = findSimilarity(src_pts, dst_pts)
|
||||||
|
else:
|
||||||
|
trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts)
|
||||||
|
|
||||||
|
return trans, trans_inv
|
||||||
|
|
||||||
|
|
||||||
|
def cvt_tform_mat_for_cv2(trans):
|
||||||
|
"""
|
||||||
|
Function:
|
||||||
|
----------
|
||||||
|
Convert Transform Matrix 'trans' into 'cv2_trans' which could be
|
||||||
|
directly used by cv2.warpAffine():
|
||||||
|
u = src_pts[:, 0]
|
||||||
|
v = src_pts[:, 1]
|
||||||
|
x = dst_pts[:, 0]
|
||||||
|
y = dst_pts[:, 1]
|
||||||
|
[x, y].T = cv_trans * [u, v, 1].T
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
----------
|
||||||
|
@trans: 3x3 np.array
|
||||||
|
transform matrix from uv to xy
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
----------
|
||||||
|
@cv2_trans: 2x3 np.array
|
||||||
|
transform matrix from src_pts to dst_pts, could be directly used
|
||||||
|
for cv2.warpAffine()
|
||||||
|
"""
|
||||||
|
cv2_trans = trans[:, 0:2].T
|
||||||
|
|
||||||
|
return cv2_trans
|
||||||
|
|
||||||
|
|
||||||
|
def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True):
|
||||||
|
"""
|
||||||
|
Function:
|
||||||
|
----------
|
||||||
|
Find Similarity Transform Matrix 'cv2_trans' which could be
|
||||||
|
directly used by cv2.warpAffine():
|
||||||
|
u = src_pts[:, 0]
|
||||||
|
v = src_pts[:, 1]
|
||||||
|
x = dst_pts[:, 0]
|
||||||
|
y = dst_pts[:, 1]
|
||||||
|
[x, y].T = cv_trans * [u, v, 1].T
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
----------
|
||||||
|
@src_pts: Kx2 np.array
|
||||||
|
source points, each row is a pair of coordinates (x, y)
|
||||||
|
@dst_pts: Kx2 np.array
|
||||||
|
destination points, each row is a pair of transformed
|
||||||
|
coordinates (x, y)
|
||||||
|
reflective: True or False
|
||||||
|
if True:
|
||||||
|
use reflective similarity transform
|
||||||
|
else:
|
||||||
|
use non-reflective similarity transform
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
----------
|
||||||
|
@cv2_trans: 2x3 np.array
|
||||||
|
transform matrix from src_pts to dst_pts, could be directly used
|
||||||
|
for cv2.warpAffine()
|
||||||
|
"""
|
||||||
|
trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective)
|
||||||
|
cv2_trans = cvt_tform_mat_for_cv2(trans)
|
||||||
|
cv2_trans_inv = cvt_tform_mat_for_cv2(trans_inv)
|
||||||
|
|
||||||
|
return cv2_trans, cv2_trans_inv
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
"""
|
||||||
|
u = [0, 6, -2]
|
||||||
|
v = [0, 3, 5]
|
||||||
|
x = [-1, 0, 4]
|
||||||
|
y = [-1, -10, 4]
|
||||||
|
|
||||||
|
# In Matlab, run:
|
||||||
|
#
|
||||||
|
# uv = [u'; v'];
|
||||||
|
# xy = [x'; y'];
|
||||||
|
# tform_sim=cp2tform(uv,xy,'similarity');
|
||||||
|
#
|
||||||
|
# trans = tform_sim.tdata.T
|
||||||
|
# ans =
|
||||||
|
# -0.0764 -1.6190 0
|
||||||
|
# 1.6190 -0.0764 0
|
||||||
|
# -3.2156 0.0290 1.0000
|
||||||
|
# trans_inv = tform_sim.tdata.Tinv
|
||||||
|
# ans =
|
||||||
|
#
|
||||||
|
# -0.0291 0.6163 0
|
||||||
|
# -0.6163 -0.0291 0
|
||||||
|
# -0.0756 1.9826 1.0000
|
||||||
|
# xy_m=tformfwd(tform_sim, u,v)
|
||||||
|
#
|
||||||
|
# xy_m =
|
||||||
|
#
|
||||||
|
# -3.2156 0.0290
|
||||||
|
# 1.1833 -9.9143
|
||||||
|
# 5.0323 2.8853
|
||||||
|
# uv_m=tforminv(tform_sim, x,y)
|
||||||
|
#
|
||||||
|
# uv_m =
|
||||||
|
#
|
||||||
|
# 0.5698 1.3953
|
||||||
|
# 6.0872 2.2733
|
||||||
|
# -2.6570 4.3314
|
||||||
|
"""
|
||||||
|
u = [0, 6, -2]
|
||||||
|
v = [0, 3, 5]
|
||||||
|
x = [-1, 0, 4]
|
||||||
|
y = [-1, -10, 4]
|
||||||
|
|
||||||
|
uv = np.array((u, v)).T
|
||||||
|
xy = np.array((x, y)).T
|
||||||
|
|
||||||
|
print('\n--->uv:')
|
||||||
|
print(uv)
|
||||||
|
print('\n--->xy:')
|
||||||
|
print(xy)
|
||||||
|
|
||||||
|
trans, trans_inv = get_similarity_transform(uv, xy)
|
||||||
|
|
||||||
|
print('\n--->trans matrix:')
|
||||||
|
print(trans)
|
||||||
|
|
||||||
|
print('\n--->trans_inv matrix:')
|
||||||
|
print(trans_inv)
|
||||||
|
|
||||||
|
print('\n---> apply transform to uv')
|
||||||
|
print('\nxy_m = uv_augmented * trans')
|
||||||
|
uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1))))
|
||||||
|
xy_m = np.dot(uv_aug, trans)
|
||||||
|
print(xy_m)
|
||||||
|
|
||||||
|
print('\nxy_m = tformfwd(trans, uv)')
|
||||||
|
xy_m = tformfwd(trans, uv)
|
||||||
|
print(xy_m)
|
||||||
|
|
||||||
|
print('\n---> apply inverse transform to xy')
|
||||||
|
print('\nuv_m = xy_augmented * trans_inv')
|
||||||
|
xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1))))
|
||||||
|
uv_m = np.dot(xy_aug, trans_inv)
|
||||||
|
print(uv_m)
|
||||||
|
|
||||||
|
print('\nuv_m = tformfwd(trans_inv, xy)')
|
||||||
|
uv_m = tformfwd(trans_inv, xy)
|
||||||
|
print(uv_m)
|
||||||
|
|
||||||
|
uv_m = tforminv(trans, xy)
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|
print('\nuv_m = tforminv(trans, xy)')
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|
print(uv_m)
|
||||||
@ -0,0 +1,107 @@
|
|||||||
|
import os
|
||||||
|
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
def resize_size(image, size=720):
|
||||||
|
h, w, c = np.shape(image)
|
||||||
|
if min(h, w) > size:
|
||||||
|
if h > w:
|
||||||
|
h, w = int(size * h / w), size
|
||||||
|
else:
|
||||||
|
h, w = size, int(size * w / h)
|
||||||
|
image = cv2.resize(image, (w, h), interpolation=cv2.INTER_AREA)
|
||||||
|
return image
|
||||||
|
|
||||||
|
|
||||||
|
def padTo16x(image):
|
||||||
|
h, w, c = np.shape(image)
|
||||||
|
if h % 16 == 0 and w % 16 == 0:
|
||||||
|
return image, h, w
|
||||||
|
nh, nw = (h // 16 + 1) * 16, (w // 16 + 1) * 16
|
||||||
|
img_new = np.ones((nh, nw, 3), np.uint8) * 255
|
||||||
|
img_new[:h, :w, :] = image
|
||||||
|
|
||||||
|
return img_new, h, w
|
||||||
|
|
||||||
|
|
||||||
|
def get_f5p(landmarks, np_img):
|
||||||
|
eye_left = find_pupil(landmarks[36:41], np_img)
|
||||||
|
eye_right = find_pupil(landmarks[42:47], np_img)
|
||||||
|
if eye_left is None or eye_right is None:
|
||||||
|
print('cannot find 5 points with find_puil, used mean instead.!')
|
||||||
|
eye_left = landmarks[36:41].mean(axis=0)
|
||||||
|
eye_right = landmarks[42:47].mean(axis=0)
|
||||||
|
nose = landmarks[30]
|
||||||
|
mouth_left = landmarks[48]
|
||||||
|
mouth_right = landmarks[54]
|
||||||
|
f5p = [[eye_left[0], eye_left[1]], [eye_right[0], eye_right[1]],
|
||||||
|
[nose[0], nose[1]], [mouth_left[0], mouth_left[1]],
|
||||||
|
[mouth_right[0], mouth_right[1]]]
|
||||||
|
return f5p
|
||||||
|
|
||||||
|
|
||||||
|
def find_pupil(landmarks, np_img):
|
||||||
|
h, w, _ = np_img.shape
|
||||||
|
xmax = int(landmarks[:, 0].max())
|
||||||
|
xmin = int(landmarks[:, 0].min())
|
||||||
|
ymax = int(landmarks[:, 1].max())
|
||||||
|
ymin = int(landmarks[:, 1].min())
|
||||||
|
|
||||||
|
if ymin >= ymax or xmin >= xmax or ymin < 0 or xmin < 0 or ymax > h or xmax > w:
|
||||||
|
return None
|
||||||
|
eye_img_bgr = np_img[ymin:ymax, xmin:xmax, :]
|
||||||
|
eye_img = cv2.cvtColor(eye_img_bgr, cv2.COLOR_BGR2GRAY)
|
||||||
|
eye_img = cv2.equalizeHist(eye_img)
|
||||||
|
n_marks = landmarks - np.array([xmin, ymin]).reshape([1, 2])
|
||||||
|
eye_mask = cv2.fillConvexPoly(
|
||||||
|
np.zeros_like(eye_img), n_marks.astype(np.int32), 1)
|
||||||
|
ret, thresh = cv2.threshold(eye_img, 100, 255,
|
||||||
|
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
|
||||||
|
thresh = (1 - thresh / 255.) * eye_mask
|
||||||
|
cnt = 0
|
||||||
|
xm = []
|
||||||
|
ym = []
|
||||||
|
for i in range(thresh.shape[0]):
|
||||||
|
for j in range(thresh.shape[1]):
|
||||||
|
if thresh[i, j] > 0.5:
|
||||||
|
xm.append(j)
|
||||||
|
ym.append(i)
|
||||||
|
cnt += 1
|
||||||
|
if cnt != 0:
|
||||||
|
xm.sort()
|
||||||
|
ym.sort()
|
||||||
|
xm = xm[cnt // 2]
|
||||||
|
ym = ym[cnt // 2]
|
||||||
|
else:
|
||||||
|
xm = thresh.shape[1] / 2
|
||||||
|
ym = thresh.shape[0] / 2
|
||||||
|
|
||||||
|
return xm + xmin, ym + ymin
|
||||||
|
|
||||||
|
|
||||||
|
def all_file(file_dir):
|
||||||
|
L = []
|
||||||
|
for root, dirs, files in os.walk(file_dir):
|
||||||
|
for file in files:
|
||||||
|
extend = os.path.splitext(file)[1]
|
||||||
|
if extend == '.png' or extend == '.jpg' or extend == '.jpeg':
|
||||||
|
L.append(os.path.join(root, file))
|
||||||
|
return L
|
||||||
|
|
||||||
|
def initialize_mask(box_width):
|
||||||
|
h, w = [box_width, box_width]
|
||||||
|
mask = np.zeros((h, w), np.uint8)
|
||||||
|
|
||||||
|
center = (int(w / 2), int(h / 2))
|
||||||
|
axes = (int(w * 0.4), int(h * 0.49))
|
||||||
|
mask = cv2.ellipse(img=mask, center=center, axes=axes, angle=0, startAngle=0, endAngle=360, color=(1),
|
||||||
|
thickness=-1)
|
||||||
|
mask = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
|
||||||
|
|
||||||
|
maxn = max(w, h) * 0.15
|
||||||
|
mask[(mask < 255) & (mask > 0)] = mask[(mask < 255) & (mask > 0)] / maxn
|
||||||
|
mask = np.clip(mask, 0, 1)
|
||||||
|
|
||||||
|
return mask.astype(float)
|
||||||
Loading…
Reference in New Issue